diff --git a/README.es.md b/README.es.md index d61504cd5..b62d2cece 100644 --- a/README.es.md +++ b/README.es.md @@ -21,9 +21,28 @@ # SurfSense -Conecta cualquier LLM a tus fuentes de conocimiento internas y chatea con él en tiempo real junto a tu equipo. Alternativa de código abierto a NotebookLM, Perplexity y Glean. -SurfSense es un agente de investigación de IA altamente personalizable, conectado a fuentes externas como motores de búsqueda (SearxNG, Tavily, LinkUp), Google Drive, OneDrive, Dropbox, Slack, Microsoft Teams, Linear, Jira, ClickUp, Confluence, BookStack, Gmail, Notion, YouTube, GitHub, Discord, Airtable, Google Calendar, Luma, Circleback, Elasticsearch, Obsidian y más por venir. +NotebookLM es una de las mejores y más útiles plataformas de IA que existen, pero una vez que comienzas a usarla regularmente también sientes sus limitaciones dejando algo que desear. + +1. Hay límites en la cantidad de fuentes que puedes agregar en un notebook. +2. Hay límites en la cantidad de notebooks que puedes tener. +3. No puedes tener fuentes que excedan 500,000 palabras y más de 200MB. +4. Estás bloqueado con los servicios de Google (LLMs, modelos de uso, etc.) sin opción de configurarlos. +5. Fuentes de datos externas e integraciones de servicios limitadas. +6. El agente de NotebookLM está específicamente optimizado solo para estudiar e investigar, pero puedes hacer mucho más con los datos de origen. +7. Falta de soporte multijugador. + +...y más. + +**SurfSense está específicamente hecho para resolver estos problemas.** SurfSense te permite: + +- **Controla Tu Flujo de Datos** - Mantén tus datos privados y seguros. +- **Sin Límites de Datos** - Agrega una cantidad ilimitada de fuentes y notebooks. +- **Sin Dependencia de Proveedores** - Configura cualquier modelo LLM, de imagen, TTS y STT. +- **25+ Fuentes de Datos Externas** - Agrega tus fuentes desde Google Drive, OneDrive, Dropbox, Notion y muchos otros servicios externos. +- **Soporte Multijugador en Tiempo Real** - Trabaja fácilmente con los miembros de tu equipo en un notebook compartido. + +...y más por venir. @@ -34,7 +53,7 @@ https://github.com/user-attachments/assets/cc0c84d3-1f2f-4f7a-b519-2ecce22310b1 ## Ejemplo de Agente de Video -https://github.com/user-attachments/assets/cc977e6d-8292-4ffe-abb8-3b0560ef5562 +https://github.com/user-attachments/assets/012a7ffa-6f76-4f06-9dda-7632b470057a @@ -133,24 +152,29 @@ Para Docker Compose, instalación manual y otras opciones de despliegue, consult

Comentarios en Tiempo Real

-## Funcionalidades Principales +## SurfSense vs Google NotebookLM -| Funcionalidad | Descripción | -|----------------|-------------| -| Alternativa OSS | Reemplazo directo de NotebookLM, Perplexity y Glean con colaboración en equipo en tiempo real | -| 50+ Formatos de Archivo | Sube documentos, imágenes, videos vía LlamaCloud, Unstructured o Docling (local) | -| Búsqueda Híbrida | Semántica + Texto completo con Índices Jerárquicos y Reciprocal Rank Fusion | -| Respuestas con Citas | Chatea con tu base de conocimiento y obtén respuestas citadas al estilo Perplexity | -| Arquitectura de Agentes Profundos | Impulsado por [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) con planificación, subagentes y acceso al sistema de archivos | -| Soporte Universal de LLM | 100+ LLMs, 6000+ modelos de embeddings, todos los principales rerankers vía OpenAI spec y LiteLLM | -| Privacidad Primero | Soporte completo de LLM local (vLLM, Ollama) tus datos son tuyos | -| Colaboración en Equipo | RBAC con roles de Propietario / Admin / Editor / Visor, chat en tiempo real e hilos de comentarios | -| Generación de Videos | Genera videos con narración y visuales | -| Generación de Presentaciones | Crea presentaciones editables basadas en diapositivas | -| Generación de Podcasts | Podcast de 3 min en menos de 20 segundos; múltiples proveedores TTS (OpenAI, Azure, Kokoro) | -| Extensión de Navegador | Extensión multi-navegador para guardar cualquier página web, incluyendo páginas protegidas por autenticación | -| 27+ Conectores | Motores de búsqueda, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord y [más](#fuentes-externas) | -| Auto-Hospedable | Código abierto, Docker en un solo comando o Docker Compose completo para producción | +| Característica | Google NotebookLM | SurfSense | +|---------|-------------------|-----------| +| **Fuentes por Notebook** | 50 (Gratis) a 600 (Ultra, $249.99/mes) | Ilimitadas | +| **Número de Notebooks** | 100 (Gratis) a 500 (planes de pago) | Ilimitados | +| **Límite de Tamaño de Fuente** | 500,000 palabras / 200MB por fuente | Sin límite | +| **Precios** | Nivel gratuito disponible; Pro $19.99/mes, Ultra $249.99/mes | Gratuito y de código abierto, auto-hospedable en tu propia infra | +| **Soporte de LLM** | Solo Google Gemini | 100+ LLMs vía OpenAI spec y LiteLLM | +| **Modelos de Embeddings** | Solo Google | 6,000+ modelos de embeddings, todos los principales rerankers | +| **LLMs Locales / Privados** | No disponible | Soporte completo (vLLM, Ollama) - tus datos son tuyos | +| **Auto-Hospedable** | No | Sí - Docker en un solo comando o Docker Compose completo | +| **Código Abierto** | No | Sí | +| **Conectores Externos** | Google Drive, YouTube, sitios web | 27+ conectores - Motores de búsqueda, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord y [más](#fuentes-externas) | +| **Soporte de Formatos de Archivo** | PDFs, Docs, Slides, Sheets, CSV, Word, EPUB, imágenes, URLs web, YouTube | 50+ formatos - documentos, imágenes, videos vía LlamaCloud, Unstructured o Docling (local) | +| **Búsqueda** | Búsqueda semántica | Búsqueda Híbrida - Semántica + Texto completo con Índices Jerárquicos y Reciprocal Rank Fusion | +| **Respuestas con Citas** | Sí | Sí - Respuestas citadas al estilo Perplexity | +| **Arquitectura de Agentes** | No | Sí - impulsado por [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) con planificación, subagentes y acceso al sistema de archivos | +| **Multijugador en Tiempo Real** | Notebooks compartidos con roles de Visor/Editor (sin chat en tiempo real) | RBAC con roles de Propietario / Admin / Editor / Visor, chat en tiempo real e hilos de comentarios | +| **Generación de Videos** | Resúmenes en video cinemáticos vía Veo 3 (solo Ultra) | Disponible (NotebookLM es mejor aquí, mejorando activamente) | +| **Generación de Presentaciones** | Diapositivas más atractivas pero no editables | Crea presentaciones editables basadas en diapositivas | +| **Generación de Podcasts** | Resúmenes de audio con hosts e idiomas personalizables | Disponible con múltiples proveedores TTS (NotebookLM es mejor aquí, mejorando activamente) | +| **Extensión de Navegador** | No | Extensión multi-navegador para guardar cualquier página web, incluyendo páginas protegidas por autenticación |
Lista completa de Fuentes Externas diff --git a/README.hi.md b/README.hi.md index 011dbf5db..b49bddc72 100644 --- a/README.hi.md +++ b/README.hi.md @@ -21,9 +21,28 @@ # SurfSense -किसी भी LLM को अपने आंतरिक ज्ञान स्रोतों से जोड़ें और अपनी टीम के साथ रीयल-टाइम में चैट करें। NotebookLM, Perplexity और Glean का ओपन सोर्स विकल्प। -SurfSense एक अत्यधिक अनुकूलन योग्य AI शोध एजेंट है, जो बाहरी स्रोतों से जुड़ा है जैसे सर्च इंजन (SearxNG, Tavily, LinkUp), Google Drive, OneDrive, Dropbox, Slack, Microsoft Teams, Linear, Jira, ClickUp, Confluence, BookStack, Gmail, Notion, YouTube, GitHub, Discord, Airtable, Google Calendar, Luma, Circleback, Elasticsearch, Obsidian और भी बहुत कुछ आने वाला है। +NotebookLM वहाँ उपलब्ध सबसे अच्छे और सबसे उपयोगी AI प्लेटफ़ॉर्म में से एक है, लेकिन जब आप इसे नियमित रूप से उपयोग करना शुरू करते हैं तो आप इसकी सीमाओं को भी महसूस करते हैं जो कुछ और की चाह छोड़ती हैं। + +1. एक notebook में जोड़े जा सकने वाले स्रोतों की मात्रा पर सीमाएं हैं। +2. आपके पास कितने notebooks हो सकते हैं इस पर सीमाएं हैं। +3. आपके पास ऐसे स्रोत नहीं हो सकते जो 500,000 शब्दों और 200MB से अधिक हों। +4. आप Google सेवाओं (LLMs, उपयोग मॉडल, आदि) में बंद हैं और उन्हें कॉन्फ़िगर करने का कोई विकल्प नहीं है। +5. सीमित बाहरी डेटा स्रोत और सेवा एकीकरण। +6. NotebookLM एजेंट विशेष रूप से केवल अध्ययन और शोध के लिए अनुकूलित है, लेकिन आप स्रोत डेटा के साथ और भी बहुत कुछ कर सकते हैं। +7. मल्टीप्लेयर सपोर्ट की कमी। + +...और भी बहुत कुछ। + +**SurfSense विशेष रूप से इन समस्याओं को हल करने के लिए बनाया गया है।** SurfSense आपको सक्षम बनाता है: + +- **अपने डेटा प्रवाह को नियंत्रित करें** - अपने डेटा को निजी और सुरक्षित रखें। +- **कोई डेटा सीमा नहीं** - असीमित मात्रा में स्रोत और notebooks जोड़ें। +- **कोई विक्रेता लॉक-इन नहीं** - किसी भी LLM, इमेज, TTS और STT मॉडल को कॉन्फ़िगर करें। +- **25+ बाहरी डेटा स्रोत** - Google Drive, OneDrive, Dropbox, Notion और कई अन्य बाहरी सेवाओं से अपने स्रोत जोड़ें। +- **रीयल-टाइम मल्टीप्लेयर सपोर्ट** - एक साझा notebook में अपनी टीम के सदस्यों के साथ आसानी से काम करें। + +...और भी बहुत कुछ आने वाला है। @@ -34,7 +53,7 @@ https://github.com/user-attachments/assets/cc0c84d3-1f2f-4f7a-b519-2ecce22310b1 ## वीडियो एजेंट नमूना -https://github.com/user-attachments/assets/cc977e6d-8292-4ffe-abb8-3b0560ef5562 +https://github.com/user-attachments/assets/012a7ffa-6f76-4f06-9dda-7632b470057a @@ -133,24 +152,29 @@ Docker Compose, मैनुअल इंस्टॉलेशन और अन

रीयल-टाइम कमेंट्स

-## प्रमुख विशेषताएं +## SurfSense vs Google NotebookLM -| विशेषता | विवरण | -|----------|--------| -| OSS विकल्प | रीयल-टाइम टीम सहयोग के साथ NotebookLM, Perplexity और Glean का सीधा प्रतिस्थापन | -| 50+ फ़ाइल फ़ॉर्मेट | LlamaCloud, Unstructured या Docling (लोकल) के माध्यम से दस्तावेज़, चित्र, वीडियो अपलोड करें | -| हाइब्रिड सर्च | हायरार्किकल इंडाइसेस और Reciprocal Rank Fusion के साथ सिमैंटिक + फुल टेक्स्ट सर्च | -| उद्धृत उत्तर | अपने ज्ञान आधार के साथ चैट करें और Perplexity शैली के उद्धृत उत्तर पाएं | -| डीप एजेंट आर्किटेक्चर | [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) द्वारा संचालित, योजना, सब-एजेंट और फ़ाइल सिस्टम एक्सेस | -| यूनिवर्सल LLM सपोर्ट | 100+ LLMs, 6000+ एम्बेडिंग मॉडल, सभी प्रमुख रीरैंकर्स OpenAI spec और LiteLLM के माध्यम से | -| प्राइवेसी फर्स्ट | पूर्ण लोकल LLM सपोर्ट (vLLM, Ollama) आपका डेटा आपका रहता है | -| टीम सहयोग | मालिक / एडमिन / संपादक / दर्शक भूमिकाओं के साथ RBAC, रीयल-टाइम चैट और कमेंट थ्रेड | -| वीडियो जनरेशन | नैरेशन और विज़ुअल के साथ वीडियो बनाएं | -| प्रेजेंटेशन जनरेशन | संपादन योग्य, स्लाइड आधारित प्रेजेंटेशन बनाएं | -| पॉडकास्ट जनरेशन | 20 सेकंड से कम में 3 मिनट का पॉडकास्ट; कई TTS प्रदाता (OpenAI, Azure, Kokoro) | -| ब्राउज़र एक्सटेंशन | किसी भी वेबपेज को सहेजने के लिए क्रॉस-ब्राउज़र एक्सटेंशन, प्रमाणीकरण सुरक्षित पेज सहित | -| 27+ कनेक्टर्स | सर्च इंजन, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord और [अधिक](#बाहरी-स्रोत) | -| सेल्फ-होस्ट करने योग्य | ओपन सोर्स, Docker एक कमांड या प्रोडक्शन के लिए पूर्ण Docker Compose | +| विशेषता | Google NotebookLM | SurfSense | +|---------|-------------------|-----------| +| **प्रति Notebook स्रोत** | 50 (मुफ़्त) से 600 (Ultra, $249.99/माह) | असीमित | +| **Notebooks की संख्या** | 100 (मुफ़्त) से 500 (सशुल्क योजनाएं) | असीमित | +| **स्रोत आकार सीमा** | 500,000 शब्द / 200MB प्रति स्रोत | कोई सीमा नहीं | +| **मूल्य निर्धारण** | मुफ़्त स्तर उपलब्ध; Pro $19.99/माह, Ultra $249.99/माह | मुफ़्त और ओपन सोर्स, अपनी इंफ्रा पर सेल्फ-होस्ट करें | +| **LLM सपोर्ट** | केवल Google Gemini | 100+ LLMs OpenAI spec और LiteLLM के माध्यम से | +| **एम्बेडिंग मॉडल** | केवल Google | 6,000+ एम्बेडिंग मॉडल, सभी प्रमुख रीरैंकर्स | +| **लोकल / प्राइवेट LLMs** | उपलब्ध नहीं | पूर्ण सपोर्ट (vLLM, Ollama) - आपका डेटा आपका रहता है | +| **सेल्फ-होस्ट करने योग्य** | नहीं | हाँ - Docker एक कमांड या पूर्ण Docker Compose | +| **ओपन सोर्स** | नहीं | हाँ | +| **बाहरी कनेक्टर्स** | Google Drive, YouTube, वेबसाइटें | 27+ कनेक्टर्स - सर्च इंजन, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord और [अधिक](#बाहरी-स्रोत) | +| **फ़ाइल फ़ॉर्मेट सपोर्ट** | PDFs, Docs, Slides, Sheets, CSV, Word, EPUB, इमेज, वेब URLs, YouTube | 50+ फ़ॉर्मेट - दस्तावेज़, इमेज, वीडियो LlamaCloud, Unstructured या Docling (लोकल) के माध्यम से | +| **सर्च** | सिमैंटिक सर्च | हाइब्रिड सर्च - हायरार्किकल इंडाइसेस और Reciprocal Rank Fusion के साथ सिमैंटिक + फुल टेक्स्ट | +| **उद्धृत उत्तर** | हाँ | हाँ - Perplexity शैली के उद्धृत उत्तर | +| **एजेंट आर्किटेक्चर** | नहीं | हाँ - [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) द्वारा संचालित, योजना, सब-एजेंट और फ़ाइल सिस्टम एक्सेस | +| **रीयल-टाइम मल्टीप्लेयर** | दर्शक/संपादक भूमिकाओं के साथ साझा notebooks (कोई रीयल-टाइम चैट नहीं) | मालिक / एडमिन / संपादक / दर्शक भूमिकाओं के साथ RBAC, रीयल-टाइम चैट और कमेंट थ्रेड | +| **वीडियो जनरेशन** | Veo 3 के माध्यम से सिनेमैटिक वीडियो ओवरव्यू (केवल Ultra) | उपलब्ध (NotebookLM यहाँ बेहतर है, सक्रिय रूप से सुधार हो रहा है) | +| **प्रेजेंटेशन जनरेशन** | बेहतर दिखने वाली स्लाइड्स लेकिन संपादन योग्य नहीं | संपादन योग्य, स्लाइड आधारित प्रेजेंटेशन बनाएं | +| **पॉडकास्ट जनरेशन** | कस्टमाइज़ेबल होस्ट और भाषाओं के साथ ऑडियो ओवरव्यू | कई TTS प्रदाताओं के साथ उपलब्ध (NotebookLM यहाँ बेहतर है, सक्रिय रूप से सुधार हो रहा है) | +| **ब्राउज़र एक्सटेंशन** | नहीं | किसी भी वेबपेज को सहेजने के लिए क्रॉस-ब्राउज़र एक्सटेंशन, प्रमाणीकरण सुरक्षित पेज सहित |
बाहरी स्रोतों की पूरी सूची diff --git a/README.md b/README.md index f007fd43c..12ea4912a 100644 --- a/README.md +++ b/README.md @@ -21,9 +21,28 @@ # SurfSense -Connect any LLM to your internal knowledge sources and chat with it in real time alongside your team. OSS alternative to NotebookLM, Perplexity, and Glean. -SurfSense is a highly customizable AI research agent, connected to external sources such as Search Engines (SearxNG, Tavily, LinkUp), Google Drive, OneDrive, Dropbox, Slack, Microsoft Teams, Linear, Jira, ClickUp, Confluence, BookStack, Gmail, Notion, YouTube, GitHub, Discord, Airtable, Google Calendar, Luma, Circleback, Elasticsearch, Obsidian and more to come. +NotebookLM is one of the best and most useful AI platforms out there, but once you start using it regularly you also feel its limitations leaving something to be desired more. + +1. There are limits on the amount of sources you can add in a notebook. +2. There are limits on the number of notebooks you can have. +3. You cannot have sources that exceed 500,000 words and are more than 200MB. +4. You are vendor locked in to Google services (LLMs, usage models, etc.) with no option to configure them. +5. Limited external data sources and service integrations. +6. NotebookLM Agent is specifically optimised for just studying and researching, but you can do so much more with the source data. +7. Lack of multiplayer support. + +...and more. + +**SurfSense is specifically made to solve these problems.** SurfSense empowers you to: + +- **Control Your Data Flow** - Keep your data private and secure. +- **No Data Limits** - Add an unlimited amount of sources and notebooks. +- **No Vendor Lock-in** - Configure any LLM, image, TTS, and STT models to use. +- **25+ External Data Sources** - Add your sources from Google Drive, OneDrive, Dropbox, Notion, and many other external services. +- **Real-Time Multiplayer Support** - Work easily with your team members in a shared notebook. + +...and more to come. @@ -134,24 +153,29 @@ For Docker Compose, manual installation, and other deployment options, see the [

Realtime Comments

-## Key Features +## SurfSense vs Google NotebookLM -| Feature | Description | -|---------|-------------| -| OSS Alternative | Drop in replacement for NotebookLM, Perplexity, and Glean with real time team collaboration | -| 50+ File Formats | Upload documents, images, videos via LlamaCloud, Unstructured, or Docling (local) | -| Hybrid Search | Semantic + Full Text Search with Hierarchical Indices and Reciprocal Rank Fusion | -| Cited Answers | Chat with your knowledge base and get Perplexity style cited responses | -| Deep Agent Architecture | Powered by [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) planning, subagents, and file system access | -| Universal LLM Support | 100+ LLMs, 6000+ embedding models, all major rerankers via OpenAI spec & LiteLLM | -| Privacy First | Full local LLM support (vLLM, Ollama) your data stays yours | -| Team Collaboration | RBAC with Owner / Admin / Editor / Viewer roles, real time chat & comment threads | -| Video Generation | Generate videos with narration and visuals | -| Presentation Generation | Create editable, slide based presentations | -| Podcast Generation | 3 min podcast in under 20 seconds; multiple TTS providers (OpenAI, Azure, Kokoro) | -| Browser Extension | Cross browser extension to save any webpage, including auth protected pages | -| 27+ Connectors | Search Engines, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord & [more](#external-sources) | -| Self Hostable | Open source, Docker one liner or full Docker Compose for production | +| Feature | Google NotebookLM | SurfSense | +|---------|-------------------|-----------| +| **Sources per Notebook** | 50 (Free) to 600 (Ultra, $249.99/mo) | Unlimited | +| **Number of Notebooks** | 100 (Free) to 500 (paid tiers) | Unlimited | +| **Source Size Limit** | 500,000 words / 200MB per source | No limit | +| **Pricing** | Free tier available; Pro $19.99/mo, Ultra $249.99/mo | Free and open source, self-host on your own infra | +| **LLM Support** | Google Gemini only | 100+ LLMs via OpenAI spec & LiteLLM | +| **Embedding Models** | Google only | 6,000+ embedding models, all major rerankers | +| **Local / Private LLMs** | Not available | Full support (vLLM, Ollama) - your data stays yours | +| **Self Hostable** | No | Yes - Docker one-liner or full Docker Compose | +| **Open Source** | No | Yes | +| **External Connectors** | Google Drive, YouTube, websites | 27+ connectors - Search Engines, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord & [more](#external-sources) | +| **File Format Support** | PDFs, Docs, Slides, Sheets, CSV, Word, EPUB, images, web URLs, YouTube | 50+ formats - documents, images, videos via LlamaCloud, Unstructured, or Docling (local) | +| **Search** | Semantic search | Hybrid Search - Semantic + Full Text with Hierarchical Indices & Reciprocal Rank Fusion | +| **Cited Answers** | Yes | Yes - Perplexity-style cited responses | +| **Agentic Architecture** | No | Yes - powered by [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) with planning, subagents, and file system access | +| **Real-Time Multiplayer** | Shared notebooks with Viewer/Editor roles (no real-time chat) | RBAC with Owner / Admin / Editor / Viewer roles, real-time chat & comment threads | +| **Video Generation** | Cinematic Video Overviews via Veo 3 (Ultra only) | Available (NotebookLM is better here, actively improving) | +| **Presentation Generation** | Better looking slides but not editable | Create editable, slide-based presentations | +| **Podcast Generation** | Audio Overviews with customizable hosts and languages | Available with multiple TTS providers (NotebookLM is better here, actively improving) | +| **Browser Extension** | No | Cross-browser extension to save any webpage, including auth-protected pages |
Full list of External Sources diff --git a/README.pt-BR.md b/README.pt-BR.md index 4306b0767..50a8b739e 100644 --- a/README.pt-BR.md +++ b/README.pt-BR.md @@ -21,9 +21,28 @@ # SurfSense -Conecte qualquer LLM às suas fontes de conhecimento internas e converse com ele em tempo real junto com sua equipe. Alternativa de código aberto ao NotebookLM, Perplexity e Glean. -SurfSense é um agente de pesquisa de IA altamente personalizável, conectado a fontes externas como mecanismos de busca (SearxNG, Tavily, LinkUp), Google Drive, OneDrive, Dropbox, Slack, Microsoft Teams, Linear, Jira, ClickUp, Confluence, BookStack, Gmail, Notion, YouTube, GitHub, Discord, Airtable, Google Calendar, Luma, Circleback, Elasticsearch, Obsidian e mais por vir. +O NotebookLM é uma das melhores e mais úteis plataformas de IA disponíveis, mas quando você começa a usá-lo regularmente também sente suas limitações deixando algo a desejar. + +1. Há limites na quantidade de fontes que você pode adicionar em um notebook. +2. Há limites no número de notebooks que você pode ter. +3. Você não pode ter fontes que excedam 500.000 palavras e mais de 200MB. +4. Você fica preso aos serviços do Google (LLMs, modelos de uso, etc.) sem opção de configurá-los. +5. Fontes de dados externas e integrações de serviços limitadas. +6. O agente do NotebookLM é especificamente otimizado apenas para estudar e pesquisar, mas você pode fazer muito mais com os dados de origem. +7. Falta de suporte multiplayer. + +...e mais. + +**O SurfSense foi feito especificamente para resolver esses problemas.** O SurfSense permite que você: + +- **Controle Seu Fluxo de Dados** - Mantenha seus dados privados e seguros. +- **Sem Limites de Dados** - Adicione uma quantidade ilimitada de fontes e notebooks. +- **Sem Dependência de Fornecedor** - Configure qualquer modelo LLM, de imagem, TTS e STT. +- **25+ Fontes de Dados Externas** - Adicione suas fontes do Google Drive, OneDrive, Dropbox, Notion e muitos outros serviços externos. +- **Suporte Multiplayer em Tempo Real** - Trabalhe facilmente com os membros da sua equipe em um notebook compartilhado. + +...e mais por vir. @@ -34,7 +53,7 @@ https://github.com/user-attachments/assets/cc0c84d3-1f2f-4f7a-b519-2ecce22310b1 ## Exemplo de Agente de Vídeo -https://github.com/user-attachments/assets/cc977e6d-8292-4ffe-abb8-3b0560ef5562 +https://github.com/user-attachments/assets/012a7ffa-6f76-4f06-9dda-7632b470057a @@ -133,24 +152,29 @@ Para Docker Compose, instalação manual e outras opções de implantação, con

Comentários em Tempo Real

-## Funcionalidades Principais +## SurfSense vs Google NotebookLM -| Funcionalidade | Descrição | -|----------------|-----------| -| Alternativa OSS | Substituto direto do NotebookLM, Perplexity e Glean com colaboração em equipe em tempo real | -| 50+ Formatos de Arquivo | Faça upload de documentos, imagens, vídeos via LlamaCloud, Unstructured ou Docling (local) | -| Busca Híbrida | Semântica + Texto completo com Índices Hierárquicos e Reciprocal Rank Fusion | -| Respostas com Citações | Converse com sua base de conhecimento e obtenha respostas citadas no estilo Perplexity | -| Arquitetura de Agentes Profundos | Alimentado por [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) com planejamento, subagentes e acesso ao sistema de arquivos | -| Suporte Universal de LLM | 100+ LLMs, 6000+ modelos de embeddings, todos os principais rerankers via OpenAI spec e LiteLLM | -| Privacidade em Primeiro Lugar | Suporte completo a LLM local (vLLM, Ollama) seus dados ficam com você | -| Colaboração em Equipe | RBAC com papéis de Proprietário / Admin / Editor / Visualizador, chat em tempo real e threads de comentários | -| Geração de Vídeos | Gera vídeos com narração e visuais | -| Geração de Apresentações | Cria apresentações editáveis baseadas em slides | -| Geração de Podcasts | Podcast de 3 min em menos de 20 segundos; múltiplos provedores TTS (OpenAI, Azure, Kokoro) | -| Extensão de Navegador | Extensão multi-navegador para salvar qualquer página web, incluindo páginas protegidas por autenticação | -| 27+ Conectores | Mecanismos de busca, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord e [mais](#fontes-externas) | -| Auto-Hospedável | Código aberto, Docker em um único comando ou Docker Compose completo para produção | +| Recurso | Google NotebookLM | SurfSense | +|---------|-------------------|-----------| +| **Fontes por Notebook** | 50 (Grátis) a 600 (Ultra, $249.99/mês) | Ilimitadas | +| **Número de Notebooks** | 100 (Grátis) a 500 (planos pagos) | Ilimitados | +| **Limite de Tamanho da Fonte** | 500.000 palavras / 200MB por fonte | Sem limite | +| **Preços** | Nível gratuito disponível; Pro $19.99/mês, Ultra $249.99/mês | Gratuito e de código aberto, auto-hospedável na sua própria infra | +| **Suporte a LLM** | Apenas Google Gemini | 100+ LLMs via OpenAI spec e LiteLLM | +| **Modelos de Embeddings** | Apenas Google | 6.000+ modelos de embeddings, todos os principais rerankers | +| **LLMs Locais / Privados** | Não disponível | Suporte completo (vLLM, Ollama) - seus dados ficam com você | +| **Auto-Hospedável** | Não | Sim - Docker em um único comando ou Docker Compose completo | +| **Código Aberto** | Não | Sim | +| **Conectores Externos** | Google Drive, YouTube, sites | 27+ conectores - Mecanismos de busca, Google Drive, OneDrive, Dropbox, Slack, Teams, Jira, Notion, GitHub, Discord e [mais](#fontes-externas) | +| **Suporte a Formatos de Arquivo** | PDFs, Docs, Slides, Sheets, CSV, Word, EPUB, imagens, URLs web, YouTube | 50+ formatos - documentos, imagens, vídeos via LlamaCloud, Unstructured ou Docling (local) | +| **Busca** | Busca semântica | Busca Híbrida - Semântica + Texto completo com Índices Hierárquicos e Reciprocal Rank Fusion | +| **Respostas com Citações** | Sim | Sim - Respostas citadas no estilo Perplexity | +| **Arquitetura de Agentes** | Não | Sim - alimentado por [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) com planejamento, subagentes e acesso ao sistema de arquivos | +| **Multiplayer em Tempo Real** | Notebooks compartilhados com papéis de Visualizador/Editor (sem chat em tempo real) | RBAC com papéis de Proprietário / Admin / Editor / Visualizador, chat em tempo real e threads de comentários | +| **Geração de Vídeos** | Visões gerais cinemáticas via Veo 3 (apenas Ultra) | Disponível (NotebookLM é melhor aqui, melhorando ativamente) | +| **Geração de Apresentações** | Slides mais bonitos mas não editáveis | Cria apresentações editáveis baseadas em slides | +| **Geração de Podcasts** | Visões gerais em áudio com hosts e idiomas personalizáveis | Disponível com múltiplos provedores TTS (NotebookLM é melhor aqui, melhorando ativamente) | +| **Extensão de Navegador** | Não | Extensão multi-navegador para salvar qualquer página web, incluindo páginas protegidas por autenticação |
Lista completa de Fontes Externas diff --git a/README.zh-CN.md b/README.zh-CN.md index 96ebb25ad..419a831ae 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -21,9 +21,28 @@ # SurfSense -将任何 LLM 连接到您的内部知识源，并与团队成员实时聊天。NotebookLM、Perplexity 和 Glean 的开源替代方案。 -SurfSense 是一个高度可定制的 AI 研究助手，可以连接外部数据源，如搜索引擎（SearxNG、Tavily、LinkUp）、Google Drive、OneDrive、Dropbox、Slack、Microsoft Teams、Linear、Jira、ClickUp、Confluence、BookStack、Gmail、Notion、YouTube、GitHub、Discord、Airtable、Google Calendar、Luma、Circleback、Elasticsearch、Obsidian 等，未来还会支持更多。 +NotebookLM 是目前最好、最实用的 AI 平台之一，但当你开始经常使用它时，你也会感受到它的局限性，总觉得还有不足之处。 + +1. 一个笔记本中可以添加的来源数量有限制。 +2. 可以拥有的笔记本数量有限制。 +3. 来源不能超过 500,000 个单词和 200MB。 +4. 你被锁定在 Google 服务中（LLM、使用模型等），没有配置选项。 +5. 有限的外部数据源和服务集成。 +6. NotebookLM 代理专门针对学习和研究进行了优化，但你可以用源数据做更多事情。 +7. 缺乏多人协作支持。 + +...还有更多。 + +**SurfSense 正是为了解决这些问题而生。** SurfSense 赋予你： + +- **控制你的数据流** - 保持数据私密和安全。 +- **无数据限制** - 添加无限数量的来源和笔记本。 +- **无供应商锁定** - 配置任何 LLM、图像、TTS 和 STT 模型。 +- **25+ 外部数据源** - 从 Google Drive、OneDrive、Dropbox、Notion 和许多其他外部服务添加你的来源。 +- **实时多人协作支持** - 在共享笔记本中轻松与团队成员协作。 + +...更多功能即将推出。 @@ -34,7 +53,7 @@ https://github.com/user-attachments/assets/cc0c84d3-1f2f-4f7a-b519-2ecce22310b1 ## 视频代理示例 -https://github.com/user-attachments/assets/cc977e6d-8292-4ffe-abb8-3b0560ef5562 +https://github.com/user-attachments/assets/012a7ffa-6f76-4f06-9dda-7632b470057a @@ -133,24 +152,29 @@ irm https://raw.githubusercontent.com/MODSetter/SurfSense/main/docker/scripts/in

