mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-04-25 00:36:31 +02:00
Merge branch 'dev' into fix/replace-transition-all-with-specific-transitions
This commit is contained in:
Soham Bhattacharjee
GitHub
commit
e404b05b11
295 changed files with 25773 additions and 10799 deletions
40
.github/workflows/desktop-release.yml
vendored
40
.github/workflows/desktop-release.yml
vendored
|
|
@ -5,6 +5,20 @@ on:
|
|||
tags:
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- 'v*'
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- 'beta-v*'
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workflow_dispatch:
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inputs:
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version:
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description: 'Version number (e.g. 0.0.15) — used for dry-run testing without a tag'
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required: true
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default: '0.0.0-test'
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publish:
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description: 'Publish to GitHub Releases'
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required: true
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type: choice
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options:
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- never
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- always
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default: 'never'
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permissions:
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contents: write
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|
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@ -25,24 +39,28 @@ jobs:
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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uses: actions/checkout@v5
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- name: Extract version from tag
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- name: Extract version
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id: version
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shell: bash
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run: |
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TAG=${GITHUB_REF#refs/tags/}
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VERSION=${TAG#beta-}
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VERSION=${VERSION#v}
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if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
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VERSION="${{ inputs.version }}"
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else
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TAG=${GITHUB_REF#refs/tags/}
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VERSION=${TAG#beta-}
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VERSION=${VERSION#v}
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fi
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echo "VERSION=$VERSION" >> "$GITHUB_OUTPUT"
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- name: Setup pnpm
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uses: pnpm/action-setup@v4
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uses: pnpm/action-setup@v5
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|
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- name: Setup Node.js
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uses: actions/setup-node@v4
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uses: actions/setup-node@v5
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with:
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node-version: 20
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node-version: 22
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cache: 'pnpm'
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cache-dependency-path: |
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surfsense_web/pnpm-lock.yaml
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@ -60,6 +78,7 @@ jobs:
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NEXT_PUBLIC_ZERO_CACHE_URL: ${{ vars.NEXT_PUBLIC_ZERO_CACHE_URL }}
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NEXT_PUBLIC_DEPLOYMENT_MODE: ${{ vars.NEXT_PUBLIC_DEPLOYMENT_MODE }}
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NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: ${{ vars.NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE }}
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NEXT_PUBLIC_POSTHOG_KEY: ${{ secrets.NEXT_PUBLIC_POSTHOG_KEY }}
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|
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- name: Install desktop dependencies
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run: pnpm install
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||||
|
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@ -70,9 +89,12 @@ jobs:
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working-directory: surfsense_desktop
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env:
|
||||
HOSTED_FRONTEND_URL: ${{ vars.HOSTED_FRONTEND_URL }}
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POSTHOG_KEY: ${{ secrets.POSTHOG_KEY }}
|
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POSTHOG_HOST: ${{ vars.POSTHOG_HOST }}
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||||
|
||||
- name: Package & Publish
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run: pnpm exec electron-builder ${{ matrix.platform }} --config electron-builder.yml --publish always -c.extraMetadata.version=${{ steps.version.outputs.VERSION }}
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shell: bash
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run: pnpm exec electron-builder ${{ matrix.platform }} --config electron-builder.yml --publish ${{ inputs.publish || 'always' }} -c.extraMetadata.version=${{ steps.version.outputs.VERSION }}
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working-directory: surfsense_desktop
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||||
env:
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GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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|
|
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|||
78
README.es.md
78
README.es.md
|
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@ -21,9 +21,29 @@
|
|||
</div>
|
||||
|
||||
# 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.
|
||||
|
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...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.
|
||||
- **Aplicación de Escritorio** - Obtén asistencia de IA en cualquier aplicación con Quick Assist, General Assist y Extreme Assist.
|
||||
|
||||
...y más por venir.
|
||||
|
||||
|
||||
|
||||
|
|
@ -34,7 +54,7 @@ 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
|
||||
|
||||
|
||||
|
||||
|
|
@ -111,6 +131,18 @@ El script de instalación configura [Watchtower](https://github.com/nicholas-fed
|
|||
|
||||
Para Docker Compose, instalación manual y otras opciones de despliegue, consulta la [documentación](https://www.surfsense.com/docs/).
|
||||
|
||||
### Aplicación de Escritorio
|
||||
|
||||
SurfSense también ofrece una aplicación de escritorio que lleva la asistencia de IA a cada aplicación en tu computadora. Descárgala desde la [última versión](https://github.com/MODSetter/SurfSense/releases/latest).
|
||||
|
||||
La aplicación de escritorio incluye tres potentes funciones:
|
||||
|
||||
- **General Assist** — Lanza SurfSense al instante desde cualquier aplicación con un atajo global.
|
||||
- **Quick Assist** — Selecciona texto en cualquier lugar, luego pide a la IA que lo explique, reescriba o actúe sobre él.
|
||||
- **Extreme Assist** — Obtén sugerencias de escritura en línea impulsadas por tu base de conocimiento mientras escribes en cualquier aplicación.
|
||||
|
||||
Las tres funciones operan contra tu espacio de búsqueda elegido, por lo que tus respuestas siempre están basadas en tus propios datos.
|
||||
|
||||
### Cómo Colaborar en Tiempo Real (Beta)
|
||||
|
||||
1. Ve a la página de Gestión de Miembros y crea una invitación.
|
||||
|
|
@ -133,24 +165,30 @@ Para Docker Compose, instalación manual y otras opciones de despliegue, consult
|
|||
|
||||
<p align="center"><img src="https://github.com/user-attachments/assets/3b04477d-8f42-4baa-be95-867c1eaeba87" alt="Comentarios en Tiempo Real" /></p>
|
||||
|
||||
## 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) |
|
||||
| **Aplicación de Escritorio** | No | Aplicación nativa con General Assist, Quick Assist y Extreme Assist — asistencia de IA en cualquier aplicación |
|
||||
| **Extensión de Navegador** | No | Extensión multi-navegador para guardar cualquier página web, incluyendo páginas protegidas por autenticación |
|
||||
|
||||
<details>
|
||||
<summary><b>Lista completa de Fuentes Externas</b></summary>
|
||||
|
|
|
|||
78
README.hi.md
78
README.hi.md
|
|
@ -21,9 +21,29 @@
|
|||
</div>
|
||||
|
||||
# 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।
|
||||
- **डेस्कटॉप ऐप** - Quick Assist, General Assist और Extreme Assist के साथ किसी भी एप्लिकेशन में AI सहायता प्राप्त करें।
|
||||
|
||||
...और भी बहुत कुछ आने वाला है।
|
||||
|
||||
|
||||
|
||||
|
|
@ -34,7 +54,7 @@ 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
|
||||
|
||||
|
||||
|
||||
|
|
@ -111,6 +131,18 @@ irm https://raw.githubusercontent.com/MODSetter/SurfSense/main/docker/scripts/in
|
|||
|
||||
Docker Compose, मैनुअल इंस्टॉलेशन और अन्य डिप्लॉयमेंट विकल्पों के लिए, [डॉक्स](https://www.surfsense.com/docs/) देखें।
|
||||
|
||||
### डेस्कटॉप ऐप
|
||||
|
||||
SurfSense एक डेस्कटॉप ऐप भी प्रदान करता है जो आपके कंप्यूटर पर हर एप्लिकेशन में AI सहायता लाता है। इसे [नवीनतम रिलीज़](https://github.com/MODSetter/SurfSense/releases/latest) से डाउनलोड करें।
|
||||
|
||||
डेस्कटॉप ऐप में तीन शक्तिशाली सुविधाएं शामिल हैं:
|
||||
|
||||
- **General Assist** — एक ग्लोबल शॉर्टकट से किसी भी एप्लिकेशन से तुरंत SurfSense लॉन्च करें।
|
||||
- **Quick Assist** — कहीं भी टेक्स्ट चुनें, फिर AI से समझाने, फिर से लिखने या उस पर कार्रवाई करने को कहें।
|
||||
- **Extreme Assist** — किसी भी ऐप में टाइप करते समय अपनी नॉलेज बेस से संचालित इनलाइन लेखन सुझाव प्राप्त करें।
|
||||
|
||||
तीनों सुविधाएं आपके चुने हुए सर्च स्पेस पर काम करती हैं, ताकि आपके उत्तर हमेशा आपके अपने डेटा पर आधारित हों।
|
||||
|
||||
### रीयल-टाइम सहयोग कैसे करें (बीटा)
|
||||
|
||||
1. सदस्य प्रबंधन पेज पर जाएं और एक आमंत्रण बनाएं।
|
||||
|
|
@ -133,24 +165,30 @@ Docker Compose, मैनुअल इंस्टॉलेशन और अन
|
|||
|
||||
<p align="center"><img src="https://github.com/user-attachments/assets/3b04477d-8f42-4baa-be95-867c1eaeba87" alt="रीयल-टाइम कमेंट्स" /></p>
|
||||
|
||||
## 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) |
|
||||
| **डेस्कटॉप ऐप** | नहीं | General Assist, Quick Assist और Extreme Assist के साथ नेटिव ऐप — किसी भी एप्लिकेशन में AI सहायता |
|
||||
| **ब्राउज़र एक्सटेंशन** | नहीं | किसी भी वेबपेज को सहेजने के लिए क्रॉस-ब्राउज़र एक्सटेंशन, प्रमाणीकरण सुरक्षित पेज सहित |
|
||||
|
||||
<details>
|
||||
<summary><b>बाहरी स्रोतों की पूरी सूची</b></summary>
|
||||
|
|
|
|||
76
README.md
76
README.md
|
|
@ -21,9 +21,29 @@
|
|||
</div>
|
||||
|
||||
# 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.
|
||||
- **Desktop App** - Get AI assistance in any application with Quick Assist, General Assist, and Extreme Assist.
|
||||
|
||||
...and more to come.
|
||||
|
||||
|
||||
|
||||
|
|
@ -112,6 +132,18 @@ The install script sets up [Watchtower](https://github.com/nicholas-fedor/watcht
|
|||
|
||||
For Docker Compose, manual installation, and other deployment options, see the [docs](https://www.surfsense.com/docs/).
|
||||
|
||||
### Desktop App
|
||||
|
||||
SurfSense also ships a desktop app that brings AI assistance to every application on your computer. Download it from the [latest release](https://github.com/MODSetter/SurfSense/releases/latest).
|
||||
|
||||
The desktop app includes three powerful features:
|
||||
|
||||
- **General Assist** — Launch SurfSense instantly from any application with a global shortcut.
|
||||
- **Quick Assist** — Select text anywhere, then ask AI to explain, rewrite, or act on it.
|
||||
- **Extreme Assist** — Get inline writing suggestions powered by your knowledge base as you type in any app.
|
||||
|
||||
All three features operate against your chosen search space, so your answers are always grounded in your own data.
|
||||
|
||||
### How to Realtime Collaborate (Beta)
|
||||
|
||||
1. Go to Manage Members page and create an invite.
|
||||
|
|
@ -134,24 +166,30 @@ For Docker Compose, manual installation, and other deployment options, see the [
|
|||
|
||||
<p align="center"><img src="https://github.com/user-attachments/assets/3b04477d-8f42-4baa-be95-867c1eaeba87" alt="Realtime Comments" /></p>
|
||||
|
||||
## 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) |
|
||||
| **Desktop App** | No | Native app with General Assist, Quick Assist, and Extreme Assist — AI help in any application |
|
||||
| **Browser Extension** | No | Cross-browser extension to save any webpage, including auth-protected pages |
|
||||
|
||||
<details>
|
||||
<summary><b>Full list of External Sources</b></summary>
|
||||
|
|
|
|||
|
|
@ -21,9 +21,29 @@
|
|||
</div>
|
||||
|
||||
# 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.
|
||||
- **Aplicativo Desktop** - Obtenha assistência de IA em qualquer aplicativo com Quick Assist, General Assist e Extreme Assist.
|
||||
|
||||
...e mais por vir.
|
||||
|
||||
|
||||
|
||||
|
|
@ -34,7 +54,7 @@ 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
|
||||
|
||||
|
||||
|
||||
|
|
@ -111,6 +131,18 @@ O script de instalação configura o [Watchtower](https://github.com/nicholas-fe
|
|||
|
||||
Para Docker Compose, instalação manual e outras opções de implantação, consulte a [documentação](https://www.surfsense.com/docs/).
|
||||
|
||||
### Aplicativo Desktop
|
||||
|
||||
O SurfSense também oferece um aplicativo desktop que traz assistência de IA para cada aplicativo no seu computador. Baixe-o na [última versão](https://github.com/MODSetter/SurfSense/releases/latest).
|
||||
|
||||
O aplicativo desktop inclui três recursos poderosos:
|
||||
|
||||
- **General Assist** — Abra o SurfSense instantaneamente de qualquer aplicativo com um atalho global.
|
||||
- **Quick Assist** — Selecione texto em qualquer lugar, depois peça à IA para explicar, reescrever ou agir sobre ele.
|
||||
- **Extreme Assist** — Receba sugestões de escrita em linha alimentadas pela sua base de conhecimento enquanto digita em qualquer aplicativo.
|
||||
|
||||
Os três recursos operam no espaço de busca escolhido, para que suas respostas sejam sempre baseadas nos seus próprios dados.