实时评论

-## 核心功能 +## SurfSense vs Google NotebookLM -| 功能 | 描述 | -|------|------| -| 开源替代方案 | 支持实时团队协作的 NotebookLM、Perplexity 和 Glean 替代品 | -| 50+ 文件格式 | 通过 LlamaCloud、Unstructured 或 Docling（本地）上传文档、图像、视频 | -| 混合搜索 | 语义搜索 + 全文搜索，结合层次化索引和倒数排名融合 | -| 引用回答 | 与知识库对话，获得 Perplexity 风格的引用回答 | -| 深度代理架构 | 基于 [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) 构建，支持规划、子代理和文件系统访问 | -| 通用 LLM 支持 | 100+ LLM、6000+ 嵌入模型、所有主流重排序器，通过 OpenAI spec 和 LiteLLM | -| 隐私优先 | 完整本地 LLM 支持（vLLM、Ollama），您的数据由您掌控 | -| 团队协作 | RBAC 角色控制（所有者/管理员/编辑者/查看者），实时聊天和评论线程 | -| 视频生成 | 生成带有旁白和视觉效果的视频 | -| 演示文稿生成 | 创建可编辑的幻灯片式演示文稿 | -| 播客生成 | 20 秒内生成 3 分钟播客；多种 TTS 提供商（OpenAI、Azure、Kokoro） | -| 浏览器扩展 | 跨浏览器扩展，保存任何网页，包括需要身份验证的页面 | -| 27+ 连接器 | 搜索引擎、Google Drive、OneDrive、Dropbox、Slack、Teams、Jira、Notion、GitHub、Discord 等[更多](#外部数据源) | -| 可自托管 | 开源，Docker 一行命令或完整 Docker Compose 用于生产环境 | +| 功能 | Google NotebookLM | SurfSense | +|---------|-------------------|-----------| +| **每个笔记本的来源数** | 50（免费）到 600（Ultra，$249.99/月） | 无限制 | +| **笔记本数量** | 100（免费）到 500（付费方案） | 无限制 | +| **来源大小限制** | 500,000 词 / 200MB 每个来源 | 无限制 | +| **定价** | 免费版可用；Pro $19.99/月，Ultra $249.99/月 | 免费开源，在自己的基础设施上自托管 | +| **LLM 支持** | 仅 Google Gemini | 100+ LLM，通过 OpenAI spec 和 LiteLLM | +| **嵌入模型** | 仅 Google | 6,000+ 嵌入模型，所有主流重排序器 | +| **本地 / 私有 LLM** | 不可用 | 完整支持（vLLM、Ollama）- 您的数据由您掌控 | +| **可自托管** | 否 | 是 - Docker 一行命令或完整 Docker Compose | +| **开源** | 否 | 是 | +| **外部连接器** | Google Drive、YouTube、网站 | 27+ 连接器 - 搜索引擎、Google Drive、OneDrive、Dropbox、Slack、Teams、Jira、Notion、GitHub、Discord 等[更多](#外部数据源) | +| **文件格式支持** | PDF、Docs、Slides、Sheets、CSV、Word、EPUB、图像、网页 URL、YouTube | 50+ 格式 - 文档、图像、视频，通过 LlamaCloud、Unstructured 或 Docling（本地） | +| **搜索** | 语义搜索 | 混合搜索 - 语义 + 全文搜索，结合层次化索引和倒数排名融合 | +| **引用回答** | 是 | 是 - Perplexity 风格的引用回答 | +| **代理架构** | 否 | 是 - 基于 [LangChain Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) 构建，支持规划、子代理和文件系统访问 | +| **实时多人协作** | 共享笔记本，支持查看者/编辑者角色（无实时聊天） | RBAC 角色控制（所有者/管理员/编辑者/查看者），实时聊天和评论线程 | +| **视频生成** | 通过 Veo 3 的电影级视频概览（仅 Ultra） | 可用（NotebookLM 在此方面更好，正在积极改进） | +| **演示文稿生成** | 更美观的幻灯片但不可编辑 | 创建可编辑的幻灯片式演示文稿 | +| **播客生成** | 可自定义主持人和语言的音频概览 | 可用，支持多种 TTS 提供商（NotebookLM 在此方面更好，正在积极改进） | +| **浏览器扩展** | 否 | 跨浏览器扩展，保存任何网页，包括需要身份验证的页面 |
外部数据源完整列表 diff --git a/docs/chinese-llm-setup.md b/docs/chinese-llm-setup.md index 1fb0ce2a1..6638dbba1 100644 --- a/docs/chinese-llm-setup.md +++ b/docs/chinese-llm-setup.md @@ -24,7 +24,7 @@ SurfSense 现已支持以下国产 LLM： 1. 登录 SurfSense Dashboard 2. 进入 **Settings** → **API Keys** (或 **LLM Configurations**) -3. 点击 **Add LLM Model** +3. 点击 **Add Model** 4. 从 **Provider** 下拉菜单中选择你的国产 LLM 提供商 5. 填写必填字段（见下方各提供商详细配置） 6. 点击 **Save** diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 000000000..9703ac09f --- /dev/null +++ b/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "SurfSense", + "lockfileVersion": 3, + "requires": true, + "packages": {} +} diff --git a/surfsense_backend/alembic/versions/116_create_zero_publication.py b/surfsense_backend/alembic/versions/116_create_zero_publication.py index 8f0d7b5d3..ff74952a9 100644 --- a/surfsense_backend/alembic/versions/116_create_zero_publication.py +++ b/surfsense_backend/alembic/versions/116_create_zero_publication.py @@ -42,9 +42,7 @@ def upgrade() -> None: if not exists: table_list = ", ".join(TABLES) conn.execute( - sa.text( - f"CREATE PUBLICATION {PUBLICATION_NAME} FOR TABLE {table_list}" - ) + sa.text(f"CREATE PUBLICATION {PUBLICATION_NAME} FOR TABLE {table_list}") ) diff --git a/surfsense_backend/alembic/versions/117_optimize_zero_publication_column_lists.py b/surfsense_backend/alembic/versions/117_optimize_zero_publication_column_lists.py new file mode 100644 index 000000000..78a26a381 --- /dev/null +++ b/surfsense_backend/alembic/versions/117_optimize_zero_publication_column_lists.py @@ -0,0 +1,123 @@ +"""optimize zero_publication with column lists + +Recreates the zero_publication using column lists for the documents +table so that large text columns (content, source_markdown, +blocknote_document, etc.) are excluded from WAL replication. +This prevents RangeError: Invalid string length in zero-cache's +change-streamer when documents have very large content. + +Also resets REPLICA IDENTITY to DEFAULT on tables that had it set +to FULL for the old Electric SQL setup (migration 66/75/76). +With DEFAULT (primary-key) identity, column-list publications +only need to include the PK — not every column. + +IMPORTANT — before AND after running this migration: + 1. Stop zero-cache (it holds replication locks that will deadlock DDL) + 2. Run: alembic upgrade head + 3. Delete / reset the zero-cache data volume + 4. Restart zero-cache (it will do a fresh initial sync) + +Revision ID: 117 +Revises: 116 +""" + +from collections.abc import Sequence + +import sqlalchemy as sa + +from alembic import op + +revision: str = "117" +down_revision: str | None = "116" +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + +PUBLICATION_NAME = "zero_publication" + +TABLES_WITH_FULL_IDENTITY = [ + "documents", + "notifications", + "search_source_connectors", + "new_chat_messages", + "chat_comments", + "chat_session_state", +] + +DOCUMENT_COLS = [ + "id", + "title", + "document_type", + "search_space_id", + "folder_id", + "created_by_id", + "status", + "created_at", + "updated_at", +] + +PUBLICATION_DDL_FULL = f"""\ +CREATE PUBLICATION {PUBLICATION_NAME} FOR TABLE + notifications, documents, folders, + search_source_connectors, new_chat_messages, + chat_comments, chat_session_state +""" + + +def _terminate_blocked_pids(conn, table: str) -> None: + """Kill backends whose locks on *table* would block our AccessExclusiveLock.""" + conn.execute( + sa.text( + "SELECT pg_terminate_backend(l.pid) " + "FROM pg_locks l " + "JOIN pg_class c ON c.oid = l.relation " + "WHERE c.relname = :tbl " + " AND l.pid != pg_backend_pid()" + ), + {"tbl": table}, + ) + + +def upgrade() -> None: + conn = op.get_bind() + + conn.execute(sa.text("SET lock_timeout = '10s'")) + + for tbl in sorted(TABLES_WITH_FULL_IDENTITY): + _terminate_blocked_pids(conn, tbl) + conn.execute(sa.text(f'LOCK TABLE "{tbl}" IN ACCESS EXCLUSIVE MODE')) + + for tbl in TABLES_WITH_FULL_IDENTITY: + conn.execute(sa.text(f'ALTER TABLE "{tbl}" REPLICA IDENTITY DEFAULT')) + + conn.execute(sa.text(f"DROP PUBLICATION IF EXISTS {PUBLICATION_NAME}")) + + has_zero_ver = conn.execute( + sa.text( + "SELECT 1 FROM information_schema.columns " + "WHERE table_name = 'documents' AND column_name = '_0_version'" + ) + ).fetchone() + + cols = DOCUMENT_COLS + (['"_0_version"'] if has_zero_ver else []) + col_list = ", ".join(cols) + + conn.execute( + sa.text( + f"CREATE PUBLICATION {PUBLICATION_NAME} FOR TABLE " + f"notifications, " + f"documents ({col_list}), " + f"folders, " + f"search_source_connectors, " + f"new_chat_messages, " + f"chat_comments, " + f"chat_session_state" + ) + ) + + +def downgrade() -> None: + conn = op.get_bind() + conn.execute(sa.text(f"DROP PUBLICATION IF EXISTS {PUBLICATION_NAME}")) + conn.execute(sa.text(PUBLICATION_DDL_FULL)) + for tbl in TABLES_WITH_FULL_IDENTITY: + conn.execute(sa.text(f'ALTER TABLE "{tbl}" REPLICA IDENTITY FULL')) diff --git a/surfsense_backend/alembic/versions/118_add_local_folder_sync_and_versioning.py b/surfsense_backend/alembic/versions/118_add_local_folder_sync_and_versioning.py new file mode 100644 index 000000000..1fef9fbcb --- /dev/null +++ b/surfsense_backend/alembic/versions/118_add_local_folder_sync_and_versioning.py @@ -0,0 +1,149 @@ +"""Add LOCAL_FOLDER_FILE document type, folder metadata, and document_versions table + +Revision ID: 118 +Revises: 117 +""" + +from collections.abc import Sequence + +import sqlalchemy as sa + +from alembic import op + +revision: str = "118" +down_revision: str | None = "117" +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + +PUBLICATION_NAME = "zero_publication" + + +def upgrade() -> None: + conn = op.get_bind() + + # Add LOCAL_FOLDER_FILE to documenttype enum + op.execute( + """ + DO $$ + BEGIN + IF NOT EXISTS ( + SELECT 1 FROM pg_type t + JOIN pg_enum e ON t.oid = e.enumtypid + WHERE t.typname = 'documenttype' AND e.enumlabel = 'LOCAL_FOLDER_FILE' + ) THEN + ALTER TYPE documenttype ADD VALUE 'LOCAL_FOLDER_FILE'; + END IF; + END + $$; + """ + ) + + # Add JSONB metadata column to folders table + col_exists = conn.execute( + sa.text( + "SELECT 1 FROM information_schema.columns " + "WHERE table_name = 'folders' AND column_name = 'metadata'" + ) + ).fetchone() + if not col_exists: + op.add_column( + "folders", + sa.Column("metadata", sa.dialects.postgresql.JSONB, nullable=True), + ) + + # Create document_versions table + table_exists = conn.execute( + sa.text( + "SELECT 1 FROM information_schema.tables WHERE table_name = 'document_versions'" + ) + ).fetchone() + if not table_exists: + op.create_table( + "document_versions", + sa.Column("id", sa.Integer(), nullable=False, autoincrement=True), + sa.Column("document_id", sa.Integer(), nullable=False), + sa.Column("version_number", sa.Integer(), nullable=False), + sa.Column("source_markdown", sa.Text(), nullable=True), + sa.Column("content_hash", sa.String(), nullable=False), + sa.Column("title", sa.String(), nullable=True), + sa.Column( + "created_at", + sa.TIMESTAMP(timezone=True), + server_default=sa.text("now()"), + nullable=False, + ), + sa.ForeignKeyConstraint( + ["document_id"], + ["documents.id"], + ondelete="CASCADE", + ), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint( + "document_id", + "version_number", + name="uq_document_version", + ), + ) + + op.execute( + "CREATE INDEX IF NOT EXISTS ix_document_versions_document_id " + "ON document_versions (document_id)" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_document_versions_created_at " + "ON document_versions (created_at)" + ) + + # Add document_versions to Zero publication + pub_exists = conn.execute( + sa.text("SELECT 1 FROM pg_publication WHERE pubname = :name"), + {"name": PUBLICATION_NAME}, + ).fetchone() + if pub_exists: + already_in_pub = conn.execute( + sa.text( + "SELECT 1 FROM pg_publication_tables " + "WHERE pubname = :name AND tablename = 'document_versions'" + ), + {"name": PUBLICATION_NAME}, + ).fetchone() + if not already_in_pub: + op.execute( + f"ALTER PUBLICATION {PUBLICATION_NAME} ADD TABLE document_versions" + ) + + +def downgrade() -> None: + conn = op.get_bind() + + # Remove from publication + pub_exists = conn.execute( + sa.text("SELECT 1 FROM pg_publication WHERE pubname = :name"), + {"name": PUBLICATION_NAME}, + ).fetchone() + if pub_exists: + already_in_pub = conn.execute( + sa.text( + "SELECT 1 FROM pg_publication_tables " + "WHERE pubname = :name AND tablename = 'document_versions'" + ), + {"name": PUBLICATION_NAME}, + ).fetchone() + if already_in_pub: + op.execute( + f"ALTER PUBLICATION {PUBLICATION_NAME} DROP TABLE document_versions" + ) + + op.execute("DROP INDEX IF EXISTS ix_document_versions_created_at") + op.execute("DROP INDEX IF EXISTS ix_document_versions_document_id") + op.execute("DROP TABLE IF EXISTS document_versions") + + # Drop metadata column from folders + col_exists = conn.execute( + sa.text( + "SELECT 1 FROM information_schema.columns " + "WHERE table_name = 'folders' AND column_name = 'metadata'" + ) + ).fetchone() + if col_exists: + op.drop_column("folders", "metadata") diff --git a/surfsense_backend/alembic/versions/51_add_new_llm_config_table.py b/surfsense_backend/alembic/versions/51_add_new_llm_config_table.py index 89a5c1246..7d90f4b13 100644 --- a/surfsense_backend/alembic/versions/51_add_new_llm_config_table.py +++ b/surfsense_backend/alembic/versions/51_add_new_llm_config_table.py @@ -17,10 +17,10 @@ depends_on: str | Sequence[str] | None = None def upgrade() -> None: """ - Add the new_llm_configs table that combines LLM model settings with prompt configuration. + Add the new_llm_configs table that combines model settings with prompt configuration. This table includes: - - LLM model configuration (provider, model_name, api_key, etc.) + - Model configuration (provider, model_name, api_key, etc.) - Configurable system instructions - Citation toggle """ @@ -41,7 +41,7 @@ def upgrade() -> None: name VARCHAR(100) NOT NULL, description VARCHAR(500), - -- LLM Model Configuration (same as llm_configs, excluding language) + -- Model Configuration (same as llm_configs, excluding language) provider litellmprovider NOT NULL, custom_provider VARCHAR(100), model_name VARCHAR(100) NOT NULL, diff --git a/surfsense_backend/app/agents/new_chat/chat_deepagent.py b/surfsense_backend/app/agents/new_chat/chat_deepagent.py index ccc06f272..fc1e80d28 100644 --- a/surfsense_backend/app/agents/new_chat/chat_deepagent.py +++ b/surfsense_backend/app/agents/new_chat/chat_deepagent.py @@ -159,6 +159,7 @@ async def create_surfsense_deep_agent( additional_tools: Sequence[BaseTool] | None = None, firecrawl_api_key: str | None = None, thread_visibility: ChatVisibility | None = None, + mentioned_document_ids: list[int] | None = None, ): """ Create a SurfSense deep agent with configurable tools and prompts. @@ -451,6 +452,7 @@ async def create_surfsense_deep_agent( search_space_id=search_space_id, available_connectors=available_connectors, available_document_types=available_document_types, + mentioned_document_ids=mentioned_document_ids, ), SurfSenseFilesystemMiddleware( search_space_id=search_space_id, diff --git a/surfsense_backend/app/agents/new_chat/middleware/filesystem.py b/surfsense_backend/app/agents/new_chat/middleware/filesystem.py index 41b24f88b..d7697ef15 100644 --- a/surfsense_backend/app/agents/new_chat/middleware/filesystem.py +++ b/surfsense_backend/app/agents/new_chat/middleware/filesystem.py @@ -66,6 +66,16 @@ the ``, identify chunks marked `matched="true"`, then use those sections instead of reading the entire file sequentially. Use `` values as citation IDs in your answers. + +## User-Mentioned Documents + +When the `ls` output tags a file with `[MENTIONED BY USER — read deeply]`, +the user **explicitly selected** that document. These files are your highest- +priority sources: +1. **Always read them thoroughly** — scan the full ``, then read + all major sections, not just matched chunks. +2. **Prefer their content** over other search results when answering. +3. **Cite from them first** whenever applicable. """ # ============================================================================= diff --git a/surfsense_backend/app/agents/new_chat/middleware/knowledge_search.py b/surfsense_backend/app/agents/new_chat/middleware/knowledge_search.py index 3728f229c..7b0dd2f71 100644 --- a/surfsense_backend/app/agents/new_chat/middleware/knowledge_search.py +++ b/surfsense_backend/app/agents/new_chat/middleware/knowledge_search.py @@ -28,7 +28,13 @@ from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession from app.agents.new_chat.utils import parse_date_or_datetime, resolve_date_range -from app.db import NATIVE_TO_LEGACY_DOCTYPE, Document, Folder, shielded_async_session +from app.db import ( + NATIVE_TO_LEGACY_DOCTYPE, + Chunk, + Document, + Folder, + shielded_async_session, +) from app.retriever.chunks_hybrid_search import ChucksHybridSearchRetriever from app.utils.document_converters import embed_texts from app.utils.perf import get_perf_logger @@ -430,21 +436,36 @@ async def _get_folder_paths( def _build_synthetic_ls( existing_files: dict[str, Any] | None, new_files: dict[str, Any], + *, + mentioned_paths: set[str] | None = None, ) -> tuple[AIMessage, ToolMessage]: """Build a synthetic ls("/documents") tool-call + result for the LLM context. - Paths are listed with *new* (rank-ordered) files first, then existing files - that were already in state from prior turns. + Mentioned files are listed first. A separate header tells the LLM which + files the user explicitly selected; the path list itself stays clean so + paths can be passed directly to ``read_file`` without stripping tags. """ + _mentioned = mentioned_paths or set() merged: dict[str, Any] = {**(existing_files or {}), **new_files} doc_paths = [ p for p, v in merged.items() if p.startswith("/documents/") and v is not None ] new_set = set(new_files) - new_paths = [p for p in doc_paths if p in new_set] + mentioned_list = [p for p in doc_paths if p in _mentioned] + new_non_mentioned = [p for p in doc_paths if p in new_set and p not in _mentioned] old_paths = [p for p in doc_paths if p not in new_set] - ordered = new_paths + old_paths + ordered = mentioned_list + new_non_mentioned + old_paths + + parts: list[str] = [] + if mentioned_list: + parts.append( + "USER-MENTIONED documents (read these thoroughly before answering):" + ) + for p in mentioned_list: + parts.append(f" {p}") + parts.append("") + parts.append(str(ordered) if ordered else "No documents found.") tool_call_id = f"auto_ls_{uuid.uuid4().hex[:12]}" ai_msg = AIMessage( @@ -452,7 +473,7 @@ def _build_synthetic_ls( tool_calls=[{"name": "ls", "args": {"path": "/documents"}, "id": tool_call_id}], ) tool_msg = ToolMessage( - content=str(ordered) if ordered else "No documents found.", + content="\n".join(parts), tool_call_id=tool_call_id, ) return ai_msg, tool_msg @@ -524,12 +545,92 @@ async def search_knowledge_base( return results[:top_k] +async def fetch_mentioned_documents( + *, + document_ids: list[int], + search_space_id: int, +) -> list[dict[str, Any]]: + """Fetch explicitly mentioned documents with *all* their chunks. + + Returns the same dict structure as ``search_knowledge_base`` so results + can be merged directly into ``build_scoped_filesystem``. Unlike search + results, every chunk is included (no top-K limiting) and none are marked + as ``matched`` since the entire document is relevant by virtue of the + user's explicit mention. + """ + if not document_ids: + return [] + + async with shielded_async_session() as session: + doc_result = await session.execute( + select(Document).where( + Document.id.in_(document_ids), + Document.search_space_id == search_space_id, + ) + ) + docs = {doc.id: doc for doc in doc_result.scalars().all()} + + if not docs: + return [] + + chunk_result = await session.execute( + select(Chunk.id, Chunk.content, Chunk.document_id) + .where(Chunk.document_id.in_(list(docs.keys()))) + .order_by(Chunk.document_id, Chunk.id) + ) + chunks_by_doc: dict[int, list[dict[str, Any]]] = {doc_id: [] for doc_id in docs} + for row in chunk_result.all(): + if row.document_id in chunks_by_doc: + chunks_by_doc[row.document_id].append( + {"chunk_id": row.id, "content": row.content} + ) + + results: list[dict[str, Any]] = [] + for doc_id in document_ids: + doc = docs.get(doc_id) + if doc is None: + continue + metadata = doc.document_metadata or {} + results.append( + { + "document_id": doc.id, + "content": "", + "score": 1.0, + "chunks": chunks_by_doc.get(doc.id, []), + "matched_chunk_ids": [], + "document": { + "id": doc.id, + "title": doc.title, + "document_type": ( + doc.document_type.value + if getattr(doc, "document_type", None) + else None + ), + "metadata": metadata, + }, + "source": ( + doc.document_type.value + if getattr(doc, "document_type", None) + else None + ), + "_user_mentioned": True, + } + ) + return results + + async def build_scoped_filesystem( *, documents: Sequence[dict[str, Any]], search_space_id: int, -) -> dict[str, dict[str, str]]: - """Build a StateBackend-compatible files dict from search results.""" +) -> tuple[dict[str, dict[str, str]], dict[int, str]]: + """Build a StateBackend-compatible files dict from search results. + + Returns ``(files, doc_id_to_path)`` so callers can reliably map a + document id back to its filesystem path without guessing by title. + Paths are collision-proof: when two documents resolve to the same + path the doc-id is appended to disambiguate. + """ async with shielded_async_session() as session: folder_paths = await _get_folder_paths(session, search_space_id) doc_ids = [ @@ -551,6 +652,7 @@ async def build_scoped_filesystem( } files: dict[str, dict[str, str]] = {} + doc_id_to_path: dict[int, str] = {} for document in documents: doc_meta = document.get("document") or {} title = str(doc_meta.get("title") or "untitled") @@ -559,6 +661,9 @@ async def build_scoped_filesystem( base_folder = folder_paths.get(folder_id, "/documents") file_name = _safe_filename(title) path = f"{base_folder}/{file_name}" + if path in files: + stem = file_name.removesuffix(".xml") + path = f"{base_folder}/{stem} ({doc_id}).xml" matched_ids = set(document.get("matched_chunk_ids") or []) xml_content = _build_document_xml(document, matched_chunk_ids=matched_ids) files[path] = { @@ -567,7 +672,9 @@ async def build_scoped_filesystem( "created_at": "", "modified_at": "", } - return files + if isinstance(doc_id, int): + doc_id_to_path[doc_id] = path + return files, doc_id_to_path class KnowledgeBaseSearchMiddleware(AgentMiddleware): # type: ignore[type-arg] @@ -583,12 +690,14 @@ class KnowledgeBaseSearchMiddleware(AgentMiddleware): # type: ignore[type-arg] available_connectors: list[str] | None = None, available_document_types: list[str] | None = None, top_k: int = 10, + mentioned_document_ids: list[int] | None = None, ) -> None: self.llm = llm self.search_space_id = search_space_id self.available_connectors = available_connectors self.available_document_types = available_document_types self.top_k = top_k + self.mentioned_document_ids = mentioned_document_ids or [] async def _plan_search_inputs( self, @@ -680,6 +789,18 @@ class KnowledgeBaseSearchMiddleware(AgentMiddleware): # type: ignore[type-arg] user_text=user_text, ) + # --- 1. Fetch mentioned documents (user-selected, all chunks) --- + mentioned_results: list[dict[str, Any]] = [] + if self.mentioned_document_ids: + mentioned_results = await fetch_mentioned_documents( + document_ids=self.mentioned_document_ids, + search_space_id=self.search_space_id, + ) + # Clear after first turn so they are not re-fetched on subsequent + # messages within the same agent instance. + self.mentioned_document_ids = [] + + # --- 2. Run KB hybrid search --- search_results = await search_knowledge_base( query=planned_query, search_space_id=self.search_space_id, @@ -689,19 +810,50 @@ class KnowledgeBaseSearchMiddleware(AgentMiddleware): # type: ignore[type-arg] start_date=start_date, end_date=end_date, ) - new_files = await build_scoped_filesystem( - documents=search_results, + + # --- 3. Merge: mentioned first, then search (dedup by doc id) --- + seen_doc_ids: set[int] = set() + merged: list[dict[str, Any]] = [] + for doc in mentioned_results: + doc_id = (doc.get("document") or {}).get("id") + if doc_id is not None: + seen_doc_ids.add(doc_id) + merged.append(doc) + for doc in search_results: + doc_id = (doc.get("document") or {}).get("id") + if doc_id is not None and doc_id in seen_doc_ids: + continue + merged.append(doc) + + # --- 4. Build scoped filesystem --- + new_files, doc_id_to_path = await build_scoped_filesystem( + documents=merged, search_space_id=self.search_space_id, ) - ai_msg, tool_msg = _build_synthetic_ls(existing_files, new_files) + # Identify which paths belong to user-mentioned documents using + # the authoritative doc_id -> path mapping (no title guessing). + mentioned_doc_ids = { + (d.get("document") or {}).get("id") for d in mentioned_results + } + mentioned_paths = { + doc_id_to_path[did] for did in mentioned_doc_ids if did in doc_id_to_path + } + + ai_msg, tool_msg = _build_synthetic_ls( + existing_files, + new_files, + mentioned_paths=mentioned_paths, + ) if t0 is not None: _perf_log.info( - "[kb_fs_middleware] completed in %.3fs query=%r optimized=%r new_files=%d total=%d", + "[kb_fs_middleware] completed in %.3fs query=%r optimized=%r " + "mentioned=%d new_files=%d total=%d", asyncio.get_event_loop().time() - t0, user_text[:80], planned_query[:120], + len(mentioned_results), len(new_files), len(new_files) + len(existing_files or {}), ) diff --git a/surfsense_backend/app/config/global_llm_config.example.yaml b/surfsense_backend/app/config/global_llm_config.example.yaml index 6ca3e95e3..49a8d0295 100644 --- a/surfsense_backend/app/config/global_llm_config.example.yaml +++ b/surfsense_backend/app/config/global_llm_config.example.yaml @@ -17,7 +17,7 @@ # - Configure router_settings below to customize the load balancing behavior # # Structure matches NewLLMConfig: -# - LLM model configuration (provider, model_name, api_key, etc.) +# - Model configuration (provider, model_name, api_key, etc.) # - Prompt configuration (system_instructions, citations_enabled) # Router Settings for Auto Mode diff --git a/surfsense_backend/app/db.py b/surfsense_backend/app/db.py index 2dfbdd97f..6e9553307 100644 --- a/surfsense_backend/app/db.py +++ b/surfsense_backend/app/db.py @@ -64,6 +64,7 @@ class DocumentType(StrEnum): COMPOSIO_GOOGLE_DRIVE_CONNECTOR = "COMPOSIO_GOOGLE_DRIVE_CONNECTOR" COMPOSIO_GMAIL_CONNECTOR = "COMPOSIO_GMAIL_CONNECTOR" COMPOSIO_GOOGLE_CALENDAR_CONNECTOR = "COMPOSIO_GOOGLE_CALENDAR_CONNECTOR" + LOCAL_FOLDER_FILE = "LOCAL_FOLDER_FILE" # Native Google document types → their legacy Composio equivalents. @@ -955,6 +956,7 @@ class Folder(BaseModel, TimestampMixin): onupdate=lambda: datetime.now(UTC), index=True, ) + folder_metadata = Column("metadata", JSONB, nullable=True) parent = relationship("Folder", remote_side="Folder.id", backref="children") search_space = relationship("SearchSpace", back_populates="folders") @@ -1039,6 +1041,26 @@ class Document(BaseModel, TimestampMixin): ) +class DocumentVersion(BaseModel, TimestampMixin): + __tablename__ = "document_versions" + __table_args__ = ( + UniqueConstraint("document_id", "version_number", name="uq_document_version"), + ) + + document_id = Column( + Integer, + ForeignKey("documents.id", ondelete="CASCADE"), + nullable=False, + index=True, + ) + version_number = Column(Integer, nullable=False) + source_markdown = Column(Text, nullable=True) + content_hash = Column(String, nullable=False) + title = Column(String, nullable=True) + + document = relationship("Document", backref="versions") + + class Chunk(BaseModel, TimestampMixin): __tablename__ = "chunks" diff --git a/surfsense_backend/app/indexing_pipeline/exceptions.py b/surfsense_backend/app/indexing_pipeline/exceptions.py index 9155e9baa..666fa4b9f 100644 --- a/surfsense_backend/app/indexing_pipeline/exceptions.py +++ b/surfsense_backend/app/indexing_pipeline/exceptions.py @@ -59,7 +59,7 @@ class PipelineMessages: LLM_AUTH = "LLM authentication failed. Check your API key." LLM_PERMISSION = "LLM request denied. Check your account permissions." - LLM_NOT_FOUND = "LLM model not found. Check your model configuration." + LLM_NOT_FOUND = "Model not found. Check your model configuration." LLM_BAD_REQUEST = "LLM rejected the request. Document content may be invalid." LLM_UNPROCESSABLE = ( "Document exceeds the LLM context window even after optimization." @@ -67,7 +67,7 @@ class PipelineMessages: LLM_RESPONSE = "LLM returned an invalid response." LLM_AUTH = "LLM authentication failed. Check your API key." LLM_PERMISSION = "LLM request denied. Check your account permissions." - LLM_NOT_FOUND = "LLM model not found. Check your model configuration." + LLM_NOT_FOUND = "Model not found. Check your model configuration." LLM_BAD_REQUEST = "LLM rejected the request. Document content may be invalid." LLM_UNPROCESSABLE = ( "Document exceeds the LLM context window even after optimization." diff --git a/surfsense_backend/app/routes/__init__.py b/surfsense_backend/app/routes/__init__.py index a063b5976..22631bc1d 100644 --- a/surfsense_backend/app/routes/__init__.py +++ b/surfsense_backend/app/routes/__init__.py @@ -85,7 +85,7 @@ router.include_router(confluence_add_connector_router) router.include_router(clickup_add_connector_router) router.include_router(dropbox_add_connector_router) router.include_router(new_llm_config_router) # LLM configs with prompt configuration -router.include_router(model_list_router) # Dynamic LLM model catalogue from OpenRouter +router.include_router(model_list_router) # Dynamic model catalogue from OpenRouter router.include_router(logs_router) router.include_router(circleback_webhook_router) # Circleback meeting webhooks router.include_router(surfsense_docs_router) # Surfsense documentation for citations diff --git a/surfsense_backend/app/routes/documents_routes.py b/surfsense_backend/app/routes/documents_routes.py index 6e69218f1..5008b1a10 100644 --- a/surfsense_backend/app/routes/documents_routes.py +++ b/surfsense_backend/app/routes/documents_routes.py @@ -1,7 +1,8 @@ # Force asyncio to use standard event loop before unstructured imports import asyncio -from fastapi import APIRouter, Depends, Form, HTTPException, UploadFile +from fastapi import APIRouter, Depends, Form, HTTPException, Query, UploadFile +from pydantic import BaseModel as PydanticBaseModel from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy.future import select from sqlalchemy.orm import selectinload @@ -10,6 +11,8 @@ from app.db import ( Chunk, Document, DocumentType, + DocumentVersion, + Folder, Permission, SearchSpace, SearchSpaceMembership, @@ -17,6 +20,7 @@ from app.db import ( get_async_session, ) from app.schemas import ( + ChunkRead, DocumentRead, DocumentsCreate, DocumentStatusBatchResponse, @@ -26,6 +30,7 @@ from app.schemas import ( DocumentTitleSearchResponse, DocumentUpdate, DocumentWithChunksRead, + FolderRead, PaginatedResponse, ) from app.services.task_dispatcher import TaskDispatcher, get_task_dispatcher @@ -45,9 +50,7 @@ os.environ["UNSTRUCTURED_HAS_PATCHED_LOOP"] = "1" router = APIRouter() -MAX_FILES_PER_UPLOAD = 10 -MAX_FILE_SIZE_BYTES = 50 * 1024 * 1024 # 50 MB per file -MAX_TOTAL_SIZE_BYTES = 200 * 1024 * 1024 # 200 MB total +MAX_FILE_SIZE_BYTES = 500 * 1024 * 1024 # 500 MB per file @router.post("/documents") @@ -156,13 +159,6 @@ async def create_documents_file_upload( if not files: raise HTTPException(status_code=400, detail="No files provided") - if len(files) > MAX_FILES_PER_UPLOAD: - raise HTTPException( - status_code=413, - detail=f"Too many files. Maximum {MAX_FILES_PER_UPLOAD} files per upload.", - ) - - total_size = 0 for file in files: file_size = file.size or 0 if file_size > MAX_FILE_SIZE_BYTES: @@ -171,14 +167,6 @@ async def create_documents_file_upload( detail=f"File '{file.filename}' ({file_size / (1024 * 1024):.1f} MB) " f"exceeds the {MAX_FILE_SIZE_BYTES // (1024 * 1024)} MB per-file limit.", ) - total_size += file_size - - if total_size > MAX_TOTAL_SIZE_BYTES: - raise HTTPException( - status_code=413, - detail=f"Total upload size ({total_size / (1024 * 1024):.1f} MB) " - f"exceeds the {MAX_TOTAL_SIZE_BYTES // (1024 * 1024)} MB limit.", - ) # ===== Read all files concurrently to avoid blocking the event loop ===== async def _read_and_save(file: UploadFile) -> tuple[str, str, int]: @@ -206,16 +194,6 @@ async def create_documents_file_upload( saved_files = await asyncio.gather(*(_read_and_save(f) for f in files)) - actual_total_size = sum(size for _, _, size in saved_files) - if actual_total_size > MAX_TOTAL_SIZE_BYTES: - for temp_path, _, _ in saved_files: - os.unlink(temp_path) - raise HTTPException( - status_code=413, - detail=f"Total upload size ({actual_total_size / (1024 * 1024):.1f} MB) " - f"exceeds the {MAX_TOTAL_SIZE_BYTES // (1024 * 1024)} MB limit.", - ) - # ===== PHASE 1: Create pending documents for all files ===== created_documents: list[Document] = [] files_to_process: list[tuple[Document, str, str]] = [] @@ -451,13 +429,15 @@ async def read_documents( reason=doc.status.get("reason"), ) + raw_content = doc.content or "" api_documents.append( DocumentRead( id=doc.id, title=doc.title, document_type=doc.document_type, document_metadata=doc.document_metadata, - content=doc.content, + content="", + content_preview=raw_content[:300], content_hash=doc.content_hash, unique_identifier_hash=doc.unique_identifier_hash, created_at=doc.created_at, @@ -609,13 +589,15 @@ async def search_documents( reason=doc.status.get("reason"), ) + raw_content = doc.content or "" api_documents.append( DocumentRead( id=doc.id, title=doc.title, document_type=doc.document_type, document_metadata=doc.document_metadata, - content=doc.content, + content="", + content_preview=raw_content[:300], content_hash=doc.content_hash, unique_identifier_hash=doc.unique_identifier_hash, created_at=doc.created_at, @@ -884,16 +866,19 @@ async def get_document_type_counts( @router.get("/documents/by-chunk/{chunk_id}", response_model=DocumentWithChunksRead) async def get_document_by_chunk_id( chunk_id: int, + chunk_window: int = Query( + 5, ge=0, description="Number of chunks before/after the cited chunk to include" + ), session: AsyncSession = Depends(get_async_session), user: User = Depends(current_active_user), ): """ - Retrieves a document based on a chunk ID, including all its chunks ordered by creation time. - Requires DOCUMENTS_READ permission for the search space. - The document's embedding and chunk embeddings are excluded from the response. + Retrieves a document based on a chunk ID, including a window of chunks around the cited one. + Uses SQL-level pagination to avoid loading all chunks into memory. """ try: - # First, get the chunk and verify it exists + from sqlalchemy import and_, func, or_ + chunk_result = await session.execute(select(Chunk).filter(Chunk.id == chunk_id)) chunk = chunk_result.scalars().first() @@ -902,11 +887,8 @@ async def get_document_by_chunk_id( status_code=404, detail=f"Chunk with id {chunk_id} not found" ) - # Get the associated document document_result = await session.execute( - select(Document) - .options(selectinload(Document.chunks)) - .filter(Document.id == chunk.document_id) + select(Document).filter(Document.id == chunk.document_id) ) document = document_result.scalars().first() @@ -916,7 +898,6 @@ async def get_document_by_chunk_id( detail="Document not found", ) - # Check permission for the search space await check_permission( session, user, @@ -925,10 +906,38 @@ async def get_document_by_chunk_id( "You don't have permission to read documents in this search space", ) - # Sort chunks by creation time - sorted_chunks = sorted(document.chunks, key=lambda x: x.created_at) + total_result = await session.execute( + select(func.count()) + .select_from(Chunk) + .filter(Chunk.document_id == document.id) + ) + total_chunks = total_result.scalar() or 0 + + cited_idx_result = await session.execute( + select(func.count()) + .select_from(Chunk) + .filter( + Chunk.document_id == document.id, + or_( + Chunk.created_at < chunk.created_at, + and_(Chunk.created_at == chunk.created_at, Chunk.id < chunk.id), + ), + ) + ) + cited_idx = cited_idx_result.scalar() or 0 + + start = max(0, cited_idx - chunk_window) + end = min(total_chunks, cited_idx + chunk_window + 1) + + windowed_result = await session.execute( + select(Chunk) + .filter(Chunk.document_id == document.id) + .order_by(Chunk.created_at, Chunk.id) + .offset(start) + .limit(end - start) + ) + windowed_chunks = windowed_result.scalars().all() - # Return the document with its chunks return DocumentWithChunksRead( id=document.id, title=document.title, @@ -940,7 +949,9 @@ async def get_document_by_chunk_id( created_at=document.created_at, updated_at=document.updated_at, search_space_id=document.search_space_id, - chunks=sorted_chunks, + chunks=windowed_chunks, + total_chunks=total_chunks, + chunk_start_index=start, ) except HTTPException: raise @@ -950,6 +961,108 @@ async def get_document_by_chunk_id( ) from e +@router.get("/documents/watched-folders", response_model=list[FolderRead]) +async def get_watched_folders( + search_space_id: int, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """Return root folders that are marked as watched (metadata->>'watched' = 'true').""" + await check_permission( + session, + user, + search_space_id, + Permission.DOCUMENTS_READ.value, + "You don't have permission to read documents in this search space", + ) + + folders = ( + ( + await session.execute( + select(Folder).where( + Folder.search_space_id == search_space_id, + Folder.parent_id.is_(None), + Folder.folder_metadata.isnot(None), + Folder.folder_metadata["watched"].astext == "true", + ) + ) + ) + .scalars() + .all() + ) + + return folders + + +@router.get( + "/documents/{document_id}/chunks", + response_model=PaginatedResponse[ChunkRead], +) +async def get_document_chunks_paginated( + document_id: int, + page: int = Query(0, ge=0), + page_size: int = Query(20, ge=1, le=100), + start_offset: int | None = Query( + None, ge=0, description="Direct offset; overrides page * page_size" + ), + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """ + Paginated chunk loading for a document. + Supports both page-based and offset-based access. + """ + try: + from sqlalchemy import func + + doc_result = await session.execute( + select(Document).filter(Document.id == document_id) + ) + document = doc_result.scalars().first() + + if not document: + raise HTTPException(status_code=404, detail="Document not found") + + await check_permission( + session, + user, + document.search_space_id, + Permission.DOCUMENTS_READ.value, + "You don't have permission to read documents in this search space", + ) + + total_result = await session.execute( + select(func.count()) + .select_from(Chunk) + .filter(Chunk.document_id == document_id) + ) + total = total_result.scalar() or 0 + + offset = start_offset if start_offset is not None else page * page_size + chunks_result = await session.execute( + select(Chunk) + .filter(Chunk.document_id == document_id) + .order_by(Chunk.created_at, Chunk.id) + .offset(offset) + .limit(page_size) + ) + chunks = chunks_result.scalars().all() + + return PaginatedResponse( + items=chunks, + total=total, + page=offset // page_size if page_size else page, + page_size=page_size, + has_more=(offset + len(chunks)) < total, + ) + except HTTPException: + raise + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Failed to fetch chunks: {e!s}" + ) from e + + @router.get("/documents/{document_id}", response_model=DocumentRead) async def read_document( document_id: int, @@ -980,13 +1093,14 @@ async def read_document( "You don't have permission to read documents in this search space", ) - # Convert database object to API-friendly format + raw_content = document.content or "" return DocumentRead( id=document.id, title=document.title, document_type=document.document_type, document_metadata=document.document_metadata, - content=document.content, + content=raw_content, + content_preview=raw_content[:300], content_hash=document.content_hash, unique_identifier_hash=document.unique_identifier_hash, created_at=document.created_at, @@ -1135,3 +1249,297 @@ async def delete_document( raise HTTPException( status_code=500, detail=f"Failed to delete document: {e!s}" ) from e + + +# ==================================================================== +# Version History Endpoints +# ==================================================================== + + +@router.get("/documents/{document_id}/versions") +async def list_document_versions( + document_id: int, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """List all versions for a document, ordered by version_number descending.""" + document = ( + await session.execute(select(Document).where(Document.id == document_id)) + ).scalar_one_or_none() + if not document: + raise HTTPException(status_code=404, detail="Document not found") + + await check_permission( + session, user, document.search_space_id, Permission.DOCUMENTS_READ.value + ) + + versions = ( + ( + await session.execute( + select(DocumentVersion) + .where(DocumentVersion.document_id == document_id) + .order_by(DocumentVersion.version_number.desc()) + ) + ) + .scalars() + .all() + ) + + return [ + { + "version_number": v.version_number, + "title": v.title, + "content_hash": v.content_hash, + "created_at": v.created_at.isoformat() if v.created_at else None, + } + for v in versions + ] + + +@router.get("/documents/{document_id}/versions/{version_number}") +async def get_document_version( + document_id: int, + version_number: int, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """Get full version content including source_markdown.""" + document = ( + await session.execute(select(Document).where(Document.id == document_id)) + ).scalar_one_or_none() + if not document: + raise HTTPException(status_code=404, detail="Document not found") + + await check_permission( + session, user, document.search_space_id, Permission.DOCUMENTS_READ.value + ) + + version = ( + await session.execute( + select(DocumentVersion).where( + DocumentVersion.document_id == document_id, + DocumentVersion.version_number == version_number, + ) + ) + ).scalar_one_or_none() + if not version: + raise HTTPException(status_code=404, detail="Version not found") + + return { + "version_number": version.version_number, + "title": version.title, + "content_hash": version.content_hash, + "source_markdown": version.source_markdown, + "created_at": version.created_at.isoformat() if version.created_at else None, + } + + +@router.post("/documents/{document_id}/versions/{version_number}/restore") +async def restore_document_version( + document_id: int, + version_number: int, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """Restore a previous version: snapshot current state, then overwrite document content.""" + document = ( + await session.execute(select(Document).where(Document.id == document_id)) + ).scalar_one_or_none() + if not document: + raise HTTPException(status_code=404, detail="Document not found") + + await check_permission( + session, user, document.search_space_id, Permission.DOCUMENTS_UPDATE.value + ) + + version = ( + await session.execute( + select(DocumentVersion).where( + DocumentVersion.document_id == document_id, + DocumentVersion.version_number == version_number, + ) + ) + ).scalar_one_or_none() + if not version: + raise HTTPException(status_code=404, detail="Version not found") + + # Snapshot current state before restoring + from app.utils.document_versioning import create_version_snapshot + + await create_version_snapshot(session, document) + + # Restore the version's content onto the document + document.source_markdown = version.source_markdown + document.title = version.title or document.title + document.content_needs_reindexing = True + await session.commit() + + from app.tasks.celery_tasks.document_reindex_tasks import reindex_document_task + + reindex_document_task.delay(document_id, str(user.id)) + + return { + "message": f"Restored version {version_number}", + "document_id": document_id, + "restored_version": version_number, + } + + +# ===== Local folder indexing endpoints ===== + + +class FolderIndexRequest(PydanticBaseModel): + folder_path: str + folder_name: str + search_space_id: int + exclude_patterns: list[str] | None = None + file_extensions: list[str] | None = None + root_folder_id: int | None = None + enable_summary: bool = False + + +class FolderIndexFilesRequest(PydanticBaseModel): + folder_path: str + folder_name: str + search_space_id: int + target_file_paths: list[str] + root_folder_id: int | None = None + enable_summary: bool = False + + +@router.post("/documents/folder-index") +async def folder_index( + request: FolderIndexRequest, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """Full-scan index of a local folder. Creates the root Folder row synchronously + and dispatches the heavy indexing work to a Celery task. + Returns the root_folder_id so the desktop can persist it. + """ + from app.config import config as app_config + + if not app_config.is_self_hosted(): + raise HTTPException( + status_code=400, + detail="Local folder indexing is only available in self-hosted mode", + ) + + await check_permission( + session, + user, + request.search_space_id, + Permission.DOCUMENTS_CREATE.value, + "You don't have permission to create documents in this search space", + ) + + watched_metadata = { + "watched": True, + "folder_path": request.folder_path, + "exclude_patterns": request.exclude_patterns, + "file_extensions": request.file_extensions, + } + + root_folder_id = request.root_folder_id + if root_folder_id: + existing = ( + await session.execute(select(Folder).where(Folder.id == root_folder_id)) + ).scalar_one_or_none() + if not existing: + root_folder_id = None + else: + existing.folder_metadata = watched_metadata + await session.commit() + + if not root_folder_id: + root_folder = Folder( + name=request.folder_name, + search_space_id=request.search_space_id, + created_by_id=str(user.id), + position="a0", + folder_metadata=watched_metadata, + ) + session.add(root_folder) + await session.flush() + root_folder_id = root_folder.id + await session.commit() + + from app.tasks.celery_tasks.document_tasks import index_local_folder_task + + index_local_folder_task.delay( + search_space_id=request.search_space_id, + user_id=str(user.id), + folder_path=request.folder_path, + folder_name=request.folder_name, + exclude_patterns=request.exclude_patterns, + file_extensions=request.file_extensions, + root_folder_id=root_folder_id, + enable_summary=request.enable_summary, + ) + + return { + "message": "Folder indexing started", + "status": "processing", + "root_folder_id": root_folder_id, + } + + +@router.post("/documents/folder-index-files") +async def folder_index_files( + request: FolderIndexFilesRequest, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """Index multiple files within a watched folder (batched chokidar trigger). + Validates that all target_file_paths are under folder_path. + Dispatches a single Celery task that processes them in parallel. + """ + from app.config import config as app_config + + if not app_config.is_self_hosted(): + raise HTTPException( + status_code=400, + detail="Local folder indexing is only available in self-hosted mode", + ) + + if not request.target_file_paths: + raise HTTPException( + status_code=400, detail="target_file_paths must not be empty" + ) + + await check_permission( + session, + user, + request.search_space_id, + Permission.DOCUMENTS_CREATE.value, + "You don't have permission to create documents in this search space", + ) + + from pathlib import Path + + for fp in request.target_file_paths: + try: + Path(fp).relative_to(request.folder_path) + except ValueError as err: + raise HTTPException( + status_code=400, + detail=f"target_file_path {fp} must be inside folder_path", + ) from err + + from app.tasks.celery_tasks.document_tasks import index_local_folder_task + + index_local_folder_task.delay( + search_space_id=request.search_space_id, + user_id=str(user.id), + folder_path=request.folder_path, + folder_name=request.folder_name, + target_file_paths=request.target_file_paths, + root_folder_id=request.root_folder_id, + enable_summary=request.enable_summary, + ) + + return { + "message": f"Batch indexing started for {len(request.target_file_paths)} file(s)", + "status": "processing", + "file_count": len(request.target_file_paths), + } diff --git a/surfsense_backend/app/routes/editor_routes.py b/surfsense_backend/app/routes/editor_routes.py index f54f18def..829b2cf69 100644 --- a/surfsense_backend/app/routes/editor_routes.py +++ b/surfsense_backend/app/routes/editor_routes.py @@ -15,11 +15,10 @@ import pypandoc import typst from fastapi import APIRouter, Depends, HTTPException, Query from fastapi.responses import StreamingResponse -from sqlalchemy import select +from sqlalchemy import func, select from sqlalchemy.ext.asyncio import AsyncSession -from sqlalchemy.orm import selectinload -from app.db import Document, DocumentType, Permission, User, get_async_session +from app.db import Chunk, Document, DocumentType, Permission, User, get_async_session from app.routes.reports_routes import ( _FILE_EXTENSIONS, _MEDIA_TYPES, @@ -44,6 +43,9 @@ router = APIRouter() async def get_editor_content( search_space_id: int, document_id: int, + max_length: int | None = Query( + None, description="Truncate source_markdown to this many characters" + ), session: AsyncSession = Depends(get_async_session), user: User = Depends(current_active_user), ): @@ -65,9 +67,7 @@ async def get_editor_content( ) result = await session.execute( - select(Document) - .options(selectinload(Document.chunks)) - .filter( + select(Document).filter( Document.id == document_id, Document.search_space_id == search_space_id, ) @@ -77,80 +77,152 @@ async def get_editor_content( if not document: raise HTTPException(status_code=404, detail="Document not found") - # Priority 1: Return source_markdown if it exists (check `is not None` to allow empty strings) - if document.source_markdown is not None: + count_result = await session.execute( + select(func.count()).select_from(Chunk).filter(Chunk.document_id == document_id) + ) + chunk_count = count_result.scalar() or 0 + + def _build_response(md: str) -> dict: + size_bytes = len(md.encode("utf-8")) + truncated = False + output_md = md + if max_length is not None and size_bytes > max_length: + output_md = md[:max_length] + truncated = True return { "document_id": document.id, "title": document.title, "document_type": document.document_type.value, - "source_markdown": document.source_markdown, + "source_markdown": output_md, + "content_size_bytes": size_bytes, + "chunk_count": chunk_count, + "truncated": truncated, "updated_at": document.updated_at.isoformat() if document.updated_at else None, } - # Priority 2: Lazy-migrate from blocknote_document (pure Python, no external deps) + if document.source_markdown is not None: + return _build_response(document.source_markdown) + if document.blocknote_document: from app.utils.blocknote_to_markdown import blocknote_to_markdown markdown = blocknote_to_markdown(document.blocknote_document) if markdown: - # Persist the migration so we don't repeat it document.source_markdown = markdown await session.commit() - return { - "document_id": document.id, - "title": document.title, - "document_type": document.document_type.value, - "source_markdown": markdown, - "updated_at": document.updated_at.isoformat() - if document.updated_at - else None, - } + return _build_response(markdown) - # Priority 3: For NOTE type with no content, return empty markdown if document.document_type == DocumentType.NOTE: empty_markdown = "" document.source_markdown = empty_markdown await session.commit() - return { - "document_id": document.id, - "title": document.title, - "document_type": document.document_type.value, - "source_markdown": empty_markdown, - "updated_at": document.updated_at.isoformat() - if document.updated_at - else None, - } + return _build_response(empty_markdown) - # Priority 4: Reconstruct from chunks - chunks = sorted(document.chunks, key=lambda c: c.id) + chunk_contents_result = await session.execute( + select(Chunk.content) + .filter(Chunk.document_id == document_id) + .order_by(Chunk.id) + ) + chunk_contents = chunk_contents_result.scalars().all() - if not chunks: + if not chunk_contents: + doc_status = document.status or {} + state = ( + doc_status.get("state", "ready") + if isinstance(doc_status, dict) + else "ready" + ) + if state in ("pending", "processing"): + raise HTTPException( + status_code=409, + detail="This document is still being processed. Please wait a moment and try again.", + ) raise HTTPException( status_code=400, - detail="This document has no content and cannot be edited. Please re-upload to enable editing.", + detail="This document has no viewable content yet. It may still be syncing. Try again in a few seconds, or re-upload if the issue persists.", ) - markdown_content = "\n\n".join(chunk.content for chunk in chunks) + markdown_content = "\n\n".join(chunk_contents) if not markdown_content.strip(): raise HTTPException( status_code=400, - detail="This document has empty content and cannot be edited.", + detail="This document appears to be empty. Try re-uploading or editing it to add content.", ) - # Persist the lazy migration document.source_markdown = markdown_content await session.commit() - return { - "document_id": document.id, - "title": document.title, - "document_type": document.document_type.value, - "source_markdown": markdown_content, - "updated_at": document.updated_at.isoformat() if document.updated_at else None, - } + return _build_response(markdown_content) + + +@router.get( + "/search-spaces/{search_space_id}/documents/{document_id}/download-markdown" +) +async def download_document_markdown( + search_space_id: int, + document_id: int, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """ + Download the full document content as a .md file. + Reconstructs markdown from source_markdown or chunks. + """ + await check_permission( + session, + user, + search_space_id, + Permission.DOCUMENTS_READ.value, + "You don't have permission to read documents in this search space", + ) + + result = await session.execute( + select(Document).filter( + Document.id == document_id, + Document.search_space_id == search_space_id, + ) + ) + document = result.scalars().first() + + if not document: + raise HTTPException(status_code=404, detail="Document not found") + + markdown: str | None = document.source_markdown + if markdown is None and document.blocknote_document: + from app.utils.blocknote_to_markdown import blocknote_to_markdown + + markdown = blocknote_to_markdown(document.blocknote_document) + if markdown is None: + chunk_contents_result = await session.execute( + select(Chunk.content) + .filter(Chunk.document_id == document_id) + .order_by(Chunk.id) + ) + chunk_contents = chunk_contents_result.scalars().all() + if chunk_contents: + markdown = "\n\n".join(chunk_contents) + + if not markdown or not markdown.strip(): + raise HTTPException( + status_code=400, detail="Document has no content to download" + ) + + safe_title = ( + "".join( + c if c.isalnum() or c in " -_" else "_" + for c in (document.title or "document") + ).strip()[:80] + or "document" + ) + + return StreamingResponse( + io.BytesIO(markdown.encode("utf-8")), + media_type="text/markdown; charset=utf-8", + headers={"Content-Disposition": f'attachment; filename="{safe_title}.md"'}, + ) @router.post("/search-spaces/{search_space_id}/documents/{document_id}/save") @@ -258,9 +330,7 @@ async def export_document( ) result = await session.execute( - select(Document) - .options(selectinload(Document.chunks)) - .filter( + select(Document).filter( Document.id == document_id, Document.search_space_id == search_space_id, ) @@ -269,16 +339,20 @@ async def export_document( if not document: raise HTTPException(status_code=404, detail="Document not found") - # Resolve markdown content (same priority as editor-content endpoint) markdown_content: str | None = document.source_markdown if markdown_content is None and document.blocknote_document: from app.utils.blocknote_to_markdown import blocknote_to_markdown markdown_content = blocknote_to_markdown(document.blocknote_document) if markdown_content is None: - chunks = sorted(document.chunks, key=lambda c: c.id) - if chunks: - markdown_content = "\n\n".join(chunk.content for chunk in chunks) + chunk_contents_result = await session.execute( + select(Chunk.content) + .filter(Chunk.document_id == document_id) + .order_by(Chunk.id) + ) + chunk_contents = chunk_contents_result.scalars().all() + if chunk_contents: + markdown_content = "\n\n".join(chunk_contents) if not markdown_content or not markdown_content.strip(): raise HTTPException(status_code=400, detail="Document has no content to export") diff --git a/surfsense_backend/app/routes/folders_routes.py b/surfsense_backend/app/routes/folders_routes.py index d688e692a..2dc9bceac 100644 --- a/surfsense_backend/app/routes/folders_routes.py +++ b/surfsense_backend/app/routes/folders_routes.py @@ -192,6 +192,33 @@ async def get_folder_breadcrumb( ) from e +@router.patch("/folders/{folder_id}/watched") +async def stop_watching_folder( + folder_id: int, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """Clear the watched flag from a folder's metadata.""" + folder = await session.get(Folder, folder_id) + if not folder: + raise HTTPException(status_code=404, detail="Folder not found") + + await check_permission( + session, + user, + folder.search_space_id, + Permission.DOCUMENTS_UPDATE.value, + "You don't have permission to update folders in this search space", + ) + + if folder.folder_metadata and isinstance(folder.folder_metadata, dict): + updated = {**folder.folder_metadata, "watched": False} + folder.folder_metadata = updated + await session.commit() + + return {"message": "Folder watch status updated"} + + @router.put("/folders/{folder_id}", response_model=FolderRead) async def update_folder( folder_id: int, @@ -340,7 +367,7 @@ async def delete_folder( session: AsyncSession = Depends(get_async_session), user: User = Depends(current_active_user), ): - """Delete a folder and cascade-delete subfolders. Documents are async-deleted via Celery.""" + """Mark documents for deletion and dispatch Celery to delete docs first, then folders.""" try: folder = await session.get(Folder, folder_id) if not folder: @@ -372,30 +399,29 @@ async def delete_folder( ) await session.commit() - await session.execute(Folder.__table__.delete().where(Folder.id == folder_id)) - await session.commit() + try: + from app.tasks.celery_tasks.document_tasks import ( + delete_folder_documents_task, + ) - if document_ids: - try: - from app.tasks.celery_tasks.document_tasks import ( - delete_folder_documents_task, - ) - - delete_folder_documents_task.delay(document_ids) - except Exception as err: + delete_folder_documents_task.delay( + document_ids, folder_subtree_ids=list(subtree_ids) + ) + except Exception as err: + if document_ids: await session.execute( Document.__table__.update() .where(Document.id.in_(document_ids)) .values(status={"state": "ready"}) ) await session.commit() - raise HTTPException( - status_code=503, - detail="Folder deleted but document cleanup could not be queued. Documents have been restored.", - ) from err + raise HTTPException( + status_code=503, + detail="Could not queue folder deletion. Documents have been restored.", + ) from err return { - "message": "Folder deleted successfully", + "message": "Folder deletion started", "documents_queued_for_deletion": len(document_ids), } diff --git a/surfsense_backend/app/routes/model_list_routes.py b/surfsense_backend/app/routes/model_list_routes.py index ef6e30514..79ae7221f 100644 --- a/surfsense_backend/app/routes/model_list_routes.py +++ b/surfsense_backend/app/routes/model_list_routes.py @@ -1,5 +1,5 @@ """ -API route for fetching the available LLM models catalogue. +API route for fetching the available models catalogue. Serves a dynamically-updated list sourced from the OpenRouter public API, with a local JSON fallback when the API is unreachable. @@ -30,7 +30,7 @@ async def list_available_models( user: User = Depends(current_active_user), ): """ - Return all available LLM models grouped by provider. + Return all available models grouped by provider. The list is sourced from the OpenRouter public API and cached for 1 hour. If the API is unreachable, a local fallback file is used instead. diff --git a/surfsense_backend/app/routes/new_llm_config_routes.py b/surfsense_backend/app/routes/new_llm_config_routes.py index f784bd273..78907c719 100644 --- a/surfsense_backend/app/routes/new_llm_config_routes.py +++ b/surfsense_backend/app/routes/new_llm_config_routes.py @@ -1,7 +1,7 @@ """ API routes for NewLLMConfig CRUD operations. -NewLLMConfig combines LLM model settings with prompt configuration: +NewLLMConfig combines model settings with prompt configuration: - LLM provider, model, API key, etc. - Configurable system instructions - Citation toggle diff --git a/surfsense_backend/app/routes/search_source_connectors_routes.py b/surfsense_backend/app/routes/search_source_connectors_routes.py index b73b8c789..d208ff910 100644 --- a/surfsense_backend/app/routes/search_source_connectors_routes.py +++ b/surfsense_backend/app/routes/search_source_connectors_routes.py @@ -55,23 +55,12 @@ from app.schemas import ( ) from app.services.composio_service import ComposioService, get_composio_service from app.services.notification_service import NotificationService -from app.tasks.connector_indexers import ( - index_airtable_records, - index_clickup_tasks, - index_confluence_pages, - index_crawled_urls, - index_discord_messages, - index_elasticsearch_documents, - index_github_repos, - index_google_calendar_events, - index_google_gmail_messages, - index_jira_issues, - index_linear_issues, - index_luma_events, - index_notion_pages, - index_slack_messages, -) from app.users import current_active_user + +# NOTE: connector indexer functions are imported lazily inside each +# ``run_*_indexing`` helper to break a circular import cycle: +# connector_indexers.__init__ → airtable_indexer → airtable_history +# → app.routes.