|
||||
|
||||
### Como Colaborar em Tempo Real (Beta)
|
||||
|
||||
1. Acesse a página de Gerenciar Membros e crie um convite.
|
||||
|
|
@ -133,24 +165,30 @@ Para Docker Compose, instalação manual e outras opções de implantação, con
|
|||
|
||||
<p align="center"><img src="https://github.com/user-attachments/assets/3b04477d-8f42-4baa-be95-867c1eaeba87" alt="Comentários em Tempo Real" /></p>
|
||||
|
||||
## 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) |
|
||||
| **Aplicativo Desktop** | Não | Aplicativo nativo com General Assist, Quick Assist e Extreme Assist — assistência de IA em qualquer aplicativo |
|
||||
| **Extensão de Navegador** | Não | Extensão multi-navegador para salvar qualquer página web, incluindo páginas protegidas por autenticação |
|
||||
|
||||
<details>
|
||||
<summary><b>Lista completa de Fontes Externas</b></summary>
|
||||
|
|
|
|||
|
|
@ -21,9 +21,29 @@
|
|||
</div>
|
||||
|
||||
# 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。
|
||||
- **桌面应用** - 通过 Quick Assist、General Assist 和 Extreme Assist 在任何应用程序中获得 AI 助手。
|
||||
|
||||
...更多功能即将推出。
|
||||
|
||||
|
||||
|
||||
|
|
@ -34,7 +54,7 @@ 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
|
||||
|
||||
|
||||
|
||||
|
|
@ -111,6 +131,18 @@ irm https://raw.githubusercontent.com/MODSetter/SurfSense/main/docker/scripts/in
|
|||
|
||||
如需 Docker Compose、手动安装及其他部署方式，请查看[文档](https://www.surfsense.com/docs/)。
|
||||
|
||||
### 桌面应用
|
||||
|
||||
SurfSense 还提供桌面应用，将 AI 助手带到您计算机上的每个应用程序中。从[最新版本](https://github.com/MODSetter/SurfSense/releases/latest)下载。
|
||||
|
||||
桌面应用包含三个强大功能：
|
||||
|
||||
- **General Assist** — 通过全局快捷键从任何应用程序即时启动 SurfSense。
|
||||
- **Quick Assist** — 在任何位置选中文本，然后让 AI 解释、改写或对其执行操作。
|
||||
- **Extreme Assist** — 在任何应用中输入时，获得基于您知识库的内联写作建议。
|
||||
|
||||
三个功能均基于您选择的搜索空间运行，确保回答始终以您自己的数据为依据。
|
||||
|
||||
### 如何实时协作（Beta）
|
||||
|
||||
1. 前往成员管理页面并创建邀请。
|
||||
|
|
@ -133,24 +165,30 @@ irm https://raw.githubusercontent.com/MODSetter/SurfSense/main/docker/scripts/in
|
|||
|
||||
<p align="center"><img src="https://github.com/user-attachments/assets/3b04477d-8f42-4baa-be95-867c1eaeba87" alt="实时评论" /></p>
|
||||
|
||||
## 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） |
|
||||
| **桌面应用** | 否 | 原生应用，包含 General Assist、Quick Assist 和 Extreme Assist — 在任何应用程序中获得 AI 助手 |
|
||||
| **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 |
|
||||
|
||||
<details>
|
||||
<summary><b>外部数据源完整列表</b></summary>
|
||||
|
|
|
|||
|
|
@ -282,6 +282,9 @@ STT_SERVICE=local/base
|
|||
|
||||
# LlamaCloud (if ETL_SERVICE=LLAMACLOUD)
|
||||
# LLAMA_CLOUD_API_KEY=
|
||||
# Optional: Azure Document Intelligence accelerator (used with LLAMACLOUD)
|
||||
# AZURE_DI_ENDPOINT=https://your-resource.cognitiveservices.azure.com/
|
||||
# AZURE_DI_KEY=
|
||||
|
||||
# ------------------------------------------------------------------------------
|
||||
# Observability (optional)
|
||||
|
|
|
|||
|
|
@ -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**
|
||||
|
|
|
|||
6
package-lock.json
generated
Normal file
6
package-lock.json
generated
Normal file
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"name": "SurfSense",
|
||||
"lockfileVersion": 3,
|
||||
"requires": true,
|
||||
"packages": {}
|
||||
}
|
||||
5
package.json
Normal file
5
package.json
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"name": "surfsense",
|
||||
"private": true,
|
||||
"packageManager": "pnpm@10.24.0"
|
||||
}
|
||||
|
|
@ -193,6 +193,9 @@ FIRECRAWL_API_KEY=fcr-01J0000000000000000000000
|
|||
ETL_SERVICE=UNSTRUCTURED or LLAMACLOUD or DOCLING
|
||||
UNSTRUCTURED_API_KEY=Tpu3P0U8iy
|
||||
LLAMA_CLOUD_API_KEY=llx-nnn
|
||||
# Optional: Azure Document Intelligence accelerator (used when ETL_SERVICE=LLAMACLOUD)
|
||||
# AZURE_DI_ENDPOINT=https://your-resource.cognitiveservices.azure.com/
|
||||
# AZURE_DI_KEY=your-key
|
||||
|
||||
# OPTIONAL: Add these for LangSmith Observability
|
||||
LANGSMITH_TRACING=true
|
||||
|
|
|
|||
|
|
@ -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}")
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -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'))
|
||||
|
|
@ -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")
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
"""119_add_vision_llm_id_to_search_spaces
|
||||
|
||||
Revision ID: 119
|
||||
Revises: 118
|
||||
|
||||
Adds vision_llm_id column to search_spaces for vision/screenshot analysis
|
||||
LLM role assignment. Defaults to 0 (Auto mode), same convention as
|
||||
agent_llm_id and document_summary_llm_id.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Sequence
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision: str = "119"
|
||||
down_revision: str | None = "118"
|
||||
branch_labels: str | Sequence[str] | None = None
|
||||
depends_on: str | Sequence[str] | None = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
existing_columns = [
|
||||
col["name"] for col in sa.inspect(conn).get_columns("searchspaces")
|
||||
]
|
||||
|
||||
if "vision_llm_id" not in existing_columns:
|
||||
op.add_column(
|
||||
"searchspaces",
|
||||
sa.Column("vision_llm_id", sa.Integer(), nullable=True, server_default="0"),
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.drop_column("searchspaces", "vision_llm_id")
|
||||
|
|
@ -0,0 +1,199 @@
|
|||
"""Add vision LLM configs table and rename preference column
|
||||
|
||||
Revision ID: 120
|
||||
Revises: 119
|
||||
|
||||
Changes:
|
||||
1. Create visionprovider enum type
|
||||
2. Create vision_llm_configs table
|
||||
3. Rename vision_llm_id -> vision_llm_config_id on searchspaces
|
||||
4. Add vision config permissions to existing system roles
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Sequence
|
||||
|
||||
import sqlalchemy as sa
|
||||
from sqlalchemy.dialects.postgresql import ENUM as PG_ENUM, UUID
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision: str = "120"
|
||||
down_revision: str | None = "119"
|
||||
branch_labels: str | Sequence[str] | None = None
|
||||
depends_on: str | Sequence[str] | None = None
|
||||
|
||||
VISION_PROVIDER_VALUES = (
|
||||
"OPENAI",
|
||||
"ANTHROPIC",
|
||||
"GOOGLE",
|
||||
"AZURE_OPENAI",
|
||||
"VERTEX_AI",
|
||||
"BEDROCK",
|
||||
"XAI",
|
||||
"OPENROUTER",
|
||||
"OLLAMA",
|
||||
"GROQ",
|
||||
"TOGETHER_AI",
|
||||
"FIREWORKS_AI",
|
||||
"DEEPSEEK",
|
||||
"MISTRAL",
|
||||
"CUSTOM",
|
||||
)
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
connection = op.get_bind()
|
||||
|
||||
# 1. Create visionprovider enum
|
||||
connection.execute(
|
||||
sa.text(
|
||||
"""
|
||||
DO $$
|
||||
BEGIN
|
||||
IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'visionprovider') THEN
|
||||
CREATE TYPE visionprovider AS ENUM (
|
||||
'OPENAI', 'ANTHROPIC', 'GOOGLE', 'AZURE_OPENAI', 'VERTEX_AI',
|
||||
'BEDROCK', 'XAI', 'OPENROUTER', 'OLLAMA', 'GROQ',
|
||||
'TOGETHER_AI', 'FIREWORKS_AI', 'DEEPSEEK', 'MISTRAL', 'CUSTOM'
|
||||
);
|
||||
END IF;
|
||||
END
|
||||
$$;
|
||||
"""
|
||||
)
|
||||
)
|
||||
|
||||
# 2. Create vision_llm_configs table
|
||||
result = connection.execute(
|
||||
sa.text(
|
||||
"SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = 'vision_llm_configs')"
|
||||
)
|
||||
)
|
||||
if not result.scalar():
|
||||
op.create_table(
|
||||
"vision_llm_configs",
|
||||
sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
|
||||
sa.Column("name", sa.String(100), nullable=False),
|
||||
sa.Column("description", sa.String(500), nullable=True),
|
||||
sa.Column(
|
||||
"provider",
|
||||
PG_ENUM(
|
||||
*VISION_PROVIDER_VALUES, name="visionprovider", create_type=False
|
||||
),
|
||||
nullable=False,
|
||||
),
|
||||
sa.Column("custom_provider", sa.String(100), nullable=True),
|
||||
sa.Column("model_name", sa.String(100), nullable=False),
|
||||
sa.Column("api_key", sa.String(), nullable=False),
|
||||
sa.Column("api_base", sa.String(500), nullable=True),
|
||||
sa.Column("api_version", sa.String(50), nullable=True),
|
||||
sa.Column("litellm_params", sa.JSON(), nullable=True),
|
||||
sa.Column("search_space_id", sa.Integer(), nullable=False),
|
||||
sa.Column("user_id", UUID(as_uuid=True), nullable=False),
|
||||
sa.Column(
|
||||
"created_at",
|
||||
sa.TIMESTAMP(timezone=True),
|
||||
server_default=sa.text("now()"),
|
||||
nullable=False,
|
||||
),
|
||||
sa.PrimaryKeyConstraint("id"),
|
||||
sa.ForeignKeyConstraint(
|
||||
["search_space_id"], ["searchspaces.id"], ondelete="CASCADE"
|
||||
),
|
||||
sa.ForeignKeyConstraint(["user_id"], ["user.id"], ondelete="CASCADE"),
|
||||
)
|
||||
op.execute(
|
||||
"CREATE INDEX IF NOT EXISTS ix_vision_llm_configs_name "
|
||||
"ON vision_llm_configs (name)"
|
||||
)
|
||||
op.execute(
|
||||
"CREATE INDEX IF NOT EXISTS ix_vision_llm_configs_search_space_id "
|
||||
"ON vision_llm_configs (search_space_id)"
|
||||
)
|
||||
|
||||
# 3. Rename vision_llm_id -> vision_llm_config_id on searchspaces
|
||||
existing_columns = [
|
||||
col["name"] for col in sa.inspect(connection).get_columns("searchspaces")
|
||||
]
|
||||
if (
|
||||
"vision_llm_id" in existing_columns
|
||||
and "vision_llm_config_id" not in existing_columns
|
||||
):
|
||||
op.alter_column(
|
||||
"searchspaces", "vision_llm_id", new_column_name="vision_llm_config_id"
|
||||
)
|
||||
elif "vision_llm_config_id" not in existing_columns:
|
||||
op.add_column(
|
||||
"searchspaces",
|
||||
sa.Column(
|
||||
"vision_llm_config_id", sa.Integer(), nullable=True, server_default="0"
|
||||
),
|
||||
)
|
||||
|
||||
# 4. Add vision config permissions to existing system roles
|
||||
connection.execute(
|
||||
sa.text(
|
||||
"""
|
||||
UPDATE search_space_roles
|
||||
SET permissions = array_cat(
|
||||
permissions,
|
||||
ARRAY['vision_configs:create', 'vision_configs:read']
|
||||
)
|
||||
WHERE is_system_role = true
|
||||
AND name = 'Editor'
|
||||
AND NOT ('vision_configs:create' = ANY(permissions))
|
||||
"""
|
||||
)
|
||||
)
|
||||
connection.execute(
|
||||
sa.text(
|
||||
"""
|
||||
UPDATE search_space_roles
|
||||
SET permissions = array_cat(
|
||||
permissions,
|
||||
ARRAY['vision_configs:read']
|
||||
)
|
||||
WHERE is_system_role = true
|
||||
AND name = 'Viewer'
|
||||
AND NOT ('vision_configs:read' = ANY(permissions))
|
||||
"""
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
connection = op.get_bind()
|
||||
|
||||
# Remove permissions
|
||||
connection.execute(
|
||||
sa.text(
|
||||
"""
|
||||
UPDATE search_space_roles
|
||||
SET permissions = array_remove(
|
||||
array_remove(
|
||||
array_remove(permissions, 'vision_configs:create'),
|
||||
'vision_configs:read'
|
||||
),
|
||||
'vision_configs:delete'
|
||||
)
|
||||
WHERE is_system_role = true
|
||||
"""
|
||||
)
|
||||
)
|
||||
|
||||
# Rename column back
|
||||
existing_columns = [
|
||||
col["name"] for col in sa.inspect(connection).get_columns("searchspaces")
|
||||
]
|
||||
if "vision_llm_config_id" in existing_columns:
|
||||
op.alter_column(
|
||||
"searchspaces", "vision_llm_config_id", new_column_name="vision_llm_id"
|
||||
)
|
||||
|
||||
# Drop table and enum
|
||||
op.execute("DROP INDEX IF EXISTS ix_vision_llm_configs_search_space_id")
|
||||
op.execute("DROP INDEX IF EXISTS ix_vision_llm_configs_name")
|
||||
op.execute("DROP TABLE IF EXISTS vision_llm_configs")
|
||||
op.execute("DROP TYPE IF EXISTS visionprovider")
|
||||
|
|
@ -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,
|
||||
|
|
|
|||
11
surfsense_backend/app/agents/autocomplete/__init__.py
Normal file
11
surfsense_backend/app/agents/autocomplete/__init__.py
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
"""Agent-based vision autocomplete with scoped filesystem exploration."""
|
||||
|
||||
from app.agents.autocomplete.autocomplete_agent import (
|
||||
create_autocomplete_agent,
|
||||
stream_autocomplete_agent,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"create_autocomplete_agent",
|
||||
"stream_autocomplete_agent",
|
||||
]
|
||||
495
surfsense_backend/app/agents/autocomplete/autocomplete_agent.py
Normal file
495
surfsense_backend/app/agents/autocomplete/autocomplete_agent.py
Normal file
|
|
@ -0,0 +1,495 @@
|
|||
"""Vision autocomplete agent with scoped filesystem exploration.
|
||||
|
||||
Converts the stateless single-shot vision autocomplete into an agent that
|
||||
seeds a virtual filesystem from KB search results and lets the vision LLM
|
||||
explore documents via ``ls``, ``read_file``, ``glob``, ``grep``, etc.
|
||||
before generating the final completion.
|
||||
|
||||
Performance: KB search and agent graph compilation run in parallel so
|
||||
the only sequential latency is KB-search (or agent compile, whichever is
|
||||
slower) + the agent's LLM turns. There is no separate "query extraction"
|
||||
LLM call — the window title is used directly as the KB search query.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import uuid
|
||||
from collections.abc import AsyncGenerator
|
||||
from typing import Any
|
||||
|
||||
from deepagents.graph import BASE_AGENT_PROMPT
|
||||
from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
|
||||
from langchain.agents import create_agent
|
||||
from langchain_anthropic.middleware import AnthropicPromptCachingMiddleware
|
||||
from langchain_core.language_models import BaseChatModel
|
||||
from langchain_core.messages import AIMessage, ToolMessage
|
||||
|
||||
from app.agents.new_chat.middleware.filesystem import SurfSenseFilesystemMiddleware
|
||||
from app.agents.new_chat.middleware.knowledge_search import (
|
||||
build_scoped_filesystem,
|
||||
search_knowledge_base,
|
||||
)
|
||||
from app.services.new_streaming_service import VercelStreamingService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
KB_TOP_K = 10
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# System prompt
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
AUTOCOMPLETE_SYSTEM_PROMPT = """You are a smart writing assistant that analyzes the user's screen to draft or complete text.
|
||||
|
||||
You will receive a screenshot of the user's screen. Your PRIMARY source of truth is the screenshot itself — the visual context determines what to write.
|
||||
|
||||
Your job:
|
||||
1. Analyze the ENTIRE screenshot to understand what the user is working on (email thread, chat conversation, document, code editor, form, etc.).
|
||||
2. Identify the text area where the user will type.
|
||||
3. Generate the text the user most likely wants to write based on the visual context.
|
||||
|
||||
You also have access to the user's knowledge base documents via filesystem tools. However:
|
||||
- ONLY consult the knowledge base if the screenshot clearly involves a topic where your KB documents are DIRECTLY relevant (e.g., the user is writing about a specific project/topic that matches a document title).
|
||||
- Do NOT explore documents just because they exist. Most autocomplete requests can be answered purely from the screenshot.
|
||||
- If you do read a document, only incorporate information that is 100% relevant to what the user is typing RIGHT NOW. Do not add extra details, background, or tangential information from the KB.
|
||||
- Keep your output SHORT — autocomplete should feel like a natural continuation, not an essay.
|
||||
|
||||
Key behavior:
|
||||
- If the text area is EMPTY, draft a concise response or message based on what you see on screen (e.g., reply to an email, respond to a chat message, continue a document).
|
||||
- If the text area already has text, continue it naturally — typically just a sentence or two.
|
||||
|
||||
Rules:
|
||||
- Be CONCISE. Prefer a single paragraph or a few sentences. Autocomplete is a quick assist, not a full draft.
|
||||
- Match the tone and formality of the surrounding context.
|
||||
- If the screen shows code, write code. If it shows a casual chat, be casual. If it shows a formal email, be formal.
|
||||
- Do NOT describe the screenshot or explain your reasoning.
|
||||
- Do NOT cite or reference documents explicitly — just let the knowledge inform your writing naturally.
|
||||
- If you cannot determine what to write, output an empty JSON array: []
|
||||
|
||||
## Output Format
|
||||
|
||||
You MUST provide exactly 3 different suggestion options. Each should be a distinct, plausible completion — vary the tone, detail level, or angle.
|
||||
|
||||
Return your suggestions as a JSON array of exactly 3 strings. Output ONLY the JSON array, nothing else — no markdown fences, no explanation, no commentary.
|
||||
|
||||
Example format:
|
||||
["First suggestion text here.", "Second suggestion — a different take.", "Third option with another approach."]
|
||||
|
||||
## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`
|
||||
|
||||
All file paths must start with a `/`.
|
||||
- ls: list files and directories at a given path.
|
||||
- read_file: read a file from the filesystem.
|
||||
- write_file: create a temporary file in the session (not persisted).
|
||||
- edit_file: edit a file in the session (not persisted for /documents/ files).
|
||||
- glob: find files matching a pattern (e.g., "**/*.xml").
|
||||
- grep: search for text within files.
|
||||
|
||||
## When to Use Filesystem Tools
|
||||
|
||||
BEFORE reaching for any tool, ask yourself: "Can I write a good completion purely from the screenshot?" If yes, just write it — do NOT explore the KB.
|
||||
|
||||
Only use tools when:
|
||||
- The user is clearly writing about a specific topic that likely has detailed information in their KB.
|
||||
- You need a specific fact, name, number, or reference that the screenshot doesn't provide.
|
||||
|
||||
When you do use tools, be surgical:
|
||||
- Check the `ls` output first. If no document title looks relevant, stop — do not read files just to see what's there.
|
||||
- If a title looks relevant, read only the `<chunk_index>` (first ~20 lines) and jump to matched chunks. Do not read entire documents.
|
||||
- Extract only the specific information you need and move on to generating the completion.
|
||||
|
||||
## Reading Documents Efficiently
|
||||
|
||||
Documents are formatted as XML. Each document contains:
|
||||
- `<document_metadata>` — title, type, URL, etc.
|
||||
- `<chunk_index>` — a table of every chunk with its **line range** and a
|
||||
`matched="true"` flag for chunks that matched the search query.
|
||||
- `<document_content>` — the actual chunks in original document order.
|
||||
|
||||
**Workflow**: read the first ~20 lines to see the `<chunk_index>`, identify
|
||||
chunks marked `matched="true"`, then use `read_file(path, offset=<start_line>,
|
||||
limit=<lines>)` to jump directly to those sections."""
|
||||
|
||||
APP_CONTEXT_BLOCK = """
|
||||
|
||||
The user is currently working in "{app_name}" (window: "{window_title}"). Use this to understand the type of application and adapt your tone and format accordingly."""
|
||||
|
||||
|
||||
def _build_autocomplete_system_prompt(app_name: str, window_title: str) -> str:
|
||||
prompt = AUTOCOMPLETE_SYSTEM_PROMPT
|
||||
if app_name:
|
||||
prompt += APP_CONTEXT_BLOCK.format(app_name=app_name, window_title=window_title)
|
||||
return prompt
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Pre-compute KB filesystem (runs in parallel with agent compilation)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class _KBResult:
|
||||
"""Container for pre-computed KB filesystem results."""
|
||||
|
||||
__slots__ = ("files", "ls_ai_msg", "ls_tool_msg")
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
files: dict[str, Any] | None = None,
|
||||
ls_ai_msg: AIMessage | None = None,
|
||||
ls_tool_msg: ToolMessage | None = None,
|
||||
) -> None:
|
||||
self.files = files
|
||||
self.ls_ai_msg = ls_ai_msg
|
||||
self.ls_tool_msg = ls_tool_msg
|
||||
|
||||
@property
|
||||
def has_documents(self) -> bool:
|
||||
return bool(self.files)
|
||||
|
||||
|
||||
async def precompute_kb_filesystem(
|
||||
search_space_id: int,
|
||||
query: str,
|
||||
top_k: int = KB_TOP_K,
|
||||
) -> _KBResult:
|
||||
"""Search the KB and build the scoped filesystem outside the agent.
|
||||
|
||||
This is designed to be called via ``asyncio.gather`` alongside agent
|
||||
graph compilation so the two run concurrently.
|
||||
"""
|
||||
if not query:
|
||||
return _KBResult()
|
||||
|
||||
try:
|
||||
search_results = await search_knowledge_base(
|
||||
query=query,
|
||||
search_space_id=search_space_id,
|
||||
top_k=top_k,
|
||||
)
|
||||
|
||||
if not search_results:
|
||||
return _KBResult()
|
||||
|
||||
new_files, _ = await build_scoped_filesystem(
|
||||
documents=search_results,
|
||||
search_space_id=search_space_id,
|
||||
)
|
||||
|
||||
if not new_files:
|
||||
return _KBResult()
|
||||
|
||||
doc_paths = [
|
||||
p
|
||||
for p, v in new_files.items()
|
||||
if p.startswith("/documents/") and v is not None
|
||||
]
|
||||
tool_call_id = f"auto_ls_{uuid.uuid4().hex[:12]}"
|
||||
ai_msg = AIMessage(
|
||||
content="",
|
||||
tool_calls=[
|
||||
{"name": "ls", "args": {"path": "/documents"}, "id": tool_call_id}
|
||||
],
|
||||
)
|
||||
tool_msg = ToolMessage(
|
||||
content=str(doc_paths) if doc_paths else "No documents found.",
|
||||
tool_call_id=tool_call_id,
|
||||
)
|
||||
return _KBResult(files=new_files, ls_ai_msg=ai_msg, ls_tool_msg=tool_msg)
|
||||
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"KB pre-computation failed, proceeding without KB", exc_info=True
|
||||
)
|
||||
return _KBResult()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Filesystem middleware — no save_document, no persistence
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class AutocompleteFilesystemMiddleware(SurfSenseFilesystemMiddleware):
|
||||
"""Filesystem middleware for autocomplete — read-only exploration only.
|
||||
|
||||
Strips ``save_document`` (permanent KB persistence) and passes
|
||||
``search_space_id=None`` so ``write_file`` / ``edit_file`` stay ephemeral.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
super().__init__(search_space_id=None, created_by_id=None)
|
||||
self.tools = [t for t in self.tools if t.name != "save_document"]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Agent factory
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _compile_agent(
|
||||
llm: BaseChatModel,
|
||||
app_name: str,
|
||||
window_title: str,
|
||||
) -> Any:
|
||||
"""Compile the agent graph (CPU-bound, runs in a thread)."""
|
||||
system_prompt = _build_autocomplete_system_prompt(app_name, window_title)
|
||||
final_system_prompt = system_prompt + "\n\n" + BASE_AGENT_PROMPT
|
||||
|
||||
middleware = [
|
||||
AutocompleteFilesystemMiddleware(),
|
||||
PatchToolCallsMiddleware(),
|
||||
AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
|
||||
]
|
||||
|
||||
agent = await asyncio.to_thread(
|
||||
create_agent,
|
||||
llm,
|
||||
system_prompt=final_system_prompt,
|
||||
tools=[],
|
||||
middleware=middleware,
|
||||
)
|
||||
return agent.with_config({"recursion_limit": 200})
|
||||
|
||||
|
||||
async def create_autocomplete_agent(
|
||||
llm: BaseChatModel,
|
||||
*,
|
||||
search_space_id: int,
|
||||
kb_query: str,
|
||||
app_name: str = "",
|
||||
window_title: str = "",
|
||||
) -> tuple[Any, _KBResult]:
|
||||
"""Create the autocomplete agent and pre-compute KB in parallel.
|
||||
|
||||
Returns ``(agent, kb_result)`` so the caller can inject the pre-computed
|
||||
filesystem into the agent's initial state without any middleware delay.
|
||||
"""
|
||||
agent, kb = await asyncio.gather(
|
||||
_compile_agent(llm, app_name, window_title),
|
||||
precompute_kb_filesystem(search_space_id, kb_query),
|
||||
)
|
||||
return agent, kb
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# JSON suggestion parsing (with fallback)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _parse_suggestions(raw: str) -> list[str]:
|
||||
"""Extract a list of suggestion strings from the agent's output.
|
||||
|
||||
Tries, in order:
|
||||
1. Direct ``json.loads``
|
||||
2. Extract content between ```json ... ``` fences
|
||||
3. Find the first ``[`` … ``]`` span
|
||||
Falls back to wrapping the raw text as a single suggestion.
|
||||
"""
|
||||
text = raw.strip()
|
||||
if not text:
|
||||
return []
|
||||
|
||||
for candidate in _json_candidates(text):
|
||||
try:
|
||||
parsed = json.loads(candidate)
|
||||
if isinstance(parsed, list) and all(isinstance(s, str) for s in parsed):
|
||||
return [s for s in parsed if s.strip()]
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
continue
|
||||
|
||||
return [text]
|
||||
|
||||
|
||||
def _json_candidates(text: str) -> list[str]:
|
||||
"""Yield candidate JSON strings from raw text."""
|
||||
candidates = [text]
|
||||
|
||||
fence = re.search(r"```(?:json)?\s*\n?(.*?)```", text, re.DOTALL)
|
||||
if fence:
|
||||
candidates.append(fence.group(1).strip())
|
||||
|
||||
bracket = re.search(r"\[.*]", text, re.DOTALL)
|
||||
if bracket:
|
||||
candidates.append(bracket.group(0))
|
||||
|
||||
return candidates
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Streaming helper
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def stream_autocomplete_agent(
|
||||
agent: Any,
|
||||
input_data: dict[str, Any],
|
||||
streaming_service: VercelStreamingService,
|
||||
*,
|
||||
emit_message_start: bool = True,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Stream agent events as Vercel SSE, with thinking steps for tool calls.
|
||||
|
||||
When ``emit_message_start`` is False the caller has already sent the
|
||||
``message_start`` event (e.g. to show preparation steps before the agent
|
||||
runs).
|
||||
"""
|
||||
thread_id = uuid.uuid4().hex
|
||||
config = {"configurable": {"thread_id": thread_id}}
|
||||
|
||||
text_buffer: list[str] = []
|
||||
active_tool_depth = 0
|
||||
thinking_step_counter = 0
|
||||
tool_step_ids: dict[str, str] = {}
|
||||
step_titles: dict[str, str] = {}
|
||||
completed_step_ids: set[str] = set()
|
||||
last_active_step_id: str | None = None
|
||||
|
||||
def next_thinking_step_id() -> str:
|
||||
nonlocal thinking_step_counter
|
||||
thinking_step_counter += 1
|
||||
return f"autocomplete-step-{thinking_step_counter}"
|
||||
|
||||
def complete_current_step() -> str | None:
|
||||
nonlocal last_active_step_id
|
||||
if last_active_step_id and last_active_step_id not in completed_step_ids:
|
||||
completed_step_ids.add(last_active_step_id)
|
||||
title = step_titles.get(last_active_step_id, "Done")
|
||||
event = streaming_service.format_thinking_step(
|
||||
step_id=last_active_step_id,
|
||||
title=title,
|
||||
status="complete",
|
||||
)
|
||||
last_active_step_id = None
|
||||
return event
|
||||
return None
|
||||
|
||||
if emit_message_start:
|
||||
yield streaming_service.format_message_start()
|
||||
|
||||
gen_step_id = next_thinking_step_id()
|
||||
last_active_step_id = gen_step_id
|
||||
step_titles[gen_step_id] = "Generating suggestions"
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=gen_step_id,
|
||||
title="Generating suggestions",
|
||||
status="in_progress",
|
||||
)
|
||||
|
||||
try:
|
||||
async for event in agent.astream_events(
|
||||
input_data, config=config, version="v2"
|
||||
):
|
||||
event_type = event.get("event", "")
|
||||
if event_type == "on_chat_model_stream":
|
||||
if active_tool_depth > 0:
|
||||
continue
|
||||
if "surfsense:internal" in event.get("tags", []):
|
||||
continue
|
||||
chunk = event.get("data", {}).get("chunk")
|
||||
if chunk and hasattr(chunk, "content"):
|
||||
content = chunk.content
|
||||
if content and isinstance(content, str):
|
||||
text_buffer.append(content)
|
||||
|
||||
elif event_type == "on_chat_model_end":
|
||||
if active_tool_depth > 0:
|
||||
continue
|
||||
if "surfsense:internal" in event.get("tags", []):
|
||||
continue
|
||||
output = event.get("data", {}).get("output")
|
||||
if output and hasattr(output, "content"):
|
||||
if getattr(output, "tool_calls", None):
|
||||
continue
|
||||
content = output.content
|
||||
if content and isinstance(content, str) and not text_buffer:
|
||||
text_buffer.append(content)
|
||||
|
||||
elif event_type == "on_tool_start":
|
||||
active_tool_depth += 1
|
||||
tool_name = event.get("name", "unknown_tool")
|
||||
run_id = event.get("run_id", "")
|
||||
tool_input = event.get("data", {}).get("input", {})
|
||||
|
||||
step_event = complete_current_step()
|
||||
if step_event:
|
||||
yield step_event
|
||||
|
||||
tool_step_id = next_thinking_step_id()
|
||||
tool_step_ids[run_id] = tool_step_id
|
||||
last_active_step_id = tool_step_id
|
||||
|
||||
title, items = _describe_tool_call(tool_name, tool_input)
|
||||
step_titles[tool_step_id] = title
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title=title,
|
||||
status="in_progress",
|
||||
items=items,
|
||||
)
|
||||
|
||||
elif event_type == "on_tool_end":
|
||||
active_tool_depth = max(0, active_tool_depth - 1)
|
||||
run_id = event.get("run_id", "")
|
||||
step_id = tool_step_ids.pop(run_id, None)
|
||||
if step_id and step_id not in completed_step_ids:
|
||||
completed_step_ids.add(step_id)
|
||||
title = step_titles.get(step_id, "Done")
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=step_id,
|
||||
title=title,
|
||||
status="complete",
|
||||
)
|
||||
if last_active_step_id == step_id:
|
||||
last_active_step_id = None
|
||||
|
||||
step_event = complete_current_step()
|
||||
if step_event:
|
||||
yield step_event
|
||||
|
||||
raw_text = "".join(text_buffer)
|
||||
suggestions = _parse_suggestions(raw_text)
|
||||
|
||||
yield streaming_service.format_data("suggestions", {"options": suggestions})
|
||||
|
||||
yield streaming_service.format_finish()
|
||||
yield streaming_service.format_done()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Autocomplete agent streaming error: {e}", exc_info=True)
|
||||
yield streaming_service.format_error("Autocomplete failed. Please try again.")
|
||||
yield streaming_service.format_done()
|
||||
|
||||
|
||||
def _describe_tool_call(tool_name: str, tool_input: Any) -> tuple[str, list[str]]:
|
||||
"""Return a human-readable (title, items) for a tool call thinking step."""