__init__ → this file → connector_indexers (not ready yet) from app.utils.connector_naming import ensure_unique_connector_name from app.utils.indexing_locks import ( acquire_connector_indexing_lock, @@ -1378,6 +1367,8 @@ async def run_slack_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_slack_messages + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -1824,6 +1815,8 @@ async def run_notion_indexing_with_new_session( Create a new session and run the Notion indexing task. This prevents session leaks by creating a dedicated session for the background task. """ + from app.tasks.connector_indexers import index_notion_pages + async with async_session_maker() as session: await _run_indexing_with_notifications( session=session, @@ -1858,6 +1851,8 @@ async def run_notion_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_notion_pages + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -1910,6 +1905,8 @@ async def run_github_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_github_repos + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -1961,6 +1958,8 @@ async def run_linear_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_linear_issues + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2011,6 +2010,8 @@ async def run_discord_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_discord_messages + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2113,6 +2114,8 @@ async def run_jira_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_jira_issues + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2166,6 +2169,8 @@ async def run_confluence_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_confluence_pages + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2217,6 +2222,8 @@ async def run_clickup_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_clickup_tasks + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2268,6 +2275,8 @@ async def run_airtable_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_airtable_records + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2321,6 +2330,8 @@ async def run_google_calendar_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_google_calendar_events + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2370,6 +2381,7 @@ async def run_google_gmail_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_google_gmail_messages # Create a wrapper function that calls index_google_gmail_messages with max_messages async def gmail_indexing_wrapper( @@ -2836,6 +2848,8 @@ async def run_luma_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_luma_events + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2888,6 +2902,8 @@ async def run_elasticsearch_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_elasticsearch_documents + await _run_indexing_with_notifications( session=session, connector_id=connector_id, @@ -2938,6 +2954,8 @@ async def run_web_page_indexing( start_date: Start date for indexing end_date: End date for indexing """ + from app.tasks.connector_indexers import index_crawled_urls + await _run_indexing_with_notifications( session=session, connector_id=connector_id, diff --git a/surfsense_backend/app/schemas/documents.py b/surfsense_backend/app/schemas/documents.py index c022a09d2..49d2836b2 100644 --- a/surfsense_backend/app/schemas/documents.py +++ b/surfsense_backend/app/schemas/documents.py @@ -53,25 +53,26 @@ class DocumentRead(BaseModel): title: str document_type: DocumentType document_metadata: dict - content: str # Changed to string to match frontend + content: str = "" + content_preview: str = "" content_hash: str unique_identifier_hash: str | None created_at: datetime updated_at: datetime | None search_space_id: int folder_id: int | None = None - created_by_id: UUID | None = None # User who created/uploaded this document + created_by_id: UUID | None = None created_by_name: str | None = None created_by_email: str | None = None - status: DocumentStatusSchema | None = ( - None # Processing status (ready, processing, failed) - ) + status: DocumentStatusSchema | None = None model_config = ConfigDict(from_attributes=True) class DocumentWithChunksRead(DocumentRead): chunks: list[ChunkRead] = [] + total_chunks: int = 0 + chunk_start_index: int = 0 model_config = ConfigDict(from_attributes=True) diff --git a/surfsense_backend/app/schemas/folders.py b/surfsense_backend/app/schemas/folders.py index 263817182..a7e065144 100644 --- a/surfsense_backend/app/schemas/folders.py +++ b/surfsense_backend/app/schemas/folders.py @@ -1,6 +1,7 @@ """Pydantic schemas for folder CRUD, move, and reorder operations.""" from datetime import datetime +from typing import Any from uuid import UUID from pydantic import BaseModel, ConfigDict, Field @@ -34,6 +35,9 @@ class FolderRead(BaseModel): created_by_id: UUID | None created_at: datetime updated_at: datetime + metadata: dict[str, Any] | None = Field( + default=None, validation_alias="folder_metadata" + ) model_config = ConfigDict(from_attributes=True) diff --git a/surfsense_backend/app/schemas/new_llm_config.py b/surfsense_backend/app/schemas/new_llm_config.py index 2deddd7f2..6c76ca512 100644 --- a/surfsense_backend/app/schemas/new_llm_config.py +++ b/surfsense_backend/app/schemas/new_llm_config.py @@ -1,7 +1,7 @@ """ Pydantic schemas for the NewLLMConfig API. -NewLLMConfig combines LLM model settings with prompt configuration: +NewLLMConfig combines model settings with prompt configuration: - LLM provider, model, API key, etc. - Configurable system instructions - Citation toggle @@ -26,7 +26,7 @@ class NewLLMConfigBase(BaseModel): None, max_length=500, description="Optional description" ) - # LLM Model Configuration + # Model Configuration provider: LiteLLMProvider = Field(..., description="LiteLLM provider type") custom_provider: str | None = Field( None, max_length=100, description="Custom provider name when provider is CUSTOM" @@ -71,7 +71,7 @@ class NewLLMConfigUpdate(BaseModel): name: str | None = Field(None, max_length=100) description: str | None = Field(None, max_length=500) - # LLM Model Configuration + # Model Configuration provider: LiteLLMProvider | None = None custom_provider: str | None = Field(None, max_length=100) model_name: str | None = Field(None, max_length=100) @@ -106,7 +106,7 @@ class NewLLMConfigPublic(BaseModel): name: str description: str | None = None - # LLM Model Configuration (no api_key) + # Model Configuration (no api_key) provider: LiteLLMProvider custom_provider: str | None = None model_name: str @@ -149,7 +149,7 @@ class GlobalNewLLMConfigRead(BaseModel): name: str description: str | None = None - # LLM Model Configuration (no api_key) + # Model Configuration (no api_key) provider: str # String because YAML doesn't enforce enum, "AUTO" for Auto mode custom_provider: str | None = None model_name: str diff --git a/surfsense_backend/app/services/model_list_service.py b/surfsense_backend/app/services/model_list_service.py index ebc0e0d7c..2a81c2d52 100644 --- a/surfsense_backend/app/services/model_list_service.py +++ b/surfsense_backend/app/services/model_list_service.py @@ -1,5 +1,5 @@ """ -Service for fetching and caching the available LLM model list. +Service for fetching and caching the available model list. Uses the OpenRouter public API as the primary source, with a local fallback JSON file when the API is unreachable. diff --git a/surfsense_backend/app/tasks/celery_tasks/document_tasks.py b/surfsense_backend/app/tasks/celery_tasks/document_tasks.py index 662b41f2a..4e9249d34 100644 --- a/surfsense_backend/app/tasks/celery_tasks/document_tasks.py +++ b/surfsense_backend/app/tasks/celery_tasks/document_tasks.py @@ -1,6 +1,7 @@ """Celery tasks for document processing.""" import asyncio +import contextlib import logging import os from uuid import UUID @@ -10,6 +11,7 @@ from app.config import config from app.services.notification_service import NotificationService from app.services.task_logging_service import TaskLoggingService from app.tasks.celery_tasks import get_celery_session_maker +from app.tasks.connector_indexers.local_folder_indexer import index_local_folder from app.tasks.document_processors import ( add_extension_received_document, add_youtube_video_document, @@ -141,21 +143,30 @@ async def _delete_document_background(document_id: int) -> None: retry_backoff_max=300, max_retries=5, ) -def delete_folder_documents_task(self, document_ids: list[int]): - """Celery task to batch-delete documents orphaned by folder deletion.""" +def delete_folder_documents_task( + self, + document_ids: list[int], + folder_subtree_ids: list[int] | None = None, +): + """Celery task to delete documents first, then the folder rows.""" loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) try: - loop.run_until_complete(_delete_folder_documents(document_ids)) + loop.run_until_complete( + _delete_folder_documents(document_ids, folder_subtree_ids) + ) finally: loop.close() -async def _delete_folder_documents(document_ids: list[int]) -> None: - """Delete chunks in batches, then document rows for each orphaned document.""" +async def _delete_folder_documents( + document_ids: list[int], + folder_subtree_ids: list[int] | None = None, +) -> None: + """Delete chunks in batches, then document rows, then folder rows.""" from sqlalchemy import delete as sa_delete, select - from app.db import Chunk, Document + from app.db import Chunk, Document, Folder async with get_celery_session_maker()() as session: batch_size = 500 @@ -177,6 +188,12 @@ async def _delete_folder_documents(document_ids: list[int]) -> None: await session.delete(doc) await session.commit() + if folder_subtree_ids: + await session.execute( + sa_delete(Folder).where(Folder.id.in_(folder_subtree_ids)) + ) + await session.commit() + @celery_app.task( name="delete_search_space_background", @@ -1243,3 +1260,154 @@ async def _process_circleback_meeting( heartbeat_task.cancel() if notification: _stop_heartbeat(notification.id) + + +# ===== Local folder indexing task ===== + + +@celery_app.task(name="index_local_folder", bind=True) +def index_local_folder_task( + self, + search_space_id: int, + user_id: str, + folder_path: str, + folder_name: str, + exclude_patterns: list[str] | None = None, + file_extensions: list[str] | None = None, + root_folder_id: int | None = None, + enable_summary: bool = False, + target_file_paths: list[str] | None = None, +): + """Celery task to index a local folder. Config is passed directly — no connector row.""" + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + try: + loop.run_until_complete( + _index_local_folder_async( + search_space_id=search_space_id, + user_id=user_id, + folder_path=folder_path, + folder_name=folder_name, + exclude_patterns=exclude_patterns, + file_extensions=file_extensions, + root_folder_id=root_folder_id, + enable_summary=enable_summary, + target_file_paths=target_file_paths, + ) + ) + finally: + loop.close() + + +async def _index_local_folder_async( + search_space_id: int, + user_id: str, + folder_path: str, + folder_name: str, + exclude_patterns: list[str] | None = None, + file_extensions: list[str] | None = None, + root_folder_id: int | None = None, + enable_summary: bool = False, + target_file_paths: list[str] | None = None, +): + """Run local folder indexing with notification + heartbeat.""" + is_batch = bool(target_file_paths) + is_full_scan = not target_file_paths + file_count = len(target_file_paths) if target_file_paths else None + + if is_batch: + doc_name = f"{folder_name} ({file_count} file{'s' if file_count != 1 else ''})" + else: + doc_name = folder_name + + notification = None + notification_id: int | None = None + heartbeat_task = None + + async with get_celery_session_maker()() as session: + try: + notification = ( + await NotificationService.document_processing.notify_processing_started( + session=session, + user_id=UUID(user_id), + document_type="LOCAL_FOLDER_FILE", + document_name=doc_name, + search_space_id=search_space_id, + ) + ) + notification_id = notification.id + _start_heartbeat(notification_id) + heartbeat_task = asyncio.create_task(_run_heartbeat_loop(notification_id)) + except Exception: + logger.warning( + "Failed to create notification for local folder indexing", + exc_info=True, + ) + + async def _heartbeat_progress(completed_count: int) -> None: + """Refresh heartbeat and optionally update notification progress.""" + if notification: + with contextlib.suppress(Exception): + await NotificationService.document_processing.notify_processing_progress( + session=session, + notification=notification, + stage="indexing", + stage_message=f"Syncing files ({completed_count}/{file_count or '?'})", + ) + + try: + _indexed, _skipped_or_failed, _rfid, err = await index_local_folder( + session=session, + search_space_id=search_space_id, + user_id=user_id, + folder_path=folder_path, + folder_name=folder_name, + exclude_patterns=exclude_patterns, + file_extensions=file_extensions, + root_folder_id=root_folder_id, + enable_summary=enable_summary, + target_file_paths=target_file_paths, + on_heartbeat_callback=_heartbeat_progress + if (is_batch or is_full_scan) + else None, + ) + + if notification: + try: + await session.refresh(notification) + if err: + await NotificationService.document_processing.notify_processing_completed( + session=session, + notification=notification, + error_message=err, + ) + else: + await NotificationService.document_processing.notify_processing_completed( + session=session, + notification=notification, + ) + except Exception: + logger.warning( + "Failed to update notification after local folder indexing", + exc_info=True, + ) + + except Exception as e: + logger.exception(f"Local folder indexing failed: {e}") + if notification: + try: + await session.refresh(notification) + await NotificationService.document_processing.notify_processing_completed( + session=session, + notification=notification, + error_message=str(e)[:200], + ) + except Exception: + pass + raise + finally: + if heartbeat_task: + heartbeat_task.cancel() + if notification_id is not None: + _stop_heartbeat(notification_id) diff --git a/surfsense_backend/app/tasks/chat/stream_new_chat.py b/surfsense_backend/app/tasks/chat/stream_new_chat.py index 7c1e3b7ea..5ff907459 100644 --- a/surfsense_backend/app/tasks/chat/stream_new_chat.py +++ b/surfsense_backend/app/tasks/chat/stream_new_chat.py @@ -39,7 +39,6 @@ from app.agents.new_chat.llm_config import ( ) from app.db import ( ChatVisibility, - Document, NewChatMessage, NewChatThread, Report, @@ -63,74 +62,6 @@ _perf_log = get_perf_logger() _background_tasks: set[asyncio.Task] = set() -def format_mentioned_documents_as_context(documents: list[Document]) -> str: - """ - Format mentioned documents as context for the agent. - - Uses the same XML structure as knowledge_base.format_documents_for_context - to ensure citations work properly with chunk IDs. - """ - if not documents: - return "" - - context_parts = [""] - context_parts.append( - "The user has explicitly mentioned the following documents from their knowledge base. " - "These documents are directly relevant to the query and should be prioritized as primary sources. " - "Use [citation:CHUNK_ID] format for citations (e.g., [citation:123])." - ) - context_parts.append("") - - for doc in documents: - # Build metadata JSON - metadata = doc.document_metadata or {} - metadata_json = json.dumps(metadata, ensure_ascii=False) - - # Get URL from metadata - url = ( - metadata.get("url") - or metadata.get("source") - or metadata.get("page_url") - or "" - ) - - context_parts.append("") - context_parts.append("") - context_parts.append(f" {doc.id}") - context_parts.append( - f" {doc.document_type.value}" - ) - context_parts.append(f" <![CDATA[{doc.title}]]>") - context_parts.append(f" ") - context_parts.append( - f" " - ) - context_parts.append("") - context_parts.append("") - context_parts.append("") - - # Use chunks if available (preferred for proper citations) - if hasattr(doc, "chunks") and doc.chunks: - for chunk in doc.chunks: - context_parts.append( - f" " - ) - else: - # Fallback to document content if chunks not loaded - # Use document ID as chunk ID prefix for consistency - context_parts.append( - f" " - ) - - context_parts.append("") - context_parts.append("") - context_parts.append("") - - context_parts.append("") - - return "\n".join(context_parts) - - def format_mentioned_surfsense_docs_as_context( documents: list[SurfsenseDocsDocument], ) -> str: @@ -1317,6 +1248,7 @@ async def stream_new_chat( firecrawl_api_key=firecrawl_api_key, thread_visibility=visibility, disabled_tools=disabled_tools, + mentioned_document_ids=mentioned_document_ids, ) _perf_log.info( "[stream_new_chat] Agent created in %.3fs", time.perf_counter() - _t0 @@ -1340,18 +1272,9 @@ async def stream_new_chat( thread.needs_history_bootstrap = False await session.commit() - # Fetch mentioned documents if any (with chunks for proper citations) - mentioned_documents: list[Document] = [] - if mentioned_document_ids: - result = await session.execute( - select(Document) - .options(selectinload(Document.chunks)) - .filter( - Document.id.in_(mentioned_document_ids), - Document.search_space_id == search_space_id, - ) - ) - mentioned_documents = list(result.scalars().all()) + # Mentioned KB documents are now handled by KnowledgeBaseSearchMiddleware + # which merges them into the scoped filesystem with full document + # structure. Only SurfSense docs and report context are inlined here. # Fetch mentioned SurfSense docs if any mentioned_surfsense_docs: list[SurfsenseDocsDocument] = [] @@ -1379,15 +1302,10 @@ async def stream_new_chat( ) recent_reports = list(recent_reports_result.scalars().all()) - # Format the user query with context (mentioned documents + SurfSense docs) + # Format the user query with context (SurfSense docs + reports only) final_query = user_query context_parts = [] - if mentioned_documents: - context_parts.append( - format_mentioned_documents_as_context(mentioned_documents) - ) - if mentioned_surfsense_docs: context_parts.append( format_mentioned_surfsense_docs_as_context(mentioned_surfsense_docs) @@ -1479,7 +1397,7 @@ async def stream_new_chat( yield streaming_service.format_start_step() # Initial thinking step - analyzing the request - if mentioned_documents or mentioned_surfsense_docs: + if mentioned_surfsense_docs: initial_title = "Analyzing referenced content" action_verb = "Analyzing" else: @@ -1490,18 +1408,6 @@ async def stream_new_chat( query_text = user_query[:80] + ("..." if len(user_query) > 80 else "") processing_parts.append(query_text) - if mentioned_documents: - doc_names = [] - for doc in mentioned_documents: - title = doc.title - if len(title) > 30: - title = title[:27] + "..." - doc_names.append(title) - if len(doc_names) == 1: - processing_parts.append(f"[{doc_names[0]}]") - else: - processing_parts.append(f"[{len(doc_names)} documents]") - if mentioned_surfsense_docs: doc_names = [] for doc in mentioned_surfsense_docs: @@ -1527,7 +1433,7 @@ async def stream_new_chat( # These ORM objects (with eagerly-loaded chunks) can be very large. # They're only needed to build context strings already copied into # final_query / langchain_messages — release them before streaming. - del mentioned_documents, mentioned_surfsense_docs, recent_reports + del mentioned_surfsense_docs, recent_reports del langchain_messages, final_query # Check if this is the first assistant response so we can generate diff --git a/surfsense_backend/app/tasks/connector_indexers/__init__.py b/surfsense_backend/app/tasks/connector_indexers/__init__.py index 9a1d17fd5..1b032d54a 100644 --- a/surfsense_backend/app/tasks/connector_indexers/__init__.py +++ b/surfsense_backend/app/tasks/connector_indexers/__init__.py @@ -42,9 +42,9 @@ from .jira_indexer import index_jira_issues # Issue tracking and project management from .linear_indexer import index_linear_issues -from .luma_indexer import index_luma_events # Documentation and knowledge management +from .luma_indexer import index_luma_events from .notion_indexer import index_notion_pages from .obsidian_indexer import index_obsidian_vault from .slack_indexer import index_slack_messages diff --git a/surfsense_backend/app/tasks/connector_indexers/local_folder_indexer.py b/surfsense_backend/app/tasks/connector_indexers/local_folder_indexer.py new file mode 100644 index 000000000..acfbce0bf --- /dev/null +++ b/surfsense_backend/app/tasks/connector_indexers/local_folder_indexer.py @@ -0,0 +1,1247 @@ +""" +Local folder indexer. + +Indexes files from a local folder on disk. Supports: +- Full-scan mode (startup reconciliation / manual trigger) +- Batch mode (chokidar real-time trigger, 1..N files) +- Filesystem folder structure mirroring into DB Folder rows +- Document versioning via create_version_snapshot +- ETL-based file parsing for binary formats (PDF, DOCX, images, audio, etc.) + +Desktop-only: all change detection is driven by chokidar in the desktop app. +Config (folder_path, exclude_patterns, etc.) is passed in from the caller — +no connector row is read. +""" + +import asyncio +import os +from collections.abc import Awaitable, Callable +from datetime import UTC, datetime +from pathlib import Path + +from sqlalchemy import select +from sqlalchemy.exc import IntegrityError, SQLAlchemyError +from sqlalchemy.ext.asyncio import AsyncSession + +from app.config import config +from app.db import ( + Document, + DocumentStatus, + DocumentType, + Folder, +) +from app.indexing_pipeline.connector_document import ConnectorDocument +from app.indexing_pipeline.document_hashing import compute_identifier_hash +from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService +from app.services.llm_service import get_user_long_context_llm +from app.services.page_limit_service import PageLimitExceededError, PageLimitService +from app.services.task_logging_service import TaskLoggingService +from app.tasks.celery_tasks import get_celery_session_maker +from app.utils.document_versioning import create_version_snapshot + +from .base import ( + check_document_by_unique_identifier, + logger, +) + +PLAINTEXT_EXTENSIONS = frozenset( + { + ".md", + ".markdown", + ".txt", + ".text", + ".json", + ".jsonl", + ".yaml", + ".yml", + ".toml", + ".ini", + ".cfg", + ".conf", + ".xml", + ".css", + ".scss", + ".less", + ".sass", + ".py", + ".pyw", + ".pyi", + ".pyx", + ".js", + ".jsx", + ".ts", + ".tsx", + ".mjs", + ".cjs", + ".java", + ".kt", + ".kts", + ".scala", + ".groovy", + ".c", + ".h", + ".cpp", + ".cxx", + ".cc", + ".hpp", + ".hxx", + ".cs", + ".fs", + ".fsx", + ".go", + ".rs", + ".rb", + ".php", + ".pl", + ".pm", + ".lua", + ".swift", + ".m", + ".mm", + ".r", + ".R", + ".jl", + ".sh", + ".bash", + ".zsh", + ".fish", + ".bat", + ".cmd", + ".ps1", + ".sql", + ".graphql", + ".gql", + ".env", + ".gitignore", + ".dockerignore", + ".editorconfig", + ".makefile", + ".cmake", + ".log", + ".rst", + ".tex", + ".bib", + ".org", + ".adoc", + ".asciidoc", + ".vue", + ".svelte", + ".astro", + ".tf", + ".hcl", + ".proto", + } +) + +AUDIO_EXTENSIONS = frozenset( + { + ".mp3", + ".mp4", + ".mpeg", + ".mpga", + ".m4a", + ".wav", + ".webm", + } +) + + +DIRECT_CONVERT_EXTENSIONS = frozenset({".csv", ".tsv", ".html", ".htm"}) + + +def _is_plaintext_file(filename: str) -> bool: + return Path(filename).suffix.lower() in PLAINTEXT_EXTENSIONS + + +def _is_audio_file(filename: str) -> bool: + return Path(filename).suffix.lower() in AUDIO_EXTENSIONS + + +def _is_direct_convert_file(filename: str) -> bool: + return Path(filename).suffix.lower() in DIRECT_CONVERT_EXTENSIONS + + +def _needs_etl(filename: str) -> bool: + """File is not plaintext, not audio, and not direct-convert — requires ETL.""" + return ( + not _is_plaintext_file(filename) + and not _is_audio_file(filename) + and not _is_direct_convert_file(filename) + ) + + +HeartbeatCallbackType = Callable[[int], Awaitable[None]] + + +def _estimate_pages_safe(page_limit_service: PageLimitService, file_path: str) -> int: + """Estimate page count with a file-size fallback.""" + try: + return page_limit_service.estimate_pages_before_processing(file_path) + except Exception: + file_size = os.path.getsize(file_path) + return max(1, file_size // (80 * 1024)) + + +async def _check_page_limit_or_skip( + page_limit_service: PageLimitService, + user_id: str, + file_path: str, +) -> int: + """Estimate pages and check the limit; raises PageLimitExceededError if over quota. + + Returns the estimated page count on success. + """ + estimated = _estimate_pages_safe(page_limit_service, file_path) + await page_limit_service.check_page_limit(user_id, estimated) + return estimated + + +def _compute_final_pages( + page_limit_service: PageLimitService, + estimated_pages: int, + content_length: int, +) -> int: + """Return the final page count as max(estimated, actual).""" + actual = page_limit_service.estimate_pages_from_content_length(content_length) + return max(estimated_pages, actual) + +DEFAULT_EXCLUDE_PATTERNS = [ + ".git", + "node_modules", + "__pycache__", + ".DS_Store", + ".obsidian", + ".trash", +] + + +def scan_folder( + folder_path: str, + file_extensions: list[str] | None = None, + exclude_patterns: list[str] | None = None, +) -> list[dict]: + """Walk a directory and return a list of file entries. + + Args: + folder_path: Absolute path to the folder to scan. + file_extensions: If provided, only include files with these extensions + (e.g. [".md", ".txt"]). ``None`` means include all files. + exclude_patterns: Directory/file names to exclude. Any path component + matching one of these strings is skipped. + + Returns: + List of dicts with keys: path, relative_path, name, modified_at, size. + """ + root = Path(folder_path) + if not root.exists(): + raise ValueError(f"Folder path does not exist: {folder_path}") + + if exclude_patterns is None: + exclude_patterns = [] + + files: list[dict] = [] + for dirpath, dirnames, filenames in os.walk(root): + rel_dir = Path(dirpath).relative_to(root) + + dirnames[:] = [d for d in dirnames if d not in exclude_patterns] + + if any(part in exclude_patterns for part in rel_dir.parts): + continue + + for fname in filenames: + if fname in exclude_patterns: + continue + + full = Path(dirpath) / fname + + if ( + file_extensions is not None + and full.suffix.lower() not in file_extensions + ): + continue + + try: + stat = full.stat() + rel_path = full.relative_to(root) + files.append( + { + "path": str(full), + "relative_path": str(rel_path), + "name": full.name, + "modified_at": datetime.fromtimestamp(stat.st_mtime, tz=UTC), + "size": stat.st_size, + } + ) + except OSError as e: + logger.warning(f"Could not stat file {full}: {e}") + + return files + + +def _read_plaintext_file(file_path: str) -> str: + """Read a plaintext/text-based file as UTF-8.""" + with open(file_path, encoding="utf-8", errors="replace") as f: + content = f.read() + if "\x00" in content: + raise ValueError( + f"File contains null bytes — likely a binary file opened as text: {file_path}" + ) + return content + + +async def _read_file_content(file_path: str, filename: str) -> str: + """Read file content, using ETL for binary formats. + + Plaintext files are read directly. Audio and document files (PDF, DOCX, etc.) + are routed through the configured ETL service (same as Google Drive / OneDrive). + + Raises ValueError if the file cannot be parsed (e.g. no ETL service configured + for a binary file). + """ + if _is_plaintext_file(filename): + return _read_plaintext_file(file_path) + + if _is_direct_convert_file(filename): + from app.tasks.document_processors._direct_converters import ( + convert_file_directly, + ) + + return convert_file_directly(file_path, filename) + + if _is_audio_file(filename): + etl_service = config.ETL_SERVICE if hasattr(config, "ETL_SERVICE") else None + stt_service_val = config.STT_SERVICE if hasattr(config, "STT_SERVICE") else None + if not stt_service_val and not etl_service: + raise ValueError( + f"No STT_SERVICE configured — cannot transcribe audio file: {filename}" + ) + + if _needs_etl(filename): + etl_service = getattr(config, "ETL_SERVICE", None) + if not etl_service: + raise ValueError( + f"No ETL_SERVICE configured — cannot parse binary file: {filename}. " + f"Set ETL_SERVICE to UNSTRUCTURED, LLAMACLOUD, or DOCLING in your .env" + ) + + from app.connectors.onedrive.content_extractor import ( + _parse_file_to_markdown, + ) + + return await _parse_file_to_markdown(file_path, filename) + + +def _content_hash(content: str, search_space_id: int) -> str: + """SHA-256 hash of content scoped to a search space. + + Matches the format used by ``compute_content_hash`` in the unified + pipeline so that dedup checks are consistent. + """ + import hashlib + + return hashlib.sha256(f"{search_space_id}:{content}".encode()).hexdigest() + + +async def _compute_file_content_hash( + file_path: str, + filename: str, + search_space_id: int, +) -> tuple[str, str]: + """Read a file (via ETL if needed) and compute its content hash. + + Returns (content_text, content_hash). + """ + content = await _read_file_content(file_path, filename) + return content, _content_hash(content, search_space_id) + + +async def _mirror_folder_structure( + session: AsyncSession, + folder_path: str, + folder_name: str, + search_space_id: int, + user_id: str, + root_folder_id: int | None = None, + exclude_patterns: list[str] | None = None, +) -> tuple[dict[str, int], int]: + """Mirror the local filesystem directory structure into DB Folder rows. + + Returns (mapping, root_folder_id) where mapping is + relative_dir_path -> folder_id. The empty string key maps to the root folder. + """ + root = Path(folder_path) + if exclude_patterns is None: + exclude_patterns = [] + + subdirs: list[str] = [] + for dirpath, dirnames, _ in os.walk(root): + dirnames[:] = [d for d in dirnames if d not in exclude_patterns] + rel = Path(dirpath).relative_to(root) + if any(part in exclude_patterns for part in rel.parts): + continue + rel_str = str(rel) if str(rel) != "." else "" + if rel_str: + subdirs.append(rel_str) + + subdirs.sort(key=lambda p: p.count(os.sep)) + + mapping: dict[str, int] = {} + + if root_folder_id: + existing = ( + await session.execute(select(Folder).where(Folder.id == root_folder_id)) + ).scalar_one_or_none() + if existing: + mapping[""] = existing.id + else: + root_folder_id = None + + if not root_folder_id: + root_folder = Folder( + name=folder_name, + search_space_id=search_space_id, + created_by_id=user_id, + position="a0", + ) + session.add(root_folder) + await session.flush() + mapping[""] = root_folder.id + root_folder_id = root_folder.id + + for rel_dir in subdirs: + dir_parts = Path(rel_dir).parts + dir_name = dir_parts[-1] + parent_rel = str(Path(*dir_parts[:-1])) if len(dir_parts) > 1 else "" + + parent_id = mapping.get(parent_rel, mapping[""]) + + existing_folder = ( + await session.execute( + select(Folder).where( + Folder.name == dir_name, + Folder.parent_id == parent_id, + Folder.search_space_id == search_space_id, + ) + ) + ).scalar_one_or_none() + + if existing_folder: + mapping[rel_dir] = existing_folder.id + else: + new_folder = Folder( + name=dir_name, + parent_id=parent_id, + search_space_id=search_space_id, + created_by_id=user_id, + position="a0", + ) + session.add(new_folder) + await session.flush() + mapping[rel_dir] = new_folder.id + + await session.flush() + return mapping, root_folder_id + + +async def _resolve_folder_for_file( + session: AsyncSession, + rel_path: str, + root_folder_id: int, + search_space_id: int, + user_id: str, +) -> int: + """Given a file's relative path, ensure all parent Folder rows exist and + return the folder_id for the file's immediate parent directory. + + For a file at "notes/daily/today.md", this ensures Folder rows exist for + "notes" and "notes/daily", and returns the id of "notes/daily". + For a file at "readme.md" (root level), returns root_folder_id. + """ + parent_dir = str(Path(rel_path).parent) + if parent_dir == ".": + return root_folder_id + + parts = Path(parent_dir).parts + current_parent_id = root_folder_id + + for part in parts: + existing = ( + await session.execute( + select(Folder).where( + Folder.name == part, + Folder.parent_id == current_parent_id, + Folder.search_space_id == search_space_id, + ) + ) + ).scalar_one_or_none() + + if existing: + current_parent_id = existing.id + else: + new_folder = Folder( + name=part, + parent_id=current_parent_id, + search_space_id=search_space_id, + created_by_id=user_id, + position="a0", + ) + session.add(new_folder) + await session.flush() + current_parent_id = new_folder.id + + return current_parent_id + + +async def _cleanup_empty_folder_chain( + session: AsyncSession, + folder_id: int, + root_folder_id: int, +) -> None: + """Walk up from folder_id toward root, deleting empty folders (no docs, no + children). Stops at root_folder_id which is never deleted.""" + current_id = folder_id + while current_id and current_id != root_folder_id: + has_doc = ( + await session.execute( + select(Document.id).where(Document.folder_id == current_id).limit(1) + ) + ).scalar_one_or_none() + if has_doc is not None: + break + + has_child = ( + await session.execute( + select(Folder.id).where(Folder.parent_id == current_id).limit(1) + ) + ).scalar_one_or_none() + if has_child is not None: + break + + folder = ( + await session.execute(select(Folder).where(Folder.id == current_id)) + ).scalar_one_or_none() + if not folder: + break + + parent_id = folder.parent_id + await session.delete(folder) + await session.flush() + current_id = parent_id + + +async def _cleanup_empty_folders( + session: AsyncSession, + root_folder_id: int, + search_space_id: int, + existing_dirs_on_disk: set[str], + folder_mapping: dict[str, int], +) -> None: + """Delete Folder rows that are empty (no docs, no children) and no longer on disk.""" + from sqlalchemy import delete as sa_delete + + id_to_rel: dict[int, str] = {fid: rel for rel, fid in folder_mapping.items() if rel} + + all_folders = ( + ( + await session.execute( + select(Folder).where( + Folder.search_space_id == search_space_id, + Folder.id != root_folder_id, + ) + ) + ) + .scalars() + .all() + ) + + candidates: list[Folder] = [] + for folder in all_folders: + rel = id_to_rel.get(folder.id) + if rel and rel in existing_dirs_on_disk: + continue + candidates.append(folder) + + changed = True + while changed: + changed = False + remaining: list[Folder] = [] + for folder in candidates: + doc_exists = ( + await session.execute( + select(Document.id).where(Document.folder_id == folder.id).limit(1) + ) + ).scalar_one_or_none() + if doc_exists is not None: + remaining.append(folder) + continue + + child_exists = ( + await session.execute( + select(Folder.id).where(Folder.parent_id == folder.id).limit(1) + ) + ).scalar_one_or_none() + if child_exists is not None: + remaining.append(folder) + continue + + await session.execute(sa_delete(Folder).where(Folder.id == folder.id)) + changed = True + candidates = remaining + + +def _build_connector_doc( + title: str, + content: str, + relative_path: str, + folder_name: str, + *, + search_space_id: int, + user_id: str, + enable_summary: bool, +) -> ConnectorDocument: + """Build a ConnectorDocument from a local file's extracted content.""" + unique_id = f"{folder_name}:{relative_path}" + metadata = { + "folder_name": folder_name, + "file_path": relative_path, + "document_type": "Local Folder File", + "connector_type": "Local Folder", + } + fallback_summary = f"File: {title}\n\n{content[:4000]}" + + return ConnectorDocument( + title=title, + source_markdown=content, + unique_id=unique_id, + document_type=DocumentType.LOCAL_FOLDER_FILE, + search_space_id=search_space_id, + connector_id=None, + created_by_id=user_id, + should_summarize=enable_summary, + fallback_summary=fallback_summary, + metadata=metadata, + ) + + +async def index_local_folder( + session: AsyncSession, + search_space_id: int, + user_id: str, + folder_path: str, + folder_name: str, + exclude_patterns: list[str] | None = None, + file_extensions: list[str] | None = None, + root_folder_id: int | None = None, + enable_summary: bool = False, + target_file_paths: list[str] | None = None, + on_heartbeat_callback: HeartbeatCallbackType | None = None, +) -> tuple[int, int, int | None, str | None]: + """Index files from a local folder. + + Supports two modes: + - Batch (target_file_paths set): processes 1..N files. + Single-file uses the caller's session; multi-file fans out with per-file sessions. + - Full scan (no target paths): walks entire folder, handles new/changed/deleted files. + + Returns (indexed_count, skipped_count, root_folder_id, error_or_warning_message). + """ + task_logger = TaskLoggingService(session, search_space_id) + + log_entry = await task_logger.log_task_start( + task_name="local_folder_indexing", + source="local_folder_indexing_task", + message=f"Starting local folder indexing for {folder_name}", + metadata={ + "folder_path": folder_path, + "user_id": str(user_id), + "target_file_paths_count": len(target_file_paths) + if target_file_paths + else None, + }, + ) + + try: + if not folder_path or not os.path.exists(folder_path): + await task_logger.log_task_failure( + log_entry, + f"Folder path missing or does not exist: {folder_path}", + "Folder not found", + {}, + ) + return ( + 0, + 0, + root_folder_id, + f"Folder path missing or does not exist: {folder_path}", + ) + + if exclude_patterns is None: + exclude_patterns = DEFAULT_EXCLUDE_PATTERNS + + # ==================================================================== + # BATCH MODE (1..N files) + # ==================================================================== + if target_file_paths: + if len(target_file_paths) == 1: + indexed, skipped, err = await _index_single_file( + session=session, + search_space_id=search_space_id, + user_id=user_id, + folder_path=folder_path, + folder_name=folder_name, + target_file_path=target_file_paths[0], + enable_summary=enable_summary, + root_folder_id=root_folder_id, + task_logger=task_logger, + log_entry=log_entry, + ) + return indexed, skipped, root_folder_id, err + + indexed, failed, err = await _index_batch_files( + search_space_id=search_space_id, + user_id=user_id, + folder_path=folder_path, + folder_name=folder_name, + target_file_paths=target_file_paths, + enable_summary=enable_summary, + root_folder_id=root_folder_id, + on_progress_callback=on_heartbeat_callback, + ) + if err: + await task_logger.log_task_success( + log_entry, + f"Batch indexing: {indexed} indexed, {failed} failed", + {"indexed": indexed, "failed": failed}, + ) + else: + await task_logger.log_task_success( + log_entry, + f"Batch indexing complete: {indexed} indexed", + {"indexed": indexed, "failed": failed}, + ) + return indexed, failed, root_folder_id, err + + # ==================================================================== + # FULL-SCAN MODE + # ==================================================================== + + await task_logger.log_task_progress( + log_entry, "Mirroring folder structure", {"stage": "folder_mirror"} + ) + + folder_mapping, root_folder_id = await _mirror_folder_structure( + session=session, + folder_path=folder_path, + folder_name=folder_name, + search_space_id=search_space_id, + user_id=user_id, + root_folder_id=root_folder_id, + exclude_patterns=exclude_patterns, + ) + await session.flush() + + try: + files = scan_folder(folder_path, file_extensions, exclude_patterns) + except Exception as e: + await task_logger.log_task_failure( + log_entry, f"Failed to scan folder: {e}", "Scan error", {} + ) + return 0, 0, root_folder_id, f"Failed to scan folder: {e}" + + logger.info(f"Found {len(files)} files in folder") + + indexed_count = 0 + skipped_count = 0 + failed_count = 0 + + page_limit_service = PageLimitService(session) + + # ================================================================ + # PHASE 1: Pre-filter files (mtime / content-hash), version changed + # ================================================================ + connector_docs: list[ConnectorDocument] = [] + file_meta_map: dict[str, dict] = {} + seen_unique_hashes: set[str] = set() + + for file_info in files: + try: + relative_path = file_info["relative_path"] + file_path_abs = file_info["path"] + + unique_identifier = f"{folder_name}:{relative_path}" + unique_identifier_hash = compute_identifier_hash( + DocumentType.LOCAL_FOLDER_FILE.value, + unique_identifier, + search_space_id, + ) + seen_unique_hashes.add(unique_identifier_hash) + + existing_document = await check_document_by_unique_identifier( + session, unique_identifier_hash + ) + + if existing_document: + stored_mtime = (existing_document.document_metadata or {}).get( + "mtime" + ) + current_mtime = file_info["modified_at"].timestamp() + + if stored_mtime and abs(current_mtime - stored_mtime) < 1.0: + if not DocumentStatus.is_state( + existing_document.status, DocumentStatus.READY + ): + existing_document.status = DocumentStatus.ready() + skipped_count += 1 + continue + + try: + estimated_pages = await _check_page_limit_or_skip( + page_limit_service, user_id, file_path_abs + ) + except PageLimitExceededError: + logger.warning( + f"Page limit exceeded, skipping: {file_path_abs}" + ) + failed_count += 1 + continue + + try: + content, content_hash = await _compute_file_content_hash( + file_path_abs, file_info["relative_path"], search_space_id + ) + except Exception as read_err: + logger.warning(f"Could not read {file_path_abs}: {read_err}") + skipped_count += 1 + continue + + if existing_document.content_hash == content_hash: + meta = dict(existing_document.document_metadata or {}) + meta["mtime"] = current_mtime + existing_document.document_metadata = meta + if not DocumentStatus.is_state( + existing_document.status, DocumentStatus.READY + ): + existing_document.status = DocumentStatus.ready() + skipped_count += 1 + continue + + await create_version_snapshot(session, existing_document) + else: + try: + estimated_pages = await _check_page_limit_or_skip( + page_limit_service, user_id, file_path_abs + ) + except PageLimitExceededError: + logger.warning( + f"Page limit exceeded, skipping: {file_path_abs}" + ) + failed_count += 1 + continue + + try: + content, content_hash = await _compute_file_content_hash( + file_path_abs, file_info["relative_path"], search_space_id + ) + except Exception as read_err: + logger.warning(f"Could not read {file_path_abs}: {read_err}") + skipped_count += 1 + continue + + if not content.strip(): + skipped_count += 1 + continue + + doc = _build_connector_doc( + title=file_info["name"], + content=content, + relative_path=relative_path, + folder_name=folder_name, + search_space_id=search_space_id, + user_id=user_id, + enable_summary=enable_summary, + ) + connector_docs.append(doc) + file_meta_map[unique_identifier] = { + "relative_path": relative_path, + "mtime": file_info["modified_at"].timestamp(), + "estimated_pages": estimated_pages, + "content_length": len(content), + } + + except Exception as e: + logger.exception(f"Phase 1 error for {file_info.get('path')}: {e}") + failed_count += 1 + + # ================================================================ + # PHASE 1.5: Delete documents no longer on disk + # ================================================================ + all_root_folder_ids = set(folder_mapping.values()) + all_db_folders = ( + ( + await session.execute( + select(Folder.id).where( + Folder.search_space_id == search_space_id, + ) + ) + ) + .scalars() + .all() + ) + all_root_folder_ids.update(all_db_folders) + + all_folder_docs = ( + ( + await session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == search_space_id, + Document.folder_id.in_(list(all_root_folder_ids)), + ) + ) + ) + .scalars() + .all() + ) + + for doc in all_folder_docs: + if doc.unique_identifier_hash not in seen_unique_hashes: + await session.delete(doc) + + await session.flush() + + # ================================================================ + # PHASE 2: Index via unified pipeline + # ================================================================ + if connector_docs: + from app.indexing_pipeline.document_hashing import ( + compute_unique_identifier_hash, + ) + + pipeline = IndexingPipelineService(session) + doc_map = {compute_unique_identifier_hash(cd): cd for cd in connector_docs} + documents = await pipeline.prepare_for_indexing(connector_docs) + + # Assign folder_id immediately so docs appear in the correct + # folder while still pending/processing (visible via Zero sync). + for document in documents: + cd = doc_map.get(document.unique_identifier_hash) + if cd is None: + continue + rel_path = (cd.metadata or {}).get("file_path", "") + parent_dir = str(Path(rel_path).parent) if rel_path else "" + if parent_dir == ".": + parent_dir = "" + document.folder_id = folder_mapping.get( + parent_dir, folder_mapping.get("") + ) + try: + await session.commit() + except IntegrityError: + await session.rollback() + for document in documents: + await session.refresh(document) + + llm = await get_user_long_context_llm(session, user_id, search_space_id) + + for document in documents: + connector_doc = doc_map.get(document.unique_identifier_hash) + if connector_doc is None: + failed_count += 1 + continue + + result = await pipeline.index(document, connector_doc, llm) + + if DocumentStatus.is_state(result.status, DocumentStatus.READY): + indexed_count += 1 + + unique_id = connector_doc.unique_id + mtime_info = file_meta_map.get(unique_id, {}) + + doc_meta = dict(result.document_metadata or {}) + doc_meta["mtime"] = mtime_info.get("mtime") + result.document_metadata = doc_meta + + est = mtime_info.get("estimated_pages", 1) + content_len = mtime_info.get("content_length", 0) + final_pages = _compute_final_pages( + page_limit_service, est, content_len + ) + await page_limit_service.update_page_usage( + user_id, final_pages, allow_exceed=True + ) + else: + failed_count += 1 + + if on_heartbeat_callback and indexed_count % 5 == 0: + await on_heartbeat_callback(indexed_count) + + # Cleanup empty folders + existing_dirs = set() + for dirpath, dirnames, _ in os.walk(folder_path): + dirnames[:] = [d for d in dirnames if d not in exclude_patterns] + rel = str(Path(dirpath).relative_to(folder_path)) + if rel == ".": + rel = "" + if rel and not any(part in exclude_patterns for part in Path(rel).parts): + existing_dirs.add(rel) + + root_fid = folder_mapping.get("") + if root_fid: + await _cleanup_empty_folders( + session, root_fid, search_space_id, existing_dirs, folder_mapping + ) + + try: + await session.commit() + except Exception as e: + if "duplicate key value violates unique constraint" in str(e).lower(): + logger.warning(f"Duplicate key during commit: {e}") + await session.rollback() + else: + raise + + warning_parts = [] + if failed_count > 0: + warning_parts.append(f"{failed_count} failed") + warning_message = ", ".join(warning_parts) if warning_parts else None + + await task_logger.log_task_success( + log_entry, + f"Completed local folder indexing for {folder_name}", + { + "indexed": indexed_count, + "skipped": skipped_count, + "failed": failed_count, + }, + ) + + return indexed_count, skipped_count, root_folder_id, warning_message + + except SQLAlchemyError as e: + logger.exception(f"Database error during local folder indexing: {e}") + await session.rollback() + await task_logger.log_task_failure( + log_entry, f"DB error: {e}", "Database error", {} + ) + return 0, 0, root_folder_id, f"Database error: {e}" + + except Exception as e: + logger.exception(f"Error during local folder indexing: {e}") + await task_logger.log_task_failure( + log_entry, f"Error: {e}", "Unexpected error", {} + ) + return 0, 0, root_folder_id, str(e) + + +BATCH_CONCURRENCY = 5 + + +async def _index_batch_files( + search_space_id: int, + user_id: str, + folder_path: str, + folder_name: str, + target_file_paths: list[str], + enable_summary: bool, + root_folder_id: int | None, + on_progress_callback: HeartbeatCallbackType | None = None, +) -> tuple[int, int, str | None]: + """Process multiple files in parallel with bounded concurrency. + + Each file gets its own DB session so they can run concurrently. + Returns (indexed_count, failed_count, error_summary_or_none). + """ + semaphore = asyncio.Semaphore(BATCH_CONCURRENCY) + indexed = 0 + failed = 0 + errors: list[str] = [] + lock = asyncio.Lock() + completed = 0 + + async def process_one(file_path: str) -> None: + nonlocal indexed, failed, completed + async with semaphore: + try: + async with get_celery_session_maker()() as file_session: + task_logger = TaskLoggingService(file_session, search_space_id) + log_entry = await task_logger.log_task_start( + task_name="local_folder_indexing", + source="local_folder_batch_indexing", + message=f"Batch: indexing {Path(file_path).name}", + metadata={"file_path": file_path}, + ) + ix, _sk, err = await _index_single_file( + session=file_session, + search_space_id=search_space_id, + user_id=user_id, + folder_path=folder_path, + folder_name=folder_name, + target_file_path=file_path, + enable_summary=enable_summary, + root_folder_id=root_folder_id, + task_logger=task_logger, + log_entry=log_entry, + ) + async with lock: + indexed += ix + if err: + failed += 1 + errors.append(f"{Path(file_path).name}: {err}") + completed += 1 + if on_progress_callback and completed % BATCH_CONCURRENCY == 0: + await on_progress_callback(completed) + except Exception as exc: + logger.exception(f"Batch: error processing {file_path}: {exc}") + async with lock: + failed += 1 + completed += 1 + errors.append(f"{Path(file_path).name}: {exc}") + + await asyncio.gather(*[process_one(fp) for fp in target_file_paths]) + + if on_progress_callback: + await on_progress_callback(completed) + + error_summary = None + if errors: + error_summary = f"{failed} file(s) failed: " + "; ".join(errors[:5]) + if len(errors) > 5: + error_summary += f" ... and {len(errors) - 5} more" + + return indexed, failed, error_summary + + +async def _index_single_file( + session: AsyncSession, + search_space_id: int, + user_id: str, + folder_path: str, + folder_name: str, + target_file_path: str, + enable_summary: bool, + root_folder_id: int | None, + task_logger, + log_entry, +) -> tuple[int, int, str | None]: + """Process a single file (chokidar real-time trigger).""" + try: + full_path = Path(target_file_path) + if not full_path.exists(): + rel = str(full_path.relative_to(folder_path)) + unique_id = f"{folder_name}:{rel}" + uid_hash = compute_identifier_hash( + DocumentType.LOCAL_FOLDER_FILE.value, unique_id, search_space_id + ) + existing = await check_document_by_unique_identifier(session, uid_hash) + if existing: + deleted_folder_id = existing.folder_id + await session.delete(existing) + await session.flush() + if deleted_folder_id and root_folder_id: + await _cleanup_empty_folder_chain( + session, deleted_folder_id, root_folder_id + ) + await session.commit() + return 0, 0, None + return 0, 0, None + + rel_path = str(full_path.relative_to(folder_path)) + + unique_id = f"{folder_name}:{rel_path}" + uid_hash = compute_identifier_hash( + DocumentType.LOCAL_FOLDER_FILE.value, unique_id, search_space_id + ) + + page_limit_service = PageLimitService(session) + try: + estimated_pages = await _check_page_limit_or_skip( + page_limit_service, user_id, str(full_path) + ) + except PageLimitExceededError as e: + return 0, 1, f"Page limit exceeded: {e}" + + try: + content, content_hash = await _compute_file_content_hash( + str(full_path), full_path.name, search_space_id + ) + except Exception as e: + return 0, 1, f"Could not read file: {e}" + + if not content.strip(): + return 0, 1, None + + existing = await check_document_by_unique_identifier(session, uid_hash) + + if existing: + if existing.content_hash == content_hash: + mtime = full_path.stat().st_mtime + meta = dict(existing.document_metadata or {}) + meta["mtime"] = mtime + existing.document_metadata = meta + await session.commit() + return 0, 1, None + + await create_version_snapshot(session, existing) + + mtime = full_path.stat().st_mtime + + connector_doc = _build_connector_doc( + title=full_path.name, + content=content, + relative_path=rel_path, + folder_name=folder_name, + search_space_id=search_space_id, + user_id=user_id, + enable_summary=enable_summary, + ) + + pipeline = IndexingPipelineService(session) + llm = await get_user_long_context_llm(session, user_id, search_space_id) + documents = await pipeline.prepare_for_indexing([connector_doc]) + + if not documents: + return 0, 1, None + + db_doc = documents[0] + + if root_folder_id: + try: + db_doc.folder_id = await _resolve_folder_for_file( + session, rel_path, root_folder_id, search_space_id, user_id + ) + await session.commit() + except IntegrityError: + await session.rollback() + await session.refresh(db_doc) + + await pipeline.index(db_doc, connector_doc, llm) + + await session.refresh(db_doc) + doc_meta = dict(db_doc.document_metadata or {}) + doc_meta["mtime"] = mtime + db_doc.document_metadata = doc_meta + await session.commit() + + indexed = ( + 1 if DocumentStatus.is_state(db_doc.status, DocumentStatus.READY) else 0 + ) + failed_msg = None if indexed else "Indexing failed" + + if indexed: + final_pages = _compute_final_pages( + page_limit_service, estimated_pages, len(content) + ) + await page_limit_service.update_page_usage( + user_id, final_pages, allow_exceed=True + ) + await task_logger.log_task_success( + log_entry, + f"Single file indexed: {rel_path}", + {"file": rel_path, "pages_processed": final_pages}, + ) + return indexed, 0 if indexed else 1, failed_msg + + except Exception as e: + logger.exception(f"Error indexing single file {target_file_path}: {e}") + await session.rollback() + return 0, 0, str(e) diff --git a/surfsense_backend/app/tasks/document_processors/__init__.py b/surfsense_backend/app/tasks/document_processors/__init__.py index e70c41cb4..2b5690d02 100644 --- a/surfsense_backend/app/tasks/document_processors/__init__.py +++ b/surfsense_backend/app/tasks/document_processors/__init__.py @@ -12,16 +12,14 @@ Available processors: - YouTube processor: Process YouTube videos and extract transcripts """ -# URL crawler # Extension processor -from .extension_processor import add_extension_received_document - -# File processors -from .file_processors import ( +# File processors (backward-compatible re-exports from _save) +from ._save import ( add_received_file_document_using_docling, add_received_file_document_using_llamacloud, add_received_file_document_using_unstructured, ) +from .extension_processor import add_extension_received_document # Markdown processor from .markdown_processor import add_received_markdown_file_document @@ -32,9 +30,9 @@ from .youtube_processor import add_youtube_video_document __all__ = [ # Extension processing "add_extension_received_document", + # File processing with different ETL services "add_received_file_document_using_docling", "add_received_file_document_using_llamacloud", - # File processing with different ETL services "add_received_file_document_using_unstructured", # Markdown file processing "add_received_markdown_file_document", diff --git a/surfsense_backend/app/tasks/document_processors/_constants.py b/surfsense_backend/app/tasks/document_processors/_constants.py new file mode 100644 index 000000000..f74d7acce --- /dev/null +++ b/surfsense_backend/app/tasks/document_processors/_constants.py @@ -0,0 +1,74 @@ +""" +Constants for file document processing. + +Centralizes file type classification, LlamaCloud retry configuration, +and timeout calculation parameters. +""" + +import ssl +from enum import Enum + +import httpx + +# --------------------------------------------------------------------------- +# File type classification +# --------------------------------------------------------------------------- + +MARKDOWN_EXTENSIONS = (".md", ".markdown", ".txt") +AUDIO_EXTENSIONS = (".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm") +DIRECT_CONVERT_EXTENSIONS = (".csv", ".tsv", ".html", ".htm") + + +class FileCategory(Enum): + MARKDOWN = "markdown" + AUDIO = "audio" + DIRECT_CONVERT = "direct_convert" + DOCUMENT = "document" + + +def classify_file(filename: str) -> FileCategory: + """Classify a file by its extension into a processing category.""" + lower = filename.lower() + if lower.endswith(MARKDOWN_EXTENSIONS): + return FileCategory.MARKDOWN + if lower.endswith(AUDIO_EXTENSIONS): + return FileCategory.AUDIO + if lower.endswith(DIRECT_CONVERT_EXTENSIONS): + return FileCategory.DIRECT_CONVERT + return FileCategory.DOCUMENT + + +# --------------------------------------------------------------------------- +# LlamaCloud retry configuration +# --------------------------------------------------------------------------- + +LLAMACLOUD_MAX_RETRIES = 5 +LLAMACLOUD_BASE_DELAY = 10 # seconds (exponential backoff base) +LLAMACLOUD_MAX_DELAY = 120 # max delay between retries (2 minutes) +LLAMACLOUD_RETRYABLE_EXCEPTIONS = ( + ssl.SSLError, + httpx.ConnectError, + httpx.ConnectTimeout, + httpx.ReadError, + httpx.ReadTimeout, + httpx.WriteError, + httpx.WriteTimeout, + httpx.RemoteProtocolError, + httpx.LocalProtocolError, + ConnectionError, + ConnectionResetError, + TimeoutError, + OSError, +) + +# --------------------------------------------------------------------------- +# Timeout calculation constants +# --------------------------------------------------------------------------- + +UPLOAD_BYTES_PER_SECOND_SLOW = ( + 100 * 1024 +) # 100 KB/s (conservative for slow connections) +MIN_UPLOAD_TIMEOUT = 120 # Minimum 2 minutes for any file +MAX_UPLOAD_TIMEOUT = 1800 # Maximum 30 minutes for very large files +BASE_JOB_TIMEOUT = 600 # 10 minutes base for job processing +PER_PAGE_JOB_TIMEOUT = 60 # 1 minute per page for processing diff --git a/surfsense_backend/app/tasks/document_processors/_direct_converters.py b/surfsense_backend/app/tasks/document_processors/_direct_converters.py new file mode 100644 index 000000000..b1a69ef4f --- /dev/null +++ b/surfsense_backend/app/tasks/document_processors/_direct_converters.py @@ -0,0 +1,90 @@ +""" +Lossless file-to-markdown converters for text-based formats. + +These converters handle file types that can be faithfully represented as +markdown without any external ETL/OCR service: + +- CSV / TSV → markdown table (stdlib ``csv``) +- HTML / HTM → markdown (``markdownify``) +""" + +from __future__ import annotations + +import csv +from collections.abc import Callable +from pathlib import Path + +from markdownify import markdownify + +# The stdlib csv module defaults to a 128 KB field-size limit which is too +# small for real-world exports (e.g. chat logs, CRM dumps). We raise it once +# at import time so every csv.reader call in this module can handle large fields. +csv.field_size_limit(2**31 - 1) + + +def _escape_pipe(cell: str) -> str: + """Escape literal pipe characters inside a markdown table cell.""" + return cell.replace("|", "\\|") + + +def csv_to_markdown(file_path: str, *, delimiter: str = ",") -> str: + """Convert a CSV (or TSV) file to a markdown table. + + The first row is treated as the header. An empty file returns an + empty string so the caller can decide how to handle it. + """ + with open(file_path, encoding="utf-8", newline="") as fh: + reader = csv.reader(fh, delimiter=delimiter) + rows = list(reader) + + if not rows: + return "" + + header, *body = rows + col_count = len(header) + + lines: list[str] = [] + + header_cells = [_escape_pipe(c.strip()) for c in header] + lines.append("| " + " | ".join(header_cells) + " |") + lines.append("| " + " | ".join(["---"] * col_count) + " |") + + for row in body: + padded = row + [""] * (col_count - len(row)) + cells = [_escape_pipe(c.strip()) for c in padded[:col_count]] + lines.append("| " + " | ".join(cells) + " |") + + return "\n".join(lines) + "\n" + + +def tsv_to_markdown(file_path: str) -> str: + """Convert a TSV file to a markdown table.""" + return csv_to_markdown(file_path, delimiter="\t") + + +def html_to_markdown(file_path: str) -> str: + """Convert an HTML file to markdown via ``markdownify``.""" + html = Path(file_path).read_text(encoding="utf-8") + return markdownify(html).strip() + + +_CONVERTER_MAP: dict[str, Callable[..., str]] = { + ".csv": csv_to_markdown, + ".tsv": tsv_to_markdown, + ".html": html_to_markdown, + ".htm": html_to_markdown, +} + + +def convert_file_directly(file_path: str, filename: str) -> str: + """Dispatch to the appropriate lossless converter based on file extension. + + Raises ``ValueError`` if the extension is not supported. + """ + suffix = Path(filename).suffix.lower() + converter = _CONVERTER_MAP.get(suffix) + if converter is None: + raise ValueError( + f"No direct converter for extension '{suffix}' (file: {filename})" + ) + return converter(file_path) diff --git a/surfsense_backend/app/tasks/document_processors/_etl.py b/surfsense_backend/app/tasks/document_processors/_etl.py new file mode 100644 index 000000000..cc3a8b1ac --- /dev/null +++ b/surfsense_backend/app/tasks/document_processors/_etl.py @@ -0,0 +1,209 @@ +""" +ETL parsing strategies for different document processing services. + +Provides parse functions for Unstructured, LlamaCloud, and Docling, along with +LlamaCloud retry logic and dynamic timeout calculations. +""" + +import asyncio +import logging +import os +import random +import warnings +from logging import ERROR, getLogger + +import httpx + +from app.config import config as app_config +from app.db import Log +from app.services.task_logging_service import TaskLoggingService + +from ._constants import ( + LLAMACLOUD_BASE_DELAY, + LLAMACLOUD_MAX_DELAY, + LLAMACLOUD_MAX_RETRIES, + LLAMACLOUD_RETRYABLE_EXCEPTIONS, + PER_PAGE_JOB_TIMEOUT, +) +from ._helpers import calculate_job_timeout, calculate_upload_timeout + +# --------------------------------------------------------------------------- +# LlamaCloud parsing with retry +# --------------------------------------------------------------------------- + + +async def parse_with_llamacloud_retry( + file_path: str, + estimated_pages: int, + task_logger: TaskLoggingService | None = None, + log_entry: Log | None = None, +): + """ + Parse a file with LlamaCloud with retry logic for transient SSL/connection errors. + + Uses dynamic timeout calculations based on file size and page count to handle + very large files reliably. + + Returns: + LlamaParse result object + + Raises: + Exception: If all retries fail + """ + from llama_cloud_services import LlamaParse + from llama_cloud_services.parse.utils import ResultType + + file_size_bytes = os.path.getsize(file_path) + file_size_mb = file_size_bytes / (1024 * 1024) + + upload_timeout = calculate_upload_timeout(file_size_bytes) + job_timeout = calculate_job_timeout(estimated_pages, file_size_bytes) + + custom_timeout = httpx.Timeout( + connect=120.0, + read=upload_timeout, + write=upload_timeout, + pool=120.0, + ) + + logging.info( + f"LlamaCloud upload configured: file_size={file_size_mb:.1f}MB, " + f"pages={estimated_pages}, upload_timeout={upload_timeout:.0f}s, " + f"job_timeout={job_timeout:.0f}s" + ) + + last_exception = None + attempt_errors: list[str] = [] + + for attempt in range(1, LLAMACLOUD_MAX_RETRIES + 1): + try: + async with httpx.AsyncClient(timeout=custom_timeout) as custom_client: + parser = LlamaParse( + api_key=app_config.LLAMA_CLOUD_API_KEY, + num_workers=1, + verbose=True, + language="en", + result_type=ResultType.MD, + max_timeout=int(max(2000, job_timeout + upload_timeout)), + job_timeout_in_seconds=job_timeout, + job_timeout_extra_time_per_page_in_seconds=PER_PAGE_JOB_TIMEOUT, + custom_client=custom_client, + ) + result = await parser.aparse(file_path) + + if attempt > 1: + logging.info( + f"LlamaCloud upload succeeded on attempt {attempt} after " + f"{len(attempt_errors)} failures" + ) + return result + + except LLAMACLOUD_RETRYABLE_EXCEPTIONS as e: + last_exception = e + error_type = type(e).__name__ + error_msg = str(e)[:200] + attempt_errors.append(f"Attempt {attempt}: {error_type} - {error_msg}") + + if attempt < LLAMACLOUD_MAX_RETRIES: + base_delay = min( + LLAMACLOUD_BASE_DELAY * (2 ** (attempt - 1)), + LLAMACLOUD_MAX_DELAY, + ) + jitter = base_delay * 0.25 * (2 * random.random() - 1) + delay = base_delay + jitter + + if task_logger and log_entry: + await task_logger.log_task_progress( + log_entry, + f"LlamaCloud upload failed " + f"(attempt {attempt}/{LLAMACLOUD_MAX_RETRIES}), " + f"retrying in {delay:.0f}s", + { + "error_type": error_type, + "error_message": error_msg, + "attempt": attempt, + "retry_delay": delay, + "file_size_mb": round(file_size_mb, 1), + "upload_timeout": upload_timeout, + }, + ) + else: + logging.warning( + f"LlamaCloud upload failed " + f"(attempt {attempt}/{LLAMACLOUD_MAX_RETRIES}): " + f"{error_type}. File: {file_size_mb:.1f}MB. " + f"Retrying in {delay:.0f}s..." + ) + + await asyncio.sleep(delay) + else: + logging.error( + f"LlamaCloud upload failed after {LLAMACLOUD_MAX_RETRIES} " + f"attempts. File size: {file_size_mb:.1f}MB, " + f"Pages: {estimated_pages}. " + f"Errors: {'; '.join(attempt_errors)}" + ) + + except Exception: + raise + + raise last_exception or RuntimeError( + f"LlamaCloud parsing failed after {LLAMACLOUD_MAX_RETRIES} retries. " + f"File size: {file_size_mb:.1f}MB" + ) + + +# --------------------------------------------------------------------------- +# Per-service parse functions +# --------------------------------------------------------------------------- + + +async def parse_with_unstructured(file_path: str): + """ + Parse a file using the Unstructured ETL service. + + Returns: + List of LangChain Document elements. + """ + from langchain_unstructured import UnstructuredLoader + + loader = UnstructuredLoader( + file_path, + mode="elements", + post_processors=[], + languages=["eng"], + include_orig_elements=False, + include_metadata=False, + strategy="auto", + ) + return await loader.aload() + + +async def parse_with_docling(file_path: str, filename: str) -> str: + """ + Parse a file using the Docling ETL service (via the Docling service wrapper). + + Returns: + Markdown content string. + """ + from app.services.docling_service import create_docling_service + + docling_service = create_docling_service() + + pdfminer_logger = getLogger("pdfminer") + original_level = pdfminer_logger.level + + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=UserWarning, module="pdfminer") + warnings.filterwarnings( + "ignore", message=".*Cannot set gray non-stroke color.*" + ) + warnings.filterwarnings("ignore", message=".*invalid float value.*") + pdfminer_logger.setLevel(ERROR) + + try: + result = await docling_service.process_document(file_path, filename) + finally: + pdfminer_logger.setLevel(original_level) + + return result["content"] diff --git a/surfsense_backend/app/tasks/document_processors/_helpers.py b/surfsense_backend/app/tasks/document_processors/_helpers.py new file mode 100644 index 000000000..7ac05932c --- /dev/null +++ b/surfsense_backend/app/tasks/document_processors/_helpers.py @@ -0,0 +1,218 @@ +""" +Document helper functions for deduplication, migration, and connector updates. + +Provides reusable logic shared across file processors and ETL strategies. +""" + +import logging + +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db import Document, DocumentStatus, DocumentType +from app.utils.document_converters import generate_unique_identifier_hash + +from ._constants import ( + BASE_JOB_TIMEOUT, + MAX_UPLOAD_TIMEOUT, + MIN_UPLOAD_TIMEOUT, + PER_PAGE_JOB_TIMEOUT, + UPLOAD_BYTES_PER_SECOND_SLOW, +) +from .base import ( + check_document_by_unique_identifier, + check_duplicate_document, +) + +# --------------------------------------------------------------------------- +# Unique identifier helpers +# --------------------------------------------------------------------------- + + +def get_google_drive_unique_identifier( + connector: dict | None, + filename: str, + search_space_id: int, +) -> tuple[str, str | None]: + """ + Get unique identifier hash, using file_id for Google Drive (stable across renames). + + Returns: + Tuple of (primary_hash, legacy_hash or None). + For Google Drive: (file_id-based hash, filename-based hash for migration). + For other sources: (filename-based hash, None). + """ + if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: + metadata = connector.get("metadata", {}) + file_id = metadata.get("google_drive_file_id") + + if file_id: + primary_hash = generate_unique_identifier_hash( + DocumentType.GOOGLE_DRIVE_FILE, file_id, search_space_id + ) + legacy_hash = generate_unique_identifier_hash( + DocumentType.GOOGLE_DRIVE_FILE, filename, search_space_id + ) + return primary_hash, legacy_hash + + primary_hash = generate_unique_identifier_hash( + DocumentType.FILE, filename, search_space_id + ) + return primary_hash, None + + +# --------------------------------------------------------------------------- +# Document deduplication and migration +# --------------------------------------------------------------------------- + + +async def handle_existing_document_update( + session: AsyncSession, + existing_document: Document, + content_hash: str, + connector: dict | None, + filename: str, + primary_hash: str, +) -> tuple[bool, Document | None]: + """ + Handle update logic for an existing document. + + Returns: + Tuple of (should_skip_processing, document_to_return): + - (True, document): Content unchanged, return existing document + - (False, None): Content changed, needs re-processing + """ + if existing_document.unique_identifier_hash != primary_hash: + existing_document.unique_identifier_hash = primary_hash + logging.info(f"Migrated document to file_id-based identifier: {filename}") + + if existing_document.content_hash == content_hash: + if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: + connector_metadata = connector.get("metadata", {}) + new_name = connector_metadata.get("google_drive_file_name") + doc_metadata = existing_document.document_metadata or {} + old_name = doc_metadata.get("FILE_NAME") or doc_metadata.get( + "google_drive_file_name" + ) + + if new_name and old_name and old_name != new_name: + from sqlalchemy.orm.attributes import flag_modified + + existing_document.title = new_name + if not existing_document.document_metadata: + existing_document.document_metadata = {} + existing_document.document_metadata["FILE_NAME"] = new_name + existing_document.document_metadata["google_drive_file_name"] = new_name + flag_modified(existing_document, "document_metadata") + await session.commit() + logging.info( + f"File renamed in Google Drive: '{old_name}' → '{new_name}' " + f"(no re-processing needed)" + ) + + logging.info(f"Document for file {filename} unchanged. Skipping.") + return True, existing_document + + # Content has changed — guard against content_hash collision before + # expensive ETL processing. + collision_doc = await check_duplicate_document(session, content_hash) + if collision_doc and collision_doc.id != existing_document.id: + logging.warning( + "Content-hash collision for %s: identical content exists in " + "document #%s (%s). Skipping re-processing.", + filename, + collision_doc.id, + collision_doc.document_type, + ) + if DocumentStatus.is_state( + existing_document.status, DocumentStatus.PENDING + ) or DocumentStatus.is_state( + existing_document.status, DocumentStatus.PROCESSING + ): + await session.delete(existing_document) + await session.commit() + return True, None + + return True, existing_document + + logging.info(f"Content changed for file {filename}. Updating document.") + return False, None + + +async def find_existing_document_with_migration( + session: AsyncSession, + primary_hash: str, + legacy_hash: str | None, + content_hash: str | None = None, +) -> Document | None: + """ + Find existing document, checking primary hash, legacy hash, and content_hash. + + Supports migration from filename-based to file_id-based hashing for + Google Drive files, with content_hash fallback for cross-source dedup. + """ + existing_document = await check_document_by_unique_identifier(session, primary_hash) + + if not existing_document and legacy_hash: + existing_document = await check_document_by_unique_identifier( + session, legacy_hash + ) + if existing_document: + logging.info( + "Found legacy document (filename-based hash), " + "will migrate to file_id-based hash" + ) + + if not existing_document and content_hash: + existing_document = await check_duplicate_document(session, content_hash) + if existing_document: + logging.info( + f"Found duplicate content from different source (content_hash match). " + f"Original document ID: {existing_document.id}, " + f"type: {existing_document.document_type}" + ) + + return existing_document + + +# --------------------------------------------------------------------------- +# Connector helpers +# --------------------------------------------------------------------------- + + +async def update_document_from_connector( + document: Document | None, + connector: dict | None, + session: AsyncSession, +) -> None: + """Update document type, metadata, and connector_id from connector info.""" + if not document or not connector: + return + if "type" in connector: + document.document_type = connector["type"] + if "metadata" in connector: + if not document.document_metadata: + document.document_metadata = connector["metadata"] + else: + merged = {**document.document_metadata, **connector["metadata"]} + document.document_metadata = merged + if "connector_id" in connector: + document.connector_id = connector["connector_id"] + await session.commit() + + +# --------------------------------------------------------------------------- +# Timeout calculations +# --------------------------------------------------------------------------- + + +def calculate_upload_timeout(file_size_bytes: int) -> float: + """Calculate upload timeout based on file size (conservative for slow connections).""" + estimated_time = (file_size_bytes / UPLOAD_BYTES_PER_SECOND_SLOW) * 1.5 + return max(MIN_UPLOAD_TIMEOUT, min(estimated_time, MAX_UPLOAD_TIMEOUT)) + + +def calculate_job_timeout(estimated_pages: int, file_size_bytes: int) -> float: + """Calculate job processing timeout based on page count and file size.""" + page_based_timeout = BASE_JOB_TIMEOUT + (estimated_pages * PER_PAGE_JOB_TIMEOUT) + size_based_timeout = BASE_JOB_TIMEOUT + (file_size_bytes / (10 * 1024 * 1024)) * 60 + return max(page_based_timeout, size_based_timeout) diff --git a/surfsense_backend/app/tasks/document_processors/_save.py b/surfsense_backend/app/tasks/document_processors/_save.py new file mode 100644 index 000000000..5088ad004 --- /dev/null +++ b/surfsense_backend/app/tasks/document_processors/_save.py @@ -0,0 +1,285 @@ +""" +Unified document save/update logic for file processors. + +Replaces the three nearly-identical ``add_received_file_document_using_*`` +functions with a single ``save_file_document`` function plus thin wrappers +for backward compatibility. +""" + +import logging + +from langchain_core.documents import Document as LangChainDocument +from sqlalchemy.exc import SQLAlchemyError +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db import Document, DocumentStatus, DocumentType +from app.services.llm_service import get_user_long_context_llm +from app.utils.document_converters import ( + create_document_chunks, + embed_text, + generate_content_hash, + generate_document_summary, +) + +from ._helpers import ( + find_existing_document_with_migration, + get_google_drive_unique_identifier, + handle_existing_document_update, +) +from .base import get_current_timestamp, safe_set_chunks + +# --------------------------------------------------------------------------- +# Summary generation +# --------------------------------------------------------------------------- + + +async def _generate_summary( + markdown_content: str, + file_name: str, + etl_service: str, + user_llm, + enable_summary: bool, +) -> tuple[str, list[float]]: + """ + Generate a document summary and embedding. + + Docling uses its own large-document summary strategy; other ETL services + use the standard ``generate_document_summary`` helper. + """ + if not enable_summary: + summary = f"File: {file_name}\n\n{markdown_content[:4000]}" + return summary, embed_text(summary) + + if etl_service == "DOCLING": + from app.services.docling_service import create_docling_service + + docling_service = create_docling_service() + summary_text = await docling_service.process_large_document_summary( + content=markdown_content, llm=user_llm, document_title=file_name + ) + + meta = { + "file_name": file_name, + "etl_service": etl_service, + "document_type": "File Document", + } + parts = ["# DOCUMENT METADATA"] + for key, value in meta.items(): + if value: + formatted_key = key.replace("_", " ").title() + parts.append(f"**{formatted_key}:** {value}") + + enhanced = "\n".join(parts) + "\n\n# DOCUMENT SUMMARY\n\n" + summary_text + return enhanced, embed_text(enhanced) + + # Standard summary (Unstructured / LlamaCloud / others) + meta = { + "file_name": file_name, + "etl_service": etl_service, + "document_type": "File Document", + } + return await generate_document_summary(markdown_content, user_llm, meta) + + +# --------------------------------------------------------------------------- +# Unified save function +# --------------------------------------------------------------------------- + + +async def save_file_document( + session: AsyncSession, + file_name: str, + markdown_content: str, + search_space_id: int, + user_id: str, + etl_service: str, + connector: dict | None = None, + enable_summary: bool = True, +) -> Document | None: + """ + Process and store a file document with deduplication and migration support. + + Handles both creating new documents and updating existing ones. This is + the single implementation behind the per-ETL-service wrapper functions. + + Args: + session: Database session + file_name: Name of the processed file + markdown_content: Markdown content to store + search_space_id: ID of the search space + user_id: ID of the user + etl_service: Name of the ETL service (UNSTRUCTURED, LLAMACLOUD, DOCLING) + connector: Optional connector info for Google Drive files + enable_summary: Whether to generate an AI summary + + Returns: + Document object if successful, None if duplicate detected + """ + try: + primary_hash, legacy_hash = get_google_drive_unique_identifier( + connector, file_name, search_space_id + ) + content_hash = generate_content_hash(markdown_content, search_space_id) + + existing_document = await find_existing_document_with_migration( + session, primary_hash, legacy_hash, content_hash + ) + + if existing_document: + should_skip, doc = await handle_existing_document_update( + session, + existing_document, + content_hash, + connector, + file_name, + primary_hash, + ) + if should_skip: + return doc + + user_llm = await get_user_long_context_llm(session, user_id, search_space_id) + if not user_llm: + raise RuntimeError( + f"No long context LLM configured for user {user_id} " + f"in search space {search_space_id}" + ) + + summary_content, summary_embedding = await _generate_summary( + markdown_content, file_name, etl_service, user_llm, enable_summary + ) + chunks = await create_document_chunks(markdown_content) + doc_metadata = {"FILE_NAME": file_name, "ETL_SERVICE": etl_service} + + if existing_document: + existing_document.title = file_name + existing_document.content = summary_content + existing_document.content_hash = content_hash + existing_document.embedding = summary_embedding + existing_document.document_metadata = doc_metadata + await safe_set_chunks(session, existing_document, chunks) + existing_document.source_markdown = markdown_content + existing_document.content_needs_reindexing = False + existing_document.updated_at = get_current_timestamp() + existing_document.status = DocumentStatus.ready() + + await session.commit() + await session.refresh(existing_document) + return existing_document + + doc_type = DocumentType.FILE + if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: + doc_type = DocumentType.GOOGLE_DRIVE_FILE + + document = Document( + search_space_id=search_space_id, + title=file_name, + document_type=doc_type, + document_metadata=doc_metadata, + content=summary_content, + embedding=summary_embedding, + chunks=chunks, + content_hash=content_hash, + unique_identifier_hash=primary_hash, + source_markdown=markdown_content, + content_needs_reindexing=False, + updated_at=get_current_timestamp(), + created_by_id=user_id, + connector_id=connector.get("connector_id") if connector else None, + status=DocumentStatus.ready(), + ) + session.add(document) + await session.commit() + await session.refresh(document) + return document + + except SQLAlchemyError as db_error: + await session.rollback() + if "ix_documents_content_hash" in str(db_error): + logging.warning( + "content_hash collision during commit for %s (%s). Skipping.", + file_name, + etl_service, + ) + return None + raise db_error + except Exception as e: + await session.rollback() + raise RuntimeError( + f"Failed to process file document using {etl_service}: {e!s}" + ) from e + + +# --------------------------------------------------------------------------- +# Backward-compatible wrapper functions +# --------------------------------------------------------------------------- + + +async def add_received_file_document_using_unstructured( + session: AsyncSession, + file_name: str, + unstructured_processed_elements: list[LangChainDocument], + search_space_id: int, + user_id: str, + connector: dict | None = None, + enable_summary: bool = True, +) -> Document | None: + """Process and store a file document using the Unstructured service.""" + from app.utils.document_converters import convert_document_to_markdown + + markdown_content = await convert_document_to_markdown( + unstructured_processed_elements + ) + return await save_file_document( + session, + file_name, + markdown_content, + search_space_id, + user_id, + "UNSTRUCTURED", + connector, + enable_summary, + ) + + +async def add_received_file_document_using_llamacloud( + session: AsyncSession, + file_name: str, + llamacloud_markdown_document: str, + search_space_id: int, + user_id: str, + connector: dict | None = None, + enable_summary: bool = True, +) -> Document | None: + """Process and store document content parsed by LlamaCloud.""" + return await save_file_document( + session, + file_name, + llamacloud_markdown_document, + search_space_id, + user_id, + "LLAMACLOUD", + connector, + enable_summary, + ) + + +async def add_received_file_document_using_docling( + session: AsyncSession, + file_name: str, + docling_markdown_document: str, + search_space_id: int, + user_id: str, + connector: dict | None = None, + enable_summary: bool = True, +) -> Document | None: + """Process and store document content parsed by Docling.""" + return await save_file_document( + session, + file_name, + docling_markdown_document, + search_space_id, + user_id, + "DOCLING", + connector, + enable_summary, + ) diff --git a/surfsense_backend/app/tasks/document_processors/file_processors.py b/surfsense_backend/app/tasks/document_processors/file_processors.py index 6c0ae1870..0c1cad52d 100644 --- a/surfsense_backend/app/tasks/document_processors/file_processors.py +++ b/surfsense_backend/app/tasks/document_processors/file_processors.py @@ -1,905 +1,685 @@ """ -File document processors for different ETL services (Unstructured, LlamaCloud, Docling). +File document processors orchestrating content extraction and indexing. + +This module is the public entry point for file processing. It delegates to +specialised sub-modules that each own a single concern: + +- ``_constants`` — file type classification and configuration constants +- ``_helpers`` — document deduplication, migration, connector helpers +- ``_direct_converters`` — lossless file-to-markdown for csv/tsv/html +- ``_etl`` — ETL parsing strategies (Unstructured, LlamaCloud, Docling) +- ``_save`` — unified document creation / update logic """ -import asyncio +from __future__ import annotations + import contextlib import logging -import ssl -import warnings +import os +from dataclasses import dataclass, field from logging import ERROR, getLogger -import httpx from fastapi import HTTPException -from langchain_core.documents import Document as LangChainDocument -from litellm import atranscription -from sqlalchemy.exc import SQLAlchemyError from sqlalchemy.ext.asyncio import AsyncSession from app.config import config as app_config -from app.db import Document, DocumentStatus, DocumentType, Log, Notification -from app.indexing_pipeline.adapters.file_upload_adapter import UploadDocumentAdapter -from app.services.llm_service import get_user_long_context_llm +from app.db import Document, Log, Notification from app.services.notification_service import NotificationService from app.services.task_logging_service import TaskLoggingService -from app.utils.document_converters import ( - convert_document_to_markdown, - create_document_chunks, - embed_text, - generate_content_hash, - generate_document_summary, - generate_unique_identifier_hash, -) -from .base import ( - check_document_by_unique_identifier, - check_duplicate_document, - get_current_timestamp, - safe_set_chunks, +from ._constants import FileCategory, classify_file +from ._direct_converters import convert_file_directly +from ._etl import ( + parse_with_docling, + parse_with_llamacloud_retry, + parse_with_unstructured, +) +from ._helpers import update_document_from_connector +from ._save import ( + add_received_file_document_using_docling, + add_received_file_document_using_llamacloud, + add_received_file_document_using_unstructured, + save_file_document, ) from .markdown_processor import add_received_markdown_file_document -# Constants for LlamaCloud retry configuration -LLAMACLOUD_MAX_RETRIES = 5 # Increased from 3 for large file resilience -LLAMACLOUD_BASE_DELAY = 10 # Base delay in seconds for exponential backoff -LLAMACLOUD_MAX_DELAY = 120 # Maximum delay between retries (2 minutes) -LLAMACLOUD_RETRYABLE_EXCEPTIONS = ( - ssl.SSLError, - httpx.ConnectError, - httpx.ConnectTimeout, - httpx.ReadTimeout, - httpx.WriteTimeout, - httpx.RemoteProtocolError, - httpx.LocalProtocolError, - ConnectionError, - ConnectionResetError, - TimeoutError, - OSError, # Catches various network-level errors -) - -# Timeout calculation constants -UPLOAD_BYTES_PER_SECOND_SLOW = ( - 100 * 1024 -) # 100 KB/s (conservative for slow connections) -MIN_UPLOAD_TIMEOUT = 120 # Minimum 2 minutes for any file -MAX_UPLOAD_TIMEOUT = 1800 # Maximum 30 minutes for very large files -BASE_JOB_TIMEOUT = 600 # 10 minutes base for job processing -PER_PAGE_JOB_TIMEOUT = 60 # 1 minute per page for processing +# Re-export public API so existing ``from file_processors import …`` keeps working. +__all__ = [ + "add_received_file_document_using_docling", + "add_received_file_document_using_llamacloud", + "add_received_file_document_using_unstructured", + "parse_with_llamacloud_retry", + "process_file_in_background", + "process_file_in_background_with_document", + "save_file_document", +] -def get_google_drive_unique_identifier( - connector: dict | None, - filename: str, - search_space_id: int, -) -> tuple[str, str | None]: - """ - Get unique identifier hash for a file, with special handling for Google Drive. - - For Google Drive files, uses file_id as the unique identifier (doesn't change on rename). - For other files, uses filename. - - Args: - connector: Optional connector info dict with type and metadata - filename: The filename (used for non-Google Drive files or as fallback) - search_space_id: The search space ID - - Returns: - Tuple of (primary_hash, legacy_hash or None) - - For Google Drive: (file_id_based_hash, filename_based_hash for migration) - - For other sources: (filename_based_hash, None) - """ - if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: - metadata = connector.get("metadata", {}) - file_id = metadata.get("google_drive_file_id") - - if file_id: - # New method: use file_id as unique identifier (doesn't change on rename) - primary_hash = generate_unique_identifier_hash( - DocumentType.GOOGLE_DRIVE_FILE, file_id, search_space_id - ) - # Legacy method: for backward compatibility with existing documents - # that were indexed with filename-based hash - legacy_hash = generate_unique_identifier_hash( - DocumentType.GOOGLE_DRIVE_FILE, filename, search_space_id - ) - return primary_hash, legacy_hash - - # For non-Google Drive files, use filename as before - primary_hash = generate_unique_identifier_hash( - DocumentType.FILE, filename, search_space_id - ) - return primary_hash, None +# --------------------------------------------------------------------------- +# Processing context (bundles parameters shared across handler functions) +# --------------------------------------------------------------------------- -async def handle_existing_document_update( - session: AsyncSession, - existing_document: Document, - content_hash: str, - connector: dict | None, - filename: str, - primary_hash: str, -) -> tuple[bool, Document | None]: - """ - Handle update logic for an existing document. +@dataclass +class _ProcessingContext: + session: AsyncSession + file_path: str + filename: str + search_space_id: int + user_id: str + task_logger: TaskLoggingService + log_entry: Log + connector: dict | None = None + notification: Notification | None = None + enable_summary: bool = field(init=False) - Args: - session: Database session - existing_document: The existing document found in database - content_hash: Hash of the new content - connector: Optional connector info - filename: Current filename - primary_hash: The primary hash (file_id based for Google Drive) - - Returns: - Tuple of (should_skip_processing, document_to_return) - - (True, document): Content unchanged, just return existing document - - (False, None): Content changed, need to re-process - """ - # Check if this document needs hash migration (found via legacy hash) - if existing_document.unique_identifier_hash != primary_hash: - existing_document.unique_identifier_hash = primary_hash - logging.info(f"Migrated document to file_id-based identifier: {filename}") - - # Check if content has changed - if existing_document.content_hash == content_hash: - # Content unchanged - check if we need to update metadata (e.g., filename changed) - if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: - connector_metadata = connector.get("metadata", {}) - new_name = connector_metadata.get("google_drive_file_name") - # Check both possible keys for old name (FILE_NAME is used in stored documents) - doc_metadata = existing_document.document_metadata or {} - old_name = doc_metadata.get("FILE_NAME") or doc_metadata.get( - "google_drive_file_name" - ) - - if new_name and old_name and old_name != new_name: - # File was renamed - update title and metadata, skip expensive processing - from sqlalchemy.orm.attributes import flag_modified - - existing_document.title = new_name - if not existing_document.document_metadata: - existing_document.document_metadata = {} - existing_document.document_metadata["FILE_NAME"] = new_name - existing_document.document_metadata["google_drive_file_name"] = new_name - flag_modified(existing_document, "document_metadata") - await session.commit() - logging.info( - f"File renamed in Google Drive: '{old_name}' → '{new_name}' (no re-processing needed)" - ) - - logging.info(f"Document for file {filename} unchanged. Skipping.") - return True, existing_document - else: - # Content has changed — guard against content_hash collision before - # expensive ETL processing. A collision means the exact same content - # already lives in a *different* document (e.g. a manual upload of the - # same file). Proceeding would trigger a unique-constraint violation - # on ix_documents_content_hash. - collision_doc = await check_duplicate_document(session, content_hash) - if collision_doc and collision_doc.id != existing_document.id: - logging.warning( - "Content-hash collision for %s: identical content exists in " - "document #%s (%s). Skipping re-processing.", - filename, - collision_doc.id, - collision_doc.document_type, - ) - if DocumentStatus.is_state( - existing_document.status, DocumentStatus.PENDING - ) or DocumentStatus.is_state( - existing_document.status, DocumentStatus.PROCESSING - ): - # Pending/processing doc has no real content yet — remove it - # so the UI doesn't show a contentless entry. - await session.delete(existing_document) - await session.commit() - return True, None - - # Document already has valid content — keep it as-is. - return True, existing_document - - logging.info(f"Content changed for file {filename}. Updating document.") - return False, None - - -async def find_existing_document_with_migration( - session: AsyncSession, - primary_hash: str, - legacy_hash: str | None, - content_hash: str | None = None, -) -> Document | None: - """ - Find existing document, checking both new hash and legacy hash for migration, - with fallback to content_hash for cross-source deduplication. - - Args: - session: Database session - primary_hash: The primary hash (file_id based for Google Drive) - legacy_hash: The legacy hash (filename based) for migration, or None - content_hash: The content hash for fallback deduplication, or None - - Returns: - Existing document if found, None otherwise - """ - # First check with primary hash (new method) - existing_document = await check_document_by_unique_identifier(session, primary_hash) - - # If not found and we have a legacy hash, check with that (migration path) - if not existing_document and legacy_hash: - existing_document = await check_document_by_unique_identifier( - session, legacy_hash - ) - if existing_document: - logging.info( - "Found legacy document (filename-based hash), will migrate to file_id-based hash" - ) - - # Fallback: check by content_hash to catch duplicates from different sources - # This prevents unique constraint violations when the same content exists - # under a different unique_identifier (e.g., manual upload vs Google Drive) - if not existing_document and content_hash: - existing_document = await check_duplicate_document(session, content_hash) - if existing_document: - logging.info( - f"Found duplicate content from different source (content_hash match). " - f"Original document ID: {existing_document.id}, type: {existing_document.document_type}" - ) - - return existing_document - - -def calculate_upload_timeout(file_size_bytes: int) -> float: - """ - Calculate appropriate upload timeout based on file size. - - Assumes a conservative slow connection speed to handle worst-case scenarios. - - Args: - file_size_bytes: Size of the file in bytes - - Returns: - Timeout in seconds - """ - # Calculate time needed at slow connection speed - # Add 50% buffer for network variability and SSL overhead - estimated_time = (file_size_bytes / UPLOAD_BYTES_PER_SECOND_SLOW) * 1.5 - - # Clamp to reasonable bounds - return max(MIN_UPLOAD_TIMEOUT, min(estimated_time, MAX_UPLOAD_TIMEOUT)) - - -def calculate_job_timeout(estimated_pages: int, file_size_bytes: int) -> float: - """ - Calculate job processing timeout based on page count and file size. - - Args: - estimated_pages: Estimated number of pages - file_size_bytes: Size of the file in bytes - - Returns: - Timeout in seconds - """ - # Base timeout + time per page - page_based_timeout = BASE_JOB_TIMEOUT + (estimated_pages * PER_PAGE_JOB_TIMEOUT) - - # Also consider file size (large images take longer to process) - # ~1 minute per 10MB of file size - size_based_timeout = BASE_JOB_TIMEOUT + (file_size_bytes / (10 * 1024 * 1024)) * 60 - - # Use the larger of the two estimates - return max(page_based_timeout, size_based_timeout) - - -async def parse_with_llamacloud_retry( - file_path: str, - estimated_pages: int, - task_logger: TaskLoggingService | None = None, - log_entry: Log | None = None, -): - """ - Parse a file with LlamaCloud with retry logic for transient SSL/connection errors. - - Uses dynamic timeout calculations based on file size and page count to handle - very large files reliably. - - Args: - file_path: Path to the file to parse - estimated_pages: Estimated number of pages for timeout calculation - task_logger: Optional task logger for progress updates - log_entry: Optional log entry for progress updates - - Returns: - LlamaParse result object - - Raises: - Exception: If all retries fail - """ - import os - import random - - from llama_cloud_services import LlamaParse - from llama_cloud_services.parse.utils import ResultType - - # Get file size for timeout calculations - file_size_bytes = os.path.getsize(file_path) - file_size_mb = file_size_bytes / (1024 * 1024) - - # Calculate dynamic timeouts based on file size and page count - upload_timeout = calculate_upload_timeout(file_size_bytes) - job_timeout = calculate_job_timeout(estimated_pages, file_size_bytes) - - # HTTP client timeouts - scaled based on file size - # Write timeout is critical for large file uploads - custom_timeout = httpx.Timeout( - connect=120.0, # 2 minutes to establish connection (handles slow DNS, etc.) - read=upload_timeout, # Dynamic based on file size - write=upload_timeout, # Dynamic based on file size (upload time) - pool=120.0, # 2 minutes to acquire connection from pool - ) - - logging.info( - f"LlamaCloud upload configured: file_size={file_size_mb:.1f}MB, " - f"pages={estimated_pages}, upload_timeout={upload_timeout:.0f}s, " - f"job_timeout={job_timeout:.0f}s" - ) - - last_exception = None - attempt_errors = [] - - for attempt in range(1, LLAMACLOUD_MAX_RETRIES + 1): - try: - # Create a fresh httpx client for each attempt - async with httpx.AsyncClient(timeout=custom_timeout) as custom_client: - # Create LlamaParse parser instance with optimized settings - parser = LlamaParse( - api_key=app_config.LLAMA_CLOUD_API_KEY, - num_workers=1, # Use single worker for file processing - verbose=True, - language="en", - result_type=ResultType.MD, - # Timeout settings for large files - max_timeout=int(max(2000, job_timeout + upload_timeout)), - job_timeout_in_seconds=job_timeout, - job_timeout_extra_time_per_page_in_seconds=PER_PAGE_JOB_TIMEOUT, - # Use our custom client with larger timeouts - custom_client=custom_client, - ) - - # Parse the file asynchronously - result = await parser.aparse(file_path) - - # Success - log if we had previous failures - if attempt > 1: - logging.info( - f"LlamaCloud upload succeeded on attempt {attempt} after " - f"{len(attempt_errors)} failures" - ) - - return result - - except LLAMACLOUD_RETRYABLE_EXCEPTIONS as e: - last_exception = e - error_type = type(e).__name__ - error_msg = str(e)[:200] - attempt_errors.append(f"Attempt {attempt}: {error_type} - {error_msg}") - - if attempt < LLAMACLOUD_MAX_RETRIES: - # Calculate exponential backoff with jitter - # Base delay doubles each attempt, capped at max delay - base_delay = min( - LLAMACLOUD_BASE_DELAY * (2 ** (attempt - 1)), LLAMACLOUD_MAX_DELAY - ) - # Add random jitter (±25%) to prevent thundering herd - jitter = base_delay * 0.25 * (2 * random.random() - 1) - delay = base_delay + jitter - - if task_logger and log_entry: - await task_logger.log_task_progress( - log_entry, - f"LlamaCloud upload failed (attempt {attempt}/{LLAMACLOUD_MAX_RETRIES}), retrying in {delay:.0f}s", - { - "error_type": error_type, - "error_message": error_msg, - "attempt": attempt, - "retry_delay": delay, - "file_size_mb": round(file_size_mb, 1), - "upload_timeout": upload_timeout, - }, - ) - else: - logging.warning( - f"LlamaCloud upload failed (attempt {attempt}/{LLAMACLOUD_MAX_RETRIES}): " - f"{error_type}. File: {file_size_mb:.1f}MB. Retrying in {delay:.0f}s..." - ) - - await asyncio.sleep(delay) - else: - logging.error( - f"LlamaCloud upload failed after {LLAMACLOUD_MAX_RETRIES} attempts. " - f"File size: {file_size_mb:.1f}MB, Pages: {estimated_pages}. " - f"Errors: {'; '.join(attempt_errors)}" - ) - - except Exception: - # Non-retryable exception, raise immediately - raise - - # All retries exhausted - raise last_exception or RuntimeError( - f"LlamaCloud parsing failed after {LLAMACLOUD_MAX_RETRIES} retries. " - f"File size: {file_size_mb:.1f}MB" - ) - - -async def add_received_file_document_using_unstructured( - session: AsyncSession, - file_name: str, - unstructured_processed_elements: list[LangChainDocument], - search_space_id: int, - user_id: str, - connector: dict | None = None, - enable_summary: bool = True, -) -> Document | None: - """ - Process and store a file document using Unstructured service. - - Args: - session: Database session - file_name: Name of the processed file - unstructured_processed_elements: Processed elements from Unstructured - search_space_id: ID of the search space - user_id: ID of the user - connector: Optional connector info for Google Drive files - - Returns: - Document object if successful, None if failed - """ - try: - file_in_markdown = await convert_document_to_markdown( - unstructured_processed_elements + def __post_init__(self) -> None: + self.enable_summary = ( + self.connector.get("enable_summary", True) if self.connector else True ) - # Generate unique identifier hash (uses file_id for Google Drive, filename for others) - primary_hash, legacy_hash = get_google_drive_unique_identifier( - connector, file_name, search_space_id - ) - # Generate content hash - content_hash = generate_content_hash(file_in_markdown, search_space_id) - - # Check if document exists (with migration support for Google Drive and content_hash fallback) - existing_document = await find_existing_document_with_migration( - session, primary_hash, legacy_hash, content_hash - ) - - if existing_document: - # Handle existing document (rename detection, content change check) - should_skip, doc = await handle_existing_document_update( - session, - existing_document, - content_hash, - connector, - file_name, - primary_hash, - ) - if should_skip: - return doc - # Content changed - continue to update - - # Get user's long context LLM (needed for both create and update) - user_llm = await get_user_long_context_llm(session, user_id, search_space_id) - if not user_llm: - raise RuntimeError( - f"No long context LLM configured for user {user_id} in search space {search_space_id}" - ) - - # Generate summary with metadata - document_metadata = { - "file_name": file_name, - "etl_service": "UNSTRUCTURED", - "document_type": "File Document", - } - if enable_summary: - summary_content, summary_embedding = await generate_document_summary( - file_in_markdown, user_llm, document_metadata - ) - else: - summary_content = f"File: {file_name}\n\n{file_in_markdown[:4000]}" - summary_embedding = embed_text(summary_content) - - # Process chunks - chunks = await create_document_chunks(file_in_markdown) - - # Update or create document - if existing_document: - # Update existing document - existing_document.title = file_name - existing_document.content = summary_content - existing_document.content_hash = content_hash - existing_document.embedding = summary_embedding - existing_document.document_metadata = { - "FILE_NAME": file_name, - "ETL_SERVICE": "UNSTRUCTURED", - } - await safe_set_chunks(session, existing_document, chunks) - existing_document.source_markdown = file_in_markdown - existing_document.content_needs_reindexing = False - existing_document.updated_at = get_current_timestamp() - existing_document.status = DocumentStatus.ready() - - await session.commit() - await session.refresh(existing_document) - document = existing_document - else: - # Create new document - doc_type = DocumentType.FILE - if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: - doc_type = DocumentType.GOOGLE_DRIVE_FILE - - document = Document( - search_space_id=search_space_id, - title=file_name, - document_type=doc_type, - document_metadata={ - "FILE_NAME": file_name, - "ETL_SERVICE": "UNSTRUCTURED", - }, - content=summary_content, - embedding=summary_embedding, - chunks=chunks, - content_hash=content_hash, - unique_identifier_hash=primary_hash, - source_markdown=file_in_markdown, - content_needs_reindexing=False, - updated_at=get_current_timestamp(), - created_by_id=user_id, - connector_id=connector.get("connector_id") if connector else None, - status=DocumentStatus.ready(), - ) - - session.add(document) - await session.commit() - await session.refresh(document) - - return document - except SQLAlchemyError as db_error: - await session.rollback() - if "ix_documents_content_hash" in str(db_error): - logging.warning( - "content_hash collision during commit for %s (Unstructured). Skipping.", - file_name, - ) - return None - raise db_error - except Exception as e: - await session.rollback() - raise RuntimeError(f"Failed to process file document: {e!s}") from e +# --------------------------------------------------------------------------- +# Notification helper +# --------------------------------------------------------------------------- -async def add_received_file_document_using_llamacloud( - session: AsyncSession, - file_name: str, - llamacloud_markdown_document: str, - search_space_id: int, - user_id: str, - connector: dict | None = None, - enable_summary: bool = True, -) -> Document | None: - """ - Process and store document content parsed by LlamaCloud. - - Args: - session: Database session - file_name: Name of the processed file - llamacloud_markdown_document: Markdown content from LlamaCloud parsing - search_space_id: ID of the search space - user_id: ID of the user - connector: Optional connector info for Google Drive files - - Returns: - Document object if successful, None if failed - """ - try: - # Combine all markdown documents into one - file_in_markdown = llamacloud_markdown_document - - # Generate unique identifier hash (uses file_id for Google Drive, filename for others) - primary_hash, legacy_hash = get_google_drive_unique_identifier( - connector, file_name, search_space_id - ) - - # Generate content hash - content_hash = generate_content_hash(file_in_markdown, search_space_id) - - # Check if document exists (with migration support for Google Drive and content_hash fallback) - existing_document = await find_existing_document_with_migration( - session, primary_hash, legacy_hash, content_hash - ) - - if existing_document: - # Handle existing document (rename detection, content change check) - should_skip, doc = await handle_existing_document_update( - session, - existing_document, - content_hash, - connector, - file_name, - primary_hash, - ) - if should_skip: - return doc - # Content changed - continue to update - - # Get user's long context LLM (needed for both create and update) - user_llm = await get_user_long_context_llm(session, user_id, search_space_id) - if not user_llm: - raise RuntimeError( - f"No long context LLM configured for user {user_id} in search space {search_space_id}" - ) - - # Generate summary with metadata - document_metadata = { - "file_name": file_name, - "etl_service": "LLAMACLOUD", - "document_type": "File Document", - } - if enable_summary: - summary_content, summary_embedding = await generate_document_summary( - file_in_markdown, user_llm, document_metadata - ) - else: - summary_content = f"File: {file_name}\n\n{file_in_markdown[:4000]}" - summary_embedding = embed_text(summary_content) - - # Process chunks - chunks = await create_document_chunks(file_in_markdown) - - # Update or create document - if existing_document: - existing_document.title = file_name - existing_document.content = summary_content - existing_document.content_hash = content_hash - existing_document.embedding = summary_embedding - existing_document.document_metadata = { - "FILE_NAME": file_name, - "ETL_SERVICE": "LLAMACLOUD", - } - await safe_set_chunks(session, existing_document, chunks) - existing_document.source_markdown = file_in_markdown - existing_document.content_needs_reindexing = False - existing_document.updated_at = get_current_timestamp() - existing_document.status = DocumentStatus.ready() - - await session.commit() - await session.refresh(existing_document) - document = existing_document - else: - doc_type = DocumentType.FILE - if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: - doc_type = DocumentType.GOOGLE_DRIVE_FILE - - document = Document( - search_space_id=search_space_id, - title=file_name, - document_type=doc_type, - document_metadata={ - "FILE_NAME": file_name, - "ETL_SERVICE": "LLAMACLOUD", - }, - content=summary_content, - embedding=summary_embedding, - chunks=chunks, - content_hash=content_hash, - unique_identifier_hash=primary_hash, - source_markdown=file_in_markdown, - content_needs_reindexing=False, - updated_at=get_current_timestamp(), - created_by_id=user_id, - connector_id=connector.get("connector_id") if connector else None, - status=DocumentStatus.ready(), - ) - - session.add(document) - await session.commit() - await session.refresh(document) - - return document - except SQLAlchemyError as db_error: - await session.rollback() - if "ix_documents_content_hash" in str(db_error): - logging.warning( - "content_hash collision during commit for %s (LlamaCloud). Skipping.", - file_name, - ) - return None - raise db_error - except Exception as e: - await session.rollback() - raise RuntimeError( - f"Failed to process file document using LlamaCloud: {e!s}" - ) from e - - -async def add_received_file_document_using_docling( - session: AsyncSession, - file_name: str, - docling_markdown_document: str, - search_space_id: int, - user_id: str, - connector: dict | None = None, - enable_summary: bool = True, -) -> Document | None: - """ - Process and store document content parsed by Docling. - - Args: - session: Database session - file_name: Name of the processed file - docling_markdown_document: Markdown content from Docling parsing - search_space_id: ID of the search space - user_id: ID of the user - connector: Optional connector info for Google Drive files - - Returns: - Document object if successful, None if failed - """ - try: - file_in_markdown = docling_markdown_document - - # Generate unique identifier hash (uses file_id for Google Drive, filename for others) - primary_hash, legacy_hash = get_google_drive_unique_identifier( - connector, file_name, search_space_id - ) - - # Generate content hash - content_hash = generate_content_hash(file_in_markdown, search_space_id) - - # Check if document exists (with migration support for Google Drive and content_hash fallback) - existing_document = await find_existing_document_with_migration( - session, primary_hash, legacy_hash, content_hash - ) - - if existing_document: - # Handle existing document (rename detection, content change check) - should_skip, doc = await handle_existing_document_update( - session, - existing_document, - content_hash, - connector, - file_name, - primary_hash, - ) - if should_skip: - return doc - # Content changed - continue to update - - # Get user's long context LLM (needed for both create and update) - user_llm = await get_user_long_context_llm(session, user_id, search_space_id) - if not user_llm: - raise RuntimeError( - f"No long context LLM configured for user {user_id} in search_space {search_space_id}" - ) - - if enable_summary: - from app.services.docling_service import create_docling_service - - docling_service = create_docling_service() - - summary_content = await docling_service.process_large_document_summary( - content=file_in_markdown, llm=user_llm, document_title=file_name - ) - - document_metadata = { - "file_name": file_name, - "etl_service": "DOCLING", - "document_type": "File Document", - } - metadata_parts = ["# DOCUMENT METADATA"] - for key, value in document_metadata.items(): - if value: - formatted_key = key.replace("_", " ").title() - metadata_parts.append(f"**{formatted_key}:** {value}") - - metadata_section = "\n".join(metadata_parts) - enhanced_summary_content = ( - f"{metadata_section}\n\n# DOCUMENT SUMMARY\n\n{summary_content}" - ) - else: - enhanced_summary_content = f"File: {file_name}\n\n{file_in_markdown[:4000]}" - - summary_embedding = embed_text(enhanced_summary_content) - - # Process chunks - chunks = await create_document_chunks(file_in_markdown) - - # Update or create document - if existing_document: - # Update existing document - existing_document.title = file_name - existing_document.content = enhanced_summary_content - existing_document.content_hash = content_hash - existing_document.embedding = summary_embedding - existing_document.document_metadata = { - "FILE_NAME": file_name, - "ETL_SERVICE": "DOCLING", - } - await safe_set_chunks(session, existing_document, chunks) - existing_document.source_markdown = file_in_markdown - existing_document.content_needs_reindexing = False - existing_document.updated_at = get_current_timestamp() - existing_document.status = DocumentStatus.ready() # Mark as ready - - await session.commit() - await session.refresh(existing_document) - document = existing_document - else: - # Create new document - # Determine document type based on connector - doc_type = DocumentType.FILE - if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: - doc_type = DocumentType.GOOGLE_DRIVE_FILE - - document = Document( - search_space_id=search_space_id, - title=file_name, - document_type=doc_type, - document_metadata={ - "FILE_NAME": file_name, - "ETL_SERVICE": "DOCLING", - }, - content=enhanced_summary_content, - embedding=summary_embedding, - chunks=chunks, - content_hash=content_hash, - unique_identifier_hash=primary_hash, - source_markdown=file_in_markdown, - content_needs_reindexing=False, - updated_at=get_current_timestamp(), - created_by_id=user_id, - connector_id=connector.get("connector_id") if connector else None, - status=DocumentStatus.ready(), # Mark as ready - ) - - session.add(document) - await session.commit() - await session.refresh(document) - - return document - except SQLAlchemyError as db_error: - await session.rollback() - if "ix_documents_content_hash" in str(db_error): - logging.warning( - "content_hash collision during commit for %s (Docling). Skipping.", - file_name, - ) - return None - raise db_error - except Exception as e: - await session.rollback() - raise RuntimeError( - f"Failed to process file document using Docling: {e!s}" - ) from e - - -async def _update_document_from_connector( - document: Document | None, connector: dict | None, session: AsyncSession +async def _notify( + ctx: _ProcessingContext, + stage: str, + stage_message: str | None = None, + **kwargs, ) -> None: - """Helper to update document type, metadata, and connector_id from connector info.""" - if document and connector: - if "type" in connector: - document.document_type = connector["type"] - if "metadata" in connector: - # Merge with existing document_metadata (the actual column name) - if not document.document_metadata: - document.document_metadata = connector["metadata"] - else: - # Expand existing metadata with connector metadata - merged = {**document.document_metadata, **connector["metadata"]} - document.document_metadata = merged - # Set connector_id if provided for de-indexing support - if "connector_id" in connector: - document.connector_id = connector["connector_id"] - await session.commit() + """Send a processing-progress notification if one is attached.""" + if not ctx.notification: + return + await NotificationService.document_processing.notify_processing_progress( + ctx.session, + ctx.notification, + stage=stage, + stage_message=stage_message, + **kwargs, + ) + + +# --------------------------------------------------------------------------- +# Page-limit helpers +# --------------------------------------------------------------------------- + + +def _estimate_pages_safe(page_limit_service, file_path: str) -> int: + """Estimate page count with a file-size fallback.""" + try: + return page_limit_service.estimate_pages_before_processing(file_path) + except Exception: + file_size = os.path.getsize(file_path) + return max(1, file_size // (80 * 1024)) + + +async def _log_page_divergence( + task_logger: TaskLoggingService, + log_entry: Log, + filename: str, + estimated: int, + actual: int, + final: int, +) -> None: + """Log a warning when the actual page count far exceeds the pre-estimate.""" + if actual > estimated * 1.5: + await task_logger.log_task_progress( + log_entry, + f"Actual page count higher than estimate: {filename}", + { + "estimated_before": estimated, + "actual_pages": actual, + "using_count": final, + }, + ) + + +# =================================================================== +# Handlers for process_file_in_background (legacy / connector path) +# =================================================================== + + +async def _process_markdown_upload(ctx: _ProcessingContext) -> Document | None: + """Read a markdown / text file and create or update a document.""" + await _notify(ctx, "parsing", "Reading file") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Processing markdown/text file: {ctx.filename}", + {"file_type": "markdown", "processing_stage": "reading_file"}, + ) + + with open(ctx.file_path, encoding="utf-8") as f: + markdown_content = f.read() + + with contextlib.suppress(Exception): + os.unlink(ctx.file_path) + + await _notify(ctx, "chunking") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Creating document from markdown content: {ctx.filename}", + { + "processing_stage": "creating_document", + "content_length": len(markdown_content), + }, + ) + + result = await add_received_markdown_file_document( + ctx.session, + ctx.filename, + markdown_content, + ctx.search_space_id, + ctx.user_id, + ctx.connector, + ) + if ctx.connector: + await update_document_from_connector(result, ctx.connector, ctx.session) + + if result: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Successfully processed markdown file: {ctx.filename}", + { + "document_id": result.id, + "content_hash": result.content_hash, + "file_type": "markdown", + }, + ) + else: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Markdown file already exists (duplicate): {ctx.filename}", + {"duplicate_detected": True, "file_type": "markdown"}, + ) + return result + + +async def _process_direct_convert_upload(ctx: _ProcessingContext) -> Document | None: + """Convert a text-based file (csv/tsv/html) to markdown without ETL.""" + await _notify(ctx, "parsing", "Converting file") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Direct-converting file to markdown: {ctx.filename}", + {"file_type": "direct_convert", "processing_stage": "converting"}, + ) + + markdown_content = convert_file_directly(ctx.file_path, ctx.filename) + + with contextlib.suppress(Exception): + os.unlink(ctx.file_path) + + await _notify(ctx, "chunking") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Creating document from converted content: {ctx.filename}", + { + "processing_stage": "creating_document", + "content_length": len(markdown_content), + }, + ) + + result = await add_received_markdown_file_document( + ctx.session, + ctx.filename, + markdown_content, + ctx.search_space_id, + ctx.user_id, + ctx.connector, + ) + if ctx.connector: + await update_document_from_connector(result, ctx.connector, ctx.session) + + if result: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Successfully direct-converted file: {ctx.filename}", + { + "document_id": result.id, + "content_hash": result.content_hash, + "file_type": "direct_convert", + }, + ) + else: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Direct-converted file already exists (duplicate): {ctx.filename}", + {"duplicate_detected": True, "file_type": "direct_convert"}, + ) + return result + + +async def _process_audio_upload(ctx: _ProcessingContext) -> Document | None: + """Transcribe an audio file and create or update a document.""" + await _notify(ctx, "parsing", "Transcribing audio") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Processing audio file for transcription: {ctx.filename}", + {"file_type": "audio", "processing_stage": "starting_transcription"}, + ) + + stt_service_type = ( + "local" + if app_config.STT_SERVICE and app_config.STT_SERVICE.startswith("local/") + else "external" + ) + + if stt_service_type == "local": + from app.services.stt_service import stt_service + + try: + stt_result = stt_service.transcribe_file(ctx.file_path) + transcribed_text = stt_result.get("text", "") + if not transcribed_text: + raise ValueError("Transcription returned empty text") + transcribed_text = ( + f"# Transcription of {ctx.filename}\n\n{transcribed_text}" + ) + except Exception as e: + raise HTTPException( + status_code=422, + detail=f"Failed to transcribe audio file {ctx.filename}: {e!s}", + ) from e + + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Local STT transcription completed: {ctx.filename}", + { + "processing_stage": "local_transcription_complete", + "language": stt_result.get("language"), + "confidence": stt_result.get("language_probability"), + "duration": stt_result.get("duration"), + }, + ) + else: + from litellm import atranscription + + with open(ctx.file_path, "rb") as audio_file: + transcription_kwargs: dict = { + "model": app_config.STT_SERVICE, + "file": audio_file, + "api_key": app_config.STT_SERVICE_API_KEY, + } + if app_config.STT_SERVICE_API_BASE: + transcription_kwargs["api_base"] = app_config.STT_SERVICE_API_BASE + + transcription_response = await atranscription(**transcription_kwargs) + transcribed_text = transcription_response.get("text", "") + if not transcribed_text: + raise ValueError("Transcription returned empty text") + + transcribed_text = f"# Transcription of {ctx.filename}\n\n{transcribed_text}" + + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Transcription completed, creating document: {ctx.filename}", + { + "processing_stage": "transcription_complete", + "transcript_length": len(transcribed_text), + }, + ) + + await _notify(ctx, "chunking") + + with contextlib.suppress(Exception): + os.unlink(ctx.file_path) + + result = await add_received_markdown_file_document( + ctx.session, + ctx.filename, + transcribed_text, + ctx.search_space_id, + ctx.user_id, + ctx.connector, + ) + if ctx.connector: + await update_document_from_connector(result, ctx.connector, ctx.session) + + if result: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Successfully transcribed and processed audio file: {ctx.filename}", + { + "document_id": result.id, + "content_hash": result.content_hash, + "file_type": "audio", + "transcript_length": len(transcribed_text), + "stt_service": stt_service_type, + }, + ) + else: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Audio file transcript already exists (duplicate): {ctx.filename}", + {"duplicate_detected": True, "file_type": "audio"}, + ) + return result + + +# --------------------------------------------------------------------------- +# Document file processing (ETL service dispatch) +# --------------------------------------------------------------------------- + + +async def _etl_unstructured( + ctx: _ProcessingContext, + page_limit_service, + estimated_pages: int, +) -> Document | None: + """Parse and save via the Unstructured ETL service.""" + await _notify(ctx, "parsing", "Extracting content") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Processing file with Unstructured ETL: {ctx.filename}", + { + "file_type": "document", + "etl_service": "UNSTRUCTURED", + "processing_stage": "loading", + }, + ) + + docs = await parse_with_unstructured(ctx.file_path) + + await _notify(ctx, "chunking", chunks_count=len(docs)) + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Unstructured ETL completed, creating document: {ctx.filename}", + {"processing_stage": "etl_complete", "elements_count": len(docs)}, + ) + + actual_pages = page_limit_service.estimate_pages_from_elements(docs) + final_pages = max(estimated_pages, actual_pages) + await _log_page_divergence( + ctx.task_logger, + ctx.log_entry, + ctx.filename, + estimated_pages, + actual_pages, + final_pages, + ) + + with contextlib.suppress(Exception): + os.unlink(ctx.file_path) + + result = await add_received_file_document_using_unstructured( + ctx.session, + ctx.filename, + docs, + ctx.search_space_id, + ctx.user_id, + ctx.connector, + enable_summary=ctx.enable_summary, + ) + if ctx.connector: + await update_document_from_connector(result, ctx.connector, ctx.session) + + if result: + await page_limit_service.update_page_usage( + ctx.user_id, final_pages, allow_exceed=True + ) + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Successfully processed file with Unstructured: {ctx.filename}", + { + "document_id": result.id, + "content_hash": result.content_hash, + "file_type": "document", + "etl_service": "UNSTRUCTURED", + "pages_processed": final_pages, + }, + ) + else: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Document already exists (duplicate): {ctx.filename}", + { + "duplicate_detected": True, + "file_type": "document", + "etl_service": "UNSTRUCTURED", + }, + ) + return result + + +async def _etl_llamacloud( + ctx: _ProcessingContext, + page_limit_service, + estimated_pages: int, +) -> Document | None: + """Parse and save via the LlamaCloud ETL service.""" + await _notify(ctx, "parsing", "Extracting content") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Processing file with LlamaCloud ETL: {ctx.filename}", + { + "file_type": "document", + "etl_service": "LLAMACLOUD", + "processing_stage": "parsing", + "estimated_pages": estimated_pages, + }, + ) + + raw_result = await parse_with_llamacloud_retry( + file_path=ctx.file_path, + estimated_pages=estimated_pages, + task_logger=ctx.task_logger, + log_entry=ctx.log_entry, + ) + + with contextlib.suppress(Exception): + os.unlink(ctx.file_path) + + markdown_documents = await raw_result.aget_markdown_documents(split_by_page=False) + + await _notify(ctx, "chunking", chunks_count=len(markdown_documents)) + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"LlamaCloud parsing completed, creating documents: {ctx.filename}", + { + "processing_stage": "parsing_complete", + "documents_count": len(markdown_documents), + }, + ) + + if not markdown_documents: + await ctx.task_logger.log_task_failure( + ctx.log_entry, + f"LlamaCloud parsing returned no documents: {ctx.filename}", + "ETL service returned empty document list", + {"error_type": "EmptyDocumentList", "etl_service": "LLAMACLOUD"}, + ) + raise ValueError(f"LlamaCloud parsing returned no documents for {ctx.filename}") + + actual_pages = page_limit_service.estimate_pages_from_markdown(markdown_documents) + final_pages = max(estimated_pages, actual_pages) + await _log_page_divergence( + ctx.task_logger, + ctx.log_entry, + ctx.filename, + estimated_pages, + actual_pages, + final_pages, + ) + + any_created = False + last_doc: Document | None = None + + for doc in markdown_documents: + doc_result = await add_received_file_document_using_llamacloud( + ctx.session, + ctx.filename, + llamacloud_markdown_document=doc.text, + search_space_id=ctx.search_space_id, + user_id=ctx.user_id, + connector=ctx.connector, + enable_summary=ctx.enable_summary, + ) + if doc_result: + any_created = True + last_doc = doc_result + + if any_created: + await page_limit_service.update_page_usage( + ctx.user_id, final_pages, allow_exceed=True + ) + if ctx.connector: + await update_document_from_connector(last_doc, ctx.connector, ctx.session) + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Successfully processed file with LlamaCloud: {ctx.filename}", + { + "document_id": last_doc.id, + "content_hash": last_doc.content_hash, + "file_type": "document", + "etl_service": "LLAMACLOUD", + "pages_processed": final_pages, + "documents_count": len(markdown_documents), + }, + ) + return last_doc + + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Document already exists (duplicate): {ctx.filename}", + { + "duplicate_detected": True, + "file_type": "document", + "etl_service": "LLAMACLOUD", + "documents_count": len(markdown_documents), + }, + ) + return None + + +async def _etl_docling( + ctx: _ProcessingContext, + page_limit_service, + estimated_pages: int, +) -> Document | None: + """Parse and save via the Docling ETL service.""" + await _notify(ctx, "parsing", "Extracting content") + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Processing file with Docling ETL: {ctx.filename}", + { + "file_type": "document", + "etl_service": "DOCLING", + "processing_stage": "parsing", + }, + ) + + content = await parse_with_docling(ctx.file_path, ctx.filename) + + with contextlib.suppress(Exception): + os.unlink(ctx.file_path) + + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Docling parsing completed, creating document: {ctx.filename}", + {"processing_stage": "parsing_complete", "content_length": len(content)}, + ) + + actual_pages = page_limit_service.estimate_pages_from_content_length(len(content)) + final_pages = max(estimated_pages, actual_pages) + await _log_page_divergence( + ctx.task_logger, + ctx.log_entry, + ctx.filename, + estimated_pages, + actual_pages, + final_pages, + ) + + await _notify(ctx, "chunking") + + result = await add_received_file_document_using_docling( + ctx.session, + ctx.filename, + docling_markdown_document=content, + search_space_id=ctx.search_space_id, + user_id=ctx.user_id, + connector=ctx.connector, + enable_summary=ctx.enable_summary, + ) + + if result: + await page_limit_service.update_page_usage( + ctx.user_id, final_pages, allow_exceed=True + ) + if ctx.connector: + await update_document_from_connector(result, ctx.connector, ctx.session) + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Successfully processed file with Docling: {ctx.filename}", + { + "document_id": result.id, + "content_hash": result.content_hash, + "file_type": "document", + "etl_service": "DOCLING", + "pages_processed": final_pages, + }, + ) + else: + await ctx.task_logger.log_task_success( + ctx.log_entry, + f"Document already exists (duplicate): {ctx.filename}", + { + "duplicate_detected": True, + "file_type": "document", + "etl_service": "DOCLING", + }, + ) + return result + + +async def _process_document_upload(ctx: _ProcessingContext) -> Document | None: + """Route a document file to the configured ETL service.""" + from app.services.page_limit_service import PageLimitExceededError, PageLimitService + + page_limit_service = PageLimitService(ctx.session) + estimated_pages = _estimate_pages_safe(page_limit_service, ctx.file_path) + + await ctx.task_logger.log_task_progress( + ctx.log_entry, + f"Estimated {estimated_pages} pages for file: {ctx.filename}", + {"estimated_pages": estimated_pages, "file_type": "document"}, + ) + + try: + await page_limit_service.check_page_limit(ctx.user_id, estimated_pages) + except PageLimitExceededError as e: + await ctx.task_logger.log_task_failure( + ctx.log_entry, + f"Page limit exceeded before processing: {ctx.filename}", + str(e), + { + "error_type": "PageLimitExceeded", + "pages_used": e.pages_used, + "pages_limit": e.pages_limit, + "estimated_pages": estimated_pages, + }, + ) + with contextlib.suppress(Exception): + os.unlink(ctx.file_path) + raise HTTPException(status_code=403, detail=str(e)) from e + + etl_dispatch = { + "UNSTRUCTURED": _etl_unstructured, + "LLAMACLOUD": _etl_llamacloud, + "DOCLING": _etl_docling, + } + handler = etl_dispatch.get(app_config.ETL_SERVICE) + if handler is None: + raise RuntimeError(f"Unknown ETL_SERVICE: {app_config.ETL_SERVICE}") + + return await handler(ctx, page_limit_service, estimated_pages) + + +# =================================================================== +# Public orchestrators +# =================================================================== async def process_file_in_background( @@ -910,726 +690,35 @@ async def process_file_in_background( session: AsyncSession, task_logger: TaskLoggingService, log_entry: Log, - connector: dict - | None = None, # Optional: {"type": "GOOGLE_DRIVE_FILE", "metadata": {...