|
||||
inp = tool_input if isinstance(tool_input, dict) else {}
|
||||
if tool_name == "ls":
|
||||
path = inp.get("path", "/")
|
||||
return "Listing files", [path]
|
||||
if tool_name == "read_file":
|
||||
fp = inp.get("file_path", "")
|
||||
display = fp if len(fp) <= 80 else "…" + fp[-77:]
|
||||
return "Reading file", [display]
|
||||
if tool_name == "write_file":
|
||||
fp = inp.get("file_path", "")
|
||||
display = fp if len(fp) <= 80 else "…" + fp[-77:]
|
||||
return "Writing file", [display]
|
||||
if tool_name == "edit_file":
|
||||
fp = inp.get("file_path", "")
|
||||
display = fp if len(fp) <= 80 else "…" + fp[-77:]
|
||||
return "Editing file", [display]
|
||||
if tool_name == "glob":
|
||||
pat = inp.get("pattern", "")
|
||||
base = inp.get("path", "/")
|
||||
return "Searching files", [f"{pat} in {base}"]
|
||||
if tool_name == "grep":
|
||||
pat = inp.get("pattern", "")
|
||||
path = inp.get("path", "")
|
||||
display_pat = pat[:60] + ("…" if len(pat) > 60 else "")
|
||||
return "Searching content", [
|
||||
f'"{display_pat}"' + (f" in {path}" if path else "")
|
||||
]
|
||||
return f"Using {tool_name}", []
|
||||
|
|
@ -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,
|
||||
|
|
|
|||
|
|
@ -66,6 +66,16 @@ the `<chunk_index>`, identify chunks marked `matched="true"`, then use
|
|||
those sections instead of reading the entire file sequentially.
|
||||
|
||||
Use `<chunk id='...'>` 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 `<chunk_index>`, 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.
|
||||
"""
|
||||
|
||||
# =============================================================================
|
||||
|
|
|
|||
|
|
@ -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 {}),
|
||||
)
|
||||
|
|
|
|||
|
|
@ -25,7 +25,12 @@ from app.agents.new_chat.checkpointer import (
|
|||
close_checkpointer,
|
||||
setup_checkpointer_tables,
|
||||
)
|
||||
from app.config import config, initialize_image_gen_router, initialize_llm_router
|
||||
from app.config import (
|
||||
config,
|
||||
initialize_image_gen_router,
|
||||
initialize_llm_router,
|
||||
initialize_vision_llm_router,
|
||||
)
|
||||
from app.db import User, create_db_and_tables, get_async_session
|
||||
from app.routes import router as crud_router
|
||||
from app.routes.auth_routes import router as auth_router
|
||||
|
|
@ -223,6 +228,7 @@ async def lifespan(app: FastAPI):
|
|||
await setup_checkpointer_tables()
|
||||
initialize_llm_router()
|
||||
initialize_image_gen_router()
|
||||
initialize_vision_llm_router()
|
||||
try:
|
||||
await asyncio.wait_for(seed_surfsense_docs(), timeout=120)
|
||||
except TimeoutError:
|
||||
|
|
|
|||
|
|
@ -18,10 +18,15 @@ def init_worker(**kwargs):
|
|||
This ensures the Auto mode (LiteLLM Router) is available for background tasks
|
||||
like document summarization and image generation.
|
||||
"""
|
||||
from app.config import initialize_image_gen_router, initialize_llm_router
|
||||
from app.config import (
|
||||
initialize_image_gen_router,
|
||||
initialize_llm_router,
|
||||
initialize_vision_llm_router,
|
||||
)
|
||||
|
||||
initialize_llm_router()
|
||||
initialize_image_gen_router()
|
||||
initialize_vision_llm_router()
|
||||
|
||||
|
||||
# Get Celery configuration from environment
|
||||
|
|
|
|||
|
|
@ -102,6 +102,44 @@ def load_global_image_gen_configs():
|
|||
return []
|
||||
|
||||
|
||||
def load_global_vision_llm_configs():
|
||||
global_config_file = BASE_DIR / "app" / "config" / "global_llm_config.yaml"
|
||||
|
||||
if not global_config_file.exists():
|
||||
return []
|
||||
|
||||
try:
|
||||
with open(global_config_file, encoding="utf-8") as f:
|
||||
data = yaml.safe_load(f)
|
||||
return data.get("global_vision_llm_configs", [])
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to load global vision LLM configs: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def load_vision_llm_router_settings():
|
||||
default_settings = {
|
||||
"routing_strategy": "usage-based-routing",
|
||||
"num_retries": 3,
|
||||
"allowed_fails": 3,
|
||||
"cooldown_time": 60,
|
||||
}
|
||||
|
||||
global_config_file = BASE_DIR / "app" / "config" / "global_llm_config.yaml"
|
||||
|
||||
if not global_config_file.exists():
|
||||
return default_settings
|
||||
|
||||
try:
|
||||
with open(global_config_file, encoding="utf-8") as f:
|
||||
data = yaml.safe_load(f)
|
||||
settings = data.get("vision_llm_router_settings", {})
|
||||
return {**default_settings, **settings}
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to load vision LLM router settings: {e}")
|
||||
return default_settings
|
||||
|
||||
|
||||
def load_image_gen_router_settings():
|
||||
"""
|
||||
Load router settings for image generation Auto mode from YAML file.
|
||||
|
|
@ -182,6 +220,29 @@ def initialize_image_gen_router():
|
|||
print(f"Warning: Failed to initialize Image Generation Router: {e}")
|
||||
|
||||
|
||||
def initialize_vision_llm_router():
|
||||
vision_configs = load_global_vision_llm_configs()
|
||||
router_settings = load_vision_llm_router_settings()
|
||||
|
||||
if not vision_configs:
|
||||
print(
|
||||
"Info: No global vision LLM configs found, "
|
||||
"Vision LLM Auto mode will not be available"
|
||||
)
|
||||
return
|
||||
|
||||
try:
|
||||
from app.services.vision_llm_router_service import VisionLLMRouterService
|
||||
|
||||
VisionLLMRouterService.initialize(vision_configs, router_settings)
|
||||
print(
|
||||
f"Info: Vision LLM Router initialized with {len(vision_configs)} models "
|
||||
f"(strategy: {router_settings.get('routing_strategy', 'usage-based-routing')})"
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to initialize Vision LLM Router: {e}")
|
||||
|
||||
|
||||
class Config:
|
||||
# Check if ffmpeg is installed
|
||||
if not is_ffmpeg_installed():
|
||||
|
|
@ -335,6 +396,12 @@ class Config:
|
|||
# Router settings for Image Generation Auto mode
|
||||
IMAGE_GEN_ROUTER_SETTINGS = load_image_gen_router_settings()
|
||||
|
||||
# Global Vision LLM Configurations (optional)
|
||||
GLOBAL_VISION_LLM_CONFIGS = load_global_vision_llm_configs()
|
||||
|
||||
# Router settings for Vision LLM Auto mode
|
||||
VISION_LLM_ROUTER_SETTINGS = load_vision_llm_router_settings()
|
||||
|
||||
# Chonkie Configuration | Edit this to your needs
|
||||
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL")
|
||||
# Azure OpenAI credentials from environment variables
|
||||
|
|
@ -394,8 +461,10 @@ class Config:
|
|||
UNSTRUCTURED_API_KEY = os.getenv("UNSTRUCTURED_API_KEY")
|
||||
|
||||
elif ETL_SERVICE == "LLAMACLOUD":
|
||||
# LlamaCloud API Key
|
||||
LLAMA_CLOUD_API_KEY = os.getenv("LLAMA_CLOUD_API_KEY")
|
||||
# Optional: Azure Document Intelligence accelerator for supported file types
|
||||
AZURE_DI_ENDPOINT = os.getenv("AZURE_DI_ENDPOINT")
|
||||
AZURE_DI_KEY = os.getenv("AZURE_DI_KEY")
|
||||
|
||||
# Residential Proxy Configuration (anonymous-proxies.net)
|
||||
# Used for web crawling and YouTube transcript fetching to avoid IP bans.
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -263,6 +263,82 @@ global_image_generation_configs:
|
|||
# rpm: 30
|
||||
# litellm_params: {}
|
||||
|
||||
# =============================================================================
|
||||
# Vision LLM Configuration
|
||||
# =============================================================================
|
||||
# These configurations power the vision autocomplete feature (screenshot analysis).
|
||||
# Only vision-capable models should be used here (e.g. GPT-4o, Gemini Pro, Claude 3).
|
||||
# Supported providers: OpenAI, Anthropic, Google, Azure OpenAI, Vertex AI, Bedrock,
|
||||
# xAI, OpenRouter, Ollama, Groq, Together AI, Fireworks AI, DeepSeek, Mistral, Custom
|
||||
#
|
||||
# Auto mode (ID 0) uses LiteLLM Router for load balancing across all vision configs.
|
||||
|
||||
# Router Settings for Vision LLM Auto Mode
|
||||
vision_llm_router_settings:
|
||||
routing_strategy: "usage-based-routing"
|
||||
num_retries: 3
|
||||
allowed_fails: 3
|
||||
cooldown_time: 60
|
||||
|
||||
global_vision_llm_configs:
|
||||
# Example: OpenAI GPT-4o (recommended for vision)
|
||||
- id: -1
|
||||
name: "Global GPT-4o Vision"
|
||||
description: "OpenAI's GPT-4o with strong vision capabilities"
|
||||
provider: "OPENAI"
|
||||
model_name: "gpt-4o"
|
||||
api_key: "sk-your-openai-api-key-here"
|
||||
api_base: ""
|
||||
rpm: 500
|
||||
tpm: 100000
|
||||
litellm_params:
|
||||
temperature: 0.3
|
||||
max_tokens: 1000
|
||||
|
||||
# Example: Google Gemini 2.0 Flash
|
||||
- id: -2
|
||||
name: "Global Gemini 2.0 Flash"
|
||||
description: "Google's fast vision model with large context"
|
||||
provider: "GOOGLE"
|
||||
model_name: "gemini-2.0-flash"
|
||||
api_key: "your-google-ai-api-key-here"
|
||||
api_base: ""
|
||||
rpm: 1000
|
||||
tpm: 200000
|
||||
litellm_params:
|
||||
temperature: 0.3
|
||||
max_tokens: 1000
|
||||
|
||||
# Example: Anthropic Claude 3.5 Sonnet
|
||||
- id: -3
|
||||
name: "Global Claude 3.5 Sonnet Vision"
|
||||
description: "Anthropic's Claude 3.5 Sonnet with vision support"
|
||||
provider: "ANTHROPIC"
|
||||
model_name: "claude-3-5-sonnet-20241022"
|
||||
api_key: "sk-ant-your-anthropic-api-key-here"
|
||||
api_base: ""
|
||||
rpm: 1000
|
||||
tpm: 100000
|
||||
litellm_params:
|
||||
temperature: 0.3
|
||||
max_tokens: 1000
|
||||
|
||||
# Example: Azure OpenAI GPT-4o
|
||||
# - id: -4
|
||||
# name: "Global Azure GPT-4o Vision"
|
||||
# description: "Azure-hosted GPT-4o for vision analysis"
|
||||
# provider: "AZURE_OPENAI"
|
||||
# model_name: "azure/gpt-4o-deployment"
|
||||
# api_key: "your-azure-api-key-here"
|
||||
# api_base: "https://your-resource.openai.azure.com"
|
||||
# api_version: "2024-02-15-preview"
|
||||
# rpm: 500
|
||||
# tpm: 100000
|
||||
# litellm_params:
|
||||
# temperature: 0.3
|
||||
# max_tokens: 1000
|
||||
# base_model: "gpt-4o"
|
||||
|
||||
# Notes:
|
||||
# - ID 0 is reserved for "Auto" mode - uses LiteLLM Router for load balancing
|
||||
# - Use negative IDs to distinguish global configs from user configs (NewLLMConfig in DB)
|
||||
|
|
@ -283,3 +359,9 @@ global_image_generation_configs:
|
|||
# - The router uses litellm.aimage_generation() for async image generation
|
||||
# - Only RPM (requests per minute) is relevant for image generation rate limiting.
|
||||
# TPM (tokens per minute) does not apply since image APIs are billed/rate-limited per request, not per token.
|
||||
#
|
||||
# VISION LLM NOTES:
|
||||
# - Vision configs use the same ID scheme (negative for global, positive for user DB)
|
||||
# - Only use vision-capable models (GPT-4o, Gemini, Claude 3, etc.)
|
||||
# - Lower temperature (0.3) is recommended for accurate screenshot analysis
|
||||
# - Lower max_tokens (1000) is sufficient since autocomplete produces short suggestions
|
||||
|
|
|
|||
23
surfsense_backend/app/config/vision_model_list_fallback.json
Normal file
23
surfsense_backend/app/config/vision_model_list_fallback.json
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
[
|
||||
{"value": "gpt-4o", "label": "GPT-4o", "provider": "OPENAI", "context_window": "128K"},
|
||||
{"value": "gpt-4o-mini", "label": "GPT-4o Mini", "provider": "OPENAI", "context_window": "128K"},
|
||||
{"value": "gpt-4-turbo", "label": "GPT-4 Turbo", "provider": "OPENAI", "context_window": "128K"},
|
||||
{"value": "claude-sonnet-4-20250514", "label": "Claude Sonnet 4", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-7-sonnet-20250219", "label": "Claude 3.7 Sonnet", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-5-sonnet-20241022", "label": "Claude 3.5 Sonnet", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-opus-20240229", "label": "Claude 3 Opus", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-haiku-20240307", "label": "Claude 3 Haiku", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "gemini-2.5-flash", "label": "Gemini 2.5 Flash", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-2.5-pro", "label": "Gemini 2.5 Pro", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-2.0-flash", "label": "Gemini 2.0 Flash", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-1.5-pro", "label": "Gemini 1.5 Pro", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-1.5-flash", "label": "Gemini 1.5 Flash", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "pixtral-large-latest", "label": "Pixtral Large", "provider": "MISTRAL", "context_window": "128K"},
|
||||
{"value": "pixtral-12b-2409", "label": "Pixtral 12B", "provider": "MISTRAL", "context_window": "128K"},
|
||||
{"value": "grok-2-vision-1212", "label": "Grok 2 Vision", "provider": "XAI", "context_window": "32K"},
|
||||
{"value": "llava", "label": "LLaVA", "provider": "OLLAMA"},
|
||||
{"value": "bakllava", "label": "BakLLaVA", "provider": "OLLAMA"},
|
||||
{"value": "llava-llama3", "label": "LLaVA Llama 3", "provider": "OLLAMA"},
|
||||
{"value": "llama-4-scout-17b-16e-instruct", "label": "Llama 4 Scout 17B", "provider": "GROQ", "context_window": "128K"},
|
||||
{"value": "meta-llama/Llama-4-Scout-17B-16E-Instruct", "label": "Llama 4 Scout 17B", "provider": "TOGETHER_AI", "context_window": "128K"}
|
||||
]
|
||||
|
|
@ -225,6 +225,55 @@ class DropboxClient:
|
|||
|
||||
return all_items, None
|
||||
|
||||
async def get_latest_cursor(self, path: str = "") -> tuple[str | None, str | None]:
|
||||
"""Get a cursor representing the current state of a folder.
|
||||
|
||||
Uses /2/files/list_folder/get_latest_cursor so we can later call
|
||||
get_changes to receive only incremental updates.