}} - notification: Notification - | None = None, # Optional notification for progress updates + connector: dict | None = None, + notification: Notification | None = None, ) -> Document | None: + ctx = _ProcessingContext( + session=session, + file_path=file_path, + filename=filename, + search_space_id=search_space_id, + user_id=user_id, + task_logger=task_logger, + log_entry=log_entry, + connector=connector, + notification=notification, + ) + try: - # Check if the file is a markdown or text file - if filename.lower().endswith((".md", ".markdown", ".txt")): - # Update notification: parsing stage - if notification: - await ( - NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Reading file", - ) - ) + category = classify_file(filename) - await task_logger.log_task_progress( - log_entry, - f"Processing markdown/text file: {filename}", - {"file_type": "markdown", "processing_stage": "reading_file"}, - ) + if category == FileCategory.MARKDOWN: + return await _process_markdown_upload(ctx) + if category == FileCategory.DIRECT_CONVERT: + return await _process_direct_convert_upload(ctx) + if category == FileCategory.AUDIO: + return await _process_audio_upload(ctx) + return await _process_document_upload(ctx) - # For markdown files, read the content directly - with open(file_path, encoding="utf-8") as f: - markdown_content = f.read() - - # Clean up the temp file - import os - - try: - os.unlink(file_path) - except Exception as e: - print("Error deleting temp file", e) - pass - - # Update notification: chunking stage - if notification: - await ( - NotificationService.document_processing.notify_processing_progress( - session, notification, stage="chunking" - ) - ) - - await task_logger.log_task_progress( - log_entry, - f"Creating document from markdown content: {filename}", - { - "processing_stage": "creating_document", - "content_length": len(markdown_content), - }, - ) - - # Process markdown directly through specialized function - result = await add_received_markdown_file_document( - session, filename, markdown_content, search_space_id, user_id, connector - ) - - if connector: - await _update_document_from_connector(result, connector, session) - - if result: - await task_logger.log_task_success( - log_entry, - f"Successfully processed markdown file: {filename}", - { - "document_id": result.id, - "content_hash": result.content_hash, - "file_type": "markdown", - }, - ) - return result - else: - await task_logger.log_task_success( - log_entry, - f"Markdown file already exists (duplicate): {filename}", - {"duplicate_detected": True, "file_type": "markdown"}, - ) - return None - - # Check if the file is an audio file - elif filename.lower().endswith( - (".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm") - ): - # Update notification: parsing stage (transcription) - if notification: - await ( - NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Transcribing audio", - ) - ) - - await task_logger.log_task_progress( - log_entry, - f"Processing audio file for transcription: {filename}", - {"file_type": "audio", "processing_stage": "starting_transcription"}, - ) - - # Determine STT service type - stt_service_type = ( - "local" - if app_config.STT_SERVICE - and app_config.STT_SERVICE.startswith("local/") - else "external" - ) - - # Check if using local STT service - if stt_service_type == "local": - # Use local Faster-Whisper for transcription - from app.services.stt_service import stt_service - - try: - result = stt_service.transcribe_file(file_path) - transcribed_text = result.get("text", "") - - if not transcribed_text: - raise ValueError("Transcription returned empty text") - - # Add metadata about the transcription - transcribed_text = ( - f"# Transcription of {filename}\n\n{transcribed_text}" - ) - except Exception as e: - raise HTTPException( - status_code=422, - detail=f"Failed to transcribe audio file {filename}: {e!s}", - ) from e - - await task_logger.log_task_progress( - log_entry, - f"Local STT transcription completed: {filename}", - { - "processing_stage": "local_transcription_complete", - "language": result.get("language"), - "confidence": result.get("language_probability"), - "duration": result.get("duration"), - }, - ) - else: - # Use LiteLLM for audio transcription - with open(file_path, "rb") as audio_file: - transcription_kwargs = { - "model": app_config.STT_SERVICE, - "file": audio_file, - "api_key": app_config.STT_SERVICE_API_KEY, - } - if app_config.STT_SERVICE_API_BASE: - transcription_kwargs["api_base"] = ( - app_config.STT_SERVICE_API_BASE - ) - - transcription_response = await atranscription( - **transcription_kwargs - ) - - # Extract the transcribed text - transcribed_text = transcription_response.get("text", "") - - if not transcribed_text: - raise ValueError("Transcription returned empty text") - - # Add metadata about the transcription - transcribed_text = ( - f"# Transcription of {filename}\n\n{transcribed_text}" - ) - - await task_logger.log_task_progress( - log_entry, - f"Transcription completed, creating document: {filename}", - { - "processing_stage": "transcription_complete", - "transcript_length": len(transcribed_text), - }, - ) - - # Update notification: chunking stage - if notification: - await ( - NotificationService.document_processing.notify_processing_progress( - session, notification, stage="chunking" - ) - ) - - # Clean up the temp file - try: - os.unlink(file_path) - except Exception as e: - print("Error deleting temp file", e) - pass - - # Process transcription as markdown document - result = await add_received_markdown_file_document( - session, filename, transcribed_text, search_space_id, user_id, connector - ) - - if connector: - await _update_document_from_connector(result, connector, session) - - if result: - await task_logger.log_task_success( - log_entry, - f"Successfully transcribed and processed audio file: {filename}", - { - "document_id": result.id, - "content_hash": result.content_hash, - "file_type": "audio", - "transcript_length": len(transcribed_text), - "stt_service": stt_service_type, - }, - ) - return result - else: - await task_logger.log_task_success( - log_entry, - f"Audio file transcript already exists (duplicate): {filename}", - {"duplicate_detected": True, "file_type": "audio"}, - ) - return None - - else: - # Import page limit service - from app.services.page_limit_service import ( - PageLimitExceededError, - PageLimitService, - ) - - # Initialize page limit service - page_limit_service = PageLimitService(session) - - # CRITICAL: Estimate page count BEFORE making expensive ETL API calls - # This prevents users from incurring costs on files that would exceed their limit - try: - estimated_pages_before = ( - page_limit_service.estimate_pages_before_processing(file_path) - ) - except Exception: - # If estimation fails, use a conservative estimate based on file size - import os - - file_size = os.path.getsize(file_path) - estimated_pages_before = max( - 1, file_size // (80 * 1024) - ) # ~80KB per page - - await task_logger.log_task_progress( - log_entry, - f"Estimated {estimated_pages_before} pages for file: {filename}", - { - "estimated_pages": estimated_pages_before, - "file_type": "document", - }, - ) - - # Check page limit BEFORE calling ETL service to avoid unnecessary costs - try: - await page_limit_service.check_page_limit( - user_id, estimated_pages_before - ) - except PageLimitExceededError as e: - await task_logger.log_task_failure( - log_entry, - f"Page limit exceeded before processing: {filename}", - str(e), - { - "error_type": "PageLimitExceeded", - "pages_used": e.pages_used, - "pages_limit": e.pages_limit, - "estimated_pages": estimated_pages_before, - }, - ) - # Clean up the temp file - import os - - with contextlib.suppress(Exception): - os.unlink(file_path) - - raise HTTPException( - status_code=403, - detail=str(e), - ) from e - - if app_config.ETL_SERVICE == "UNSTRUCTURED": - # Update notification: parsing stage - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Extracting content", - ) - - await task_logger.log_task_progress( - log_entry, - f"Processing file with Unstructured ETL: {filename}", - { - "file_type": "document", - "etl_service": "UNSTRUCTURED", - "processing_stage": "loading", - }, - ) - - from langchain_unstructured import UnstructuredLoader - - # Process the file - loader = UnstructuredLoader( - file_path, - mode="elements", - post_processors=[], - languages=["eng"], - include_orig_elements=False, - include_metadata=False, - strategy="auto", - ) - - docs = await loader.aload() - - # Update notification: chunking stage - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, notification, stage="chunking", chunks_count=len(docs) - ) - - await task_logger.log_task_progress( - log_entry, - f"Unstructured ETL completed, creating document: {filename}", - {"processing_stage": "etl_complete", "elements_count": len(docs)}, - ) - - # Verify actual page count from parsed documents - actual_pages = page_limit_service.estimate_pages_from_elements(docs) - - # Use the higher of the two estimates for safety (in case pre-estimate was too low) - final_page_count = max(estimated_pages_before, actual_pages) - - # If actual is significantly higher than estimate, log a warning - if actual_pages > estimated_pages_before * 1.5: - await task_logger.log_task_progress( - log_entry, - f"Actual page count higher than estimate: {filename}", - { - "estimated_before": estimated_pages_before, - "actual_pages": actual_pages, - "using_count": final_page_count, - }, - ) - - # Clean up the temp file - import os - - try: - os.unlink(file_path) - except Exception as e: - print("Error deleting temp file", e) - pass - - enable_summary = ( - connector.get("enable_summary", True) if connector else True - ) - result = await add_received_file_document_using_unstructured( - session, - filename, - docs, - search_space_id, - user_id, - connector, - enable_summary=enable_summary, - ) - - if connector: - await _update_document_from_connector(result, connector, session) - - if result: - # Update page usage after successful processing - # allow_exceed=True because document was already created after passing initial check - await page_limit_service.update_page_usage( - user_id, final_page_count, allow_exceed=True - ) - - await task_logger.log_task_success( - log_entry, - f"Successfully processed file with Unstructured: {filename}", - { - "document_id": result.id, - "content_hash": result.content_hash, - "file_type": "document", - "etl_service": "UNSTRUCTURED", - "pages_processed": final_page_count, - }, - ) - return result - else: - await task_logger.log_task_success( - log_entry, - f"Document already exists (duplicate): {filename}", - { - "duplicate_detected": True, - "file_type": "document", - "etl_service": "UNSTRUCTURED", - }, - ) - return None - - elif app_config.ETL_SERVICE == "LLAMACLOUD": - # Update notification: parsing stage - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Extracting content", - ) - - await task_logger.log_task_progress( - log_entry, - f"Processing file with LlamaCloud ETL: {filename}", - { - "file_type": "document", - "etl_service": "LLAMACLOUD", - "processing_stage": "parsing", - "estimated_pages": estimated_pages_before, - }, - ) - - # Parse file with retry logic for SSL/connection errors (common with large files) - result = await parse_with_llamacloud_retry( - file_path=file_path, - estimated_pages=estimated_pages_before, - task_logger=task_logger, - log_entry=log_entry, - ) - - # Clean up the temp file - import os - - try: - os.unlink(file_path) - except Exception as e: - print("Error deleting temp file", e) - pass - - # Get markdown documents from the result - markdown_documents = await result.aget_markdown_documents( - split_by_page=False - ) - - # Update notification: chunking stage - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="chunking", - chunks_count=len(markdown_documents), - ) - - await task_logger.log_task_progress( - log_entry, - f"LlamaCloud parsing completed, creating documents: {filename}", - { - "processing_stage": "parsing_complete", - "documents_count": len(markdown_documents), - }, - ) - - # Check if LlamaCloud returned any documents - if not markdown_documents or len(markdown_documents) == 0: - await task_logger.log_task_failure( - log_entry, - f"LlamaCloud parsing returned no documents: {filename}", - "ETL service returned empty document list", - { - "error_type": "EmptyDocumentList", - "etl_service": "LLAMACLOUD", - }, - ) - raise ValueError( - f"LlamaCloud parsing returned no documents for {filename}" - ) - - # Verify actual page count from parsed markdown documents - actual_pages = page_limit_service.estimate_pages_from_markdown( - markdown_documents - ) - - # Use the higher of the two estimates for safety (in case pre-estimate was too low) - final_page_count = max(estimated_pages_before, actual_pages) - - # If actual is significantly higher than estimate, log a warning - if actual_pages > estimated_pages_before * 1.5: - await task_logger.log_task_progress( - log_entry, - f"Actual page count higher than estimate: {filename}", - { - "estimated_before": estimated_pages_before, - "actual_pages": actual_pages, - "using_count": final_page_count, - }, - ) - - # Track if any document was successfully created (not a duplicate) - any_doc_created = False - last_created_doc = None - - for doc in markdown_documents: - # Extract text content from the markdown documents - markdown_content = doc.text - - enable_summary = ( - connector.get("enable_summary", True) if connector else True - ) - doc_result = await add_received_file_document_using_llamacloud( - session, - filename, - llamacloud_markdown_document=markdown_content, - search_space_id=search_space_id, - user_id=user_id, - connector=connector, - enable_summary=enable_summary, - ) - - # Track if this document was successfully created - if doc_result: - any_doc_created = True - last_created_doc = doc_result - - # Update page usage once after processing all documents - # Only update if at least one document was created (not all duplicates) - if any_doc_created: - # Update page usage after successful processing - # allow_exceed=True because document was already created after passing initial check - await page_limit_service.update_page_usage( - user_id, final_page_count, allow_exceed=True - ) - - if connector: - await _update_document_from_connector( - last_created_doc, connector, session - ) - - await task_logger.log_task_success( - log_entry, - f"Successfully processed file with LlamaCloud: {filename}", - { - "document_id": last_created_doc.id, - "content_hash": last_created_doc.content_hash, - "file_type": "document", - "etl_service": "LLAMACLOUD", - "pages_processed": final_page_count, - "documents_count": len(markdown_documents), - }, - ) - return last_created_doc - else: - # All documents were duplicates (markdown_documents was not empty, but all returned None) - await task_logger.log_task_success( - log_entry, - f"Document already exists (duplicate): {filename}", - { - "duplicate_detected": True, - "file_type": "document", - "etl_service": "LLAMACLOUD", - "documents_count": len(markdown_documents), - }, - ) - return None - - elif app_config.ETL_SERVICE == "DOCLING": - # Update notification: parsing stage - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Extracting content", - ) - - await task_logger.log_task_progress( - log_entry, - f"Processing file with Docling ETL: {filename}", - { - "file_type": "document", - "etl_service": "DOCLING", - "processing_stage": "parsing", - }, - ) - - # Use Docling service for document processing - from app.services.docling_service import create_docling_service - - # Create Docling service - docling_service = create_docling_service() - - # Suppress pdfminer warnings that can cause processing to hang - # These warnings are harmless but can spam logs and potentially halt processing - # Suppress both Python warnings and logging warnings from pdfminer - pdfminer_logger = getLogger("pdfminer") - original_level = pdfminer_logger.level - - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", category=UserWarning, module="pdfminer" - ) - warnings.filterwarnings( - "ignore", - message=".*Cannot set gray non-stroke color.*", - ) - warnings.filterwarnings("ignore", message=".*invalid float value.*") - - # Temporarily suppress pdfminer logging warnings - pdfminer_logger.setLevel(ERROR) - - try: - # Process the document - result = await docling_service.process_document( - file_path, filename - ) - finally: - # Restore original logging level - pdfminer_logger.setLevel(original_level) - - # Clean up the temp file - import os - - try: - os.unlink(file_path) - except Exception as e: - print("Error deleting temp file", e) - pass - - await task_logger.log_task_progress( - log_entry, - f"Docling parsing completed, creating document: {filename}", - { - "processing_stage": "parsing_complete", - "content_length": len(result["content"]), - }, - ) - - # Verify actual page count from content length - actual_pages = page_limit_service.estimate_pages_from_content_length( - len(result["content"]) - ) - - # Use the higher of the two estimates for safety (in case pre-estimate was too low) - final_page_count = max(estimated_pages_before, actual_pages) - - # If actual is significantly higher than estimate, log a warning - if actual_pages > estimated_pages_before * 1.5: - await task_logger.log_task_progress( - log_entry, - f"Actual page count higher than estimate: {filename}", - { - "estimated_before": estimated_pages_before, - "actual_pages": actual_pages, - "using_count": final_page_count, - }, - ) - - # Update notification: chunking stage - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, notification, stage="chunking" - ) - - enable_summary = ( - connector.get("enable_summary", True) if connector else True - ) - doc_result = await add_received_file_document_using_docling( - session, - filename, - docling_markdown_document=result["content"], - search_space_id=search_space_id, - user_id=user_id, - connector=connector, - enable_summary=enable_summary, - ) - - if doc_result: - # Update page usage after successful processing - # allow_exceed=True because document was already created after passing initial check - await page_limit_service.update_page_usage( - user_id, final_page_count, allow_exceed=True - ) - - if connector: - await _update_document_from_connector( - doc_result, connector, session - ) - - await task_logger.log_task_success( - log_entry, - f"Successfully processed file with Docling: {filename}", - { - "document_id": doc_result.id, - "content_hash": doc_result.content_hash, - "file_type": "document", - "etl_service": "DOCLING", - "pages_processed": final_page_count, - }, - ) - return doc_result - else: - await task_logger.log_task_success( - log_entry, - f"Document already exists (duplicate): {filename}", - { - "duplicate_detected": True, - "file_type": "document", - "etl_service": "DOCLING", - }, - ) - return None except Exception as e: await session.rollback() - # For page limit errors, use the detailed message from the exception from app.services.page_limit_service import PageLimitExceededError if isinstance(e, PageLimitExceededError): @@ -1645,10 +734,225 @@ async def process_file_in_background( str(e), {"error_type": type(e).__name__, "filename": filename}, ) - import logging - logging.error(f"Error processing file in background: {error_message}") - raise # Re-raise so the wrapper can also handle it + raise + + +# =================================================================== +# 2-phase handler (process_file_in_background_with_document) +# =================================================================== + + +async def _extract_file_content( + file_path: str, + filename: str, + session: AsyncSession, + user_id: str, + task_logger: TaskLoggingService, + log_entry: Log, + notification: Notification | None, +) -> tuple[str, str]: + """ + Extract markdown content from a file regardless of type. + + Returns: + Tuple of (markdown_content, etl_service_name). + """ + category = classify_file(filename) + + if category == FileCategory.MARKDOWN: + if notification: + await NotificationService.document_processing.notify_processing_progress( + session, + notification, + stage="parsing", + stage_message="Reading file", + ) + await task_logger.log_task_progress( + log_entry, + f"Processing markdown/text file: {filename}", + {"file_type": "markdown", "processing_stage": "reading_file"}, + ) + with open(file_path, encoding="utf-8") as f: + content = f.read() + with contextlib.suppress(Exception): + os.unlink(file_path) + return content, "MARKDOWN" + + if category == FileCategory.DIRECT_CONVERT: + if notification: + await NotificationService.document_processing.notify_processing_progress( + session, + notification, + stage="parsing", + stage_message="Converting file", + ) + await task_logger.log_task_progress( + log_entry, + f"Direct-converting file to markdown: {filename}", + {"file_type": "direct_convert", "processing_stage": "converting"}, + ) + content = convert_file_directly(file_path, filename) + with contextlib.suppress(Exception): + os.unlink(file_path) + return content, "DIRECT_CONVERT" + + if category == FileCategory.AUDIO: + if notification: + await NotificationService.document_processing.notify_processing_progress( + session, + notification, + stage="parsing", + stage_message="Transcribing audio", + ) + await task_logger.log_task_progress( + log_entry, + f"Processing audio file for transcription: {filename}", + {"file_type": "audio", "processing_stage": "starting_transcription"}, + ) + transcribed_text = await _transcribe_audio(file_path, filename) + with contextlib.suppress(Exception): + os.unlink(file_path) + return transcribed_text, "AUDIO_TRANSCRIPTION" + + # Document file — use ETL service + return await _extract_document_content( + file_path, + filename, + session, + user_id, + task_logger, + log_entry, + notification, + ) + + +async def _transcribe_audio(file_path: str, filename: str) -> str: + """Transcribe an audio file and return formatted markdown text.""" + stt_service_type = ( + "local" + if app_config.STT_SERVICE and app_config.STT_SERVICE.startswith("local/") + else "external" + ) + + if stt_service_type == "local": + from app.services.stt_service import stt_service + + result = stt_service.transcribe_file(file_path) + text = result.get("text", "") + if not text: + raise ValueError("Transcription returned empty text") + else: + from litellm import atranscription + + with open(file_path, "rb") as audio_file: + kwargs: dict = { + "model": app_config.STT_SERVICE, + "file": audio_file, + "api_key": app_config.STT_SERVICE_API_KEY, + } + if app_config.STT_SERVICE_API_BASE: + kwargs["api_base"] = app_config.STT_SERVICE_API_BASE + response = await atranscription(**kwargs) + text = response.get("text", "") + if not text: + raise ValueError("Transcription returned empty text") + + return f"# Transcription of {filename}\n\n{text}" + + +async def _extract_document_content( + file_path: str, + filename: str, + session: AsyncSession, + user_id: str, + task_logger: TaskLoggingService, + log_entry: Log, + notification: Notification | None, +) -> tuple[str, str]: + """ + Parse a document file via the configured ETL service. + + Returns: + Tuple of (markdown_content, etl_service_name). + """ + from app.services.page_limit_service import PageLimitService + + page_limit_service = PageLimitService(session) + + try: + estimated_pages = page_limit_service.estimate_pages_before_processing(file_path) + except Exception: + file_size = os.path.getsize(file_path) + estimated_pages = max(1, file_size // (80 * 1024)) + + await page_limit_service.check_page_limit(user_id, estimated_pages) + + etl_service = app_config.ETL_SERVICE + markdown_content: str | None = None + + if notification: + await NotificationService.document_processing.notify_processing_progress( + session, + notification, + stage="parsing", + stage_message="Extracting content", + ) + + if etl_service == "UNSTRUCTURED": + from app.utils.document_converters import convert_document_to_markdown + + docs = await parse_with_unstructured(file_path) + markdown_content = await convert_document_to_markdown(docs) + actual_pages = page_limit_service.estimate_pages_from_elements(docs) + final_pages = max(estimated_pages, actual_pages) + await page_limit_service.update_page_usage( + user_id, final_pages, allow_exceed=True + ) + + elif etl_service == "LLAMACLOUD": + raw_result = await parse_with_llamacloud_retry( + file_path=file_path, + estimated_pages=estimated_pages, + task_logger=task_logger, + log_entry=log_entry, + ) + markdown_documents = await raw_result.aget_markdown_documents( + split_by_page=False + ) + if not markdown_documents: + raise RuntimeError(f"LlamaCloud parsing returned no documents: {filename}") + markdown_content = markdown_documents[0].text + await page_limit_service.update_page_usage( + user_id, estimated_pages, allow_exceed=True + ) + + elif etl_service == "DOCLING": + getLogger("docling.pipeline.base_pipeline").setLevel(ERROR) + getLogger("docling.document_converter").setLevel(ERROR) + getLogger("docling_core.transforms.chunker.hierarchical_chunker").setLevel( + ERROR + ) + + from docling.document_converter import DocumentConverter + + converter = DocumentConverter() + result = converter.convert(file_path) + markdown_content = result.document.export_to_markdown() + await page_limit_service.update_page_usage( + user_id, estimated_pages, allow_exceed=True + ) + + else: + raise RuntimeError(f"Unknown ETL_SERVICE: {etl_service}") + + with contextlib.suppress(Exception): + os.unlink(file_path) + + if not markdown_content: + raise RuntimeError(f"Failed to extract content from file: {filename}") + + return markdown_content, etl_service async def process_file_in_background_with_document( @@ -1667,272 +971,50 @@ async def process_file_in_background_with_document( """ Process file and update existing pending document (2-phase pattern). - This function is Phase 2 of the real-time document status updates: - - Phase 1 (API): Created document with pending status - - Phase 2 (this): Process file and update document to ready/failed - - The document already exists with pending status. This function: - 1. Parses the file content (markdown, audio, or ETL services) - 2. Updates the document with content, embeddings, and chunks - 3. Sets status to 'ready' on success - - Args: - document: Existing document with pending status - file_path: Path to the uploaded file - filename: Original filename - search_space_id: ID of the search space - user_id: ID of the user - session: Database session - task_logger: Task logging service - log_entry: Log entry for this task - connector: Optional connector info for Google Drive files - notification: Optional notification for progress updates - - Returns: - Updated Document object if successful, None if duplicate content detected + Phase 1 (API layer): Created document with pending status. + Phase 2 (this function): Process file and update document to ready/failed. """ - import os - - from app.config import config as app_config + from app.indexing_pipeline.adapters.file_upload_adapter import ( + UploadDocumentAdapter, + ) from app.services.llm_service import get_user_long_context_llm + from app.utils.document_converters import generate_content_hash + + from .base import check_duplicate_document doc_id = document.id try: - markdown_content = None - etl_service = None - - # ===== STEP 1: Parse file content based on type ===== - - # Check if the file is a markdown or text file - if filename.lower().endswith((".md", ".markdown", ".txt")): - # Update notification: parsing stage - if notification: - await ( - NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Reading file", - ) - ) - - await task_logger.log_task_progress( - log_entry, - f"Processing markdown/text file: {filename}", - {"file_type": "markdown", "processing_stage": "reading_file"}, - ) - - # Read markdown content directly - with open(file_path, encoding="utf-8") as f: - markdown_content = f.read() - etl_service = "MARKDOWN" - - # Clean up temp file - with contextlib.suppress(Exception): - os.unlink(file_path) - - # Check if the file is an audio file - elif filename.lower().endswith( - (".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm") - ): - # Update notification: parsing stage (transcription) - if notification: - await ( - NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Transcribing audio", - ) - ) - - await task_logger.log_task_progress( - log_entry, - f"Processing audio file for transcription: {filename}", - {"file_type": "audio", "processing_stage": "starting_transcription"}, - ) - - # Transcribe audio - stt_service_type = ( - "local" - if app_config.STT_SERVICE - and app_config.STT_SERVICE.startswith("local/") - else "external" - ) - - if stt_service_type == "local": - from app.services.stt_service import stt_service - - result = stt_service.transcribe_file(file_path) - transcribed_text = result.get("text", "") - if not transcribed_text: - raise ValueError("Transcription returned empty text") - markdown_content = ( - f"# Transcription of {filename}\n\n{transcribed_text}" - ) - else: - with open(file_path, "rb") as audio_file: - transcription_kwargs = { - "model": app_config.STT_SERVICE, - "file": audio_file, - "api_key": app_config.STT_SERVICE_API_KEY, - } - if app_config.STT_SERVICE_API_BASE: - transcription_kwargs["api_base"] = ( - app_config.STT_SERVICE_API_BASE - ) - transcription_response = await atranscription( - **transcription_kwargs - ) - transcribed_text = transcription_response.get("text", "") - if not transcribed_text: - raise ValueError("Transcription returned empty text") - markdown_content = ( - f"# Transcription of {filename}\n\n{transcribed_text}" - ) - - etl_service = "AUDIO_TRANSCRIPTION" - # Clean up temp file - with contextlib.suppress(Exception): - os.unlink(file_path) - - else: - # Document files - use ETL service - from app.services.page_limit_service import ( - PageLimitExceededError, - PageLimitService, - ) - - page_limit_service = PageLimitService(session) - - # Estimate page count - try: - estimated_pages = page_limit_service.estimate_pages_before_processing( - file_path - ) - except Exception: - file_size = os.path.getsize(file_path) - estimated_pages = max(1, file_size // (80 * 1024)) - - # Check page limit - await page_limit_service.check_page_limit(user_id, estimated_pages) - - if app_config.ETL_SERVICE == "UNSTRUCTURED": - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Extracting content", - ) - - from langchain_unstructured import UnstructuredLoader - - loader = UnstructuredLoader( - file_path, - mode="elements", - post_processors=[], - languages=["eng"], - include_orig_elements=False, - include_metadata=False, - strategy="auto", - ) - docs = await loader.aload() - markdown_content = await convert_document_to_markdown(docs) - actual_pages = page_limit_service.estimate_pages_from_elements(docs) - final_page_count = max(estimated_pages, actual_pages) - etl_service = "UNSTRUCTURED" - - # Update page usage - await page_limit_service.update_page_usage( - user_id, final_page_count, allow_exceed=True - ) - - elif app_config.ETL_SERVICE == "LLAMACLOUD": - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Extracting content", - ) - - result = await parse_with_llamacloud_retry( - file_path=file_path, - estimated_pages=estimated_pages, - task_logger=task_logger, - log_entry=log_entry, - ) - markdown_documents = await result.aget_markdown_documents( - split_by_page=False - ) - if not markdown_documents: - raise RuntimeError( - f"LlamaCloud parsing returned no documents: {filename}" - ) - markdown_content = markdown_documents[0].text - etl_service = "LLAMACLOUD" - - # Update page usage - await page_limit_service.update_page_usage( - user_id, estimated_pages, allow_exceed=True - ) - - elif app_config.ETL_SERVICE == "DOCLING": - if notification: - await NotificationService.document_processing.notify_processing_progress( - session, - notification, - stage="parsing", - stage_message="Extracting content", - ) - - # Suppress logging during Docling import - getLogger("docling.pipeline.base_pipeline").setLevel(ERROR) - getLogger("docling.document_converter").setLevel(ERROR) - getLogger( - "docling_core.transforms.chunker.hierarchical_chunker" - ).setLevel(ERROR) - - from docling.document_converter import DocumentConverter - - converter = DocumentConverter() - result = converter.convert(file_path) - markdown_content = result.document.export_to_markdown() - etl_service = "DOCLING" - - # Update page usage - await page_limit_service.update_page_usage( - user_id, estimated_pages, allow_exceed=True - ) - - else: - raise RuntimeError(f"Unknown ETL_SERVICE: {app_config.ETL_SERVICE}") - - # Clean up temp file - with contextlib.suppress(Exception): - os.unlink(file_path) + # Step 1: extract content + markdown_content, etl_service = await _extract_file_content( + file_path, + filename, + session, + user_id, + task_logger, + log_entry, + notification, + ) if not markdown_content: raise RuntimeError(f"Failed to extract content from file: {filename}") - # ===== STEP 2: Check for duplicate content ===== + # Step 2: duplicate check content_hash = generate_content_hash(markdown_content, search_space_id) - existing_by_content = await check_duplicate_document(session, content_hash) if existing_by_content and existing_by_content.id != doc_id: - # Duplicate content found - mark this document as failed logging.info( f"Duplicate content detected for {filename}, " f"matches document {existing_by_content.id}" ) return None - # ===== STEP 3+4: Index via pipeline ===== + # Step 3: index via pipeline if notification: await NotificationService.document_processing.notify_processing_progress( - session, notification, stage="chunking" + session, + notification, + stage="chunking", ) user_llm = await get_user_long_context_llm(session, user_id, search_space_id) @@ -1957,7 +1039,6 @@ async def process_file_in_background_with_document( "file_type": etl_service, }, ) - return document except Exception as e: diff --git a/surfsense_backend/app/tasks/document_processors/markdown_processor.py b/surfsense_backend/app/tasks/document_processors/markdown_processor.py index 2fb711bf8..0ff340c0e 100644 --- a/surfsense_backend/app/tasks/document_processors/markdown_processor.py +++ b/surfsense_backend/app/tasks/document_processors/markdown_processor.py @@ -14,88 +14,19 @@ from app.utils.document_converters import ( create_document_chunks, generate_content_hash, generate_document_summary, - generate_unique_identifier_hash, ) +from ._helpers import ( + find_existing_document_with_migration, + get_google_drive_unique_identifier, +) from .base import ( - check_document_by_unique_identifier, check_duplicate_document, get_current_timestamp, safe_set_chunks, ) -def _get_google_drive_unique_identifier( - connector: dict | None, - filename: str, - search_space_id: int, -) -> tuple[str, str | None]: - """ - Get unique identifier hash for a file, with special handling for Google Drive. - - For Google Drive files, uses file_id as the unique identifier (doesn't change on rename). - For other files, uses filename. - - Args: - connector: Optional connector info dict with type and metadata - filename: The filename (used for non-Google Drive files or as fallback) - search_space_id: The search space ID - - Returns: - Tuple of (primary_hash, legacy_hash or None) - """ - if connector and connector.get("type") == DocumentType.GOOGLE_DRIVE_FILE: - metadata = connector.get("metadata", {}) - file_id = metadata.get("google_drive_file_id") - - if file_id: - primary_hash = generate_unique_identifier_hash( - DocumentType.GOOGLE_DRIVE_FILE, file_id, search_space_id - ) - legacy_hash = generate_unique_identifier_hash( - DocumentType.GOOGLE_DRIVE_FILE, filename, search_space_id - ) - return primary_hash, legacy_hash - - primary_hash = generate_unique_identifier_hash( - DocumentType.FILE, filename, search_space_id - ) - return primary_hash, None - - -async def _find_existing_document_with_migration( - session: AsyncSession, - primary_hash: str, - legacy_hash: str | None, - content_hash: str | None = None, -) -> Document | None: - """ - Find existing document, checking both new hash and legacy hash for migration, - with fallback to content_hash for cross-source deduplication. - """ - existing_document = await check_document_by_unique_identifier(session, primary_hash) - - if not existing_document and legacy_hash: - existing_document = await check_document_by_unique_identifier( - session, legacy_hash - ) - if existing_document: - logging.info( - "Found legacy document (filename-based hash), will migrate to file_id-based hash" - ) - - # Fallback: check by content_hash to catch duplicates from different sources - if not existing_document and content_hash: - existing_document = await check_duplicate_document(session, content_hash) - if existing_document: - logging.info( - f"Found duplicate content from different source (content_hash match). " - f"Original document ID: {existing_document.id}, type: {existing_document.document_type}" - ) - - return existing_document - - async def _handle_existing_document_update( session: AsyncSession, existing_document: Document, @@ -224,7 +155,7 @@ async def add_received_markdown_file_document( try: # Generate unique identifier hash (uses file_id for Google Drive, filename for others) - primary_hash, legacy_hash = _get_google_drive_unique_identifier( + primary_hash, legacy_hash = get_google_drive_unique_identifier( connector, file_name, search_space_id ) @@ -232,7 +163,7 @@ async def add_received_markdown_file_document( content_hash = generate_content_hash(file_in_markdown, search_space_id) # Check if document exists (with migration support for Google Drive and content_hash fallback) - existing_document = await _find_existing_document_with_migration( + existing_document = await find_existing_document_with_migration( session, primary_hash, legacy_hash, content_hash ) diff --git a/surfsense_backend/app/utils/document_versioning.py b/surfsense_backend/app/utils/document_versioning.py new file mode 100644 index 000000000..e6ad1fb06 --- /dev/null +++ b/surfsense_backend/app/utils/document_versioning.py @@ -0,0 +1,107 @@ +"""Document versioning: snapshot creation and cleanup. + +Rules: +- 30-minute debounce window: if the latest version was created < 30 min ago, + overwrite it instead of creating a new row. +- Maximum 20 versions per document. +- Versions older than 90 days are cleaned up. +""" + +from datetime import UTC, datetime, timedelta + +from sqlalchemy import delete, func, select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db import Document, DocumentVersion + +MAX_VERSIONS_PER_DOCUMENT = 20 +DEBOUNCE_MINUTES = 30 +RETENTION_DAYS = 90 + + +def _now() -> datetime: + return datetime.now(UTC) + + +async def create_version_snapshot( + session: AsyncSession, + document: Document, +) -> DocumentVersion | None: + """Snapshot the document's current state into a DocumentVersion row. + + Returns the created/updated DocumentVersion, or None if nothing was done. + """ + now = _now() + + latest = ( + await session.execute( + select(DocumentVersion) + .where(DocumentVersion.document_id == document.id) + .order_by(DocumentVersion.version_number.desc()) + .limit(1) + ) + ).scalar_one_or_none() + + if latest is not None: + age = now - latest.created_at.replace(tzinfo=UTC) + if age < timedelta(minutes=DEBOUNCE_MINUTES): + latest.source_markdown = document.source_markdown + latest.content_hash = document.content_hash + latest.title = document.title + latest.created_at = now + await session.flush() + return latest + + max_num = ( + await session.execute( + select(func.coalesce(func.max(DocumentVersion.version_number), 0)).where( + DocumentVersion.document_id == document.id + ) + ) + ).scalar_one() + + version = DocumentVersion( + document_id=document.id, + version_number=max_num + 1, + source_markdown=document.source_markdown, + content_hash=document.content_hash, + title=document.title, + created_at=now, + ) + session.add(version) + await session.flush() + + # Cleanup: remove versions older than 90 days + cutoff = now - timedelta(days=RETENTION_DAYS) + await session.execute( + delete(DocumentVersion).where( + DocumentVersion.document_id == document.id, + DocumentVersion.created_at < cutoff, + ) + ) + + # Cleanup: cap at MAX_VERSIONS_PER_DOCUMENT + count = ( + await session.execute( + select(func.count()) + .select_from(DocumentVersion) + .where(DocumentVersion.document_id == document.id) + ) + ).scalar_one() + + if count > MAX_VERSIONS_PER_DOCUMENT: + excess = count - MAX_VERSIONS_PER_DOCUMENT + oldest_ids_result = await session.execute( + select(DocumentVersion.id) + .where(DocumentVersion.document_id == document.id) + .order_by(DocumentVersion.version_number.asc()) + .limit(excess) + ) + oldest_ids = [row[0] for row in oldest_ids_result.all()] + if oldest_ids: + await session.execute( + delete(DocumentVersion).where(DocumentVersion.id.in_(oldest_ids)) + ) + + await session.flush() + return version diff --git a/surfsense_backend/tests/integration/document_upload/test_upload_limits.py b/surfsense_backend/tests/integration/document_upload/test_upload_limits.py index a8dab43f0..a56398baa 100644 --- a/surfsense_backend/tests/integration/document_upload/test_upload_limits.py +++ b/surfsense_backend/tests/integration/document_upload/test_upload_limits.py @@ -2,12 +2,11 @@ Integration tests for backend file upload limit enforcement. These tests verify that the API rejects uploads that exceed: - - Max files per upload (10) - - Max per-file size (50 MB) - - Max total upload size (200 MB) + - Max per-file size (500 MB) -The limits mirror the frontend's DocumentUploadTab.tsx constants and are -enforced server-side to protect against direct API calls. +No file count or total size limits are enforced — the frontend batches +uploads in groups of 5 and there is no cap on how many files a user can +upload in a single session. Prerequisites: - PostgreSQL + pgvector @@ -24,60 +23,12 @@ pytestmark = pytest.mark.integration # --------------------------------------------------------------------------- -# Test A: File count limit -# --------------------------------------------------------------------------- - - -class TestFileCountLimit: - """Uploading more than 10 files in a single request should be rejected.""" - - async def test_11_files_returns_413( - self, - client: httpx.AsyncClient, - headers: dict[str, str], - search_space_id: int, - ): - files = [ - ("files", (f"file_{i}.txt", io.BytesIO(b"test content"), "text/plain")) - for i in range(11) - ] - resp = await client.post( - "/api/v1/documents/fileupload", - headers=headers, - files=files, - data={"search_space_id": str(search_space_id)}, - ) - assert resp.status_code == 413 - assert "too many files" in resp.json()["detail"].lower() - - async def test_10_files_accepted( - self, - client: httpx.AsyncClient, - headers: dict[str, str], - search_space_id: int, - cleanup_doc_ids: list[int], - ): - files = [ - ("files", (f"file_{i}.txt", io.BytesIO(b"test content"), "text/plain")) - for i in range(10) - ] - resp = await client.post( - "/api/v1/documents/fileupload", - headers=headers, - files=files, - data={"search_space_id": str(search_space_id)}, - ) - assert resp.status_code == 200 - cleanup_doc_ids.extend(resp.json().get("document_ids", [])) - - -# --------------------------------------------------------------------------- -# Test B: Per-file size limit +# Test: Per-file size limit (500 MB) # --------------------------------------------------------------------------- class TestPerFileSizeLimit: - """A single file exceeding 50 MB should be rejected.""" + """A single file exceeding 500 MB should be rejected.""" async def test_oversized_file_returns_413( self, @@ -85,7 +36,7 @@ class TestPerFileSizeLimit: headers: dict[str, str], search_space_id: int, ): - oversized = io.BytesIO(b"\x00" * (50 * 1024 * 1024 + 1)) + oversized = io.BytesIO(b"\x00" * (500 * 1024 * 1024 + 1)) resp = await client.post( "/api/v1/documents/fileupload", headers=headers, @@ -102,11 +53,11 @@ class TestPerFileSizeLimit: search_space_id: int, cleanup_doc_ids: list[int], ): - at_limit = io.BytesIO(b"\x00" * (50 * 1024 * 1024)) + at_limit = io.BytesIO(b"\x00" * (500 * 1024 * 1024)) resp = await client.post( "/api/v1/documents/fileupload", headers=headers, - files=[("files", ("exact50mb.txt", at_limit, "text/plain"))], + files=[("files", ("exact500mb.txt", at_limit, "text/plain"))], data={"search_space_id": str(search_space_id)}, ) assert resp.status_code == 200 @@ -114,26 +65,23 @@ class TestPerFileSizeLimit: # --------------------------------------------------------------------------- -# Test C: Total upload size limit +# Test: Multiple files accepted without count limit # --------------------------------------------------------------------------- -class TestTotalSizeLimit: - """Multiple files whose combined size exceeds 200 MB should be rejected.""" +class TestNoFileCountLimit: + """Many files in a single request should be accepted.""" - async def test_total_size_over_200mb_returns_413( + async def test_many_files_accepted( self, client: httpx.AsyncClient, headers: dict[str, str], search_space_id: int, + cleanup_doc_ids: list[int], ): - chunk_size = 45 * 1024 * 1024 # 45 MB each files = [ - ( - "files", - (f"chunk_{i}.txt", io.BytesIO(b"\x00" * chunk_size), "text/plain"), - ) - for i in range(5) # 5 x 45 MB = 225 MB > 200 MB + ("files", (f"file_{i}.txt", io.BytesIO(b"test content"), "text/plain")) + for i in range(20) ] resp = await client.post( "/api/v1/documents/fileupload", @@ -141,5 +89,5 @@ class TestTotalSizeLimit: files=files, data={"search_space_id": str(search_space_id)}, ) - assert resp.status_code == 413 - assert "total upload size" in resp.json()["detail"].lower() + assert resp.status_code == 200 + cleanup_doc_ids.extend(resp.json().get("document_ids", [])) diff --git a/surfsense_backend/tests/integration/indexing_pipeline/test_local_folder_pipeline.py b/surfsense_backend/tests/integration/indexing_pipeline/test_local_folder_pipeline.py new file mode 100644 index 000000000..4d9bda7ee --- /dev/null +++ b/surfsense_backend/tests/integration/indexing_pipeline/test_local_folder_pipeline.py @@ -0,0 +1,1180 @@ +"""Integration tests for local folder indexer — Tier 3 (I1-I5), Tier 4 (F1-F7), Tier 5 (P1), Tier 6 (B1-B2).""" + +import os +from contextlib import asynccontextmanager +from pathlib import Path + +import pytest +from sqlalchemy import func, select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db import ( + Document, + DocumentStatus, + DocumentType, + DocumentVersion, + Folder, + SearchSpace, + User, +) + +pytestmark = pytest.mark.integration + +UNIFIED_FIXTURES = ( + "patched_summarize", + "patched_embed_texts", + "patched_chunk_text", +) + + +class _FakeSessionMaker: + """Wraps an existing AsyncSession so ``async with factory()`` yields it + without closing it. Used to route batch-mode DB operations through the + test's savepoint-wrapped session.""" + + def __init__(self, session: AsyncSession): + self._session = session + + def __call__(self): + @asynccontextmanager + async def _ctx(): + yield self._session + + return _ctx() + + +@pytest.fixture +def patched_batch_sessions(monkeypatch, db_session): + """Make ``_index_batch_files`` use the test session and run sequentially.""" + monkeypatch.setattr( + "app.tasks.connector_indexers.local_folder_indexer.get_celery_session_maker", + lambda: _FakeSessionMaker(db_session), + ) + monkeypatch.setattr( + "app.tasks.connector_indexers.local_folder_indexer.BATCH_CONCURRENCY", + 1, + ) + + +# ==================================================================== +# Tier 3: Full Indexer Integration (I1-I5) +# ==================================================================== + + +class TestFullIndexer: + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_i1_new_file_indexed( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """I1: Single new .md file is indexed with status READY.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "note.md").write_text("# Hello World\n\nContent here.") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + assert err is None + assert count == 1 + + docs = ( + ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ) + .scalars() + .all() + ) + assert len(docs) == 1 + assert docs[0].document_type == DocumentType.LOCAL_FOLDER_FILE + assert DocumentStatus.is_state(docs[0].status, DocumentStatus.READY) + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_i2_unchanged_skipped( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """I2: Second run on unchanged directory creates no new documents.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "note.md").write_text("# Hello\n\nSame content.") + + count1, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + assert count1 == 1 + + count2, _, _, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + root_folder_id=root_folder_id, + ) + assert count2 == 0 + + total = ( + await db_session.execute( + select(func.count()) + .select_from(Document) + .where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ).scalar_one() + assert total == 1 + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_i3_changed_reindexed( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """I3: Modified file content triggers re-index and creates a version.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + f = tmp_path / "note.md" + f.write_text("# Version 1\n\nOriginal.") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + f.write_text("# Version 2\n\nUpdated.") + os.utime(f, (f.stat().st_atime + 10, f.stat().st_mtime + 10)) + + count, _, _, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + root_folder_id=root_folder_id, + ) + assert count == 1 + + versions = ( + ( + await db_session.execute( + select(DocumentVersion) + .join(Document) + .where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ) + .scalars() + .all() + ) + assert len(versions) >= 1 + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_i4_deleted_removed( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """I4: Deleted file is removed from DB on re-sync.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + f = tmp_path / "to_delete.md" + f.write_text("# Delete me") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + docs_before = ( + await db_session.execute( + select(func.count()) + .select_from(Document) + .where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ).scalar_one() + assert docs_before == 1 + + f.unlink() + + await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + root_folder_id=root_folder_id, + ) + + docs_after = ( + await db_session.execute( + select(func.count()) + .select_from(Document) + .where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ).scalar_one() + assert docs_after == 0 + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_i5_single_file_mode( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """I5: Batch mode with a single file only processes that file.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "a.md").write_text("File A") + (tmp_path / "b.md").write_text("File B") + (tmp_path / "c.md").write_text("File C") + + count, _, _, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[str(tmp_path / "b.md")], + ) + assert count == 1 + + docs = ( + ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ) + .scalars() + .all() + ) + assert len(docs) == 1 + assert docs[0].title == "b.md" + + +# ==================================================================== +# Tier 4: Folder Mirroring (F1-F7) +# ==================================================================== + + +class TestFolderMirroring: + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_f1_root_folder_created( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """F1: First sync creates a root Folder and returns root_folder_id.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "root.md").write_text("Root file") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + assert root_folder_id is not None + + root_folder = ( + await db_session.execute(select(Folder).where(Folder.id == root_folder_id)) + ).scalar_one() + assert root_folder.name == "test-folder" + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_f2_nested_folder_rows( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """F2: Nested dirs create Folder rows with correct parent_id chain.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + daily = tmp_path / "notes" / "daily" + daily.mkdir(parents=True) + weekly = tmp_path / "notes" / "weekly" + weekly.mkdir(parents=True) + (daily / "today.md").write_text("today") + (weekly / "review.md").write_text("review") + + await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + folders = ( + ( + await db_session.execute( + select(Folder).where(Folder.search_space_id == db_search_space.id) + ) + ) + .scalars() + .all() + ) + + folder_names = {f.name for f in folders} + assert "notes" in folder_names + assert "daily" in folder_names + assert "weekly" in folder_names + + notes_folder = next(f for f in folders if f.name == "notes") + daily_folder = next(f for f in folders if f.name == "daily") + weekly_folder = next(f for f in folders if f.name == "weekly") + + assert daily_folder.parent_id == notes_folder.id + assert weekly_folder.parent_id == notes_folder.id + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_f3_resync_reuses_folders( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """F3: Re-sync reuses existing Folder rows, no duplicates.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + sub = tmp_path / "docs" + sub.mkdir() + (sub / "file.md").write_text("content") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + folders_before = ( + ( + await db_session.execute( + select(Folder).where(Folder.search_space_id == db_search_space.id) + ) + ) + .scalars() + .all() + ) + ids_before = {f.id for f in folders_before} + + await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + root_folder_id=root_folder_id, + ) + + folders_after = ( + ( + await db_session.execute( + select(Folder).where(Folder.search_space_id == db_search_space.id) + ) + ) + .scalars() + .all() + ) + ids_after = {f.id for f in folders_after} + + assert ids_before == ids_after + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_f4_folder_id_assigned( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """F4: Documents get correct folder_id based on their directory.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + daily = tmp_path / "notes" / "daily" + daily.mkdir(parents=True) + (daily / "today.md").write_text("today note") + (tmp_path / "root.md").write_text("root note") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + docs = ( + ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ) + .scalars() + .all() + ) + + today_doc = next(d for d in docs if d.title == "today.md") + root_doc = next(d for d in docs if d.title == "root.md") + + daily_folder = ( + await db_session.execute(select(Folder).where(Folder.name == "daily")) + ).scalar_one() + + assert today_doc.folder_id == daily_folder.id + + assert root_doc.folder_id == root_folder_id + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_f5_empty_folder_cleanup( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """F5: Deleted dir's empty Folder row is cleaned up on re-sync.""" + import shutil + + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + daily = tmp_path / "notes" / "daily" + daily.mkdir(parents=True) + weekly = tmp_path / "notes" / "weekly" + weekly.mkdir(parents=True) + (daily / "today.md").write_text("today") + (weekly / "review.md").write_text("review") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + weekly_folder = ( + await db_session.execute(select(Folder).where(Folder.name == "weekly")) + ).scalar_one_or_none() + assert weekly_folder is not None + + shutil.rmtree(weekly) + + await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + root_folder_id=root_folder_id, + ) + + weekly_after = ( + await db_session.execute(select(Folder).where(Folder.name == "weekly")) + ).scalar_one_or_none() + assert weekly_after is None + + daily_after = ( + await db_session.execute(select(Folder).where(Folder.name == "daily")) + ).scalar_one_or_none() + assert daily_after is not None + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_f6_single_file_creates_subfolder( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """F6: Single-file mode creates missing Folder rows and assigns correct folder_id.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "root.md").write_text("root") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + sub = tmp_path / "notes" / "daily" + sub.mkdir(parents=True) + (sub / "new.md").write_text("new note in subfolder") + + count, _, _, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[str(sub / "new.md")], + root_folder_id=root_folder_id, + ) + assert count == 1 + + doc = ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.title == "new.md", + ) + ) + ).scalar_one() + + daily_folder = ( + await db_session.execute(select(Folder).where(Folder.name == "daily")) + ).scalar_one() + + assert doc.folder_id == daily_folder.id + assert daily_folder.parent_id is not None + + notes_folder = ( + await db_session.execute(select(Folder).where(Folder.name == "notes")) + ).scalar_one() + assert daily_folder.parent_id == notes_folder.id + assert notes_folder.parent_id == root_folder_id + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_f7_single_file_delete_cleans_empty_folders( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """F7: Deleting the only file in a subfolder via batch mode removes empty Folder rows.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + sub = tmp_path / "notes" / "ephemeral" + sub.mkdir(parents=True) + (sub / "temp.md").write_text("temporary") + (tmp_path / "keep.md").write_text("keep this") + + _, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + eph_folder = ( + await db_session.execute(select(Folder).where(Folder.name == "ephemeral")) + ).scalar_one_or_none() + assert eph_folder is not None + + target = sub / "temp.md" + target.unlink() + + await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[str(target)], + root_folder_id=root_folder_id, + ) + + eph_after = ( + await db_session.execute(select(Folder).where(Folder.name == "ephemeral")) + ).scalar_one_or_none() + assert eph_after is None + + notes_after = ( + await db_session.execute(select(Folder).where(Folder.name == "notes")) + ).scalar_one_or_none() + assert notes_after is None + + +# ==================================================================== +# Tier 6: Batch Mode (B1-B2) +# ==================================================================== + + +class TestBatchMode: + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_b1_batch_indexes_multiple_files( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + patched_batch_sessions, + ): + """B1: Batch with 3 files indexes all of them.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "a.md").write_text("File A content") + (tmp_path / "b.md").write_text("File B content") + (tmp_path / "c.md").write_text("File C content") + + count, failed, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[ + str(tmp_path / "a.md"), + str(tmp_path / "b.md"), + str(tmp_path / "c.md"), + ], + ) + + assert count == 3 + assert failed == 0 + assert err is None + + docs = ( + ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ) + .scalars() + .all() + ) + assert len(docs) == 3 + assert {d.title for d in docs} == {"a.md", "b.md", "c.md"} + assert all( + DocumentStatus.is_state(d.status, DocumentStatus.READY) for d in docs + ) + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_b2_partial_failure( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + patched_batch_sessions, + ): + """B2: One unreadable file fails gracefully; the other two still get indexed.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "good1.md").write_text("Good file one") + (tmp_path / "good2.md").write_text("Good file two") + (tmp_path / "bad.md").write_bytes(b"\x00binary garbage") + + count, failed, _, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[ + str(tmp_path / "good1.md"), + str(tmp_path / "bad.md"), + str(tmp_path / "good2.md"), + ], + ) + + assert count == 2 + assert failed == 1 + assert err is not None + + docs = ( + ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ) + .scalars() + .all() + ) + assert len(docs) == 2 + assert {d.title for d in docs} == {"good1.md", "good2.md"} + + +# ==================================================================== +# Tier 5: Pipeline Integration (P1) +# ==================================================================== + + +class TestPipelineIntegration: + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_p1_local_folder_file_through_pipeline( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + mocker, + ): + """P1: LOCAL_FOLDER_FILE ConnectorDocument through prepare+index to READY.""" + from app.indexing_pipeline.connector_document import ConnectorDocument + from app.indexing_pipeline.indexing_pipeline_service import ( + IndexingPipelineService, + ) + + doc = ConnectorDocument( + title="Test Local File", + source_markdown="## Local file\n\nContent from disk.", + unique_id="test-folder:test.md", + document_type=DocumentType.LOCAL_FOLDER_FILE, + search_space_id=db_search_space.id, + connector_id=None, + created_by_id=str(db_user.id), + ) + + service = IndexingPipelineService(session=db_session) + prepared = await service.prepare_for_indexing([doc]) + assert len(prepared) == 1 + + db_doc = prepared[0] + result = await service.index(db_doc, doc, llm=mocker.Mock()) + assert result is not None + + docs = ( + ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ) + .scalars() + .all() + ) + assert len(docs) == 1 + assert DocumentStatus.is_state(docs[0].status, DocumentStatus.READY) + + +# ==================================================================== +# Tier 7: Direct Converters (DC1-DC4) +# ==================================================================== + + +class TestDirectConvert: + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_dc1_csv_produces_markdown_table( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """DC1: CSV file is indexed as a markdown table, not raw comma-separated text.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "data.csv").write_text("name,age,city\nAlice,30,NYC\nBob,25,LA\n") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + assert err is None + assert count == 1 + + doc = ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ).scalar_one() + + assert "| name" in doc.source_markdown + assert "| Alice" in doc.source_markdown + assert "name,age,city" not in doc.source_markdown + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_dc2_tsv_produces_markdown_table( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """DC2: TSV file is indexed as a markdown table.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "data.tsv").write_text( + "name\tage\tcity\nAlice\t30\tNYC\nBob\t25\tLA\n" + ) + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + assert err is None + assert count == 1 + + doc = ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ).scalar_one() + + assert "| name" in doc.source_markdown + assert "| Alice" in doc.source_markdown + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_dc3_html_produces_clean_markdown( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """DC3: HTML file is indexed as clean markdown, not raw HTML.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "page.html").write_text("