|
||||
"""
|
||||
resp = await self._request(
|
||||
"/2/files/list_folder/get_latest_cursor",
|
||||
{"path": path, "recursive": False, "include_non_downloadable_files": True},
|
||||
)
|
||||
if resp.status_code != 200:
|
||||
return None, f"Failed to get cursor: {resp.status_code} - {resp.text}"
|
||||
return resp.json().get("cursor"), None
|
||||
|
||||
async def get_changes(
|
||||
self, cursor: str
|
||||
) -> tuple[list[dict[str, Any]], str | None, str | None]:
|
||||
"""Fetch incremental changes since the given cursor.
|
||||
|
||||
Calls /2/files/list_folder/continue and handles pagination.
|
||||
Returns (entries, new_cursor, error).
|
||||
"""
|
||||
all_entries: list[dict[str, Any]] = []
|
||||
|
||||
resp = await self._request("/2/files/list_folder/continue", {"cursor": cursor})
|
||||
if resp.status_code == 401:
|
||||
return [], None, "Dropbox authentication expired (401)"
|
||||
if resp.status_code != 200:
|
||||
return [], None, f"Failed to get changes: {resp.status_code} - {resp.text}"
|
||||
|
||||
data = resp.json()
|
||||
all_entries.extend(data.get("entries", []))
|
||||
|
||||
while data.get("has_more"):
|
||||
cursor = data["cursor"]
|
||||
resp = await self._request(
|
||||
"/2/files/list_folder/continue", {"cursor": cursor}
|
||||
)
|
||||
if resp.status_code != 200:
|
||||
return (
|
||||
all_entries,
|
||||
data.get("cursor"),
|
||||
f"Pagination failed: {resp.status_code}",
|
||||
)
|
||||
data = resp.json()
|
||||
all_entries.extend(data.get("entries", []))
|
||||
|
||||
return all_entries, data.get("cursor"), None
|
||||
|
||||
async def get_metadata(self, path: str) -> tuple[dict[str, Any] | None, str | None]:
|
||||
resp = await self._request("/2/files/get_metadata", {"path": path})
|
||||
if resp.status_code != 200:
|
||||
|
|
|
|||
|
|
@ -53,7 +53,8 @@ async def download_and_extract_content(
|
|||
file_name = file.get("name", "Unknown")
|
||||
file_id = file.get("id", "")
|
||||
|
||||
if should_skip_file(file):
|
||||
skip, _unsup_ext = should_skip_file(file)
|
||||
if skip:
|
||||
return None, {}, "Skipping non-indexable item"
|
||||
|
||||
logger.info(f"Downloading file for content extraction: {file_name}")
|
||||
|
|
@ -87,9 +88,13 @@ async def download_and_extract_content(
|
|||
if error:
|
||||
return None, metadata, error
|
||||
|
||||
from app.connectors.onedrive.content_extractor import _parse_file_to_markdown
|
||||
from app.etl_pipeline.etl_document import EtlRequest
|
||||
from app.etl_pipeline.etl_pipeline_service import EtlPipelineService
|
||||
|
||||
markdown = await _parse_file_to_markdown(temp_file_path, file_name)
|
||||
result = await EtlPipelineService().extract(
|
||||
EtlRequest(file_path=temp_file_path, filename=file_name)
|
||||
)
|
||||
markdown = result.markdown_content
|
||||
return markdown, metadata, None
|
||||
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
"""File type handlers for Dropbox."""
|
||||
|
||||
PAPER_EXTENSION = ".paper"
|
||||
from app.etl_pipeline.file_classifier import should_skip_for_service
|
||||
|
||||
SKIP_EXTENSIONS: frozenset[str] = frozenset()
|
||||
PAPER_EXTENSION = ".paper"
|
||||
|
||||
MIME_TO_EXTENSION: dict[str, str] = {
|
||||
"application/pdf": ".pdf",
|
||||
|
|
@ -42,17 +42,25 @@ def is_paper_file(item: dict) -> bool:
|
|||
return ext == PAPER_EXTENSION
|
||||
|
||||
|
||||
def should_skip_file(item: dict) -> bool:
|
||||
def should_skip_file(item: dict) -> tuple[bool, str | None]:
|
||||
"""Skip folders and truly non-indexable files.
|
||||
|
||||
Paper docs are non-downloadable but exportable, so they are NOT skipped.
|
||||
Returns (should_skip, unsupported_extension_or_None).
|
||||
"""
|
||||
if is_folder(item):
|
||||
return True
|
||||
return True, None
|
||||
if is_paper_file(item):
|
||||
return False
|
||||
return False, None
|
||||
if not item.get("is_downloadable", True):
|
||||
return True
|
||||
return True, None
|
||||
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
name = item.get("name", "")
|
||||
ext = get_extension_from_name(name).lower()
|
||||
return ext in SKIP_EXTENSIONS
|
||||
if should_skip_for_service(name, app_config.ETL_SERVICE):
|
||||
ext = PurePosixPath(name).suffix.lower()
|
||||
return True, ext
|
||||
return False, None
|
||||
|
|
|
|||
|
|
@ -64,8 +64,10 @@ async def get_files_in_folder(
|
|||
)
|
||||
continue
|
||||
files.extend(sub_files)
|
||||
elif not should_skip_file(item):
|
||||
files.append(item)
|
||||
else:
|
||||
skip, _unsup_ext = should_skip_file(item)
|
||||
if not skip:
|
||||
files.append(item)
|
||||
|
||||
return files, None
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,9 @@
|
|||
"""Content extraction for Google Drive files."""
|
||||
|
||||
import asyncio
|
||||
import contextlib
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
|
@ -20,6 +17,7 @@ from .file_types import (
|
|||
get_export_mime_type,
|
||||
get_extension_from_mime,
|
||||
is_google_workspace_file,
|
||||
should_skip_by_extension,
|
||||
should_skip_file,
|
||||
)
|
||||
|
||||
|
|
@ -45,6 +43,11 @@ async def download_and_extract_content(
|
|||
if should_skip_file(mime_type):
|
||||
return None, {}, f"Skipping {mime_type}"
|
||||
|
||||
if not is_google_workspace_file(mime_type):
|
||||
ext_skip, _unsup_ext = should_skip_by_extension(file_name)
|
||||
if ext_skip:
|
||||
return None, {}, f"Skipping unsupported extension: {file_name}"
|
||||
|
||||
logger.info(f"Downloading file for content extraction: {file_name} ({mime_type})")
|
||||
|
||||
drive_metadata: dict[str, Any] = {
|
||||
|
|
@ -97,7 +100,10 @@ async def download_and_extract_content(
|
|||
if error:
|
||||
return None, drive_metadata, error
|
||||
|
||||
markdown = await _parse_file_to_markdown(temp_file_path, file_name)
|
||||
etl_filename = (
|
||||
file_name + extension if is_google_workspace_file(mime_type) else file_name
|
||||
)
|
||||
markdown = await _parse_file_to_markdown(temp_file_path, etl_filename)
|
||||
return markdown, drive_metadata, None
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -110,99 +116,14 @@ async def download_and_extract_content(
|
|||
|
||||
|
||||
async def _parse_file_to_markdown(file_path: str, filename: str) -> str:
|
||||
"""Parse a local file to markdown using the configured ETL service."""
|
||||
lower = filename.lower()
|
||||
"""Parse a local file to markdown using the unified ETL pipeline."""
|
||||
from app.etl_pipeline.etl_document import EtlRequest
|
||||
from app.etl_pipeline.etl_pipeline_service import EtlPipelineService
|
||||
|
||||
if lower.endswith((".md", ".markdown", ".txt")):
|
||||
with open(file_path, encoding="utf-8") as f:
|
||||
return f.read()
|
||||
|
||||
if lower.endswith((".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm")):
|
||||
from litellm import atranscription
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
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
|
||||
|
||||
t0 = time.monotonic()
|
||||
logger.info(
|
||||
f"[local-stt] START file={filename} thread={threading.current_thread().name}"
|
||||
)
|
||||
result = await asyncio.to_thread(stt_service.transcribe_file, file_path)
|
||||
logger.info(
|
||||
f"[local-stt] END file={filename} elapsed={time.monotonic() - t0:.2f}s"
|
||||
)
|
||||
text = result.get("text", "")
|
||||
else:
|
||||
with open(file_path, "rb") as audio_file:
|
||||
kwargs: dict[str, Any] = {
|
||||
"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
|
||||
resp = await atranscription(**kwargs)
|
||||
text = resp.get("text", "")
|
||||
|
||||
if not text:
|
||||
raise ValueError("Transcription returned empty text")
|
||||
return f"# Transcription of {filename}\n\n{text}"
|
||||
|
||||
# Document files -- use configured ETL service
|
||||
from app.config import config as app_config
|
||||
|
||||
if app_config.ETL_SERVICE == "UNSTRUCTURED":
|
||||
from langchain_unstructured import UnstructuredLoader
|
||||
|
||||
from app.utils.document_converters import convert_document_to_markdown
|
||||
|
||||
loader = UnstructuredLoader(
|
||||
file_path,
|
||||
mode="elements",
|
||||
post_processors=[],
|
||||
languages=["eng"],
|
||||
include_orig_elements=False,
|
||||
include_metadata=False,
|
||||
strategy="auto",
|
||||
)
|
||||
docs = await loader.aload()
|
||||
return await convert_document_to_markdown(docs)
|
||||
|
||||
if app_config.ETL_SERVICE == "LLAMACLOUD":
|
||||
from app.tasks.document_processors.file_processors import (
|
||||
parse_with_llamacloud_retry,
|
||||
)
|
||||
|
||||
result = await parse_with_llamacloud_retry(
|
||||
file_path=file_path, estimated_pages=50
|
||||
)
|
||||
markdown_documents = await result.aget_markdown_documents(split_by_page=False)
|
||||
if not markdown_documents:
|
||||
raise RuntimeError(f"LlamaCloud returned no documents for {filename}")
|
||||
return markdown_documents[0].text
|
||||
|
||||
if app_config.ETL_SERVICE == "DOCLING":
|
||||
from docling.document_converter import DocumentConverter
|
||||
|
||||
converter = DocumentConverter()
|
||||
t0 = time.monotonic()
|
||||
logger.info(
|
||||
f"[docling] START file={filename} thread={threading.current_thread().name}"
|
||||
)
|
||||
result = await asyncio.to_thread(converter.convert, file_path)
|
||||
logger.info(
|
||||
f"[docling] END file={filename} elapsed={time.monotonic() - t0:.2f}s"
|
||||
)
|
||||
return result.document.export_to_markdown()
|
||||
|
||||
raise RuntimeError(f"Unknown ETL_SERVICE: {app_config.ETL_SERVICE}")
|
||||
result = await EtlPipelineService().extract(
|
||||
EtlRequest(file_path=file_path, filename=filename)
|
||||
)
|
||||
return result.markdown_content
|
||||
|
||||
|
||||
async def download_and_process_file(
|
||||
|
|
@ -236,10 +157,14 @@ async def download_and_process_file(
|
|||
file_name = file.get("name", "Unknown")
|
||||
mime_type = file.get("mimeType", "")
|
||||
|
||||
# Skip folders and shortcuts
|
||||
if should_skip_file(mime_type):
|
||||
return None, f"Skipping {mime_type}", None
|
||||
|
||||
if not is_google_workspace_file(mime_type):
|
||||
ext_skip, _unsup_ext = should_skip_by_extension(file_name)
|
||||
if ext_skip:
|
||||
return None, f"Skipping unsupported extension: {file_name}", None
|
||||
|
||||
logger.info(f"Downloading file: {file_name} ({mime_type})")
|
||||
|
||||
temp_file_path = None
|
||||
|
|
@ -310,10 +235,13 @@ async def download_and_process_file(
|
|||
"."
|
||||
)[-1]
|
||||
|
||||
etl_filename = (
|
||||
file_name + extension if is_google_workspace_file(mime_type) else file_name
|
||||
)
|
||||
logger.info(f"Processing {file_name} with Surfsense's file processor")
|
||||
await process_file_in_background(
|
||||
file_path=temp_file_path,
|
||||
filename=file_name,
|
||||
filename=etl_filename,
|
||||
search_space_id=search_space_id,
|
||||
user_id=user_id,
|
||||
session=session,
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
"""File type handlers for Google Drive."""
|
||||
|
||||
from app.etl_pipeline.file_classifier import should_skip_for_service
|
||||
|
||||
GOOGLE_DOC = "application/vnd.google-apps.document"
|
||||
GOOGLE_SHEET = "application/vnd.google-apps.spreadsheet"
|
||||
GOOGLE_SLIDE = "application/vnd.google-apps.presentation"
|
||||
|
|
@ -46,6 +48,21 @@ def should_skip_file(mime_type: str) -> bool:
|
|||
return mime_type in [GOOGLE_FOLDER, GOOGLE_SHORTCUT]
|
||||
|
||||
|
||||
def should_skip_by_extension(filename: str) -> tuple[bool, str | None]:
|
||||
"""Check if the file extension is not parseable by the configured ETL service.
|
||||
|
||||
Returns (should_skip, unsupported_extension_or_None).
|
||||
"""
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
if should_skip_for_service(filename, app_config.ETL_SERVICE):
|
||||
ext = PurePosixPath(filename).suffix.lower()
|
||||
return True, ext
|
||||
return False, None
|
||||
|
||||
|
||||
def get_export_mime_type(mime_type: str) -> str | None:
|
||||
"""Get export MIME type for Google Workspace files."""
|
||||
return EXPORT_FORMATS.get(mime_type)
|
||||
|
|
|
|||
|
|
@ -1,16 +1,9 @@
|
|||
"""Content extraction for OneDrive files.
|
||||
"""Content extraction for OneDrive files."""
|
||||
|
||||
Reuses the same ETL parsing logic as Google Drive since file parsing is
|
||||
extension-based, not provider-specific.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import contextlib
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
|
@ -31,7 +24,8 @@ async def download_and_extract_content(
|
|||
item_id = file.get("id")
|
||||
file_name = file.get("name", "Unknown")
|
||||
|
||||
if should_skip_file(file):
|
||||
skip, _unsup_ext = should_skip_file(file)
|
||||
if skip:
|
||||
return None, {}, "Skipping non-indexable item"
|
||||
|
||||
file_info = file.get("file", {})
|
||||
|
|
@ -84,98 +78,11 @@ async def download_and_extract_content(
|
|||
|
||||
|
||||
async def _parse_file_to_markdown(file_path: str, filename: str) -> str:
|
||||
"""Parse a local file to markdown using the configured ETL service.
|
||||
"""Parse a local file to markdown using the unified ETL pipeline."""
|
||||
from app.etl_pipeline.etl_document import EtlRequest
|
||||
from app.etl_pipeline.etl_pipeline_service import EtlPipelineService
|
||||
|
||||
Same logic as Google Drive -- file parsing is extension-based.
|
||||
"""
|
||||
lower = filename.lower()
|
||||
|
||||
if lower.endswith((".md", ".markdown", ".txt")):
|
||||
with open(file_path, encoding="utf-8") as f:
|
||||
return f.read()
|
||||
|
||||
if lower.endswith((".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm")):
|
||||
from litellm import atranscription
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
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
|
||||
|
||||
t0 = time.monotonic()
|
||||
logger.info(
|
||||
f"[local-stt] START file={filename} thread={threading.current_thread().name}"
|
||||
)
|
||||
result = await asyncio.to_thread(stt_service.transcribe_file, file_path)
|
||||
logger.info(
|
||||
f"[local-stt] END file={filename} elapsed={time.monotonic() - t0:.2f}s"
|
||||
)
|
||||
text = result.get("text", "")
|
||||
else:
|
||||
with open(file_path, "rb") as audio_file:
|
||||
kwargs: dict[str, Any] = {
|
||||
"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
|
||||
resp = await atranscription(**kwargs)
|
||||
text = resp.get("text", "")
|
||||
|
||||
if not text:
|
||||
raise ValueError("Transcription returned empty text")
|
||||
return f"# Transcription of {filename}\n\n{text}"
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
if app_config.ETL_SERVICE == "UNSTRUCTURED":
|
||||
from langchain_unstructured import UnstructuredLoader
|
||||
|
||||
from app.utils.document_converters import convert_document_to_markdown
|
||||
|
||||
loader = UnstructuredLoader(
|
||||
file_path,
|
||||
mode="elements",
|
||||
post_processors=[],
|
||||
languages=["eng"],
|
||||
include_orig_elements=False,
|
||||
include_metadata=False,
|
||||
strategy="auto",
|
||||
)
|
||||
docs = await loader.aload()
|
||||
return await convert_document_to_markdown(docs)
|
||||
|
||||
if app_config.ETL_SERVICE == "LLAMACLOUD":
|
||||
from app.tasks.document_processors.file_processors import (
|
||||
parse_with_llamacloud_retry,
|
||||
)
|
||||
|
||||
result = await parse_with_llamacloud_retry(
|
||||
file_path=file_path, estimated_pages=50
|
||||
)
|
||||
markdown_documents = await result.aget_markdown_documents(split_by_page=False)
|
||||
if not markdown_documents:
|
||||
raise RuntimeError(f"LlamaCloud returned no documents for {filename}")
|
||||
return markdown_documents[0].text
|
||||
|
||||
if app_config.ETL_SERVICE == "DOCLING":
|
||||
from docling.document_converter import DocumentConverter
|
||||
|
||||
converter = DocumentConverter()
|
||||
t0 = time.monotonic()
|
||||
logger.info(
|
||||
f"[docling] START file={filename} thread={threading.current_thread().name}"
|
||||
)
|
||||
result = await asyncio.to_thread(converter.convert, file_path)
|
||||
logger.info(
|
||||
f"[docling] END file={filename} elapsed={time.monotonic() - t0:.2f}s"
|
||||
)
|
||||
return result.document.export_to_markdown()
|
||||
|
||||
raise RuntimeError(f"Unknown ETL_SERVICE: {app_config.ETL_SERVICE}")
|
||||
result = await EtlPipelineService().extract(
|
||||
EtlRequest(file_path=file_path, filename=filename)
|
||||
)
|
||||
return result.markdown_content
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
"""File type handlers for Microsoft OneDrive."""
|
||||
|
||||
from app.etl_pipeline.file_classifier import should_skip_for_service
|
||||
|
||||
ONEDRIVE_FOLDER_FACET = "folder"
|
||||
ONENOTE_MIME = "application/msonenote"
|
||||
|
||||
|
|
@ -38,13 +40,28 @@ def is_folder(item: dict) -> bool:
|
|||
return ONEDRIVE_FOLDER_FACET in item
|
||||
|
||||
|
||||
def should_skip_file(item: dict) -> bool:
|
||||
"""Skip folders, OneNote files, remote items (shared links), and packages."""
|
||||
def should_skip_file(item: dict) -> tuple[bool, str | None]:
|
||||
"""Skip folders, OneNote files, remote items, packages, and unsupported extensions.
|
||||
|
||||
Returns (should_skip, unsupported_extension_or_None).
|
||||
The second element is only set when the skip is due to an unsupported extension.
|
||||
"""
|
||||
if is_folder(item):
|
||||
return True
|
||||
return True, None
|
||||
if "remoteItem" in item:
|
||||
return True
|
||||
return True, None
|
||||
if "package" in item:
|
||||
return True
|
||||
return True, None
|
||||
mime = item.get("file", {}).get("mimeType", "")
|
||||
return mime in SKIP_MIME_TYPES
|
||||
if mime in SKIP_MIME_TYPES:
|
||||
return True, None
|
||||
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
name = item.get("name", "")
|
||||
if should_skip_for_service(name, app_config.ETL_SERVICE):
|
||||
ext = PurePosixPath(name).suffix.lower()
|
||||
return True, ext
|
||||
return False, None
|
||||
|
|
|
|||
|
|
@ -71,8 +71,10 @@ async def get_files_in_folder(
|
|||
)
|
||||
continue
|
||||
files.extend(sub_files)
|
||||
elif not should_skip_file(item):
|
||||
files.append(item)
|
||||
else:
|
||||
skip, _unsup_ext = should_skip_file(item)
|
||||
if not skip:
|
||||
files.append(item)
|
||||
|
||||
return files, None
|
||||
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
|
@ -259,6 +260,24 @@ class ImageGenProvider(StrEnum):
|
|||
NSCALE = "NSCALE"
|
||||
|
||||
|
||||
class VisionProvider(StrEnum):
|
||||
OPENAI = "OPENAI"
|
||||
ANTHROPIC = "ANTHROPIC"
|
||||
GOOGLE = "GOOGLE"
|
||||
AZURE_OPENAI = "AZURE_OPENAI"
|
||||
VERTEX_AI = "VERTEX_AI"
|
||||
BEDROCK = "BEDROCK"
|
||||
XAI = "XAI"
|
||||
OPENROUTER = "OPENROUTER"
|
||||
OLLAMA = "OLLAMA"
|
||||
GROQ = "GROQ"
|
||||
TOGETHER_AI = "TOGETHER_AI"
|
||||
FIREWORKS_AI = "FIREWORKS_AI"
|
||||
DEEPSEEK = "DEEPSEEK"
|
||||
MISTRAL = "MISTRAL"
|
||||
CUSTOM = "CUSTOM"
|
||||
|
||||
|
||||
class LogLevel(StrEnum):
|
||||
DEBUG = "DEBUG"
|
||||
INFO = "INFO"
|
||||
|
|
@ -376,6 +395,11 @@ class Permission(StrEnum):
|
|||
IMAGE_GENERATIONS_READ = "image_generations:read"
|
||||
IMAGE_GENERATIONS_DELETE = "image_generations:delete"
|
||||
|
||||
# Vision LLM Configs
|
||||
VISION_CONFIGS_CREATE = "vision_configs:create"
|
||||
VISION_CONFIGS_READ = "vision_configs:read"
|
||||
VISION_CONFIGS_DELETE = "vision_configs:delete"
|
||||
|
||||
# Connectors
|
||||
CONNECTORS_CREATE = "connectors:create"
|
||||
CONNECTORS_READ = "connectors:read"
|
||||
|
|
@ -444,6 +468,9 @@ DEFAULT_ROLE_PERMISSIONS = {
|
|||
# Image Generations (create and read, no delete)
|
||||
Permission.IMAGE_GENERATIONS_CREATE.value,
|
||||
Permission.IMAGE_GENERATIONS_READ.value,
|
||||
# Vision Configs (create and read, no delete)
|
||||
Permission.VISION_CONFIGS_CREATE.value,
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
# Connectors (no delete)
|
||||
Permission.CONNECTORS_CREATE.value,
|
||||
Permission.CONNECTORS_READ.value,
|
||||
|
|
@ -477,6 +504,8 @@ DEFAULT_ROLE_PERMISSIONS = {
|
|||
Permission.VIDEO_PRESENTATIONS_READ.value,
|
||||
# Image Generations (read only)
|
||||
Permission.IMAGE_GENERATIONS_READ.value,
|
||||
# Vision Configs (read only)
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
# Connectors (read only)
|
||||
Permission.CONNECTORS_READ.value,
|
||||
# Logs (read only)
|
||||
|
|
@ -955,6 +984,7 @@ 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 +1069,26 @@ 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"
|
||||
|
||||
|
|
@ -1241,6 +1291,33 @@ class ImageGenerationConfig(BaseModel, TimestampMixin):
|
|||
user = relationship("User", back_populates="image_generation_configs")
|
||||
|
||||
|
||||
class VisionLLMConfig(BaseModel, TimestampMixin):
|
||||
__tablename__ = "vision_llm_configs"
|
||||
|
||||
name = Column(String(100), nullable=False, index=True)
|
||||
description = Column(String(500), nullable=True)
|
||||
|
||||
provider = Column(SQLAlchemyEnum(VisionProvider), nullable=False)
|
||||
custom_provider = Column(String(100), nullable=True)
|
||||
model_name = Column(String(100), nullable=False)
|
||||
|
||||
api_key = Column(String, nullable=False)
|
||||
api_base = Column(String(500), nullable=True)
|
||||
api_version = Column(String(50), nullable=True)
|
||||
|
||||
litellm_params = Column(JSON, nullable=True, default={})
|
||||
|
||||
search_space_id = Column(
|
||||
Integer, ForeignKey("searchspaces.id", ondelete="CASCADE"), nullable=False
|
||||
)
|
||||
search_space = relationship("SearchSpace", back_populates="vision_llm_configs")
|
||||
|
||||
user_id = Column(
|
||||
UUID(as_uuid=True), ForeignKey("user.id", ondelete="CASCADE"), nullable=False
|
||||
)
|
||||
user = relationship("User", back_populates="vision_llm_configs")
|
||||
|
||||
|
||||
class ImageGeneration(BaseModel, TimestampMixin):
|
||||
"""
|
||||
Stores image generation requests and results using litellm.aimage_generation().
|
||||
|
|
@ -1329,6 +1406,9 @@ class SearchSpace(BaseModel, TimestampMixin):
|
|||
image_generation_config_id = Column(
|
||||
Integer, nullable=True, default=0
|
||||
) # For image generation, defaults to Auto mode
|
||||
vision_llm_config_id = Column(
|
||||
Integer, nullable=True, default=0
|
||||
) # For vision/screenshot analysis, defaults to Auto mode
|
||||
|
||||
user_id = Column(
|
||||
UUID(as_uuid=True), ForeignKey("user.id", ondelete="CASCADE"), nullable=False
|
||||
|
|
@ -1407,6 +1487,12 @@ class SearchSpace(BaseModel, TimestampMixin):
|
|||
order_by="ImageGenerationConfig.id",
|
||||
cascade="all, delete-orphan",
|
||||
)
|
||||
vision_llm_configs = relationship(
|
||||
"VisionLLMConfig",
|
||||
back_populates="search_space",
|
||||
order_by="VisionLLMConfig.id",
|
||||
cascade="all, delete-orphan",
|
||||
)
|
||||
|
||||
# RBAC relationships
|
||||
roles = relationship(
|
||||
|
|
@ -1936,6 +2022,12 @@ if config.AUTH_TYPE == "GOOGLE":
|
|||
passive_deletes=True,
|
||||
)
|
||||
|
||||
vision_llm_configs = relationship(
|
||||
"VisionLLMConfig",
|
||||
back_populates="user",
|
||||
passive_deletes=True,
|
||||
)
|
||||
|
||||
# User memories for personalized AI responses
|
||||
memories = relationship(
|
||||
"UserMemory",
|
||||
|
|
@ -2050,6 +2142,12 @@ else:
|
|||
passive_deletes=True,
|
||||
)
|
||||
|
||||
vision_llm_configs = relationship(
|
||||
"VisionLLMConfig",
|
||||
back_populates="user",
|
||||
passive_deletes=True,
|
||||
)
|
||||
|
||||
# User memories for personalized AI responses
|
||||
memories = relationship(
|
||||
"UserMemory",
|
||||
|
|
|
|||
0
surfsense_backend/app/etl_pipeline/__init__.py
Normal file
0
surfsense_backend/app/etl_pipeline/__init__.py
Normal file
39
surfsense_backend/app/etl_pipeline/constants.py
Normal file
39
surfsense_backend/app/etl_pipeline/constants.py
Normal file
|
|
@ -0,0 +1,39 @@
|
|||
import ssl
|
||||
|
||||
import httpx
|
||||
|
||||
LLAMACLOUD_MAX_RETRIES = 5
|
||||
LLAMACLOUD_BASE_DELAY = 10
|
||||
LLAMACLOUD_MAX_DELAY = 120
|
||||
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,
|
||||
)
|
||||
|
||||
UPLOAD_BYTES_PER_SECOND_SLOW = 100 * 1024
|
||||
MIN_UPLOAD_TIMEOUT = 120
|
||||
MAX_UPLOAD_TIMEOUT = 1800
|
||||
BASE_JOB_TIMEOUT = 600
|
||||
PER_PAGE_JOB_TIMEOUT = 60
|
||||
|
||||
|
||||
def calculate_upload_timeout(file_size_bytes: int) -> float:
|
||||
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:
|
||||
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)
|
||||
21
surfsense_backend/app/etl_pipeline/etl_document.py
Normal file
21
surfsense_backend/app/etl_pipeline/etl_document.py
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
from pydantic import BaseModel, field_validator
|
||||
|
||||
|
||||
class EtlRequest(BaseModel):
|
||||
file_path: str
|
||||
filename: str
|
||||
estimated_pages: int = 0
|
||||
|
||||
@field_validator("filename")
|
||||
@classmethod
|
||||
def filename_must_not_be_empty(cls, v: str) -> str:
|
||||
if not v.strip():
|
||||
raise ValueError("filename must not be empty")
|
||||
return v
|
||||
|
||||
|
||||
class EtlResult(BaseModel):
|
||||
markdown_content: str
|
||||
etl_service: str
|
||||
actual_pages: int = 0
|
||||
content_type: str
|
||||
125
surfsense_backend/app/etl_pipeline/etl_pipeline_service.py
Normal file
125
surfsense_backend/app/etl_pipeline/etl_pipeline_service.py
Normal file
|
|
@ -0,0 +1,125 @@
|
|||
import logging
|
||||
|
||||
from app.config import config as app_config
|
||||
from app.etl_pipeline.etl_document import EtlRequest, EtlResult
|
||||
from app.etl_pipeline.exceptions import (
|
||||
EtlServiceUnavailableError,
|
||||
EtlUnsupportedFileError,
|
||||
)
|
||||
from app.etl_pipeline.file_classifier import FileCategory, classify_file
|
||||
from app.etl_pipeline.parsers.audio import transcribe_audio
|
||||
from app.etl_pipeline.parsers.direct_convert import convert_file_directly
|
||||
from app.etl_pipeline.parsers.plaintext import read_plaintext
|
||||
|
||||
|
||||
class EtlPipelineService:
|
||||
"""Single pipeline for extracting markdown from files. All callers use this."""