Title

Hello world

") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + assert err is None + assert count == 1 + + doc = ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ).scalar_one() + + assert "Title" in doc.source_markdown + assert "

" not in doc.source_markdown + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_dc4_csv_single_file_mode( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """DC4: CSV via single-file batch mode also produces a markdown table.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + (tmp_path / "data.csv").write_text("name,age,city\nAlice,30,NYC\nBob,25,LA\n") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[str(tmp_path / "data.csv")], + ) + + assert err is None + assert count == 1 + + doc = ( + await db_session.execute( + select(Document).where( + Document.document_type == DocumentType.LOCAL_FOLDER_FILE, + Document.search_space_id == db_search_space.id, + ) + ) + ).scalar_one() + + assert "| name" in doc.source_markdown + assert "name,age,city" not in doc.source_markdown + + +# ==================================================================== +# Tier 8: Page Limits (PL1-PL6) +# ==================================================================== + + +class TestPageLimits: + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_pl1_full_scan_increments_pages_used( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """PL1: Successful full-scan sync increments user.pages_used.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + db_user.pages_used = 0 + db_user.pages_limit = 500 + await db_session.flush() + + (tmp_path / "note.md").write_text("# Hello World\n\nContent here.") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + assert err is None + assert count == 1 + + await db_session.refresh(db_user) + assert db_user.pages_used > 0, "pages_used should increase after indexing" + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_pl2_full_scan_blocked_when_limit_exhausted( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """PL2: Full-scan skips file when page limit is exhausted.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + db_user.pages_used = 100 + db_user.pages_limit = 100 + await db_session.flush() + + (tmp_path / "note.md").write_text("# Hello World\n\nContent here.") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + + assert count == 0 + + await db_session.refresh(db_user) + assert db_user.pages_used == 100, "pages_used should not change on rejection" + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_pl3_single_file_increments_pages_used( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """PL3: Single-file mode increments user.pages_used on success.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + db_user.pages_used = 0 + db_user.pages_limit = 500 + await db_session.flush() + + (tmp_path / "note.md").write_text("# Hello World\n\nContent here.") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[str(tmp_path / "note.md")], + ) + + assert err is None + assert count == 1 + + await db_session.refresh(db_user) + assert db_user.pages_used > 0, "pages_used should increase after indexing" + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_pl4_single_file_blocked_when_limit_exhausted( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """PL4: Single-file mode skips file when page limit is exhausted.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + db_user.pages_used = 100 + db_user.pages_limit = 100 + await db_session.flush() + + (tmp_path / "note.md").write_text("# Hello World\n\nContent here.") + + count, _skipped, _root_folder_id, err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[str(tmp_path / "note.md")], + ) + + assert count == 0 + assert err is not None + assert "page limit" in err.lower() + + await db_session.refresh(db_user) + assert db_user.pages_used == 100, "pages_used should not change on rejection" + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_pl5_unchanged_resync_no_extra_pages( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + ): + """PL5: Re-syncing an unchanged file does not consume additional pages.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + db_user.pages_used = 0 + db_user.pages_limit = 500 + await db_session.flush() + + (tmp_path / "note.md").write_text("# Hello\n\nSame content.") + + count1, _, root_folder_id, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + ) + assert count1 == 1 + + await db_session.refresh(db_user) + pages_after_first = db_user.pages_used + assert pages_after_first > 0 + + count2, _, _, _ = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + root_folder_id=root_folder_id, + ) + assert count2 == 0 + + await db_session.refresh(db_user) + assert db_user.pages_used == pages_after_first, ( + "pages_used should not increase for unchanged files" + ) + + @pytest.mark.usefixtures(*UNIFIED_FIXTURES) + async def test_pl6_batch_partial_page_limit_exhaustion( + self, + db_session: AsyncSession, + db_user: User, + db_search_space: SearchSpace, + tmp_path: Path, + patched_batch_sessions, + ): + """PL6: Batch mode with a very low page limit: some files succeed, rest fail.""" + from app.tasks.connector_indexers.local_folder_indexer import index_local_folder + + db_user.pages_used = 0 + db_user.pages_limit = 1 + await db_session.flush() + + (tmp_path / "a.md").write_text("File A content") + (tmp_path / "b.md").write_text("File B content") + (tmp_path / "c.md").write_text("File C content") + + count, failed, _root_folder_id, _err = await index_local_folder( + session=db_session, + search_space_id=db_search_space.id, + user_id=str(db_user.id), + folder_path=str(tmp_path), + folder_name="test-folder", + target_file_paths=[ + str(tmp_path / "a.md"), + str(tmp_path / "b.md"), + str(tmp_path / "c.md"), + ], + ) + + assert count >= 1, "at least one file should succeed" + assert failed >= 1, "at least one file should fail due to page limit" + assert count + failed == 3 + + await db_session.refresh(db_user) + assert db_user.pages_used > 0 + assert db_user.pages_used <= db_user.pages_limit + 1 diff --git a/surfsense_backend/tests/integration/test_document_versioning.py b/surfsense_backend/tests/integration/test_document_versioning.py new file mode 100644 index 000000000..9bd03d219 --- /dev/null +++ b/surfsense_backend/tests/integration/test_document_versioning.py @@ -0,0 +1,167 @@ +"""Integration tests for document versioning snapshot + cleanup.""" + +from datetime import UTC, datetime, timedelta + +import pytest +import pytest_asyncio +from sqlalchemy import func, select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db import Document, DocumentType, DocumentVersion, SearchSpace, User + +pytestmark = pytest.mark.integration + + +@pytest_asyncio.fixture +async def db_document( + db_session: AsyncSession, db_user: User, db_search_space: SearchSpace +) -> Document: + doc = Document( + title="Test Doc", + document_type=DocumentType.LOCAL_FOLDER_FILE, + document_metadata={}, + content="Summary of test doc.", + content_hash="abc123", + unique_identifier_hash="local_folder:test-folder:test.md", + source_markdown="# Test\n\nOriginal content.", + search_space_id=db_search_space.id, + created_by_id=db_user.id, + ) + db_session.add(doc) + await db_session.flush() + return doc + + +async def _version_count(session: AsyncSession, document_id: int) -> int: + result = await session.execute( + select(func.count()) + .select_from(DocumentVersion) + .where(DocumentVersion.document_id == document_id) + ) + return result.scalar_one() + + +async def _get_versions( + session: AsyncSession, document_id: int +) -> list[DocumentVersion]: + result = await session.execute( + select(DocumentVersion) + .where(DocumentVersion.document_id == document_id) + .order_by(DocumentVersion.version_number) + ) + return list(result.scalars().all()) + + +class TestCreateVersionSnapshot: + """V1-V5: TDD slices for create_version_snapshot.""" + + async def test_v1_creates_first_version(self, db_session, db_document): + """V1: First snapshot creates version 1 with the document's current state.""" + from app.utils.document_versioning import create_version_snapshot + + await create_version_snapshot(db_session, db_document) + + versions = await _get_versions(db_session, db_document.id) + assert len(versions) == 1 + assert versions[0].version_number == 1 + assert versions[0].source_markdown == "# Test\n\nOriginal content." + assert versions[0].content_hash == "abc123" + assert versions[0].title == "Test Doc" + assert versions[0].document_id == db_document.id + + async def test_v2_creates_version_2_after_30_min( + self, db_session, db_document, monkeypatch + ): + """V2: After 30+ minutes, a new version is created (not overwritten).""" + from app.utils.document_versioning import create_version_snapshot + + t0 = datetime(2025, 1, 1, 12, 0, 0, tzinfo=UTC) + monkeypatch.setattr("app.utils.document_versioning._now", lambda: t0) + await create_version_snapshot(db_session, db_document) + + # Simulate content change and time passing + db_document.source_markdown = "# Test\n\nUpdated content." + db_document.content_hash = "def456" + t1 = t0 + timedelta(minutes=31) + monkeypatch.setattr("app.utils.document_versioning._now", lambda: t1) + await create_version_snapshot(db_session, db_document) + + versions = await _get_versions(db_session, db_document.id) + assert len(versions) == 2 + assert versions[0].version_number == 1 + assert versions[1].version_number == 2 + assert versions[1].source_markdown == "# Test\n\nUpdated content." + + async def test_v3_overwrites_within_30_min( + self, db_session, db_document, monkeypatch + ): + """V3: Within 30 minutes, the latest version is overwritten.""" + from app.utils.document_versioning import create_version_snapshot + + t0 = datetime(2025, 1, 1, 12, 0, 0, tzinfo=UTC) + monkeypatch.setattr("app.utils.document_versioning._now", lambda: t0) + await create_version_snapshot(db_session, db_document) + count_after_first = await _version_count(db_session, db_document.id) + assert count_after_first == 1 + + # Simulate quick edit within 30 minutes + db_document.source_markdown = "# Test\n\nQuick edit." + db_document.content_hash = "quick123" + t1 = t0 + timedelta(minutes=10) + monkeypatch.setattr("app.utils.document_versioning._now", lambda: t1) + await create_version_snapshot(db_session, db_document) + + count_after_second = await _version_count(db_session, db_document.id) + assert count_after_second == 1 # still 1, not 2 + + versions = await _get_versions(db_session, db_document.id) + assert versions[0].source_markdown == "# Test\n\nQuick edit." + assert versions[0].content_hash == "quick123" + + async def test_v4_cleanup_90_day_old_versions( + self, db_session, db_document, monkeypatch + ): + """V4: Versions older than 90 days are cleaned up.""" + from app.utils.document_versioning import create_version_snapshot + + base = datetime(2025, 1, 1, 12, 0, 0, tzinfo=UTC) + + # Create 5 versions spread across time: 3 older than 90 days, 2 recent + for i in range(5): + db_document.source_markdown = f"Content v{i + 1}" + db_document.content_hash = f"hash_{i + 1}" + t = base + timedelta(days=i) if i < 3 else base + timedelta(days=100 + i) + monkeypatch.setattr("app.utils.document_versioning._now", lambda _t=t: _t) + await create_version_snapshot(db_session, db_document) + + # Now trigger cleanup from a "current" time that makes the first 3 versions > 90 days old + now = base + timedelta(days=200) + monkeypatch.setattr("app.utils.document_versioning._now", lambda: now) + db_document.source_markdown = "Content v6" + db_document.content_hash = "hash_6" + await create_version_snapshot(db_session, db_document) + + versions = await _get_versions(db_session, db_document.id) + # The first 3 (old) should be cleaned up; versions 4, 5, 6 remain + for v in versions: + age = now - v.created_at.replace(tzinfo=UTC) + assert age <= timedelta(days=90), f"Version {v.version_number} is too old" + + async def test_v5_cap_at_20_versions(self, db_session, db_document, monkeypatch): + """V5: More than 20 versions triggers cap — oldest gets deleted.""" + from app.utils.document_versioning import create_version_snapshot + + base = datetime(2025, 6, 1, 12, 0, 0, tzinfo=UTC) + + # Create 21 versions (all within 90 days, each 31 min apart) + for i in range(21): + db_document.source_markdown = f"Content v{i + 1}" + db_document.content_hash = f"hash_{i + 1}" + t = base + timedelta(minutes=31 * i) + monkeypatch.setattr("app.utils.document_versioning._now", lambda _t=t: _t) + await create_version_snapshot(db_session, db_document) + + versions = await _get_versions(db_session, db_document.id) + assert len(versions) == 20 + # The lowest version_number should be 2 (version 1 was the oldest and got capped) + assert versions[0].version_number == 2 diff --git a/surfsense_backend/tests/unit/connector_indexers/test_local_folder_scan.py b/surfsense_backend/tests/unit/connector_indexers/test_local_folder_scan.py new file mode 100644 index 000000000..c6e7b160c --- /dev/null +++ b/surfsense_backend/tests/unit/connector_indexers/test_local_folder_scan.py @@ -0,0 +1,78 @@ +"""Unit tests for scan_folder() pure logic — Tier 2 TDD slices (S1-S4).""" + +from pathlib import Path + +import pytest + +pytestmark = pytest.mark.unit + + +class TestScanFolder: + """S1-S4: scan_folder() with real tmp_path filesystem.""" + + def test_s1_single_md_file(self, tmp_path: Path): + """S1: scan_folder on a dir with one .md file returns correct entry.""" + from app.tasks.connector_indexers.local_folder_indexer import scan_folder + + md = tmp_path / "note.md" + md.write_text("# Hello") + + results = scan_folder(str(tmp_path)) + + assert len(results) == 1 + entry = results[0] + assert entry["relative_path"] == "note.md" + assert entry["size"] > 0 + assert "modified_at" in entry + assert entry["path"] == str(md) + + def test_s2_extension_filter(self, tmp_path: Path): + """S2: file_extensions filter returns only matching files.""" + from app.tasks.connector_indexers.local_folder_indexer import scan_folder + + (tmp_path / "a.md").write_text("md") + (tmp_path / "b.txt").write_text("txt") + (tmp_path / "c.pdf").write_bytes(b"%PDF") + + results = scan_folder(str(tmp_path), file_extensions=[".md"]) + names = {r["relative_path"] for r in results} + + assert names == {"a.md"} + + def test_s3_exclude_patterns(self, tmp_path: Path): + """S3: exclude_patterns skips files inside excluded directories.""" + from app.tasks.connector_indexers.local_folder_indexer import scan_folder + + (tmp_path / "good.md").write_text("good") + nm = tmp_path / "node_modules" + nm.mkdir() + (nm / "dep.js").write_text("module") + git = tmp_path / ".git" + git.mkdir() + (git / "config").write_text("gitconfig") + + results = scan_folder(str(tmp_path), exclude_patterns=["node_modules", ".git"]) + names = {r["relative_path"] for r in results} + + assert "good.md" in names + assert not any("node_modules" in n for n in names) + assert not any(".git" in n for n in names) + + def test_s4_nested_dirs(self, tmp_path: Path): + """S4: nested subdirectories produce correct relative paths.""" + from app.tasks.connector_indexers.local_folder_indexer import scan_folder + + daily = tmp_path / "notes" / "daily" + daily.mkdir(parents=True) + weekly = tmp_path / "notes" / "weekly" + weekly.mkdir(parents=True) + (daily / "today.md").write_text("today") + (weekly / "review.md").write_text("review") + (tmp_path / "root.txt").write_text("root") + + results = scan_folder(str(tmp_path)) + paths = {r["relative_path"] for r in results} + + assert "notes/daily/today.md" in paths or "notes\\daily\\today.md" in paths + assert "notes/weekly/review.md" in paths or "notes\\weekly\\review.md" in paths + assert "root.txt" in paths diff --git a/surfsense_backend/tests/unit/middleware/test_knowledge_search.py b/surfsense_backend/tests/unit/middleware/test_knowledge_search.py index 163dd0d1d..a8cf5c93b 100644 --- a/surfsense_backend/tests/unit/middleware/test_knowledge_search.py +++ b/surfsense_backend/tests/unit/middleware/test_knowledge_search.py @@ -248,7 +248,7 @@ class TestKnowledgeBaseSearchMiddlewarePlanner: return [] async def fake_build_scoped_filesystem(**kwargs): - return {} + return {}, {} monkeypatch.setattr( "app.agents.new_chat.middleware.knowledge_search.search_knowledge_base", @@ -298,7 +298,7 @@ class TestKnowledgeBaseSearchMiddlewarePlanner: return [] async def fake_build_scoped_filesystem(**kwargs): - return {} + return {}, {} monkeypatch.setattr( "app.agents.new_chat.middleware.knowledge_search.search_knowledge_base", @@ -334,7 +334,7 @@ class TestKnowledgeBaseSearchMiddlewarePlanner: return [] async def fake_build_scoped_filesystem(**kwargs): - return {} + return {}, {} monkeypatch.setattr( "app.agents.new_chat.middleware.knowledge_search.search_knowledge_base", diff --git a/surfsense_desktop/package.json b/surfsense_desktop/package.json index ab4fa0b8f..58c053c04 100644 --- a/surfsense_desktop/package.json +++ b/surfsense_desktop/package.json @@ -30,6 +30,8 @@ }, "dependencies": { "bindings": "^1.5.0", + "chokidar": "^5.0.0", + "electron-store": "^11.0.2", "electron-updater": "^6.8.3", "get-port-please": "^3.2.0", "node-mac-permissions": "^2.5.0" diff --git a/surfsense_desktop/pnpm-lock.yaml b/surfsense_desktop/pnpm-lock.yaml index 96541c579..e1df34fb2 100644 --- a/surfsense_desktop/pnpm-lock.yaml +++ b/surfsense_desktop/pnpm-lock.yaml @@ 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resolution: {integrity: sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==} engines: {node: '>= 8'} @@ -1850,6 +1939,10 @@ snapshots: agent-base@7.1.4: {} + ajv-formats@3.0.1(ajv@8.18.0): + optionalDependencies: + ajv: 8.18.0 + ajv-keywords@3.5.2(ajv@6.14.0): dependencies: ajv: 6.14.0 @@ -1861,6 +1954,13 @@ snapshots: json-schema-traverse: 0.4.1 uri-js: 4.4.1 + ajv@8.18.0: + dependencies: + fast-deep-equal: 3.1.3 + fast-uri: 3.1.0 + json-schema-traverse: 1.0.0 + require-from-string: 2.0.2 + ansi-regex@5.0.1: {} ansi-regex@6.2.2: {} @@ -1932,6 +2032,11 @@ snapshots: at-least-node@1.0.0: {} + atomically@2.1.1: + dependencies: + stubborn-fs: 2.0.0 + when-exit: 2.1.5 + axios@1.13.6: dependencies: follow-redirects: 1.15.11 @@ -2046,6 +2151,10 @@ snapshots: ansi-styles: 4.3.0 supports-color: 7.2.0 + chokidar@5.0.0: + dependencies: + readdirp: 5.0.0 + chownr@3.0.0: {} chromium-pickle-js@0.2.0: {} @@ -2106,6 +2215,18 @@ snapshots: tree-kill: 1.2.2 yargs: 17.7.2 + conf@15.1.0: + dependencies: + ajv: 8.18.0 + ajv-formats: 3.0.1(ajv@8.18.0) + atomically: 2.1.1 + debounce-fn: 6.0.0 + dot-prop: 10.1.0 + env-paths: 3.0.0 + json-schema-typed: 8.0.2 + semver: 7.7.4 + uint8array-extras: 1.5.0 + core-util-is@1.0.2: optional: true @@ -2123,6 +2244,10 @@ snapshots: shebang-command: 2.0.0 which: 2.0.2 + debounce-fn@6.0.0: + dependencies: + mimic-function: 5.0.1 + debug@4.4.3: dependencies: ms: 2.1.3 @@ -2188,6 +2313,10 @@ snapshots: verror: 1.10.1 optional: true + dot-prop@10.1.0: + dependencies: + type-fest: 5.5.0 + dotenv-expand@11.0.7: dependencies: dotenv: 16.6.1 @@ -2246,6 +2375,11 @@ snapshots: transitivePeerDependencies: - supports-color + electron-store@11.0.2: + dependencies: + conf: 15.1.0 + type-fest: 5.5.0 + electron-updater@6.8.3: dependencies: builder-util-runtime: 9.5.1 @@ -2264,7 +2398,7 @@ snapshots: '@electron/asar': 3.4.1 debug: 4.4.3 fs-extra: 7.0.1 - lodash: 4.17.23 + lodash: 4.18.1 temp: 0.9.4 optionalDependencies: '@electron/windows-sign': 1.2.2 @@ -2294,6 +2428,8 @@ snapshots: env-paths@2.2.1: {} + env-paths@3.0.0: {} + err-code@2.0.3: {} es-define-property@1.0.1: {} @@ -2367,6 +2503,8 @@ snapshots: fast-json-stable-stringify@2.1.0: {} + fast-uri@3.1.0: {} + fd-slicer@1.1.0: dependencies: pend: 1.2.0 @@ -2624,6 +2762,10 @@ snapshots: json-schema-traverse@0.4.1: {} + json-schema-traverse@1.0.0: {} + + json-schema-typed@8.0.2: {} + json-stringify-safe@5.0.1: optional: true @@ -2651,6 +2793,8 @@ snapshots: lodash@4.17.23: {} + lodash@4.18.1: {} + log-symbols@4.1.0: dependencies: chalk: 4.1.2 @@ -2697,6 +2841,8 @@ snapshots: mimic-fn@2.1.0: {} + mimic-function@5.0.1: {} + mimic-response@1.0.1: {} mimic-response@3.1.0: {} @@ -2899,8 +3045,12 @@ snapshots: string_decoder: 1.3.0 util-deprecate: 1.0.2 + readdirp@5.0.0: {} + require-directory@2.1.1: {} + require-from-string@2.0.2: {} + resedit@1.7.2: dependencies: pe-library: 0.4.1 @@ -3038,6 +3188,12 @@ snapshots: dependencies: ansi-regex: 6.2.2 + stubborn-fs@2.0.0: + dependencies: + stubborn-utils: 1.0.2 + + stubborn-utils@1.0.2: {} + sumchecker@3.0.1: dependencies: debug: 4.4.3 @@ -3052,6 +3208,8 @@ snapshots: dependencies: has-flag: 4.0.0 + tagged-tag@1.0.0: {} + tar@7.5.11: dependencies: '@isaacs/fs-minipass': 4.0.1 @@ -3098,8 +3256,14 @@ snapshots: type-fest@0.13.1: optional: true + type-fest@5.5.0: + dependencies: + tagged-tag: 1.0.0 + typescript@5.9.3: {} + uint8array-extras@1.5.0: {} + undici-types@7.16.0: {} undici-types@7.18.2: {} @@ -3145,6 +3309,8 @@ snapshots: dependencies: defaults: 1.0.4 + when-exit@2.1.5: {} + which@2.0.2: dependencies: isexe: 2.0.0 diff --git a/surfsense_desktop/src/ipc/channels.ts b/surfsense_desktop/src/ipc/channels.ts index d0e2b9ab4..2a50de75f 100644 --- a/surfsense_desktop/src/ipc/channels.ts +++ b/surfsense_desktop/src/ipc/channels.ts @@ -17,4 +17,19 @@ export const IPC_CHANNELS = { DISMISS_SUGGESTION: 'dismiss-suggestion', SET_AUTOCOMPLETE_ENABLED: 'set-autocomplete-enabled', GET_AUTOCOMPLETE_ENABLED: 'get-autocomplete-enabled', + // Folder sync channels + FOLDER_SYNC_SELECT_FOLDER: 'folder-sync:select-folder', + FOLDER_SYNC_ADD_FOLDER: 'folder-sync:add-folder', + FOLDER_SYNC_REMOVE_FOLDER: 'folder-sync:remove-folder', + FOLDER_SYNC_GET_FOLDERS: 'folder-sync:get-folders', + FOLDER_SYNC_GET_STATUS: 'folder-sync:get-status', + FOLDER_SYNC_FILE_CHANGED: 'folder-sync:file-changed', + FOLDER_SYNC_WATCHER_READY: 'folder-sync:watcher-ready', + FOLDER_SYNC_PAUSE: 'folder-sync:pause', + FOLDER_SYNC_RESUME: 'folder-sync:resume', + FOLDER_SYNC_RENDERER_READY: 'folder-sync:renderer-ready', + FOLDER_SYNC_GET_PENDING_EVENTS: 'folder-sync:get-pending-events', + FOLDER_SYNC_ACK_EVENTS: 'folder-sync:ack-events', + BROWSE_FILES: 'browse:files', + READ_LOCAL_FILES: 'browse:read-local-files', } as const; diff --git a/surfsense_desktop/src/ipc/handlers.ts b/surfsense_desktop/src/ipc/handlers.ts index 11cbfee05..de7cdb659 100644 --- a/surfsense_desktop/src/ipc/handlers.ts +++ b/surfsense_desktop/src/ipc/handlers.ts @@ -6,6 +6,20 @@ import { requestScreenRecording, restartApp, } from '../modules/permissions'; +import { + selectFolder, + addWatchedFolder, + removeWatchedFolder, + getWatchedFolders, + getWatcherStatus, + getPendingFileEvents, + acknowledgeFileEvents, + pauseWatcher, + resumeWatcher, + markRendererReady, + browseFiles, + readLocalFiles, +} from '../modules/folder-watcher'; export function registerIpcHandlers(): void { ipcMain.on(IPC_CHANNELS.OPEN_EXTERNAL, (_event, url: string) => { @@ -38,4 +52,41 @@ export function registerIpcHandlers(): void { ipcMain.handle(IPC_CHANNELS.RESTART_APP, () => { restartApp(); }); + + // Folder sync handlers + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_SELECT_FOLDER, () => selectFolder()); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_ADD_FOLDER, (_event, config) => + addWatchedFolder(config) + ); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_REMOVE_FOLDER, (_event, folderPath: string) => + removeWatchedFolder(folderPath) + ); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_GET_FOLDERS, () => getWatchedFolders()); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_GET_STATUS, () => getWatcherStatus()); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_PAUSE, () => pauseWatcher()); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_RESUME, () => resumeWatcher()); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_RENDERER_READY, () => { + markRendererReady(); + }); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_GET_PENDING_EVENTS, () => + getPendingFileEvents() + ); + + ipcMain.handle(IPC_CHANNELS.FOLDER_SYNC_ACK_EVENTS, (_event, eventIds: string[]) => + acknowledgeFileEvents(eventIds) + ); + + ipcMain.handle(IPC_CHANNELS.BROWSE_FILES, () => browseFiles()); + + ipcMain.handle(IPC_CHANNELS.READ_LOCAL_FILES, (_event, paths: string[]) => + readLocalFiles(paths) + ); } diff --git a/surfsense_desktop/src/main.ts b/surfsense_desktop/src/main.ts index 584e930fe..7ef0ad5be 100644 --- a/surfsense_desktop/src/main.ts +++ b/surfsense_desktop/src/main.ts @@ -7,6 +7,7 @@ import { setupAutoUpdater } from './modules/auto-updater'; import { setupMenu } from './modules/menu'; import { registerQuickAsk, unregisterQuickAsk } from './modules/quick-ask'; import { registerAutocomplete, unregisterAutocomplete } from './modules/autocomplete'; +import { registerFolderWatcher, unregisterFolderWatcher } from './modules/folder-watcher'; import { registerIpcHandlers } from './ipc/handlers'; registerGlobalErrorHandlers(); @@ -30,6 +31,7 @@ app.whenReady().then(async () => { createMainWindow('/dashboard'); registerQuickAsk(); registerAutocomplete(); + registerFolderWatcher(); setupAutoUpdater(); handlePendingDeepLink(); @@ -50,4 +52,5 @@ app.on('window-all-closed', () => { app.on('will-quit', () => { unregisterQuickAsk(); unregisterAutocomplete(); + unregisterFolderWatcher(); }); diff --git a/surfsense_desktop/src/modules/folder-watcher.ts b/surfsense_desktop/src/modules/folder-watcher.ts new file mode 100644 index 000000000..969dabe97 --- /dev/null +++ b/surfsense_desktop/src/modules/folder-watcher.ts @@ -0,0 +1,534 @@ +import { BrowserWindow, dialog } from 'electron'; +import chokidar, { type FSWatcher } from 'chokidar'; +import { randomUUID } from 'crypto'; +import * as path from 'path'; +import * as fs from 'fs'; +import { IPC_CHANNELS } from '../ipc/channels'; + +export interface WatchedFolderConfig { + path: string; + name: string; + excludePatterns: string[]; + fileExtensions: string[] | null; + rootFolderId: number | null; + searchSpaceId: number; + active: boolean; +} + +interface WatcherEntry { + config: WatchedFolderConfig; + watcher: FSWatcher | null; +} + +type MtimeMap = Record; +type FolderSyncAction = 'add' | 'change' | 'unlink'; + +export interface FolderSyncFileChangedEvent { + id: string; + rootFolderId: number | null; + searchSpaceId: number; + folderPath: string; + folderName: string; + relativePath: string; + fullPath: string; + action: FolderSyncAction; + timestamp: number; +} + +const STORE_KEY = 'watchedFolders'; +const OUTBOX_STORE_KEY = 'events'; +const MTIME_TOLERANCE_S = 1.0; + +let store: any = null; +let mtimeStore: any = null; +let outboxStore: any = null; +let watchers: Map = new Map(); + +/** + * In-memory cache of mtime maps, keyed by folder path. + * Persisted to electron-store on mutation. + */ +const mtimeMaps: Map = new Map(); + +let rendererReady = false; +const outboxEvents: Map = new Map(); +let outboxLoaded = false; + +export function markRendererReady() { + rendererReady = true; +} + +async function getStore() { + if (!store) { + const { default: Store } = await import('electron-store'); + store = new Store({ + name: 'folder-watcher', + defaults: { + [STORE_KEY]: [] as WatchedFolderConfig[], + }, + }); + } + return store; +} + +async function getMtimeStore() { + if (!mtimeStore) { + const { default: Store } = await import('electron-store'); + mtimeStore = new Store({ + name: 'folder-mtime-maps', + defaults: {} as Record, + }); + } + return mtimeStore; +} + +async function getOutboxStore() { + if (!outboxStore) { + const { default: Store } = await import('electron-store'); + outboxStore = new Store({ + name: 'folder-sync-outbox', + defaults: { + [OUTBOX_STORE_KEY]: [] as FolderSyncFileChangedEvent[], + }, + }); + } + return outboxStore; +} + +function makeEventKey(event: Pick): string { + return `${event.folderPath}:${event.relativePath}`; +} + +function persistOutbox() { + getOutboxStore().then((s) => { + s.set(OUTBOX_STORE_KEY, Array.from(outboxEvents.values())); + }); +} + +async function loadOutbox() { + if (outboxLoaded) return; + const s = await getOutboxStore(); + const stored: FolderSyncFileChangedEvent[] = s.get(OUTBOX_STORE_KEY, []); + outboxEvents.clear(); + for (const event of stored) { + if (!event?.id || !event.folderPath || !event.relativePath) continue; + outboxEvents.set(makeEventKey(event), event); + } + outboxLoaded = true; +} + +function sendFileChangedEvent( + data: Omit +) { + const event: FolderSyncFileChangedEvent = { + id: randomUUID(), + ...data, + }; + + outboxEvents.set(makeEventKey(event), event); + persistOutbox(); + + if (rendererReady) { + sendToRenderer(IPC_CHANNELS.FOLDER_SYNC_FILE_CHANGED, event); + } +} + +function loadMtimeMap(folderPath: string): MtimeMap { + return mtimeMaps.get(folderPath) ?? {}; +} + +function persistMtimeMap(folderPath: string) { + const map = mtimeMaps.get(folderPath) ?? {}; + getMtimeStore().then((s) => s.set(folderPath, map)); +} + +function walkFolderMtimes(config: WatchedFolderConfig): MtimeMap { + const root = config.path; + const result: MtimeMap = {}; + const excludes = new Set(config.excludePatterns); + + function walk(dir: string) { + let entries: fs.Dirent[]; + try { + entries = fs.readdirSync(dir, { withFileTypes: true }); + } catch { + return; + } + + for (const entry of entries) { + const name = entry.name; + + if (name.startsWith('.') || excludes.has(name)) continue; + + const full = path.join(dir, name); + + if (entry.isDirectory()) { + walk(full); + } else if (entry.isFile()) { + if ( + config.fileExtensions && + config.fileExtensions.length > 0 + ) { + const ext = path.extname(name).toLowerCase(); + if (!config.fileExtensions.includes(ext)) continue; + } + + try { + const stat = fs.statSync(full); + const rel = path.relative(root, full); + result[rel] = stat.mtimeMs; + } catch { + // File may have been removed between readdir and stat + } + } + } + } + + walk(root); + return result; +} + +function getMainWindow(): BrowserWindow | null { + const windows = BrowserWindow.getAllWindows(); + return windows.length > 0 ? windows[0] : null; +} + +function sendToRenderer(channel: string, data: any) { + const win = getMainWindow(); + if (win && !win.isDestroyed()) { + win.webContents.send(channel, data); + } +} + +async function startWatcher(config: WatchedFolderConfig) { + if (watchers.has(config.path)) { + return; + } + + const ms = await getMtimeStore(); + const storedMap: MtimeMap = ms.get(config.path) ?? {}; + mtimeMaps.set(config.path, { ...storedMap }); + + const ignored = [ + /(^|[/\\])\../, // dotfiles by default + ...config.excludePatterns.map((p) => `**/${p}/**`), + ]; + + const watcher = chokidar.watch(config.path, { + persistent: true, + ignoreInitial: true, + awaitWriteFinish: { + stabilityThreshold: 500, + pollInterval: 100, + }, + ignored, + }); + + let ready = false; + + watcher.on('ready', () => { + ready = true; + + const currentMap = walkFolderMtimes(config); + const storedSnapshot = loadMtimeMap(config.path); + const now = Date.now(); + + // Track which files are unchanged so we can selectively update the mtime map + const unchangedMap: MtimeMap = {}; + + for (const [rel, currentMtime] of Object.entries(currentMap)) { + const storedMtime = storedSnapshot[rel]; + if (storedMtime === undefined) { + sendFileChangedEvent({ + rootFolderId: config.rootFolderId, + searchSpaceId: config.searchSpaceId, + folderPath: config.path, + folderName: config.name, + relativePath: rel, + fullPath: path.join(config.path, rel), + action: 'add', + timestamp: now, + }); + } else if (Math.abs(currentMtime - storedMtime) >= MTIME_TOLERANCE_S * 1000) { + sendFileChangedEvent({ + rootFolderId: config.rootFolderId, + searchSpaceId: config.searchSpaceId, + folderPath: config.path, + folderName: config.name, + relativePath: rel, + fullPath: path.join(config.path, rel), + action: 'change', + timestamp: now, + }); + } else { + unchangedMap[rel] = currentMtime; + } + } + + for (const rel of Object.keys(storedSnapshot)) { + if (!(rel in currentMap)) { + sendFileChangedEvent({ + rootFolderId: config.rootFolderId, + searchSpaceId: config.searchSpaceId, + folderPath: config.path, + folderName: config.name, + relativePath: rel, + fullPath: path.join(config.path, rel), + action: 'unlink', + timestamp: now, + }); + } + } + + // Only update the mtime map for unchanged files; changed files keep their + // stored mtime so they'll be re-detected if the app crashes before indexing. + mtimeMaps.set(config.path, unchangedMap); + persistMtimeMap(config.path); + + sendToRenderer(IPC_CHANNELS.FOLDER_SYNC_WATCHER_READY, { + rootFolderId: config.rootFolderId, + folderPath: config.path, + }); + }); + + const handleFileEvent = (filePath: string, action: FolderSyncAction) => { + if (!ready) return; + + const relativePath = path.relative(config.path, filePath); + + if ( + config.fileExtensions && + config.fileExtensions.length > 0 + ) { + const ext = path.extname(filePath).toLowerCase(); + if (!config.fileExtensions.includes(ext)) return; + } + + const map = mtimeMaps.get(config.path); + if (map) { + if (action === 'unlink') { + delete map[relativePath]; + } else { + try { + map[relativePath] = fs.statSync(filePath).mtimeMs; + } catch { + // File may have been removed between event and stat + } + } + persistMtimeMap(config.path); + } + + sendFileChangedEvent({ + rootFolderId: config.rootFolderId, + searchSpaceId: config.searchSpaceId, + folderPath: config.path, + folderName: config.name, + relativePath, + fullPath: filePath, + action, + timestamp: Date.now(), + }); + }; + + watcher.on('add', (fp) => handleFileEvent(fp, 'add')); + watcher.on('change', (fp) => handleFileEvent(fp, 'change')); + watcher.on('unlink', (fp) => handleFileEvent(fp, 'unlink')); + + watchers.set(config.path, { config, watcher }); +} + +function stopWatcher(folderPath: string) { + persistMtimeMap(folderPath); + const entry = watchers.get(folderPath); + if (entry?.watcher) { + entry.watcher.close(); + } + watchers.delete(folderPath); +} + +export async function selectFolder(): Promise { + const result = await dialog.showOpenDialog({ + properties: ['openDirectory'], + title: 'Select a folder to watch', + }); + if (result.canceled || result.filePaths.length === 0) { + return null; + } + return result.filePaths[0]; +} + +export async function addWatchedFolder( + config: WatchedFolderConfig +): Promise { + const s = await getStore(); + const folders: WatchedFolderConfig[] = s.get(STORE_KEY, []); + + const existing = folders.findIndex((f: WatchedFolderConfig) => f.path === config.path); + if (existing >= 0) { + folders[existing] = config; + } else { + folders.push(config); + } + + s.set(STORE_KEY, folders); + + if (config.active) { + await startWatcher(config); + } + + return folders; +} + +export async function removeWatchedFolder( + folderPath: string +): Promise { + const s = await getStore(); + const folders: WatchedFolderConfig[] = s.get(STORE_KEY, []); + const updated = folders.filter((f: WatchedFolderConfig) => f.path !== folderPath); + s.set(STORE_KEY, updated); + + stopWatcher(folderPath); + + mtimeMaps.delete(folderPath); + const ms = await getMtimeStore(); + ms.delete(folderPath); + + return updated; +} + +export async function getWatchedFolders(): Promise { + const s = await getStore(); + return s.get(STORE_KEY, []); +} + +export async function getWatcherStatus(): Promise< + { path: string; active: boolean; watching: boolean }[] +> { + const s = await getStore(); + const folders: WatchedFolderConfig[] = s.get(STORE_KEY, []); + return folders.map((f: WatchedFolderConfig) => ({ + path: f.path, + active: f.active, + watching: watchers.has(f.path), + })); +} + +export async function getPendingFileEvents(): Promise { + await loadOutbox(); + return Array.from(outboxEvents.values()).sort((a, b) => a.timestamp - b.timestamp); +} + +export async function acknowledgeFileEvents(eventIds: string[]): Promise<{ acknowledged: number }> { + if (!eventIds || eventIds.length === 0) return { acknowledged: 0 }; + await loadOutbox(); + + const ackSet = new Set(eventIds); + let acknowledged = 0; + + for (const [key, event] of outboxEvents.entries()) { + if (ackSet.has(event.id)) { + outboxEvents.delete(key); + acknowledged += 1; + } + } + + if (acknowledged > 0) { + persistOutbox(); + } + + return { acknowledged }; +} + +export async function pauseWatcher(): Promise { + for (const [, entry] of watchers) { + if (entry.watcher) { + await entry.watcher.close(); + entry.watcher = null; + } + } +} + +export async function resumeWatcher(): Promise { + for (const [, entry] of watchers) { + if (!entry.watcher && entry.config.active) { + await startWatcher(entry.config); + } + } +} + +export async function registerFolderWatcher(): Promise { + await loadOutbox(); + const s = await getStore(); + const folders: WatchedFolderConfig[] = s.get(STORE_KEY, []); + + for (const config of folders) { + if (config.active && fs.existsSync(config.path)) { + await startWatcher(config); + } + } +} + +export async function unregisterFolderWatcher(): Promise { + for (const [folderPath] of watchers) { + stopWatcher(folderPath); + } + watchers.clear(); +} + +export async function browseFiles(): Promise { + const result = await dialog.showOpenDialog({ + properties: ['openFile', 'multiSelections'], + title: 'Select files', + }); + if (result.canceled || result.filePaths.length === 0) return null; + return result.filePaths; +} + +const MIME_MAP: Record = { + '.pdf': 'application/pdf', + '.docx': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', + '.xlsx': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', + '.pptx': 'application/vnd.openxmlformats-officedocument.presentationml.presentation', + '.html': 'text/html', '.htm': 'text/html', + '.csv': 'text/csv', + '.txt': 'text/plain', + '.md': 'text/markdown', '.markdown': 'text/markdown', + '.mp3': 'audio/mpeg', '.mpeg': 'audio/mpeg', '.mpga': 'audio/mpeg', + '.mp4': 'audio/mp4', '.m4a': 'audio/mp4', + '.wav': 'audio/wav', + '.webm': 'audio/webm', + '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg', + '.png': 'image/png', + '.bmp': 'image/bmp', + '.webp': 'image/webp', + '.tiff': 'image/tiff', + '.doc': 'application/msword', + '.rtf': 'application/rtf', + '.xml': 'application/xml', + '.epub': 'application/epub+zip', + '.xls': 'application/vnd.ms-excel', + '.ppt': 'application/vnd.ms-powerpoint', + '.eml': 'message/rfc822', + '.odt': 'application/vnd.oasis.opendocument.text', + '.msg': 'application/vnd.ms-outlook', +}; + +export interface LocalFileData { + name: string; + data: ArrayBuffer; + mimeType: string; + size: number; +} + +export function readLocalFiles(filePaths: string[]): LocalFileData[] { + return filePaths.map((p) => { + const buf = fs.readFileSync(p); + const ext = path.extname(p).toLowerCase(); + return { + name: path.basename(p), + data: buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength), + mimeType: MIME_MAP[ext] || 'application/octet-stream', + size: buf.byteLength, + }; + }); +} diff --git a/surfsense_desktop/src/preload.ts b/surfsense_desktop/src/preload.ts index 2bd09f13c..6a9190693 100644 --- a/surfsense_desktop/src/preload.ts +++ b/surfsense_desktop/src/preload.ts @@ -38,4 +38,34 @@ contextBridge.exposeInMainWorld('electronAPI', { dismissSuggestion: () => ipcRenderer.invoke(IPC_CHANNELS.DISMISS_SUGGESTION), setAutocompleteEnabled: (enabled: boolean) => ipcRenderer.invoke(IPC_CHANNELS.SET_AUTOCOMPLETE_ENABLED, enabled), getAutocompleteEnabled: () => ipcRenderer.invoke(IPC_CHANNELS.GET_AUTOCOMPLETE_ENABLED), + + // Folder sync + selectFolder: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_SELECT_FOLDER), + addWatchedFolder: (config: any) => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_ADD_FOLDER, config), + removeWatchedFolder: (folderPath: string) => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_REMOVE_FOLDER, folderPath), + getWatchedFolders: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_GET_FOLDERS), + getWatcherStatus: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_GET_STATUS), + onFileChanged: (callback: (data: any) => void) => { + const listener = (_event: unknown, data: any) => callback(data); + ipcRenderer.on(IPC_CHANNELS.FOLDER_SYNC_FILE_CHANGED, listener); + return () => { + ipcRenderer.removeListener(IPC_CHANNELS.FOLDER_SYNC_FILE_CHANGED, listener); + }; + }, + onWatcherReady: (callback: (data: any) => void) => { + const listener = (_event: unknown, data: any) => callback(data); + ipcRenderer.on(IPC_CHANNELS.FOLDER_SYNC_WATCHER_READY, listener); + return () => { + ipcRenderer.removeListener(IPC_CHANNELS.FOLDER_SYNC_WATCHER_READY, listener); + }; + }, + pauseWatcher: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_PAUSE), + resumeWatcher: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_RESUME), + signalRendererReady: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_RENDERER_READY), + getPendingFileEvents: () => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_GET_PENDING_EVENTS), + acknowledgeFileEvents: (eventIds: string[]) => ipcRenderer.invoke(IPC_CHANNELS.FOLDER_SYNC_ACK_EVENTS, eventIds), + + // Browse files via native dialog + browseFiles: () => ipcRenderer.invoke(IPC_CHANNELS.BROWSE_FILES), + readLocalFiles: (paths: string[]) => ipcRenderer.invoke(IPC_CHANNELS.READ_LOCAL_FILES, paths), }); diff --git a/surfsense_web/app/(home)/changelog/page.tsx b/surfsense_web/app/(home)/changelog/page.tsx index 8d38cb687..c14218dab 100644 --- a/surfsense_web/app/(home)/changelog/page.tsx +++ b/surfsense_web/app/(home)/changelog/page.tsx @@ -29,7 +29,7 @@ interface ChangelogPageItem { export default async function ChangelogPage() { const allPages = source.getPages() as ChangelogPageItem[]; - const sortedChangelogs = allPages.sort((a, b) => { + const sortedChangelogs = allPages.toSorted((a, b) => { const dateA = new Date(a.data.date).getTime(); const dateB = new Date(b.data.date).getTime(); return dateB - dateA; diff --git a/surfsense_web/app/(home)/login/LocalLoginForm.tsx b/surfsense_web/app/(home)/login/LocalLoginForm.tsx index ee3b47683..e94857334 100644 --- a/surfsense_web/app/(home)/login/LocalLoginForm.tsx +++ b/surfsense_web/app/(home)/login/LocalLoginForm.tsx @@ -160,10 +160,10 @@ export function LocalLoginForm() { placeholder="you@example.com" value={username} onChange={(e) => setUsername(e.target.value)} - className={`mt-1 block w-full rounded-md border px-3 py-1.5 md:py-2 shadow-sm focus:outline-none focus:ring-2 focus:ring-offset-2 bg-background text-foreground transition-all ${ + className={`mt-1 block w-full rounded-md border px-3 py-1.5 md:py-2 shadow-sm focus:outline-none focus:ring-1 bg-background text-foreground transition-all ${ error.title - ? "border-destructive focus:border-destructive focus:ring-destructive" - : "border-border focus:border-primary focus:ring-primary" + ? "border-destructive focus:border-destructive focus:ring-destructive/40" + : "border-border focus:border-primary focus:ring-primary/40" }`} disabled={isLoggingIn} /> @@ -181,10 +181,10 @@ export function LocalLoginForm() { placeholder="Enter your password" value={password} onChange={(e) => setPassword(e.target.value)} - className={`mt-1 block w-full rounded-md border pr-10 px-3 py-1.5 md:py-2 shadow-sm focus:outline-none focus:ring-2 focus:ring-offset-2 bg-background text-foreground transition-all ${ + className={`mt-1 block w-full rounded-md border pr-10 px-3 py-1.5 md:py-2 shadow-sm focus:outline-none focus:ring-1 bg-background text-foreground transition-all ${ error.title - ? "border-destructive focus:border-destructive focus:ring-destructive" - : "border-border focus:border-primary focus:ring-primary" + ? "border-destructive focus:border-destructive focus:ring-destructive/40" + : "border-border focus:border-primary focus:ring-primary/40" }`} disabled={isLoggingIn} /> diff --git a/surfsense_web/app/(home)/login/page.tsx b/surfsense_web/app/(home)/login/page.tsx index 8b3be3805..09bf770d8 100644 --- a/surfsense_web/app/(home)/login/page.tsx +++ b/surfsense_web/app/(home)/login/page.tsx @@ -115,7 +115,7 @@ function LoginContent() {
- +

{t("sign_in")}

diff --git a/surfsense_web/app/(home)/register/page.tsx b/surfsense_web/app/(home)/register/page.tsx index b9200c68f..1ec179b35 100644 --- a/surfsense_web/app/(home)/register/page.tsx +++ b/surfsense_web/app/(home)/register/page.tsx @@ -160,7 +160,7 @@ export default function RegisterPage() {
- +

{t("create_account")}

@@ -229,10 +229,7 @@ export default function RegisterPage() {
-
-
@@ -1020,6 +1056,10 @@ export function DocumentsTableShell({ )} diff --git a/surfsense_web/app/dashboard/page.tsx b/surfsense_web/app/dashboard/page.tsx index 525060bed..09ace6542 100644 --- a/surfsense_web/app/dashboard/page.tsx +++ b/surfsense_web/app/dashboard/page.tsx @@ -3,7 +3,7 @@ import { useAtomValue } from "jotai"; import { AlertCircle, Plus, Search } from "lucide-react"; import { motion } from "motion/react"; -import { useRouter, useSearchParams } from "next/navigation"; +import { useRouter } from "next/navigation"; import { useTranslations } from "next-intl"; import { useEffect, useState } from "react"; import { searchSpacesAtom } from "@/atoms/search-spaces/search-space-query.atoms"; @@ -89,7 +89,6 @@ function EmptyState({ onCreateClick }: { onCreateClick: () => void }) { export default function DashboardPage() { const router = useRouter(); - const searchParams = useSearchParams(); const [showCreateDialog, setShowCreateDialog] = useState(false); const t = useTranslations("dashboard"); @@ -99,11 +98,12 @@ export default function DashboardPage() { if (isLoading) return; if (searchSpaces.length > 0) { - const params = searchParams.toString(); - const query = params ? `?${params}` : ""; + // Read the query string at the time of redirect — no subscription needed. + // (Vercel Best Practice: rerender-defer-reads 5.2) + const query = window.location.search; router.replace(`/dashboard/${searchSpaces[0].id}/new-chat${query}`); } - }, [isLoading, searchSpaces, router, searchParams]); + }, [isLoading, searchSpaces, router]); // Show loading while fetching or while we have spaces and are about to redirect const shouldShowLoading = isLoading || searchSpaces.length > 0; diff --git a/surfsense_web/app/docs/[[...slug]]/page.tsx b/surfsense_web/app/docs/[[...slug]]/page.tsx index 204148bf9..0905be8a0 100644 --- a/surfsense_web/app/docs/[[...slug]]/page.tsx +++ b/surfsense_web/app/docs/[[...slug]]/page.tsx @@ -1,11 +1,16 @@ import { DocsBody, DocsDescription, DocsPage, DocsTitle } from "fumadocs-ui/page"; import { notFound } from "next/navigation"; +import { cache } from "react"; import { source } from "@/lib/source"; import { getMDXComponents } from "@/mdx-components"; +const getDocPage = cache((slug?: string[]) => { + return source.getPage(slug); +}); + export default async function Page(props: { params: Promise<{ slug?: string[] }> }) { const params = await props.params; - const page = source.getPage(params.slug); + const page = getDocPage(params.slug); if (!page) notFound(); const MDX = page.data.body; @@ -37,7 +42,7 @@ export async function generateStaticParams() { export async function generateMetadata(props: { params: Promise<{ slug?: string[] }> }) { const params = await props.params; - const page = source.getPage(params.slug); + const page = getDocPage(params.slug); if (!page) notFound(); return { diff --git a/surfsense_web/app/error.tsx b/surfsense_web/app/error.tsx index 7bbd74e0e..70c3d9632 100644 --- a/surfsense_web/app/error.tsx +++ b/surfsense_web/app/error.tsx @@ -1,6 +1,5 @@ "use client"; -import posthog from "posthog-js"; import { useEffect } from "react"; export default function ErrorPage({ @@ -11,7 +10,11 @@ export default function ErrorPage({ reset: () => void; }) { useEffect(() => { - posthog.captureException(error); + import("posthog-js") + .then(({ default: posthog }) => { + posthog.captureException(error); + }) + .catch(() => {}); }, [error]); return ( diff --git a/surfsense_web/app/globals.css b/surfsense_web/app/globals.css index 087c5b676..a37ddb8f3 100644 --- a/surfsense_web/app/globals.css +++ b/surfsense_web/app/globals.css @@ -246,6 +246,17 @@ button { } } +/* content-visibility utilities — skip layout/paint for off-screen list items */ +.list-item-lazy { + content-visibility: auto; + contain-intrinsic-size: 0 48px; +} + +.sidebar-item-lazy { + content-visibility: auto; + contain-intrinsic-size: 0 40px; +} + @source "../node_modules/@llamaindex/chat-ui/**/*.{ts,tsx}"; @source "../node_modules/streamdown/dist/*.js"; @source "../node_modules/@streamdown/code/dist/*.js"; diff --git a/surfsense_web/atoms/new-llm-config/new-llm-config-mutation.atoms.ts b/surfsense_web/atoms/new-llm-config/new-llm-config-mutation.atoms.ts index 861606f80..2a048ca3e 100644 --- a/surfsense_web/atoms/new-llm-config/new-llm-config-mutation.atoms.ts +++ b/surfsense_web/atoms/new-llm-config/new-llm-config-mutation.atoms.ts @@ -34,7 +34,7 @@ export const createNewLLMConfigMutationAtom = atomWithMutation((get) => { }); }, onError: (error: Error) => { - toast.error(error.message || "Failed to create LLM model"); + toast.error(error.message || "Failed to create model"); }, }; }); @@ -109,10 +109,11 @@ export const updateLLMPreferencesMutationAtom = atomWithMutation((get) => { mutationFn: async (request: UpdateLLMPreferencesRequest) => { return newLLMConfigApiService.updateLLMPreferences(request); }, - onSuccess: () => { - queryClient.invalidateQueries({ - queryKey: cacheKeys.newLLMConfigs.preferences(Number(searchSpaceId)), - }); + onSuccess: (_data, request: UpdateLLMPreferencesRequest) => { + queryClient.setQueryData( + cacheKeys.newLLMConfigs.preferences(Number(searchSpaceId)), + (old: Record | undefined) => ({ ...old, ...request.data }) + ); }, onError: (error: Error) => { toast.error(error.message || "Failed to update LLM preferences"); diff --git a/surfsense_web/atoms/new-llm-config/new-llm-config-query.atoms.ts b/surfsense_web/atoms/new-llm-config/new-llm-config-query.atoms.ts index e4c8bcffc..32250d394 100644 --- a/surfsense_web/atoms/new-llm-config/new-llm-config-query.atoms.ts +++ b/surfsense_web/atoms/new-llm-config/new-llm-config-query.atoms.ts @@ -66,7 +66,7 @@ export const defaultSystemInstructionsAtom = atomWithQuery(() => { }); /** - * Query atom for the dynamic LLM model catalogue. + * Query atom for the dynamic model catalogue. * Fetched from the backend (which proxies OpenRouter's public API). * Falls back to the static hardcoded list on error. */ diff --git a/surfsense_web/components/Logo.tsx b/surfsense_web/components/Logo.tsx index 121185757..0c1b2cce2 100644 --- a/surfsense_web/components/Logo.tsx +++ b/surfsense_web/components/Logo.tsx @@ -5,9 +5,11 @@ import { cn } from "@/lib/utils"; export const Logo = ({ className, disableLink = false, + priority = false, }: { className?: string; disableLink?: boolean; + priority?: boolean; }) => { const image = ( logo ); diff --git a/surfsense_web/components/TokenHandler.tsx b/surfsense_web/components/TokenHandler.tsx index 230cda81a..02a7089d9 100644 --- a/surfsense_web/components/TokenHandler.tsx +++ b/surfsense_web/components/TokenHandler.tsx @@ -1,6 +1,5 @@ "use client"; -import { useSearchParams } from "next/navigation"; import { useEffect } from "react"; import { useGlobalLoadingEffect } from "@/hooks/use-global-loading"; import { getAndClearRedirectPath, setBearerToken, setRefreshToken } from "@/lib/auth-utils"; @@ -26,8 +25,6 @@ const TokenHandler = ({ tokenParamName = "token", storageKey = "surfsense_bearer_token", }: TokenHandlerProps) => { - const searchParams = useSearchParams(); - // Always show loading for this component - spinner animation won't reset useGlobalLoadingEffect(true); @@ -35,9 +32,13 @@ const TokenHandler = ({ // Only run on client-side if (typeof window === "undefined") return; - // Get tokens from URL parameters - const token = searchParams.get(tokenParamName); - const refreshToken = searchParams.get("refresh_token"); + // Read tokens from URL at mount time — no subscription needed. + // TokenHandler only runs once after an auth redirect, so a stale read + // is impossible and useSearchParams() would add a pointless subscription. + // (Vercel Best Practice: rerender-defer-reads 5.2) + const params = new URLSearchParams(window.location.search); + const token = params.get(tokenParamName); + const refreshToken = params.get("refresh_token"); if (token) { try { @@ -74,7 +75,7 @@ const TokenHandler = ({ window.location.href = redirectPath; } } - }, [searchParams, tokenParamName, storageKey, redirectPath]); + }, [tokenParamName, storageKey, redirectPath]); // Return null - the global provider handles the loading UI return null; diff --git a/surfsense_web/components/assistant-ui/connector-popup.tsx b/surfsense_web/components/assistant-ui/connector-popup.tsx index d1f6dd31f..fe7b29f28 100644 --- a/surfsense_web/components/assistant-ui/connector-popup.tsx +++ b/surfsense_web/components/assistant-ui/connector-popup.tsx @@ -11,7 +11,6 @@ import { } from "@/atoms/new-llm-config/new-llm-config-query.atoms"; import { activeSearchSpaceIdAtom } from "@/atoms/search-spaces/search-space-query.atoms"; import { searchSpaceSettingsDialogAtom } from "@/atoms/settings/settings-dialog.atoms"; -import { currentUserAtom } from "@/atoms/user/user-query.atoms"; import { Alert, AlertDescription, AlertTitle } from "@/components/ui/alert"; import { Button } from "@/components/ui/button"; import { Dialog, DialogContent, DialogTitle } from "@/components/ui/dialog"; @@ -47,7 +46,6 @@ export const ConnectorIndicator = forwardRef { const searchSpaceId = useAtomValue(activeSearchSpaceIdAtom); const setSearchSpaceSettingsDialog = useSetAtom(searchSpaceSettingsDialogAtom); - useAtomValue(currentUserAtom); const { data: preferences = {}, isFetching: preferencesLoading } = useAtomValue(llmPreferencesAtom); const { data: globalConfigs = [], isFetching: globalConfigsLoading } = @@ -376,14 +374,17 @@ export const ConnectorIndicator = forwardRef {/* LLM Configuration Warning */} {!llmConfigLoading && !hasDocumentSummaryLLM && ( - + LLM Configuration Required

{isAutoMode && !hasGlobalConfigs - ? "Auto mode is selected but no global LLM configurations are available. Please configure a custom LLM in Settings to process and summarize documents from your connected sources." - : "You need to configure a Document Summary LLM before adding connectors. This LLM is used to process and summarize documents from your connected sources."} + ? "Auto mode requires a global LLM configuration. Please add one in Settings" + : "A Document Summary LLM is required to process uploads, configure one in Settings"}

- - {/* Bottom fade shadow - hidden on very small screens */} -
); diff --git a/surfsense_web/components/assistant-ui/image.tsx b/surfsense_web/components/assistant-ui/image.tsx index 65059bcdc..c147eede4 100644 --- a/surfsense_web/components/assistant-ui/image.tsx +++ b/surfsense_web/components/assistant-ui/image.tsx @@ -6,6 +6,7 @@ import { ImageIcon, ImageOffIcon } from "lucide-react"; import { memo, type PropsWithChildren, useEffect, useRef, useState } from "react"; import { createPortal } from "react-dom"; import { cn } from "@/lib/utils"; +import NextImage from 'next/image'; const imageVariants = cva("aui-image-root relative overflow-hidden rounded-lg", { variants: { @@ -86,23 +87,57 @@ function ImagePreview({ >
- ) : ( + ) : isDataOrBlobUrl(src) ? ( + // biome-ignore lint/performance/noImgElement: data/blob URLs need plain img + {alt} { + if (typeof src === "string") setLoadedSrc(src); + onLoad?.(e); + }} + onError={(e) => { + if (typeof src === "string") setErrorSrc(src); + onError?.(e); + }} + {...props} + /> + ) : ( // biome-ignore lint/performance/noImgElement: intentional for dynamic external URLs - {alt} { - if (typeof src === "string") setLoadedSrc(src); - onLoad?.(e); - }} - onError={(e) => { - if (typeof src === "string") setErrorSrc(src); - onError?.(e); - }} - {...props} - /> + // {alt} { + // if (typeof src === "string") setLoadedSrc(src); + // onLoad?.(e); + // }} + // onError={(e) => { + // if (typeof src === "string") setErrorSrc(src); + // onError?.(e); + // }} + // {...props} + // /> + { + if (typeof src === "string") setLoadedSrc(src); + onLoad?.(); + }} + onError={() => { + if (typeof src === "string") setErrorSrc(src); + onError?.(); + }} + unoptimized={false} + {...props} + /> )}
); @@ -126,7 +161,10 @@ type ImageZoomProps = PropsWithChildren<{ src: string; alt?: string; }>; - +function isDataOrBlobUrl(src: string | undefined): boolean { + if (!src || typeof src !== "string") return false; + return src.startsWith("data:") || src.startsWith("blob:"); +} function ImageZoom({ src, alt = "Image preview", children }: ImageZoomProps) { const [isMounted, setIsMounted] = useState(false); const [isOpen, setIsOpen] = useState(false); @@ -177,22 +215,39 @@ function ImageZoom({ src, alt = "Image preview", children }: ImageZoomProps) { aria-label="Close zoomed image" > {/** biome-ignore lint/performance/noImgElement: */} - {alt} { - e.stopPropagation(); - handleClose(); - }} - onKeyDown={(e) => { - if (e.key === "Enter") { - e.stopPropagation(); - handleClose(); - } - }} - /> + {isDataOrBlobUrl(src) ? ( + // biome-ignore lint/performance/noImgElement: data/blob URLs need plain img + {alt} { + e.stopPropagation(); + handleClose(); + }} + onKeyDown={(e) => { + if (e.key === "Enter") { + e.stopPropagation(); + handleClose(); + } + }} + /> + ) : ( + { + e.stopPropagation(); + handleClose(); + }} + unoptimized={false} + /> + )} , document.body )} diff --git a/surfsense_web/components/assistant-ui/inline-citation.tsx b/surfsense_web/components/assistant-ui/inline-citation.tsx index 15ad11d94..0c99090e6 100644 --- a/surfsense_web/components/assistant-ui/inline-citation.tsx +++ b/surfsense_web/components/assistant-ui/inline-citation.tsx @@ -32,7 +32,7 @@ export const InlineCitation: FC = ({ chunkId, isDocsChunk =
{group.tools.map((tool) => { - const isDisabled = disabledTools.includes(tool.name); + const isDisabled = disabledToolsSet.has(tool.name); const ToolIcon = getToolIcon(tool.name); return (
= ({ isBlockedByOtherUser = false const iconKey = group.connectorIcon ?? ""; const iconInfo = CONNECTOR_TOOL_ICON_PATHS[iconKey]; const toolNames = group.tools.map((t) => t.name); - const allDisabled = toolNames.every((n) => disabledTools.includes(n)); + const allDisabled = toolNames.every((n) => disabledToolsSet.has(n)); return (
= ({ isBlockedByOtherUser = false >
Manage Tools
= ({ isBlockedByOtherUser = false {group.label}
{group.tools.map((tool) => { - const isDisabled = disabledTools.includes(tool.name); + const isDisabled = disabledToolsSet.has(tool.name); const ToolIcon = getToolIcon(tool.name); const row = (
@@ -1115,7 +1128,7 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false const iconKey = group.connectorIcon ?? ""; const iconInfo = CONNECTOR_TOOL_ICON_PATHS[iconKey]; const toolNames = group.tools.map((t) => t.name); - const allDisabled = toolNames.every((n) => disabledTools.includes(n)); + const allDisabled = toolNames.every((n) => disabledToolsSet.has(n)); const groupDef = TOOL_GROUPS.find((g) => g.label === group.label); const row = (
@@ -1146,7 +1159,11 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false {row} {groupDef?.tooltip ?? - group.tools.map((t) => t.description).join(" · ")} + group.tools.flatMap((t, i) => + i === 0 + ? [t.description] + : [, t.description] + )} ); diff --git a/surfsense_web/components/assistant-ui/tool-fallback.tsx b/surfsense_web/components/assistant-ui/tool-fallback.tsx index 17d99549e..40118d2e4 100644 --- a/surfsense_web/components/assistant-ui/tool-fallback.tsx +++ b/surfsense_web/components/assistant-ui/tool-fallback.tsx @@ -1,6 +1,6 @@ import type { ToolCallMessagePartComponent } from "@assistant-ui/react"; import { CheckIcon, ChevronDownIcon, ChevronUpIcon, XCircleIcon } from "lucide-react"; -import { useState } from "react"; +import { useMemo, useState } from "react"; import { getToolIcon } from "@/contracts/enums/toolIcons"; import { cn } from "@/lib/utils"; @@ -19,17 +19,28 @@ export const ToolFallback: ToolCallMessagePartComponent = ({ const isCancelled = status?.type === "incomplete" && status.reason === "cancelled"; const isError = status?.type === "incomplete" && status.reason === "error"; const isRunning = status?.type === "running" || status?.type === "requires-action"; + const errorData = status?.type === "incomplete" ? status.error : undefined; + const serializedError = useMemo( + () => (errorData && typeof errorData !== "string" ? JSON.stringify(errorData) : null), + [errorData] + ); + + const serializedResult = useMemo( + () => (result !== undefined && typeof result !== "string" ? JSON.stringify(result, null, 2) : null), + [result] + ); + const cancelledReason = isCancelled && status.error ? typeof status.error === "string" ? status.error - : JSON.stringify(status.error) + : serializedError : null; const errorReason = isError && status.error ? typeof status.error === "string" ? status.error - : JSON.stringify(status.error) + : serializedError : null; const Icon = getToolIcon(toolName); @@ -122,7 +133,7 @@ export const ToolFallback: ToolCallMessagePartComponent = ({

Result

-										{typeof result === "string" ? result : JSON.stringify(result, null, 2)}
+										{typeof result === "string" ? result : serializedResult}
diff --git a/surfsense_web/components/chat-comments/comment-composer/comment-composer.tsx b/surfsense_web/components/chat-comments/comment-composer/comment-composer.tsx index 3e9b4504f..3d6ea384b 100644 --- a/surfsense_web/components/chat-comments/comment-composer/comment-composer.tsx +++ b/surfsense_web/components/chat-comments/comment-composer/comment-composer.tsx @@ -15,13 +15,14 @@ function convertDisplayToData(displayContent: string, mentions: InsertedMention[ const sortedMentions = [...mentions].sort((a, b) => b.displayName.length - a.displayName.length); - for (const mention of sortedMentions) { - const displayPattern = new RegExp( - `@${escapeRegExp(mention.displayName)}(?=\\s|$|[.,!?;:])`, - "g" - ); - const dataFormat = `@[${mention.id}]`; - result = result.replace(displayPattern, dataFormat); + const mentionPatterns = sortedMentions.map((mention) => ({ + pattern: new RegExp(`@${escapeRegExp(mention.displayName)}(?=\\s|$|[.,!?;:])`, "g"), + dataFormat: `@[${mention.id}]`, + })); + + for (const { pattern, dataFormat } of mentionPatterns) { + pattern.lastIndex = 0; // reset global regex state + result = result.replace(pattern, dataFormat); } return result; diff --git a/surfsense_web/components/documents/DocumentNode.tsx b/surfsense_web/components/documents/DocumentNode.tsx index dd7731f22..919f904d4 100644 --- a/surfsense_web/components/documents/DocumentNode.tsx +++ b/surfsense_web/components/documents/DocumentNode.tsx @@ -5,6 +5,7 @@ import { Clock, Download, Eye, + History, MoreHorizontal, Move, PenLine, @@ -39,6 +40,7 @@ import { Tooltip, TooltipContent, TooltipTrigger } from "@/components/ui/tooltip import type { DocumentTypeEnum } from "@/contracts/types/document.types"; import { cn } from "@/lib/utils"; import { DND_TYPES } from "./FolderNode"; +import { isVersionableType } from "./version-history"; const EDITABLE_DOCUMENT_TYPES = new Set(["FILE", "NOTE"]); @@ -60,6 +62,7 @@ interface DocumentNodeProps { onDelete: (doc: DocumentNodeDoc) => void; onMove: (doc: DocumentNodeDoc) => void; onExport?: (doc: DocumentNodeDoc, format: string) => void; + onVersionHistory?: (doc: DocumentNodeDoc) => void; contextMenuOpen?: boolean; onContextMenuOpenChange?: (open: boolean) => void; } @@ -74,6 +77,7 @@ export const DocumentNode = React.memo(function DocumentNode({ onDelete, onMove, onExport, + onVersionHistory, contextMenuOpen, onContextMenuOpenChange, }: DocumentNodeProps) { @@ -195,12 +199,17 @@ export const DocumentNode = React.memo(function DocumentNode({ {doc.title} - - {getDocumentTypeIcon( - doc.document_type as DocumentTypeEnum, - "h-3.5 w-3.5 text-muted-foreground" - )} - + {getDocumentTypeIcon( + doc.document_type as DocumentTypeEnum, + "h-3.5 w-3.5 text-muted-foreground" + ) && ( + + {getDocumentTypeIcon( + doc.document_type as DocumentTypeEnum, + "h-3.5 w-3.5 text-muted-foreground" + )} + + )} @@ -219,7 +228,7 @@ export const DocumentNode = React.memo(function DocumentNode({ e.stopPropagation()}> - onPreview(doc)}> + onPreview(doc)} disabled={isProcessing}> Open @@ -235,7 +244,7 @@ export const DocumentNode = React.memo(function DocumentNode({ {onExport && ( - + Export @@ -244,6 +253,12 @@ export const DocumentNode = React.memo(function DocumentNode({ )} + {onVersionHistory && isVersionableType(doc.document_type) && ( + onVersionHistory(doc)}> + + Versions + + )} e.stopPropagation()}> - onPreview(doc)}> + onPreview(doc)} disabled={isProcessing}> Open @@ -275,7 +290,7 @@ export const DocumentNode = React.memo(function DocumentNode({ {onExport && ( - + Export @@ -284,6 +299,12 @@ export const DocumentNode = React.memo(function DocumentNode({ )} + {onVersionHistory && isVersionableType(doc.document_type) && ( + onVersionHistory(doc)}> + + Versions + + )} void; onToggleExpand: (folderId: number) => void; onRename: (folder: FolderDisplay, newName: string) => void; @@ -70,6 +77,9 @@ interface FolderNodeProps { disabledDropIds?: Set; contextMenuOpen?: boolean; onContextMenuOpenChange?: (open: boolean) => void; + isWatched?: boolean; + onRescan?: (folder: FolderDisplay) => void; + onStopWatching?: (folder: FolderDisplay) => void; } function getDropZone( @@ -93,6 +103,7 @@ export const FolderNode = React.memo(function FolderNode({ isRenaming, childCount, selectionState, + processingState, onToggleSelect, onToggleExpand, onRename, @@ -107,6 +118,9 @@ export const FolderNode = React.memo(function FolderNode({ disabledDropIds, contextMenuOpen, onContextMenuOpenChange, + isWatched, + onRescan, + onStopWatching, }: FolderNodeProps) { const [renameValue, setRenameValue] = useState(folder.name); const inputRef = useRef(null); @@ -242,7 +256,9 @@ export const FolderNode = React.memo(function FolderNode({ isOver && !canDrop && "cursor-not-allowed" )} style={{ paddingLeft: `${depth * 16 + 4}px` }} - onClick={() => onToggleExpand(folder.id)} + onClick={() => { + onToggleExpand(folder.id); + }} onKeyDown={(e) => { if (e.key === "Enter" || e.key === " ") { e.preventDefault(); @@ -262,14 +278,45 @@ export const FolderNode = React.memo(function FolderNode({ )} - e.stopPropagation()} - className="h-3.5 w-3.5 shrink-0" - /> + {processingState !== "idle" && selectionState === "none" ? ( + <> + + + + {processingState === "processing" ? ( + + ) : ( + + )} + + + + {processingState === "processing" + ? "Syncing folder contents" + : "Some files failed to process"} + + + e.stopPropagation()} + className="h-3.5 w-3.5 shrink-0 hidden group-hover:flex" + /> + + ) : ( + e.stopPropagation()} + className="h-3.5 w-3.5 shrink-0" + /> + )} @@ -308,6 +355,28 @@ export const FolderNode = React.memo(function FolderNode({ + {isWatched && onRescan && ( + { + e.stopPropagation(); + onRescan(folder); + }} + > + + Re-scan + + )} + {isWatched && onStopWatching && ( + { + e.stopPropagation(); + onStopWatching(folder); + }} + > + + Stop watching + + )} { e.stopPropagation(); @@ -353,6 +422,18 @@ export const FolderNode = React.memo(function FolderNode({ {!isRenaming && contextMenuOpen && ( + {isWatched && onRescan && ( + onRescan(folder)}> + + Re-scan + + )} + {isWatched && onStopWatching && ( + onStopWatching(folder)}> + + Stop watching + + )} onCreateSubfolder(folder.id)}> New subfolder diff --git a/surfsense_web/components/documents/FolderTreeView.tsx b/surfsense_web/components/documents/FolderTreeView.tsx index f63d5da5c..1df007c0b 100644 --- a/surfsense_web/components/documents/FolderTreeView.tsx +++ b/surfsense_web/components/documents/FolderTreeView.tsx @@ -1,7 +1,7 @@ "use client"; import { useAtom } from "jotai"; -import { CirclePlus } from "lucide-react"; +import { Search } from "lucide-react"; import { useCallback, useMemo, useState } from "react"; import { DndProvider } from "react-dnd"; import { HTML5Backend } from "react-dnd-html5-backend"; @@ -32,6 +32,7 @@ interface FolderTreeViewProps { onDeleteDocument: (doc: DocumentNodeDoc) => void; onMoveDocument: (doc: DocumentNodeDoc) => void; onExportDocument?: (doc: DocumentNodeDoc, format: string) => void; + onVersionHistory?: (doc: DocumentNodeDoc) => void; activeTypes: DocumentTypeEnum[]; searchQuery?: string; onDropIntoFolder?: ( @@ -40,6 +41,9 @@ interface FolderTreeViewProps { targetFolderId: number | null ) => void; onReorderFolder?: (folderId: number, beforePos: string | null, afterPos: string | null) => void; + watchedFolderIds?: Set; + onRescanFolder?: (folder: FolderDisplay) => void; + onStopWatchingFolder?: (folder: FolderDisplay) => void; } function groupBy(items: T[], keyFn: (item: T) => string | number): Record { @@ -69,10 +73,14 @@ export function FolderTreeView({ onDeleteDocument, onMoveDocument, onExportDocument, + onVersionHistory, activeTypes, searchQuery, onDropIntoFolder, onReorderFolder, + watchedFolderIds, + onRescanFolder, + onStopWatchingFolder, }: FolderTreeViewProps) { const foldersByParent = useMemo(() => groupBy(folders, (f) => f.parentId ?? "root"), [folders]); @@ -158,6 +166,35 @@ export function FolderTreeView({ return states; }, [folders, docsByFolder, foldersByParent, mentionedDocIds]); + const folderProcessingStates = useMemo(() => { + const states: Record = {}; + + function compute(folderId: number): { hasProcessing: boolean; hasFailed: boolean } { + const directDocs = docsByFolder[folderId] ?? []; + let hasProcessing = directDocs.some( + (d) => d.status?.state === "pending" || d.status?.state === "processing" + ); + let hasFailed = directDocs.some((d) => d.status?.state === "failed"); + + for (const child of foldersByParent[folderId] ?? []) { + const sub = compute(child.id); + hasProcessing = hasProcessing || sub.hasProcessing; + hasFailed = hasFailed || sub.hasFailed; + } + + if (hasProcessing) states[folderId] = "processing"; + else if (hasFailed) states[folderId] = "failed"; + else states[folderId] = "idle"; + + return { hasProcessing, hasFailed }; + } + + for (const f of folders) { + if (states[f.id] === undefined) compute(f.id); + } + return states; + }, [folders, docsByFolder, foldersByParent]); + function renderLevel(parentId: number | null, depth: number): React.ReactNode[] { const key = parentId ?? "root"; const childFolders = (foldersByParent[key] ?? []) @@ -191,6 +228,7 @@ export function FolderTreeView({ isRenaming={renamingFolderId === f.id} childCount={folderChildCounts[f.id] ?? 0} selectionState={folderSelectionStates[f.id] ?? "none"} + processingState={folderProcessingStates[f.id] ?? "idle"} onToggleSelect={onToggleFolderSelect} onToggleExpand={onToggleExpand} onRename={onRenameFolder} @@ -204,6 +242,9 @@ export function FolderTreeView({ siblingPositions={siblingPositions} contextMenuOpen={openContextMenuId === `folder-${f.id}`} onContextMenuOpenChange={(open) => setOpenContextMenuId(open ? `folder-${f.id}` : null)} + isWatched={watchedFolderIds?.has(f.id)} + onRescan={onRescanFolder} + onStopWatching={onStopWatchingFolder} /> ); @@ -225,6 +266,7 @@ export function FolderTreeView({ onDelete={onDeleteDocument} onMove={onMoveDocument} onExport={onExportDocument} + onVersionHistory={onVersionHistory} contextMenuOpen={openContextMenuId === `doc-${d.id}`} onContextMenuOpenChange={(open) => setOpenContextMenuId(open ? `doc-${d.id}` : null)} /> @@ -250,8 +292,9 @@ export function FolderTreeView({ if (treeNodes.length === 0 && (activeTypes.length > 0 || searchQuery)) { return (
- -

No matching documents

+ +

No matching documents

+

Try a different search term

); } diff --git a/surfsense_web/components/documents/version-history.tsx b/surfsense_web/components/documents/version-history.tsx new file mode 100644 index 000000000..27343dc6a --- /dev/null +++ b/surfsense_web/components/documents/version-history.tsx @@ -0,0 +1,258 @@ +"use client"; + +import { Check, ChevronRight, Clock, Copy, RotateCcw } from "lucide-react"; +import { useCallback, useEffect, useState } from "react"; +import { toast } from "sonner"; +import { Button } from "@/components/ui/button"; +import { Dialog, DialogContent, DialogTitle, DialogTrigger } from "@/components/ui/dialog"; +import { Separator } from "@/components/ui/separator"; +import { Spinner } from "@/components/ui/spinner"; +import { documentsApiService } from "@/lib/apis/documents-api.service"; +import { cn } from "@/lib/utils"; + +interface DocumentVersionSummary { + version_number: number; + title: string; + content_hash: string; + created_at: string | null; +} + +interface VersionHistoryProps { + documentId: number; + documentType: string; +} + +const VERSION_DOCUMENT_TYPES = new Set(["LOCAL_FOLDER_FILE", "OBSIDIAN_CONNECTOR"]); + +export function isVersionableType(documentType: string) { + return VERSION_DOCUMENT_TYPES.has(documentType); +} + +const DIALOG_CLASSES = + "select-none max-w-[900px] w-[95vw] md:w-[90vw] h-[90vh] md:h-[80vh] max-h-[640px] flex flex-col md:flex-row p-0 gap-0 overflow-hidden [--card:var(--background)] dark:[--card:oklch(0.205_0_0)] dark:[--background:oklch(0.205_0_0)]"; + +export function VersionHistoryButton({ documentId, documentType }: VersionHistoryProps) { + if (!isVersionableType(documentType)) return null; + + return ( + + + + + + Version History + + + + ); +} + +export function VersionHistoryDialog({ + open, + onOpenChange, + documentId, +}: { + open: boolean; + onOpenChange: (open: boolean) => void; + documentId: number; +}) { + return ( + + + Version History + {open && } + + + ); +} + +function formatRelativeTime(dateStr: string): string { + const now = Date.now(); + const then = new Date(dateStr).getTime(); + const diffMs = now - then; + const diffMin = Math.floor(diffMs / 60_000); + if (diffMin < 1) return "Just now"; + if (diffMin < 60) return `${diffMin} minute${diffMin !== 1 ? "s" : ""} ago`; + const diffHr = Math.floor(diffMin / 60); + if (diffHr < 24) return `${diffHr} hour${diffHr !== 1 ? "s" : ""} ago`; + return new Date(dateStr).toLocaleDateString(undefined, { + weekday: "short", + month: "short", + day: "numeric", + year: "numeric", + hour: "numeric", + minute: "2-digit", + }); +} + +function VersionHistoryPanel({ documentId }: { documentId: number }) { + const [versions, setVersions] = useState([]); + const [loading, setLoading] = useState(true); + const [selectedVersion, setSelectedVersion] = useState(null); + const [versionContent, setVersionContent] = useState(""); + const [contentLoading, setContentLoading] = useState(false); + const [restoring, setRestoring] = useState(false); + const [copied, setCopied] = useState(false); + + const loadVersions = useCallback(async () => { + setLoading(true); + try { + const data = await documentsApiService.listDocumentVersions(documentId); + setVersions(data as DocumentVersionSummary[]); + } catch { + toast.error("Failed to load version history"); + } finally { + setLoading(false); + } + }, [documentId]); + + useEffect(() => { + loadVersions(); + }, [loadVersions]); + + const handleSelectVersion = async (versionNumber: number) => { + if (selectedVersion === versionNumber) return; + setSelectedVersion(versionNumber); + setContentLoading(true); + try { + const data = (await documentsApiService.getDocumentVersion(documentId, versionNumber)) as { + source_markdown: string; + }; + setVersionContent(data.source_markdown || ""); + } catch { + toast.error("Failed to load version content"); + } finally { + setContentLoading(false); + } + }; + + const handleRestore = async (versionNumber: number) => { + setRestoring(true); + try { + await documentsApiService.restoreDocumentVersion(documentId, versionNumber); + toast.success(`Restored version ${versionNumber}`); + await loadVersions(); + } catch { + toast.error("Failed to restore version"); + } finally { + setRestoring(false); + } + }; + + const handleCopy = () => { + navigator.clipboard.writeText(versionContent); + setCopied(true); + setTimeout(() => setCopied(false), 2000); + }; + + if (loading) { + return ( +
+ +
+ ); + } + + if (versions.length === 0) { + return ( +
+

No version history available yet

+

Versions are created when file content changes

+
+ ); + } + + const selectedVersionData = versions.find((v) => v.version_number === selectedVersion); + + return ( + <> + {/* Left panel — version list */} + + + {/* Right panel — content preview */} +
+ {selectedVersion !== null && selectedVersionData ? ( + <> +
+

+ {selectedVersionData.title || `Version ${selectedVersion}`} +

+
+ + +
+
+ +
+ {contentLoading ? ( +
+ +
+ ) : ( + 
+									{versionContent || "(empty)"}
+
+ )} +
+ + ) : ( +
+