|
||||
|
||||
async def extract(self, request: EtlRequest) -> EtlResult:
|
||||
category = classify_file(request.filename)
|
||||
|
||||
if category == FileCategory.UNSUPPORTED:
|
||||
raise EtlUnsupportedFileError(
|
||||
f"File type not supported for parsing: {request.filename}"
|
||||
)
|
||||
|
||||
if category == FileCategory.PLAINTEXT:
|
||||
content = read_plaintext(request.file_path)
|
||||
return EtlResult(
|
||||
markdown_content=content,
|
||||
etl_service="PLAINTEXT",
|
||||
content_type="plaintext",
|
||||
)
|
||||
|
||||
if category == FileCategory.DIRECT_CONVERT:
|
||||
content = convert_file_directly(request.file_path, request.filename)
|
||||
return EtlResult(
|
||||
markdown_content=content,
|
||||
etl_service="DIRECT_CONVERT",
|
||||
content_type="direct_convert",
|
||||
)
|
||||
|
||||
if category == FileCategory.AUDIO:
|
||||
content = await transcribe_audio(request.file_path, request.filename)
|
||||
return EtlResult(
|
||||
markdown_content=content,
|
||||
etl_service="AUDIO",
|
||||
content_type="audio",
|
||||
)
|
||||
|
||||
return await self._extract_document(request)
|
||||
|
||||
async def _extract_document(self, request: EtlRequest) -> EtlResult:
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
from app.utils.file_extensions import get_document_extensions_for_service
|
||||
|
||||
etl_service = app_config.ETL_SERVICE
|
||||
if not etl_service:
|
||||
raise EtlServiceUnavailableError(
|
||||
"No ETL_SERVICE configured. "
|
||||
"Set ETL_SERVICE to UNSTRUCTURED, LLAMACLOUD, or DOCLING in your .env"
|
||||
)
|
||||
|
||||
ext = PurePosixPath(request.filename).suffix.lower()
|
||||
supported = get_document_extensions_for_service(etl_service)
|
||||
if ext not in supported:
|
||||
raise EtlUnsupportedFileError(
|
||||
f"File type {ext} is not supported by {etl_service}"
|
||||
)
|
||||
|
||||
if etl_service == "DOCLING":
|
||||
from app.etl_pipeline.parsers.docling import parse_with_docling
|
||||
|
||||
content = await parse_with_docling(request.file_path, request.filename)
|
||||
elif etl_service == "UNSTRUCTURED":
|
||||
from app.etl_pipeline.parsers.unstructured import parse_with_unstructured
|
||||
|
||||
content = await parse_with_unstructured(request.file_path)
|
||||
elif etl_service == "LLAMACLOUD":
|
||||
content = await self._extract_with_llamacloud(request)
|
||||
else:
|
||||
raise EtlServiceUnavailableError(f"Unknown ETL_SERVICE: {etl_service}")
|
||||
|
||||
return EtlResult(
|
||||
markdown_content=content,
|
||||
etl_service=etl_service,
|
||||
content_type="document",
|
||||
)
|
||||
|
||||
async def _extract_with_llamacloud(self, request: EtlRequest) -> str:
|
||||
"""Try Azure Document Intelligence first (when configured) then LlamaCloud.
|
||||
|
||||
Azure DI is an internal accelerator: cheaper and faster for its supported
|
||||
file types. If it is not configured, or the file extension is not in
|
||||
Azure DI's supported set, LlamaCloud is used directly. If Azure DI
|
||||
fails for any reason, LlamaCloud is used as a fallback.
|
||||
"""
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
from app.utils.file_extensions import AZURE_DI_DOCUMENT_EXTENSIONS
|
||||
|
||||
ext = PurePosixPath(request.filename).suffix.lower()
|
||||
azure_configured = bool(
|
||||
getattr(app_config, "AZURE_DI_ENDPOINT", None)
|
||||
and getattr(app_config, "AZURE_DI_KEY", None)
|
||||
)
|
||||
|
||||
if azure_configured and ext in AZURE_DI_DOCUMENT_EXTENSIONS:
|
||||
try:
|
||||
from app.etl_pipeline.parsers.azure_doc_intelligence import (
|
||||
parse_with_azure_doc_intelligence,
|
||||
)
|
||||
|
||||
return await parse_with_azure_doc_intelligence(request.file_path)
|
||||
except Exception:
|
||||
logging.warning(
|
||||
"Azure Document Intelligence failed for %s, "
|
||||
"falling back to LlamaCloud",
|
||||
request.filename,
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
from app.etl_pipeline.parsers.llamacloud import parse_with_llamacloud
|
||||
|
||||
return await parse_with_llamacloud(request.file_path, request.estimated_pages)
|
||||
10
surfsense_backend/app/etl_pipeline/exceptions.py
Normal file
10
surfsense_backend/app/etl_pipeline/exceptions.py
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
class EtlParseError(Exception):
|
||||
"""Raised when an ETL parser fails to produce content."""
|
||||
|
||||
|
||||
class EtlServiceUnavailableError(Exception):
|
||||
"""Raised when the configured ETL_SERVICE is not recognised."""
|
||||
|
||||
|
||||
class EtlUnsupportedFileError(Exception):
|
||||
"""Raised when a file type cannot be parsed by any ETL pipeline."""
|
||||
137
surfsense_backend/app/etl_pipeline/file_classifier.py
Normal file
137
surfsense_backend/app/etl_pipeline/file_classifier.py
Normal file
|
|
@ -0,0 +1,137 @@
|
|||
from enum import Enum
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
from app.utils.file_extensions import (
|
||||
DOCUMENT_EXTENSIONS,
|
||||
get_document_extensions_for_service,
|
||||
)
|
||||
|
||||
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",
|
||||
".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", ".xhtml"})
|
||||
|
||||
|
||||
class FileCategory(Enum):
|
||||
PLAINTEXT = "plaintext"
|
||||
AUDIO = "audio"
|
||||
DIRECT_CONVERT = "direct_convert"
|
||||
UNSUPPORTED = "unsupported"
|
||||
DOCUMENT = "document"
|
||||
|
||||
|
||||
def classify_file(filename: str) -> FileCategory:
|
||||
suffix = PurePosixPath(filename).suffix.lower()
|
||||
if suffix in PLAINTEXT_EXTENSIONS:
|
||||
return FileCategory.PLAINTEXT
|
||||
if suffix in AUDIO_EXTENSIONS:
|
||||
return FileCategory.AUDIO
|
||||
if suffix in DIRECT_CONVERT_EXTENSIONS:
|
||||
return FileCategory.DIRECT_CONVERT
|
||||
if suffix in DOCUMENT_EXTENSIONS:
|
||||
return FileCategory.DOCUMENT
|
||||
return FileCategory.UNSUPPORTED
|
||||
|
||||
|
||||
def should_skip_for_service(filename: str, etl_service: str | None) -> bool:
|
||||
"""Return True if *filename* cannot be processed by *etl_service*.
|
||||
|
||||
Plaintext, audio, and direct-convert files are parser-agnostic and never
|
||||
skipped. Document files are checked against the per-parser extension set.
|
||||
"""
|
||||
category = classify_file(filename)
|
||||
if category == FileCategory.UNSUPPORTED:
|
||||
return True
|
||||
if category == FileCategory.DOCUMENT:
|
||||
suffix = PurePosixPath(filename).suffix.lower()
|
||||
return suffix not in get_document_extensions_for_service(etl_service)
|
||||
return False
|
||||
0
surfsense_backend/app/etl_pipeline/parsers/__init__.py
Normal file
0
surfsense_backend/app/etl_pipeline/parsers/__init__.py
Normal file
34
surfsense_backend/app/etl_pipeline/parsers/audio.py
Normal file
34
surfsense_backend/app/etl_pipeline/parsers/audio.py
Normal file
|
|
@ -0,0 +1,34 @@
|
|||
from litellm import atranscription
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
|
||||
async def transcribe_audio(file_path: str, filename: str) -> str:
|
||||
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:
|
||||
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}"
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
import random
|
||||
|
||||
from app.config import config as app_config
|
||||
|
||||
MAX_RETRIES = 5
|
||||
BASE_DELAY = 10
|
||||
MAX_DELAY = 120
|
||||
|
||||
|
||||
async def parse_with_azure_doc_intelligence(file_path: str) -> str:
|
||||
from azure.ai.documentintelligence.aio import DocumentIntelligenceClient
|
||||
from azure.ai.documentintelligence.models import DocumentContentFormat
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
from azure.core.exceptions import (
|
||||
ClientAuthenticationError,
|
||||
HttpResponseError,
|
||||
ServiceRequestError,
|
||||
ServiceResponseError,
|
||||
)
|
||||
|
||||
file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
|
||||
retryable_exceptions = (ServiceRequestError, ServiceResponseError)
|
||||
|
||||
last_exception = None
|
||||
attempt_errors: list[str] = []
|
||||
|
||||
for attempt in range(1, MAX_RETRIES + 1):
|
||||
try:
|
||||
client = DocumentIntelligenceClient(
|
||||
endpoint=app_config.AZURE_DI_ENDPOINT,
|
||||
credential=AzureKeyCredential(app_config.AZURE_DI_KEY),
|
||||
)
|
||||
async with client:
|
||||
with open(file_path, "rb") as f:
|
||||
poller = await client.begin_analyze_document(
|
||||
"prebuilt-read",
|
||||
body=f,
|
||||
output_content_format=DocumentContentFormat.MARKDOWN,
|
||||
)
|
||||
result = await poller.result()
|
||||
|
||||
if attempt > 1:
|
||||
logging.info(
|
||||
f"Azure Document Intelligence succeeded on attempt {attempt} "
|
||||
f"after {len(attempt_errors)} failures"
|
||||
)
|
||||
|
||||
if not result.content:
|
||||
return ""
|
||||
|
||||
return result.content
|
||||
|
||||
except ClientAuthenticationError:
|
||||
raise
|
||||
except HttpResponseError as e:
|
||||
if e.status_code and 400 <= e.status_code < 500:
|
||||
raise
|
||||
last_exception = e
|
||||
error_type = type(e).__name__
|
||||
error_msg = str(e)[:200]
|
||||
attempt_errors.append(f"Attempt {attempt}: {error_type} - {error_msg}")
|
||||
except 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 < MAX_RETRIES:
|
||||
base_delay = min(BASE_DELAY * (2 ** (attempt - 1)), MAX_DELAY)
|
||||
jitter = base_delay * 0.25 * (2 * random.random() - 1)
|
||||
delay = base_delay + jitter
|
||||
|
||||
logging.warning(
|
||||
f"Azure Document Intelligence failed "
|
||||
f"(attempt {attempt}/{MAX_RETRIES}): "
|
||||
f"{attempt_errors[-1]}. File: {file_size_mb:.1f}MB. "
|
||||
f"Retrying in {delay:.0f}s..."
|
||||
)
|
||||
await asyncio.sleep(delay)
|
||||
else:
|
||||
logging.error(
|
||||
f"Azure Document Intelligence failed after {MAX_RETRIES} "
|
||||
f"attempts. File size: {file_size_mb:.1f}MB. "
|
||||
f"Errors: {'; '.join(attempt_errors)}"
|
||||
)
|
||||
|
||||
raise last_exception or RuntimeError(
|
||||
f"Azure Document Intelligence parsing failed after {MAX_RETRIES} retries. "
|
||||
f"File size: {file_size_mb:.1f}MB"
|
||||
)
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
from app.tasks.document_processors._direct_converters import convert_file_directly
|
||||
|
||||
__all__ = ["convert_file_directly"]
|
||||
26
surfsense_backend/app/etl_pipeline/parsers/docling.py
Normal file
26
surfsense_backend/app/etl_pipeline/parsers/docling.py
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
import warnings
|
||||
from logging import ERROR, getLogger
|
||||
|
||||
|
||||
async def parse_with_docling(file_path: str, filename: str) -> str:
|
||||
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"]
|
||||
123
surfsense_backend/app/etl_pipeline/parsers/llamacloud.py
Normal file
123
surfsense_backend/app/etl_pipeline/parsers/llamacloud.py
Normal file
|
|
@ -0,0 +1,123 @@
|
|||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
import random
|
||||
|
||||
import httpx
|
||||
|
||||
from app.config import config as app_config
|
||||
from app.etl_pipeline.constants import (
|
||||
LLAMACLOUD_BASE_DELAY,
|
||||
LLAMACLOUD_MAX_DELAY,
|
||||
LLAMACLOUD_MAX_RETRIES,
|
||||
LLAMACLOUD_RETRYABLE_EXCEPTIONS,
|
||||
PER_PAGE_JOB_TIMEOUT,
|
||||
calculate_job_timeout,
|
||||
calculate_upload_timeout,
|
||||
)
|
||||
|
||||
|
||||
async def parse_with_llamacloud(file_path: str, estimated_pages: int) -> str:
|
||||
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"
|
||||
)
|
||||
|
||||
if hasattr(result, "get_markdown_documents"):
|
||||
markdown_docs = result.get_markdown_documents(split_by_page=False)
|
||||
if markdown_docs and hasattr(markdown_docs[0], "text"):
|
||||
return markdown_docs[0].text
|
||||
if hasattr(result, "pages") and result.pages:
|
||||
return "\n\n".join(
|
||||
p.md for p in result.pages if hasattr(p, "md") and p.md
|
||||
)
|
||||
return str(result)
|
||||
|
||||
if isinstance(result, list):
|
||||
if result and hasattr(result[0], "text"):
|
||||
return result[0].text
|
||||
return "\n\n".join(
|
||||
doc.page_content if hasattr(doc, "page_content") else str(doc)
|
||||
for doc in result
|
||||
)
|
||||
|
||||
return str(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
|
||||
|
||||
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"
|
||||
)
|
||||
8
surfsense_backend/app/etl_pipeline/parsers/plaintext.py
Normal file
8
surfsense_backend/app/etl_pipeline/parsers/plaintext.py
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
def read_plaintext(file_path: str) -> str:
|
||||
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
|
||||
14
surfsense_backend/app/etl_pipeline/parsers/unstructured.py
Normal file
14
surfsense_backend/app/etl_pipeline/parsers/unstructured.py
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
async def parse_with_unstructured(file_path: str) -> str:
|
||||
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()
|
||||
return "\n\n".join(doc.page_content for doc in docs if doc.page_content)
|
||||
|
|
@ -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."
|
||||
|
|
|
|||
|
|
@ -273,17 +273,18 @@ class IndexingPipelineService:
|
|||
continue
|
||||
|
||||
dup_check = await self.session.execute(
|
||||
select(Document.id).filter(
|
||||
select(Document.id, Document.title).filter(
|
||||
Document.content_hash == content_hash,
|
||||
Document.id != existing.id,
|
||||
)
|
||||
)
|
||||
if dup_check.scalars().first() is not None:
|
||||
dup_row = dup_check.first()
|
||||
if dup_row is not None:
|
||||
if not DocumentStatus.is_state(
|
||||
existing.status, DocumentStatus.READY
|
||||
):
|
||||
existing.status = DocumentStatus.failed(
|
||||
"Duplicate content — already indexed by another document"
|
||||
f"Duplicate content: matches '{dup_row.title}'"
|
||||
)
|
||||
continue
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ from fastapi import APIRouter
|
|||
from .airtable_add_connector_route import (
|
||||
router as airtable_add_connector_router,
|
||||
)
|
||||
from .autocomplete_routes import router as autocomplete_router
|
||||
from .chat_comments_routes import router as chat_comments_router
|
||||
from .circleback_webhook_route import router as circleback_webhook_router
|
||||
from .clickup_add_connector_route import router as clickup_add_connector_router
|
||||
|
|
@ -48,6 +49,7 @@ from .stripe_routes import router as stripe_router
|
|||
from .surfsense_docs_routes import router as surfsense_docs_router
|
||||
from .teams_add_connector_route import router as teams_add_connector_router
|
||||
from .video_presentations_routes import router as video_presentations_router
|
||||
from .vision_llm_routes import router as vision_llm_router
|
||||
from .youtube_routes import router as youtube_router
|
||||
|
||||
router = APIRouter()
|
||||
|
|
@ -67,6 +69,7 @@ router.include_router(
|
|||
) # Video presentation status and streaming
|
||||
router.include_router(reports_router) # Report CRUD and multi-format export
|
||||
router.include_router(image_generation_router) # Image generation via litellm
|
||||
router.include_router(vision_llm_router) # Vision LLM configs for screenshot analysis
|
||||
router.include_router(search_source_connectors_router)
|
||||
router.include_router(google_calendar_add_connector_router)
|
||||
router.include_router(google_gmail_add_connector_router)
|
||||
|
|
@ -84,7 +87,7 @@ 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
|
||||
|
|
@ -95,3 +98,4 @@ router.include_router(incentive_tasks_router) # Incentive tasks for earning fre
|
|||
router.include_router(stripe_router) # Stripe checkout for additional page packs
|
||||
router.include_router(youtube_router) # YouTube playlist resolution
|
||||
router.include_router(prompts_router)
|
||||
router.include_router(autocomplete_router) # Lightweight autocomplete with KB context
|
||||
|
|
|
|||
|
|
@ -1,7 +1,5 @@
|
|||
import base64
|
||||
import hashlib
|
||||
import logging
|
||||
import secrets
|
||||
from datetime import UTC, datetime, timedelta
|
||||
from uuid import UUID
|
||||
|
||||
|
|
@ -26,7 +24,11 @@ from app.utils.connector_naming import (
|
|||
check_duplicate_connector,
|
||||
generate_unique_connector_name,
|
||||
)
|
||||
from app.utils.oauth_security import OAuthStateManager, TokenEncryption
|
||||
from app.utils.oauth_security import (
|
||||
OAuthStateManager,
|
||||
TokenEncryption,
|
||||
generate_pkce_pair,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -75,28 +77,6 @@ def make_basic_auth_header(client_id: str, client_secret: str) -> str:
|
|||
return f"Basic {b64}"
|
||||
|
||||
|
||||
def generate_pkce_pair() -> tuple[str, str]:
|
||||
"""
|
||||
Generate PKCE code verifier and code challenge.