Select a version to preview

+
+ )} +
+ + ); +} diff --git a/surfsense_web/components/editor-panel/editor-panel.tsx b/surfsense_web/components/editor-panel/editor-panel.tsx index 3ea36f800..248fe68eb 100644 --- a/surfsense_web/components/editor-panel/editor-panel.tsx +++ b/surfsense_web/components/editor-panel/editor-panel.tsx @@ -1,12 +1,14 @@ "use client"; import { useAtomValue, useSetAtom } from "jotai"; -import { AlertCircle, XIcon } from "lucide-react"; +import { Download, FileQuestionMark, FileText, Loader2, RefreshCw, XIcon } from "lucide-react"; import dynamic from "next/dynamic"; import { useCallback, useEffect, useRef, useState } from "react"; import { toast } from "sonner"; import { closeEditorPanelAtom, editorPanelAtom } from "@/atoms/editor/editor-panel.atom"; +import { VersionHistoryButton } from "@/components/documents/version-history"; import { MarkdownViewer } from "@/components/markdown-viewer"; +import { Alert, AlertDescription } from "@/components/ui/alert"; import { Button } from "@/components/ui/button"; import { Drawer, DrawerContent, DrawerHandle, DrawerTitle } from "@/components/ui/drawer"; import { Skeleton } from "@/components/ui/skeleton"; @@ -18,11 +20,16 @@ const PlateEditor = dynamic( { ssr: false, loading: () => } ); +const LARGE_DOCUMENT_THRESHOLD = 2 * 1024 * 1024; // 2MB + interface EditorContent { document_id: number; title: string; document_type?: string; source_markdown: string; + content_size_bytes?: number; + chunk_count?: number; + truncated?: boolean; } const EDITABLE_DOCUMENT_TYPES = new Set(["FILE", "NOTE"]); @@ -62,6 +69,7 @@ export function EditorPanelContent({ const [isLoading, setIsLoading] = useState(true); const [error, setError] = useState(null); const [saving, setSaving] = useState(false); + const [downloading, setDownloading] = useState(false); const [editedMarkdown, setEditedMarkdown] = useState(null); const markdownRef = useRef(""); @@ -69,8 +77,10 @@ export function EditorPanelContent({ const changeCountRef = useRef(0); const [displayTitle, setDisplayTitle] = useState(title || "Untitled"); + const isLargeDocument = (editorDoc?.content_size_bytes ?? 0) > LARGE_DOCUMENT_THRESHOLD; + useEffect(() => { - let cancelled = false; + const controller = new AbortController(); setIsLoading(true); setError(null); setEditorDoc(null); @@ -78,7 +88,7 @@ export function EditorPanelContent({ initialLoadDone.current = false; changeCountRef.current = 0; - const fetchContent = async () => { + const doFetch = async () => { const token = getBearerToken(); if (!token) { redirectToLogin(); @@ -88,10 +98,15 @@ export function EditorPanelContent({ try { const response = await authenticatedFetch( `${process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL}/api/v1/search-spaces/${searchSpaceId}/documents/${documentId}/editor-content`, - { method: "GET" } + { method: "GET", signal: controller.signal } + const url = new URL( + `${process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL}/api/v1/search-spaces/${searchSpaceId}/documents/${documentId}/editor-content` ); + url.searchParams.set("max_length", String(LARGE_DOCUMENT_THRESHOLD)); - if (cancelled) return; + const response = await authenticatedFetch(url.toString(), { method: "GET" }); + + if (controller.signal.aborted) return; if (!response.ok) { const errorData = await response @@ -115,18 +130,16 @@ export function EditorPanelContent({ setEditorDoc(data); initialLoadDone.current = true; } catch (err) { - if (cancelled) return; + if (controller.signal.aborted) return; console.error("Error fetching document:", err); setError(err instanceof Error ? err.message : "Failed to fetch document"); } finally { - if (!cancelled) setIsLoading(false); + if (!controller.signal.aborted) setIsLoading(false); } }; - fetchContent(); - return () => { - cancelled = true; - }; + doFetch().catch(() => {}); + return () => controller.abort(); }, [documentId, searchSpaceId, title]); const handleMarkdownChange = useCallback((md: string) => { @@ -175,7 +188,7 @@ export function EditorPanelContent({ }, [documentId, searchSpaceId]); const isEditableType = editorDoc - ? EDITABLE_DOCUMENT_TYPES.has(editorDoc.document_type ?? "") + ? EDITABLE_DOCUMENT_TYPES.has(editorDoc.document_type ?? "") && !isLargeDocument : false; return ( @@ -187,12 +200,17 @@ export function EditorPanelContent({

Unsaved changes

)}
- {onClose && ( - - )} +
+ {editorDoc?.document_type && ( + + )} + {onClose && ( + + )} +
@@ -200,12 +218,79 @@ export function EditorPanelContent({ ) : error || !editorDoc ? (
- -
-

Failed to load document

-

{error || "An unknown error occurred"}

+ {error?.toLowerCase().includes("still being processed") ? ( +
+ +
+ ) : ( +
+ +
+ )} +
+

+ {error?.toLowerCase().includes("still being processed") + ? "Document is processing" + : "Document unavailable"} +

+

+ {error || "An unknown error occurred"} +

+ ) : isLargeDocument ? ( +
+ + + + + This document is too large for the editor ( + {Math.round((editorDoc.content_size_bytes ?? 0) / 1024 / 1024)}MB,{" "} + {editorDoc.chunk_count ?? 0} chunks). Showing a preview below. + + + + + +
) : isEditableType ? ( +
+ {title} +

{title}

diff --git a/surfsense_web/components/layout/providers/LayoutDataProvider.tsx b/surfsense_web/components/layout/providers/LayoutDataProvider.tsx index fd6b45c52..6138b67fb 100644 --- a/surfsense_web/components/layout/providers/LayoutDataProvider.tsx +++ b/surfsense_web/components/layout/providers/LayoutDataProvider.tsx @@ -775,7 +775,8 @@ export function LayoutDataProvider({ searchSpaceId, children }: LayoutDataProvid {t("delete_chat")} - {t("delete_chat_confirm")} {chatToDelete?.name}?{" "} + {t("delete_chat_confirm")}{" "} + {chatToDelete?.name}?{" "} {t("action_cannot_undone")} @@ -835,9 +836,7 @@ export function LayoutDataProvider({ searchSpaceId, children }: LayoutDataProvid {tSidebar("rename") || "Rename"} - {isRenamingChat && ( - - )} + {isRenamingChat && } @@ -865,9 +864,7 @@ export function LayoutDataProvider({ searchSpaceId, children }: LayoutDataProvid className="relative bg-destructive text-destructive-foreground hover:bg-destructive/90" > {tCommon("delete")} - {isDeletingSearchSpace && ( - - )} + {isDeletingSearchSpace && } @@ -895,9 +892,7 @@ export function LayoutDataProvider({ searchSpaceId, children }: LayoutDataProvid className="relative bg-destructive text-destructive-foreground hover:bg-destructive/90" > {t("leave")} - {isLeavingSearchSpace && ( - - )} + {isLeavingSearchSpace && } diff --git a/surfsense_web/components/layout/ui/right-panel/RightPanel.tsx b/surfsense_web/components/layout/ui/right-panel/RightPanel.tsx index ac2f65065..717f5a459 100644 --- a/surfsense_web/components/layout/ui/right-panel/RightPanel.tsx +++ b/surfsense_web/components/layout/ui/right-panel/RightPanel.tsx @@ -19,7 +19,7 @@ const EditorPanelContent = dynamic( import("@/components/editor-panel/editor-panel").then((m) => ({ default: m.EditorPanelContent, })), - { ssr: false, loading: () => } + { ssr: false, loading: () => null } ); const HitlEditPanelContent = dynamic( diff --git a/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx b/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx index 1bc773374..41bd01a99 100644 --- a/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx +++ b/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx @@ -109,6 +109,7 @@ export function AllPrivateChatsSidebarContent({ queryKey: ["all-threads", searchSpaceId], queryFn: () => fetchThreads(Number(searchSpaceId)), enabled: !!searchSpaceId && !isSearchMode, + placeholderData: () => queryClient.getQueryData(["threads", searchSpaceId, { limit: 40 }]), }); const { @@ -349,7 +350,7 @@ export function AllPrivateChatsSidebarContent({
([]); + const [watchedFolderIds, setWatchedFolderIds] = useState>(new Set()); + + useEffect(() => { + const api = typeof window !== "undefined" ? window.electronAPI : null; + if (!api?.getWatchedFolders) return; + + async function loadWatchedIds() { + const folders = await api!.getWatchedFolders(); + + if (folders.length === 0) { + try { + const backendFolders = await documentsApiService.getWatchedFolders(searchSpaceId); + for (const bf of backendFolders) { + const meta = bf.metadata as Record | null; + if (!meta?.watched || !meta.folder_path) continue; + await api!.addWatchedFolder({ + path: meta.folder_path as string, + name: bf.name, + rootFolderId: bf.id, + searchSpaceId: bf.search_space_id, + excludePatterns: (meta.exclude_patterns as string[]) ?? [], + fileExtensions: (meta.file_extensions as string[] | null) ?? null, + active: true, + }); + } + const recovered = await api!.getWatchedFolders(); + const ids = new Set( + recovered.filter((f) => f.rootFolderId != null).map((f) => f.rootFolderId as number) + ); + setWatchedFolderIds(ids); + return; + } catch (err) { + console.error("[DocumentsSidebar] Recovery from backend failed:", err); + } + } + + const ids = new Set( + folders.filter((f) => f.rootFolderId != null).map((f) => f.rootFolderId as number) + ); + setWatchedFolderIds(ids); + } + + loadWatchedIds(); + }, [searchSpaceId]); const { mutateAsync: deleteDocumentMutation } = useAtomValue(deleteDocumentMutationAtom); const [sidebarDocs, setSidebarDocs] = useAtom(sidebarSelectedDocumentsAtom); @@ -134,7 +180,12 @@ export function DocumentsSidebar({ const treeDocuments: DocumentNodeDoc[] = useMemo(() => { const zeroDocs = (zeroAllDocs ?? []) - .filter((d) => d.title && d.title.trim() !== "") + .filter((d) => { + if (!d.title || d.title.trim() === "") return false; + const state = (d.status as { state?: string } | undefined)?.state; + if (state === "deleting") return false; + return true; + }) .map((d) => ({ id: d.id, title: d.title, @@ -223,6 +274,53 @@ export function DocumentsSidebar({ [createFolderParentId, searchSpaceId, setExpandedFolderMap] ); + const handleRescanFolder = useCallback( + async (folder: FolderDisplay) => { + const api = window.electronAPI; + if (!api) return; + + const watchedFolders = await api.getWatchedFolders(); + const matched = watchedFolders.find((wf) => wf.rootFolderId === folder.id); + if (!matched) { + toast.error("This folder is not being watched"); + return; + } + + try { + await documentsApiService.folderIndex(searchSpaceId, { + folder_path: matched.path, + folder_name: matched.name, + search_space_id: searchSpaceId, + root_folder_id: folder.id, + }); + toast.success(`Re-scanning folder: ${matched.name}`); + } catch (err) { + toast.error((err as Error)?.message || "Failed to re-scan folder"); + } + }, + [searchSpaceId] + ); + + const handleStopWatching = useCallback(async (folder: FolderDisplay) => { + const api = window.electronAPI; + if (!api) return; + + const watchedFolders = await api.getWatchedFolders(); + const matched = watchedFolders.find((wf) => wf.rootFolderId === folder.id); + if (!matched) { + toast.error("This folder is not being watched"); + return; + } + + await api.removeWatchedFolder(matched.path); + try { + await foldersApiService.stopWatching(folder.id); + } catch (err) { + console.error("[DocumentsSidebar] Failed to clear watched metadata:", err); + } + toast.success(`Stopped watching: ${matched.name}`); + }, []); + const handleRenameFolder = useCallback(async (folder: FolderDisplay, newName: string) => { try { await foldersApiService.updateFolder(folder.id, { name: newName }); @@ -235,6 +333,14 @@ export function DocumentsSidebar({ const handleDeleteFolder = useCallback(async (folder: FolderDisplay) => { if (!confirm(`Delete folder "${folder.name}" and all its contents?`)) return; try { + const api = window.electronAPI; + if (api) { + const watchedFolders = await api.getWatchedFolders(); + const matched = watchedFolders.find((wf) => wf.rootFolderId === folder.id); + if (matched) { + await api.removeWatchedFolder(matched.path); + } + } await foldersApiService.deleteFolder(folder.id); toast.success("Folder deleted"); } catch (e: unknown) { @@ -448,6 +554,7 @@ export function DocumentsSidebar({ const [bulkDeleteConfirmOpen, setBulkDeleteConfirmOpen] = useState(false); const [isBulkDeleting, setIsBulkDeleting] = useState(false); + const [versionDocId, setVersionDocId] = useState(null); const handleBulkDeleteSelected = useCallback(async () => { if (deletableSelectedIds.length === 0) return; @@ -651,56 +758,72 @@ export function DocumentsSidebar({ />
- {deletableSelectedIds.length > 0 && ( -
- -
- )} +
+ {deletableSelectedIds.length > 0 && ( +
+ +
+ )} - { - openEditorPanel({ - documentId: doc.id, - searchSpaceId, - title: doc.title, - }); - }} - onEditDocument={(doc) => { - openEditorPanel({ - documentId: doc.id, - searchSpaceId, - title: doc.title, - }); - }} - onDeleteDocument={(doc) => handleDeleteDocument(doc.id)} - onMoveDocument={handleMoveDocument} - onExportDocument={handleExportDocument} - activeTypes={activeTypes} - onDropIntoFolder={handleDropIntoFolder} - onReorderFolder={handleReorderFolder} - /> + { + openEditorPanel({ + documentId: doc.id, + searchSpaceId, + title: doc.title, + }); + }} + onEditDocument={(doc) => { + openEditorPanel({ + documentId: doc.id, + searchSpaceId, + title: doc.title, + }); + }} + onDeleteDocument={(doc) => handleDeleteDocument(doc.id)} + onMoveDocument={handleMoveDocument} + onExportDocument={handleExportDocument} + onVersionHistory={(doc) => setVersionDocId(doc.id)} + activeTypes={activeTypes} + onDropIntoFolder={handleDropIntoFolder} + onReorderFolder={handleReorderFolder} + watchedFolderIds={watchedFolderIds} + onRescanFolder={handleRescanFolder} + onStopWatchingFolder={handleStopWatching} + /> +
+ {versionDocId !== null && ( + { + if (!open) setVersionDocId(null); + }} + documentId={versionDocId} + /> + )} + (null); const [connectorScrollPos, setConnectorScrollPos] = useState<"top" | "middle" | "bottom">("top"); + const connectorRafRef = useRef(); const handleConnectorScroll = useCallback((e: React.UIEvent) => { const el = e.currentTarget; - const atTop = el.scrollTop <= 2; - const atBottom = el.scrollHeight - el.scrollTop - el.clientHeight <= 2; - setConnectorScrollPos(atTop ? "top" : atBottom ? "bottom" : "middle"); + if (connectorRafRef.current) return; + connectorRafRef.current = requestAnimationFrame(() => { + const atTop = el.scrollTop <= 2; + const atBottom = el.scrollHeight - el.scrollTop - el.clientHeight <= 2; + setConnectorScrollPos(atTop ? "top" : atBottom ? "bottom" : "middle"); + connectorRafRef.current = undefined; + }); }, []); + useEffect( + () => () => { + if (connectorRafRef.current) cancelAnimationFrame(connectorRafRef.current); + }, + [] + ); const [filterDrawerOpen, setFilterDrawerOpen] = useState(false); const [markingAsReadId, setMarkingAsReadId] = useState(null); @@ -289,15 +300,14 @@ export function InboxSidebarContent({ [activeFilter] ); + // Defer non-urgent list updates so the search input stays responsive. + // The deferred snapshot lags one render behind the live value intentionally. + const deferredTabItems = useDeferredValue(activeSource.items); + const deferredSearchItems = useDeferredValue(searchResponse?.items ?? []); + // Two data paths: search mode (API) or default (per-tab data source) const filteredItems = useMemo(() => { - let tabItems: InboxItem[]; - - if (isSearchMode) { - tabItems = searchResponse?.items ?? []; - } else { - tabItems = activeSource.items; - } + const tabItems: InboxItem[] = isSearchMode ? deferredSearchItems : deferredTabItems; let result = tabItems; if (activeFilter !== "all") { @@ -310,8 +320,8 @@ export function InboxSidebarContent({ return result; }, [ isSearchMode, - searchResponse, - activeSource.items, + deferredSearchItems, + deferredTabItems, activeTab, activeFilter, selectedSource, @@ -920,6 +930,7 @@ export function InboxSidebarContent({ "transition-colors cursor-pointer", isMarkingAsRead && "opacity-50 pointer-events-none" )} + style={{ contentVisibility: "auto", containIntrinsicSize: "0 80px" }} > {isMobile ? ( + )}
); } - const isEditable = EDITABLE_DOCUMENT_TYPES.has(doc.document_type ?? ""); + const isEditable = EDITABLE_DOCUMENT_TYPES.has(doc.document_type ?? "") && !isLargeDocument; - if (isEditing) { + if (isEditing && !isLargeDocument) { return (
@@ -229,14 +259,69 @@ export function DocumentTabContent({ documentId, searchSpaceId, title }: Documen onClick={() => setIsEditing(true)} className="gap-1.5" > - + Edit )}
- + {isLargeDocument ? ( + <> + + + + + This document is too large for the editor ( + {Math.round((doc.content_size_bytes ?? 0) / 1024 / 1024)}MB,{" "} + {doc.chunk_count ?? 0} chunks). Showing a preview below. + + + + + + + ) : ( + + )}
diff --git a/surfsense_web/components/layout/ui/tabs/TabBar.tsx b/surfsense_web/components/layout/ui/tabs/TabBar.tsx index 18e1ba141..8d0d986d3 100644 --- a/surfsense_web/components/layout/ui/tabs/TabBar.tsx +++ b/surfsense_web/components/layout/ui/tabs/TabBar.tsx @@ -72,7 +72,7 @@ export function TabBar({ onTabSwitch, onNewChat, rightActions, className }: TabB if (tabs.length <= 1) return null; return ( -
+
maxLength; + const displayContent = isTruncated ? content.slice(0, maxLength) : content; + const processedContent = convertLatexDelimiters(stripOuterMarkdownFence(displayContent)); const components: StreamdownProps["components"] = { p: ({ children, ...props }) => (

@@ -124,16 +129,31 @@ export function MarkdownViewer({ content, className }: MarkdownViewerProps) {

), hr: ({ ...props }) =>
, - img: ({ src, alt, width: _w, height: _h, ...props }) => ( - // eslint-disable-next-line @next/next/no-img-element - {alt - ), + img: ({ src, alt, width: _w, height: _h, ...props }) => { + const isDataOrUnknownUrl = typeof src === "string" && (src.startsWith("data:") || !src.startsWith("http")); + + return isDataOrUnknownUrl ? ( + // eslint-disable-next-line @next/next/no-img-element + {alt + ) : ( + {alt + ); +}, table: ({ ...props }) => (
@@ -171,6 +191,12 @@ export function MarkdownViewer({ content, className }: MarkdownViewerProps) { > {processedContent} + {isTruncated && ( +

+ Content truncated ({Math.round(content.length / 1024)}KB total). Showing first{" "} + {Math.round(maxLength / 1024)}KB. +

+ )} ); } diff --git a/surfsense_web/components/new-chat/document-mention-picker.tsx b/surfsense_web/components/new-chat/document-mention-picker.tsx index ee053a9f5..9c6521f31 100644 --- a/surfsense_web/components/new-chat/document-mention-picker.tsx +++ b/surfsense_web/components/new-chat/document-mention-picker.tsx @@ -4,6 +4,7 @@ import { keepPreviousData, useQuery } from "@tanstack/react-query"; import { forwardRef, useCallback, + useDeferredValue, useEffect, useImperativeHandle, useMemo, @@ -81,6 +82,9 @@ export const DocumentMentionPicker = forwardRef< // Debounced search value to minimize API calls and prevent race conditions const search = externalSearch; const debouncedSearch = useDebounced(search, DEBOUNCE_MS); + // Deferred snapshot of debouncedSearch — client-side filtering uses this so it + // is treated as a non-urgent update, keeping the input responsive. + const deferredSearch = useDeferredValue(debouncedSearch); const [highlightedIndex, setHighlightedIndex] = useState(0); const itemRefs = useRef>(new Map()); const scrollContainerRef = useRef(null); @@ -245,12 +249,14 @@ export const DocumentMentionPicker = forwardRef< * Client-side filtering for single character searches. * Filters cached documents locally for instant feedback without additional API calls. * Server-side search is reserved for 2+ character queries to leverage database indexing. + * Uses deferredSearch (a deferred snapshot of debouncedSearch) so this memo is treated + * as non-urgent — React can interrupt it to keep the input responsive. */ const clientFilteredDocs = useMemo(() => { if (!isSingleCharSearch) return null; - const searchLower = debouncedSearch.trim().toLowerCase(); + const searchLower = deferredSearch.trim().toLowerCase(); return accumulatedDocuments.filter((doc) => doc.title.toLowerCase().includes(searchLower)); - }, [isSingleCharSearch, debouncedSearch, accumulatedDocuments]); + }, [isSingleCharSearch, deferredSearch, accumulatedDocuments]); // Select data source based on search length: client-filtered for single char, server results for 2+ const actualDocuments = isSingleCharSearch ? (clientFilteredDocs ?? []) : accumulatedDocuments; diff --git a/surfsense_web/components/new-chat/model-selector.tsx b/surfsense_web/components/new-chat/model-selector.tsx index 7a2a471ba..b207d82b4 100644 --- a/surfsense_web/components/new-chat/model-selector.tsx +++ b/surfsense_web/components/new-chat/model-selector.tsx @@ -498,7 +498,7 @@ export function ModelSelector({ }} > - Add LLM Model + Add Model diff --git a/surfsense_web/components/new-chat/prompt-picker.tsx b/surfsense_web/components/new-chat/prompt-picker.tsx index 9fc435111..3e6457b8c 100644 --- a/surfsense_web/components/new-chat/prompt-picker.tsx +++ b/surfsense_web/components/new-chat/prompt-picker.tsx @@ -5,6 +5,7 @@ import { Plus, Zap } from "lucide-react"; import { forwardRef, useCallback, + useDeferredValue, useEffect, useImperativeHandle, useMemo, @@ -41,15 +42,19 @@ export const PromptPicker = forwardRef(funct const shouldScrollRef = useRef(false); const itemRefs = useRef>(new Map()); + // Defer the search value so filtering is non-urgent and the input stays responsive + const deferredSearch = useDeferredValue(externalSearch); + const filtered = useMemo(() => { const list = prompts ?? []; - if (!externalSearch) return list; - return list.filter((a) => a.name.toLowerCase().includes(externalSearch.toLowerCase())); - }, [prompts, externalSearch]); + if (!deferredSearch) return list; + return list.filter((a) => a.name.toLowerCase().includes(deferredSearch.toLowerCase())); + }, [prompts, deferredSearch]); - const prevSearchRef = useRef(externalSearch); - if (prevSearchRef.current !== externalSearch) { - prevSearchRef.current = externalSearch; + // Reset highlight when the deferred (filtered) search changes + const prevSearchRef = useRef(deferredSearch); + if (prevSearchRef.current !== deferredSearch) { + prevSearchRef.current = deferredSearch; if (highlightedIndex !== 0) { setHighlightedIndex(0); } diff --git a/surfsense_web/components/new-chat/source-detail-panel.tsx b/surfsense_web/components/new-chat/source-detail-panel.tsx index b02b2e217..ed6c7e4ac 100644 --- a/surfsense_web/components/new-chat/source-detail-panel.tsx +++ b/surfsense_web/components/new-chat/source-detail-panel.tsx @@ -1,7 +1,18 @@ "use client"; import { useQuery } from "@tanstack/react-query"; -import { BookOpen, ChevronDown, ExternalLink, FileText, Hash, Sparkles, X } from "lucide-react"; +import { + BookOpen, + ChevronDown, + ChevronUp, + ExternalLink, + FileQuestionMark, + FileText, + Hash, + Loader2, + Sparkles, + X, +} from "lucide-react"; import { AnimatePresence, motion, useReducedMotion } from "motion/react"; import { useTranslations } from "next-intl"; import type React from "react"; @@ -10,7 +21,6 @@ import { createPortal } from "react-dom"; import { MarkdownViewer } from "@/components/markdown-viewer"; import { Badge } from "@/components/ui/badge"; import { Button } from "@/components/ui/button"; -import { Collapsible, CollapsibleContent, CollapsibleTrigger } from "@/components/ui/collapsible"; import { ScrollArea } from "@/components/ui/scroll-area"; import { Spinner } from "@/components/ui/spinner"; import type { @@ -48,7 +58,8 @@ const formatDocumentType = (type: string) => { // which break auto-scroll functionality interface ChunkCardProps { chunk: { id: number; content: string }; - index: number; + localIndex: number; + chunkNumber: number; totalChunks: number; isCited: boolean; isActive: boolean; @@ -56,51 +67,52 @@ interface ChunkCardProps { } const ChunkCard = memo( - forwardRef(({ chunk, index, totalChunks, isCited }, ref) => { - return ( -
- {/* Cited indicator glow effect */} - {isCited &&
} - - {/* Header */} -
-
-
- {index + 1} -
- of {totalChunks} chunks -
- {isCited && ( - - - Cited Source - + forwardRef( + ({ chunk, localIndex, chunkNumber, totalChunks, isCited }, ref) => { + return ( +
+ > + {isCited &&
} - {/* Content */} -
- +
+
+
+ {chunkNumber} +
+ + Chunk {chunkNumber} of {totalChunks} + +
+ {isCited && ( + + + Cited Source + + )} +
+ +
+ +
-
- ); - }) + ); + } + ) ); ChunkCard.displayName = "ChunkCard"; @@ -118,7 +130,6 @@ export function SourceDetailPanel({ const t = useTranslations("dashboard"); const scrollAreaRef = useRef(null); const hasScrolledRef = useRef(false); // Use ref to avoid stale closures - const [summaryOpen, setSummaryOpen] = useState(false); const [activeChunkIndex, setActiveChunkIndex] = useState(null); const [mounted, setMounted] = useState(false); const [_hasScrolledToCited, setHasScrolledToCited] = useState(false); @@ -140,20 +151,93 @@ export function SourceDetailPanel({ if (isDocsChunk) { return documentsApiService.getSurfsenseDocByChunk(chunkId); } - return documentsApiService.getDocumentByChunk({ chunk_id: chunkId }); + return documentsApiService.getDocumentByChunk({ chunk_id: chunkId, chunk_window: 5 }); }, enabled: !!chunkId && open, staleTime: 5 * 60 * 1000, }); + const totalChunks = + documentData && "total_chunks" in documentData + ? (documentData.total_chunks ?? documentData.chunks.length) + : (documentData?.chunks?.length ?? 0); + const [beforeChunks, setBeforeChunks] = useState< + Array<{ id: number; content: string; created_at: string }> + >([]); + const [afterChunks, setAfterChunks] = useState< + Array<{ id: number; content: string; created_at: string }> + >([]); + const [loadingBefore, setLoadingBefore] = useState(false); + const [loadingAfter, setLoadingAfter] = useState(false); + + useEffect(() => { + setBeforeChunks([]); + setAfterChunks([]); + }, [chunkId, open]); + + const chunkStartIndex = + documentData && "chunk_start_index" in documentData ? (documentData.chunk_start_index ?? 0) : 0; + const initialChunks = documentData?.chunks ?? []; + const allChunks = [...beforeChunks, ...initialChunks, ...afterChunks]; + const absoluteStart = chunkStartIndex - beforeChunks.length; + const absoluteEnd = chunkStartIndex + initialChunks.length + afterChunks.length; + const canLoadBefore = absoluteStart > 0; + const canLoadAfter = absoluteEnd < totalChunks; + + const EXPAND_SIZE = 10; + + const loadBefore = useCallback(async () => { + if (!documentData || !("search_space_id" in documentData) || !canLoadBefore) return; + setLoadingBefore(true); + try { + const count = Math.min(EXPAND_SIZE, absoluteStart); + const result = await documentsApiService.getDocumentChunks({ + document_id: documentData.id, + page: 0, + page_size: count, + start_offset: absoluteStart - count, + }); + const existingIds = new Set(allChunks.map((c) => c.id)); + const newChunks = result.items + .filter((c) => !existingIds.has(c.id)) + .map((c) => ({ id: c.id, content: c.content, created_at: c.created_at })); + setBeforeChunks((prev) => [...newChunks, ...prev]); + } catch (err) { + console.error("Failed to load earlier chunks:", err); + } finally { + setLoadingBefore(false); + } + }, [documentData, absoluteStart, canLoadBefore, allChunks]); + + const loadAfter = useCallback(async () => { + if (!documentData || !("search_space_id" in documentData) || !canLoadAfter) return; + setLoadingAfter(true); + try { + const result = await documentsApiService.getDocumentChunks({ + document_id: documentData.id, + page: 0, + page_size: EXPAND_SIZE, + start_offset: absoluteEnd, + }); + const existingIds = new Set(allChunks.map((c) => c.id)); + const newChunks = result.items + .filter((c) => !existingIds.has(c.id)) + .map((c) => ({ id: c.id, content: c.content, created_at: c.created_at })); + setAfterChunks((prev) => [...prev, ...newChunks]); + } catch (err) { + console.error("Failed to load later chunks:", err); + } finally { + setLoadingAfter(false); + } + }, [documentData, absoluteEnd, canLoadAfter, allChunks]); + const isDirectRenderSource = sourceType === "TAVILY_API" || sourceType === "LINKUP_API" || sourceType === "SEARXNG_API" || sourceType === "BAIDU_SEARCH_API"; - // Find cited chunk index - const citedChunkIndex = documentData?.chunks?.findIndex((chunk) => chunk.id === chunkId) ?? -1; + const citedChunkIndex = allChunks.findIndex((chunk) => chunk.id === chunkId); // Simple scroll function that scrolls to a chunk by index const scrollToChunkByIndex = useCallback( @@ -336,10 +420,10 @@ export function SourceDetailPanel({ {documentData && "document_type" in documentData ? formatDocumentType(documentData.document_type) : sourceType && formatDocumentType(sourceType)} - {documentData?.chunks && ( + {totalChunks > 0 && ( - • {documentData.chunks.length} chunk - {documentData.chunks.length !== 1 ? "s" : ""} + • {totalChunks} chunk{totalChunks !== 1 ? "s" : ""} + {allChunks.length < totalChunks && ` (showing ${allChunks.length})`} )}

@@ -392,13 +476,11 @@ export function SourceDetailPanel({ animate={{ opacity: 1, scale: 1 }} className="flex flex-col items-center gap-4 text-center px-6" > -
- +
+
-

- Failed to load document -

+

Document unavailable

{documentByChunkFetchingError.message || "An unexpected error occurred. Please try again."} @@ -450,7 +532,7 @@ export function SourceDetailPanel({ {!isDirectRenderSource && documentData && (

{/* Chunk Navigation Sidebar */} - {documentData.chunks.length > 1 && ( + {allChunks.length > 1 && (
- {documentData.chunks.map((chunk, idx) => { + {allChunks.map((chunk, idx) => { + const absNum = absoluteStart + idx + 1; const isCited = chunk.id === chunkId; const isActive = activeChunkIndex === idx; return ( @@ -478,9 +561,9 @@ export function SourceDetailPanel({ ? "bg-muted text-foreground" : "bg-muted/50 text-muted-foreground hover:bg-muted hover:text-foreground" )} - title={isCited ? `Chunk ${idx + 1} (Cited)` : `Chunk ${idx + 1}`} + title={isCited ? `Chunk ${absNum} (Cited)` : `Chunk ${absNum}`} > - {idx + 1} + {absNum} {isCited && ( @@ -524,44 +607,11 @@ export function SourceDetailPanel({ )} - {/* Summary Collapsible */} - {documentData.content && ( - - - - - - Document Summary - - - - - - - - - - - - - )} - {/* Chunks Header */} -
+

- Content Chunks + Chunks {absoluteStart + 1}–{absoluteEnd} of {totalChunks}

{citedChunkIndex !== -1 && ( +
+ )} + {/* Chunks */}
- {documentData.chunks.map((chunk, idx) => { + {allChunks.map((chunk, idx) => { const isCited = chunk.id === chunkId; + const chunkNumber = absoluteStart + idx + 1; return ( 30} + disableLayoutAnimation={allChunks.length > 30} /> ); })}
+ + {/* Load Later */} + {canLoadAfter && ( +
+ +
+ )}
diff --git a/surfsense_web/components/onboarding-tour.tsx b/surfsense_web/components/onboarding-tour.tsx index 1c52169cb..a7d8f108b 100644 --- a/surfsense_web/components/onboarding-tour.tsx +++ b/surfsense_web/components/onboarding-tour.tsx @@ -429,6 +429,7 @@ export function OnboardingTour() { const pathname = usePathname(); const retryCountRef = useRef(0); const retryTimerRef = useRef | null>(null); + const startCheckTimerRef = useRef | null>(null); const maxRetries = 10; // Track previous user ID to detect user changes const previousUserIdRef = useRef(null); @@ -439,8 +440,8 @@ export function OnboardingTour() { // Fetch threads data const { data: threadsData } = useQuery({ - queryKey: ["threads", searchSpaceId, { limit: 1 }], - queryFn: () => fetchThreads(Number(searchSpaceId), 1), // Only need to check if any exist + queryKey: ["threads", searchSpaceId, { limit: 40 }], // Same key as layout + queryFn: () => fetchThreads(Number(searchSpaceId), 40), enabled: !!searchSpaceId, }); @@ -460,6 +461,7 @@ export function OnboardingTour() { // Find and track target element with retry logic const updateTarget = useCallback(() => { + if (retryTimerRef.current) clearTimeout(retryTimerRef.current); if (!currentStep) return; const el = document.querySelector(currentStep.target); @@ -480,11 +482,13 @@ export function OnboardingTour() { } }, 200); } + }, [currentStep]); + useEffect(() => { return () => { if (retryTimerRef.current) clearTimeout(retryTimerRef.current); }; - }, [currentStep]); + }, []); // Check if tour should run: localStorage + data validation with user ID tracking useEffect(() => { @@ -573,15 +577,15 @@ export function OnboardingTour() { setPosition(calculatePosition(connectorEl, TOUR_STEPS[0].placement)); } else { // Retry after delay - setTimeout(checkAndStartTour, 200); + startCheckTimerRef.current = setTimeout(checkAndStartTour, 200); } }; // Start checking after initial delay - const timer = setTimeout(checkAndStartTour, 500); + startCheckTimerRef.current = setTimeout(checkAndStartTour, 500); return () => { cancelled = true; - clearTimeout(timer); + if (startCheckTimerRef.current) clearTimeout(startCheckTimerRef.current); }; }, [mounted, user?.id, searchSpaceId, pathname, threadsData, documentTypeCounts, connectors]); diff --git a/surfsense_web/components/public-chat-snapshots/public-chat-snapshot-row.tsx b/surfsense_web/components/public-chat-snapshots/public-chat-snapshot-row.tsx index ddf4746aa..4bb49c48d 100644 --- a/surfsense_web/components/public-chat-snapshots/public-chat-snapshot-row.tsx +++ b/surfsense_web/components/public-chat-snapshots/public-chat-snapshot-row.tsx @@ -1,6 +1,6 @@ "use client"; -import { Check, Copy, ExternalLink, MessageSquare, Trash2 } from "lucide-react"; +import { Check, Copy, Dot, ExternalLink, MessageSquare, Trash2 } from "lucide-react"; import { useCallback, useRef, useState } from "react"; import { Avatar, AvatarFallback, AvatarImage } from "@/components/ui/avatar"; import { Badge } from "@/components/ui/badge"; @@ -153,7 +153,7 @@ export function PublicChatSnapshotRow({ {formattedDate} {member && ( <> - · + diff --git a/surfsense_web/components/public-chat-snapshots/public-chat-snapshots-empty-state.tsx b/surfsense_web/components/public-chat-snapshots/public-chat-snapshots-empty-state.tsx index 4a4a57770..4e8ec5bb6 100644 --- a/surfsense_web/components/public-chat-snapshots/public-chat-snapshots-empty-state.tsx +++ b/surfsense_web/components/public-chat-snapshots/public-chat-snapshots-empty-state.tsx @@ -11,11 +11,8 @@ export function PublicChatSnapshotsEmptyState({ }: PublicChatSnapshotsEmptyStateProps) { return (
-
- -
-

{title}

-

{description}

+

{title}

+

{description}

); } diff --git a/surfsense_web/components/public-chat/public-chat-footer.tsx b/surfsense_web/components/public-chat/public-chat-footer.tsx index 79b317ddf..e341a9a0c 100644 --- a/surfsense_web/components/public-chat/public-chat-footer.tsx +++ b/surfsense_web/components/public-chat/public-chat-footer.tsx @@ -1,7 +1,7 @@ "use client"; import { Copy } from "lucide-react"; -import { useRouter, useSearchParams } from "next/navigation"; +import { useRouter } from "next/navigation"; import { useCallback, useEffect, useRef, useState } from "react"; import { toast } from "sonner"; import { Button } from "@/components/ui/button"; @@ -15,7 +15,6 @@ interface PublicChatFooterProps { export function PublicChatFooter({ shareToken }: PublicChatFooterProps) { const router = useRouter(); - const searchParams = useSearchParams(); const [isCloning, setIsCloning] = useState(false); const hasAutoCloned = useRef(false); @@ -36,9 +35,11 @@ export function PublicChatFooter({ shareToken }: PublicChatFooterProps) { } }, [shareToken, router]); - // Auto-trigger clone if user just logged in with action=clone + // Auto-trigger clone if user just logged in with action=clone. + // Read from window.location.search on mount — no subscription needed since + // this is a one-time post-login check. (Vercel Best Practice: rerender-defer-reads 5.2) useEffect(() => { - const action = searchParams.get("action"); + const action = new URLSearchParams(window.location.search).get("action"); const token = getBearerToken(); // Only auto-clone once, if authenticated and action=clone is present @@ -46,7 +47,7 @@ export function PublicChatFooter({ shareToken }: PublicChatFooterProps) { hasAutoCloned.current = true; triggerClone(); } - }, [searchParams, isCloning, triggerClone]); + }, [isCloning, triggerClone]); const handleCopyAndContinue = async () => { const token = getBearerToken(); diff --git a/surfsense_web/components/settings/image-model-manager.tsx b/surfsense_web/components/settings/image-model-manager.tsx index 8f08b7db3..23162b629 100644 --- a/surfsense_web/components/settings/image-model-manager.tsx +++ b/surfsense_web/components/settings/image-model-manager.tsx @@ -1,7 +1,7 @@ "use client"; import { useAtomValue } from "jotai"; -import { AlertCircle, Edit3, Info, Plus, RefreshCw, Trash2, Wand2 } from "lucide-react"; +import { AlertCircle, Dot, Edit3, Info, RefreshCw, Trash2, Wand2 } from "lucide-react"; import { useMemo, useState } from "react"; import { deleteImageGenConfigMutationAtom } from "@/atoms/image-gen-config/image-gen-config-mutation.atoms"; import { @@ -240,27 +240,14 @@ export function ImageModelManager({ searchSpaceId }: ImageModelManagerProps) { {!isLoading && (
{(userConfigs?.length ?? 0) === 0 ? ( - + -
- -
-

No Image Models Yet

-

+

No Image Models Yet

+

{canCreate ? "Add your own image generation model (DALL-E 3, GPT Image 1, etc.)" : "No image models have been added to this space yet. Contact a space owner to add one."}

- {canCreate && ( - - )} ) : ( @@ -343,7 +330,7 @@ export function ImageModelManager({ searchSpaceId }: ImageModelManagerProps) { {member && ( <> - · + diff --git a/surfsense_web/components/settings/llm-role-manager.tsx b/surfsense_web/components/settings/llm-role-manager.tsx index eaf14d8e8..386845d7d 100644 --- a/surfsense_web/components/settings/llm-role-manager.tsx +++ b/surfsense_web/components/settings/llm-role-manager.tsx @@ -4,17 +4,15 @@ import { useAtomValue } from "jotai"; import { AlertCircle, Bot, - CheckCircle, + CircleCheck, CircleDashed, Eye, FileText, ImageIcon, RefreshCw, - RotateCcw, - Save, Shuffle, } from "lucide-react"; -import { useEffect, useState } from "react"; +import { useCallback, useEffect, useRef, useState } from "react"; import { toast } from "sonner"; import { globalImageGenConfigsAtom, @@ -41,6 +39,7 @@ import { SelectValue, } from "@/components/ui/select"; import { Skeleton } from "@/components/ui/skeleton"; +import { Spinner } from "@/components/ui/spinner"; import { getProviderIcon } from "@/lib/provider-icons"; import { cn } from "@/lib/utils"; @@ -49,8 +48,8 @@ const ROLE_DESCRIPTIONS = { icon: Bot, title: "Agent LLM", description: "Primary LLM for chat interactions and agent operations", - color: "text-blue-600 dark:text-blue-400", - bgColor: "bg-blue-500/10", + color: "text-muted-foreground", + bgColor: "bg-muted", prefKey: "agent_llm_id" as const, configType: "llm" as const, }, @@ -58,8 +57,8 @@ const ROLE_DESCRIPTIONS = { icon: FileText, title: "Document Summary LLM", description: "Handles document summarization and research synthesis", - color: "text-purple-600 dark:text-purple-400", - bgColor: "bg-purple-500/10", + color: "text-muted-foreground", + bgColor: "bg-muted", prefKey: "document_summary_llm_id" as const, configType: "llm" as const, }, @@ -67,8 +66,8 @@ const ROLE_DESCRIPTIONS = { icon: ImageIcon, title: "Image Generation Model", description: "Model used for AI image generation (DALL-E, GPT Image, etc.)", - color: "text-teal-600 dark:text-teal-400", - bgColor: "bg-teal-500/10", + color: "text-muted-foreground", + bgColor: "bg-muted", prefKey: "image_generation_config_id" as const, configType: "image" as const, }, @@ -129,18 +128,18 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { vision_llm_id: preferences.vision_llm_id ?? "", })); - const [hasChanges, setHasChanges] = useState(false); - const [isSaving, setIsSaving] = useState(false); + const [savingRole, setSavingRole] = useState(null); + const savingRef = useRef(false); useEffect(() => { - const newAssignments = { - agent_llm_id: preferences.agent_llm_id ?? "", - document_summary_llm_id: preferences.document_summary_llm_id ?? "", - image_generation_config_id: preferences.image_generation_config_id ?? "", - vision_llm_id: preferences.vision_llm_id ?? "", - }; - setAssignments(newAssignments); - setHasChanges(false); + if (!savingRef.current) { + setAssignments({ + agent_llm_id: preferences.agent_llm_id ?? "", + document_summary_llm_id: preferences.document_summary_llm_id ?? "", + image_generation_config_id: preferences.image_generation_config_id ?? "", + vision_llm_id: preferences.vision_llm_id ?? "", + }); + } }, [ preferences?.agent_llm_id, preferences?.document_summary_llm_id, @@ -148,77 +147,27 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { preferences?.vision_llm_id, ]); - const handleRoleAssignment = (prefKey: string, configId: string) => { - const newAssignments = { - ...assignments, - [prefKey]: configId === "unassigned" ? "" : parseInt(configId), - }; + const handleRoleAssignment = useCallback( + async (prefKey: string, configId: string) => { + const value = configId === "unassigned" ? "" : parseInt(configId); - setAssignments(newAssignments); + setAssignments((prev) => ({ ...prev, [prefKey]: value })); + setSavingRole(prefKey); + savingRef.current = true; - const currentPrefs = { - agent_llm_id: preferences.agent_llm_id ?? "", - document_summary_llm_id: preferences.document_summary_llm_id ?? "", - image_generation_config_id: preferences.image_generation_config_id ?? "", - vision_llm_id: preferences.vision_llm_id ?? "", - }; - - const hasChangesNow = Object.keys(newAssignments).some( - (key) => - newAssignments[key as keyof typeof newAssignments] !== - currentPrefs[key as keyof typeof currentPrefs] - ); - - setHasChanges(hasChangesNow); - }; - - const handleSave = async () => { - setIsSaving(true); - - const toNumericOrUndefined = (val: string | number) => - typeof val === "string" ? (val ? parseInt(val) : undefined) : val; - - const numericAssignments = { - agent_llm_id: toNumericOrUndefined(assignments.agent_llm_id), - document_summary_llm_id: toNumericOrUndefined(assignments.document_summary_llm_id), - image_generation_config_id: toNumericOrUndefined(assignments.image_generation_config_id), - vision_llm_id: toNumericOrUndefined(assignments.vision_llm_id), - }; - - await updatePreferences({ - search_space_id: searchSpaceId, - data: numericAssignments, - }); - - setHasChanges(false); - toast.success("Role assignments saved successfully!"); - - setIsSaving(false); - }; - - const handleReset = () => { - setAssignments({ - agent_llm_id: preferences.agent_llm_id ?? "", - document_summary_llm_id: preferences.document_summary_llm_id ?? "", - image_generation_config_id: preferences.image_generation_config_id ?? "", - vision_llm_id: preferences.vision_llm_id ?? "", - }); - setHasChanges(false); - }; - - const isAssignmentComplete = - assignments.agent_llm_id !== "" && - assignments.agent_llm_id !== null && - assignments.agent_llm_id !== undefined && - assignments.document_summary_llm_id !== "" && - assignments.document_summary_llm_id !== null && - assignments.document_summary_llm_id !== undefined && - assignments.image_generation_config_id !== "" && - assignments.image_generation_config_id !== null && - assignments.image_generation_config_id !== undefined && - assignments.vision_llm_id !== "" && - assignments.vision_llm_id !== null && - assignments.vision_llm_id !== undefined; + try { + await updatePreferences({ + search_space_id: searchSpaceId, + data: { [prefKey]: value || undefined }, + }); + toast.success("Role assignment updated"); + } finally { + setSavingRole(null); + savingRef.current = false; + } + }, + [updatePreferences, searchSpaceId] + ); // Combine global and custom LLM configs const allLLMConfigs = [ @@ -232,6 +181,11 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { ...(userImageConfigs ?? []).filter((config) => config.id && config.id.toString().trim() !== ""), ]; + const isAssignmentComplete = + allLLMConfigs.some((c) => c.id === assignments.agent_llm_id) && + allLLMConfigs.some((c) => c.id === assignments.document_summary_llm_id) && + allImageConfigs.some((c) => c.id === assignments.image_generation_config_id); + const isLoading = configsLoading || preferencesLoading || @@ -261,11 +215,8 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { Refresh {isAssignmentComplete && !isLoading && !hasError && ( - - + + All roles assigned )} @@ -351,10 +302,7 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { const roleAllConfigs = isImageRole ? allImageConfigs : allLLMConfigs; const assignedConfig = roleAllConfigs.find((config) => config.id === currentAssignment); - const isAssigned = - currentAssignment !== "" && - currentAssignment !== null && - currentAssignment !== undefined; + const isAssigned = !!assignedConfig; const isAutoMode = assignedConfig && "is_auto_mode" in assignedConfig && assignedConfig.is_auto_mode; @@ -380,8 +328,10 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) {

- {isAssigned ? ( - + {savingRole === role.prefKey ? ( + + ) : isAssigned ? ( + ) : ( )} @@ -393,7 +343,7 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { Configuration - {isFileCountLimitReached ? ( -
- -
-

- {t("file_limit_reached")} -

-

- {t("file_limit_reached_desc", { max: MAX_FILES })} -

-
-
- ) : isDragActive ? ( -
- -

{t("drop_files")}

-
- ) : ( -
- -
-

{t("drag_drop")}

-

{t("or_browse")}

-
- {files.length > 0 && ( -

- {t("remaining_capacity", { files: remainingFiles, sizeMB: remainingSizeMB })} -

- )} -
- )} - {!isFileCountLimitReached && ( -
- -
- )} + + + Files + + + + Folder + + + + ); + } + + return ( + + e.stopPropagation()}> + + + e.stopPropagation()} + > + fileInputRef.current?.click()}> + + {t("browse_files")} + + folderInputRef.current?.click()}> + + {t("browse_folder")} + + + + ); + }; + + return ( +
+ {/* Hidden file input */} + + + {/* Hidden folder input for web folder browsing */} + )} + /> + + {/* MOBILE DROP ZONE */} +
+ {hasContent ? ( + !selectedFolder && + (isElectron ? ( +
{renderBrowseButton({ compact: true, fullWidth: true })}
+ ) : ( + + )) + ) : ( +
{ + if (!isElectron) fileInputRef.current?.click(); + }} + > + +
+

+ {isElectron ? "Select files or folder" : "Tap to select files or folder"} +

+

{t("file_size_limit")}

+
+
e.stopPropagation()}> + {renderBrowseButton({ fullWidth: true })} +
- - + )} +
- {files.length > 0 && ( - - -
-
- - {t("selected_files", { count: files.length })} - - - {t("total_size")}: {formatFileSize(totalFileSize)} - -
- -
- - -
- {files.map((entry) => ( -
-
- -
-

{entry.file.name}

-
- - {formatFileSize(entry.file.size)} - - - {entry.file.type || "Unknown type"} - -
-
-
- -
- ))} -
- - {isUploading && ( -
- -
-
- {t("uploading_files")} - {Math.round(uploadProgress)}% -
- -
+ {/* DESKTOP DROP ZONE */} +
+ {hasContent ? ( +
+ + + {isDragActive ? t("drop_files") : t("drag_drop_more")} + + {renderBrowseButton({ compact: true })} +
+ ) : ( +
+ {isDragActive && ( +
+ +

{t("drop_files")}

)} - -
- +
+ +

{t("drag_drop")}

+

{t("file_size_limit")}

+
{renderBrowseButton()}
+
+ )} +
-
- + {/* FOLDER SELECTED (Electron only — web flattens folder contents into file list) */} + {isElectron && selectedFolder && ( +
+
+ +
+

{selectedFolder.name}

+

{selectedFolder.path}

- - + +
+ +
+
+
+

Watch folder

+

Auto-sync when files change

+
+ +
+
+
+

Enable AI Summary

+

+ Improves search quality but adds latency +

+
+ +
+
+ + +
)} + {/* FILES SELECTED */} + {files.length > 0 && ( +
+
+

+ {t("selected_files", { count: files.length })} + + {formatFileSize(totalFileSize)} +

+ +
+ +
+ {files.map((entry) => ( +
+ + {entry.file.name.split(".").pop() || "?"} + + {entry.file.name} + + {formatFileSize(entry.file.size)} + + +
+ ))} +
+ + {isUploading && ( +
+
+ {t("uploading_files")} + {Math.round(uploadProgress)}% +
+ +
+ )} + +
+
+

Enable AI Summary

+

+ Improves search quality but adds latency +

+
+ +
+ + +
+ )} + + {/* SUPPORTED FORMATS */} - - -
-
-
- {t("supported_file_types")} -
-
- {t("file_types_desc")} -
-
-
+ + + + {t("supported_file_types")} + - -
+ +
{supportedExtensions.map((ext) => ( - + {ext} ))} diff --git a/surfsense_web/components/tool-ui/citation/citation-list.tsx b/surfsense_web/components/tool-ui/citation/citation-list.tsx index 3151917b6..75b02bf3d 100644 --- a/surfsense_web/components/tool-ui/citation/citation-list.tsx +++ b/surfsense_web/components/tool-ui/citation/citation-list.tsx @@ -7,6 +7,8 @@ import { openSafeNavigationHref, resolveSafeNavigationHref } from "../shared/med import { cn, Popover, PopoverContent, PopoverTrigger } from "./_adapter"; import { Citation } from "./citation"; import type { CitationType, CitationVariant, SerializableCitation } from "./schema"; +import NextImage from 'next/image'; + const TYPE_ICONS: Record = { webpage: Globe, @@ -253,18 +255,18 @@ function OverflowItem({ citation, onClick }: OverflowItemProps) { className="group hover:bg-muted focus-visible:bg-muted flex w-full cursor-pointer items-center gap-2.5 rounded-md px-2 py-2 text-left transition-colors focus-visible:outline-none" > {citation.favicon ? ( - // biome-ignore lint/performance/noImgElement: external favicon from arbitrary domain — next/image requires remotePatterns config - - ) : ( -