|
||||
|
||||
Returns:
|
||||
Tuple of (code_verifier, code_challenge)
|
||||
"""
|
||||
# Generate code verifier (43-128 characters)
|
||||
code_verifier = (
|
||||
base64.urlsafe_b64encode(secrets.token_bytes(32)).decode("utf-8").rstrip("=")
|
||||
)
|
||||
|
||||
# Generate code challenge (SHA256 hash of verifier, base64url encoded)
|
||||
code_challenge = (
|
||||
base64.urlsafe_b64encode(hashlib.sha256(code_verifier.encode("utf-8")).digest())
|
||||
.decode("utf-8")
|
||||
.rstrip("=")
|
||||
)
|
||||
|
||||
return code_verifier, code_challenge
|
||||
|
||||
|
||||
@router.get("/auth/airtable/connector/add")
|
||||
async def connect_airtable(space_id: int, user: User = Depends(current_active_user)):
|
||||
"""
|
||||
|
|
|
|||
45
surfsense_backend/app/routes/autocomplete_routes.py
Normal file
45
surfsense_backend/app/routes/autocomplete_routes.py
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
from fastapi import APIRouter, Depends
|
||||
from fastapi.responses import StreamingResponse
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.db import User, get_async_session
|
||||
from app.services.new_streaming_service import VercelStreamingService
|
||||
from app.services.vision_autocomplete_service import stream_vision_autocomplete
|
||||
from app.users import current_active_user
|
||||
from app.utils.rbac import check_search_space_access
|
||||
|
||||
router = APIRouter(prefix="/autocomplete", tags=["autocomplete"])
|
||||
|
||||
MAX_SCREENSHOT_SIZE = 20 * 1024 * 1024 # 20 MB base64 ceiling
|
||||
|
||||
|
||||
class VisionAutocompleteRequest(BaseModel):
|
||||
screenshot: str = Field(..., max_length=MAX_SCREENSHOT_SIZE)
|
||||
search_space_id: int
|
||||
app_name: str = ""
|
||||
window_title: str = ""
|
||||
|
||||
|
||||
@router.post("/vision/stream")
|
||||
async def vision_autocomplete_stream(
|
||||
body: VisionAutocompleteRequest,
|
||||
user: User = Depends(current_active_user),
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
):
|
||||
await check_search_space_access(session, user, body.search_space_id)
|
||||
|
||||
return StreamingResponse(
|
||||
stream_vision_autocomplete(
|
||||
body.screenshot,
|
||||
body.search_space_id,
|
||||
session,
|
||||
app_name=body.app_name,
|
||||
window_title=body.window_title,
|
||||
),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
**VercelStreamingService.get_response_headers(),
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
|
|
@ -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),
|
||||
}
|
||||
|
|
|
|||
|
|
@ -311,9 +311,11 @@ async def dropbox_callback(
|
|||
)
|
||||
|
||||
existing_cursor = db_connector.config.get("cursor")
|
||||
existing_folder_cursors = db_connector.config.get("folder_cursors")
|
||||
db_connector.config = {
|
||||
**connector_config,
|
||||
"cursor": existing_cursor,
|
||||
"folder_cursors": existing_folder_cursors,
|
||||
"auth_expired": False,
|
||||
}
|
||||
flag_modified(db_connector, "config")
|
||||
|
|
|
|||
|
|
@ -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")
|
||||
|
|
|
|||
|
|
@ -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),
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -28,7 +28,11 @@ from app.utils.connector_naming import (
|
|||
check_duplicate_connector,
|
||||
generate_unique_connector_name,
|
||||
)
|
||||
from app.utils.oauth_security import OAuthStateManager, TokenEncryption
|
||||
from app.utils.oauth_security import (
|
||||
OAuthStateManager,
|
||||
TokenEncryption,
|
||||
generate_code_verifier,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -96,9 +100,14 @@ async def connect_calendar(space_id: int, user: User = Depends(current_active_us
|
|||
|
||||
flow = get_google_flow()
|
||||
|
||||
# Generate secure state parameter with HMAC signature
|
||||
code_verifier = generate_code_verifier()
|
||||
flow.code_verifier = code_verifier
|
||||
|
||||
# Generate secure state parameter with HMAC signature (includes PKCE code_verifier)
|
||||
state_manager = get_state_manager()
|
||||
state_encoded = state_manager.generate_secure_state(space_id, user.id)
|
||||
state_encoded = state_manager.generate_secure_state(
|
||||
space_id, user.id, code_verifier=code_verifier
|
||||
)
|
||||
|
||||
auth_url, _ = flow.authorization_url(
|
||||
access_type="offline",
|
||||
|
|
@ -146,8 +155,11 @@ async def reauth_calendar(
|
|||
|
||||
flow = get_google_flow()
|
||||
|
||||
code_verifier = generate_code_verifier()
|
||||
flow.code_verifier = code_verifier
|
||||
|
||||
state_manager = get_state_manager()
|
||||
extra: dict = {"connector_id": connector_id}
|
||||
extra: dict = {"connector_id": connector_id, "code_verifier": code_verifier}
|
||||
if return_url and return_url.startswith("/"):
|
||||
extra["return_url"] = return_url
|
||||
state_encoded = state_manager.generate_secure_state(space_id, user.id, **extra)
|
||||
|
|
@ -225,6 +237,7 @@ async def calendar_callback(
|
|||
|
||||
user_id = UUID(data["user_id"])
|
||||
space_id = data["space_id"]
|
||||
code_verifier = data.get("code_verifier")
|
||||
|
||||
# Validate redirect URI (security: ensure it matches configured value)
|
||||
if not config.GOOGLE_CALENDAR_REDIRECT_URI:
|
||||
|
|
@ -233,6 +246,7 @@ async def calendar_callback(
|
|||
)
|
||||
|
||||
flow = get_google_flow()
|
||||
flow.code_verifier = code_verifier
|
||||
flow.fetch_token(code=code)
|
||||
|
||||
creds = flow.credentials
|
||||
|
|
|
|||
|
|
@ -41,7 +41,11 @@ from app.utils.connector_naming import (
|
|||
check_duplicate_connector,
|
||||
generate_unique_connector_name,
|
||||
)
|
||||
from app.utils.oauth_security import OAuthStateManager, TokenEncryption
|
||||
from app.utils.oauth_security import (
|
||||
OAuthStateManager,
|
||||
TokenEncryption,
|
||||
generate_code_verifier,
|
||||
)
|
||||
|
||||
# Relax token scope validation for Google OAuth
|
||||
os.environ["OAUTHLIB_RELAX_TOKEN_SCOPE"] = "1"
|
||||
|
|
@ -127,14 +131,19 @@ async def connect_drive(space_id: int, user: User = Depends(current_active_user)
|
|||
|
||||
flow = get_google_flow()
|
||||
|
||||
# Generate secure state parameter with HMAC signature
|
||||
code_verifier = generate_code_verifier()
|
||||
flow.code_verifier = code_verifier
|
||||
|
||||
# Generate secure state parameter with HMAC signature (includes PKCE code_verifier)
|
||||
state_manager = get_state_manager()
|
||||
state_encoded = state_manager.generate_secure_state(space_id, user.id)
|
||||
state_encoded = state_manager.generate_secure_state(
|
||||
space_id, user.id, code_verifier=code_verifier
|
||||
)
|
||||
|
||||
# Generate authorization URL
|
||||
auth_url, _ = flow.authorization_url(
|
||||
access_type="offline", # Get refresh token
|
||||
prompt="consent", # Force consent screen to get refresh token
|
||||
access_type="offline",
|
||||
prompt="consent",
|
||||
include_granted_scopes="true",
|
||||
state=state_encoded,
|
||||
)
|
||||
|
|
@ -193,8 +202,11 @@ async def reauth_drive(
|
|||
|
||||
flow = get_google_flow()
|
||||
|
||||
code_verifier = generate_code_verifier()
|
||||
flow.code_verifier = code_verifier
|
||||
|
||||
state_manager = get_state_manager()
|
||||
extra: dict = {"connector_id": connector_id}
|
||||
extra: dict = {"connector_id": connector_id, "code_verifier": code_verifier}
|
||||
if return_url and return_url.startswith("/"):
|
||||
extra["return_url"] = return_url
|
||||
state_encoded = state_manager.generate_secure_state(space_id, user.id, **extra)
|
||||
|
|
@ -285,6 +297,7 @@ async def drive_callback(
|
|||
space_id = data["space_id"]
|
||||
reauth_connector_id = data.get("connector_id")
|
||||
reauth_return_url = data.get("return_url")
|
||||
code_verifier = data.get("code_verifier")
|
||||
|
||||
logger.info(
|
||||
f"Processing Google Drive callback for user {user_id}, space {space_id}"
|
||||
|
|
@ -296,8 +309,9 @@ async def drive_callback(
|
|||
status_code=500, detail="GOOGLE_DRIVE_REDIRECT_URI not configured"
|
||||
)
|
||||
|
||||
# Exchange authorization code for tokens
|
||||
# Exchange authorization code for tokens (restore PKCE code_verifier from state)
|
||||
flow = get_google_flow()
|
||||
flow.code_verifier = code_verifier
|
||||
flow.fetch_token(code=code)
|
||||
|
||||
creds = flow.credentials
|
||||
|
|
|
|||
|
|
@ -28,7 +28,11 @@ from app.utils.connector_naming import (
|
|||
check_duplicate_connector,
|
||||
generate_unique_connector_name,
|
||||
)
|
||||
from app.utils.oauth_security import OAuthStateManager, TokenEncryption
|
||||
from app.utils.oauth_security import (
|
||||
OAuthStateManager,
|
||||
TokenEncryption,
|
||||
generate_code_verifier,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -109,9 +113,14 @@ async def connect_gmail(space_id: int, user: User = Depends(current_active_user)
|
|||
|
||||
flow = get_google_flow()
|
||||
|
||||
# Generate secure state parameter with HMAC signature
|
||||
code_verifier = generate_code_verifier()
|
||||
flow.code_verifier = code_verifier
|
||||
|
||||
# Generate secure state parameter with HMAC signature (includes PKCE code_verifier)
|
||||
state_manager = get_state_manager()
|
||||
state_encoded = state_manager.generate_secure_state(space_id, user.id)
|
||||
state_encoded = state_manager.generate_secure_state(
|
||||
space_id, user.id, code_verifier=code_verifier
|
||||
)
|
||||
|
||||
auth_url, _ = flow.authorization_url(
|
||||
access_type="offline",
|
||||
|
|
@ -164,8 +173,11 @@ async def reauth_gmail(
|
|||
|
||||
flow = get_google_flow()
|
||||
|
||||
code_verifier = generate_code_verifier()
|
||||
flow.code_verifier = code_verifier
|
||||
|
||||
state_manager = get_state_manager()
|
||||
extra: dict = {"connector_id": connector_id}
|
||||
extra: dict = {"connector_id": connector_id, "code_verifier": code_verifier}
|
||||
if return_url and return_url.startswith("/"):
|
||||
extra["return_url"] = return_url
|
||||
state_encoded = state_manager.generate_secure_state(space_id, user.id, **extra)
|
||||
|
|
@ -256,6 +268,7 @@ async def gmail_callback(
|
|||
|
||||
user_id = UUID(data["user_id"])
|
||||
space_id = data["space_id"]
|
||||
code_verifier = data.get("code_verifier")
|
||||
|
||||
# Validate redirect URI (security: ensure it matches configured value)
|
||||
if not config.GOOGLE_GMAIL_REDIRECT_URI:
|
||||
|
|
@ -264,6 +277,7 @@ async def gmail_callback(
|
|||
)
|
||||
|
||||
flow = get_google_flow()
|
||||
flow.code_verifier = code_verifier
|
||||
flow.fetch_token(code=code)
|
||||
|
||||
creds = flow.credentials
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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(
|
||||
|
|
@ -2465,6 +2477,8 @@ async def run_google_drive_indexing(
|
|||
stage="fetching",
|
||||
)
|
||||
|
||||
total_unsupported = 0
|
||||
|
||||
# Index each folder with indexing options
|
||||
for folder in items.folders:
|
||||
try:
|
||||
|
|
@ -2472,6 +2486,7 @@ async def run_google_drive_indexing(
|
|||
indexed_count,
|
||||
skipped_count,
|
||||
error_message,
|
||||
unsupported_count,
|
||||
) = await index_google_drive_files(
|
||||
session,
|
||||
connector_id,
|
||||
|
|
@ -2485,6 +2500,7 @@ async def run_google_drive_indexing(
|
|||
include_subfolders=indexing_options.include_subfolders,
|
||||
)
|
||||
total_skipped += skipped_count
|
||||
total_unsupported += unsupported_count
|
||||
if error_message:
|
||||
errors.append(f"Folder '{folder.name}': {error_message}")
|
||||
else:
|
||||
|
|
@ -2560,6 +2576,7 @@ async def run_google_drive_indexing(
|
|||
indexed_count=total_indexed,
|
||||
error_message=error_message,
|
||||
skipped_count=total_skipped,
|
||||
unsupported_count=total_unsupported,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -2630,7 +2647,12 @@ async def run_onedrive_indexing(
|
|||
stage="fetching",
|
||||
)
|
||||
|
||||
total_indexed, total_skipped, error_message = await index_onedrive_files(
|
||||
(
|
||||
total_indexed,
|
||||
total_skipped,
|
||||
error_message,
|
||||
total_unsupported,
|
||||
) = await index_onedrive_files(
|
||||
session,
|
||||
connector_id,
|
||||
search_space_id,
|
||||
|
|
@ -2671,6 +2693,7 @@ async def run_onedrive_indexing(
|
|||
indexed_count=total_indexed,
|
||||
error_message=error_message,
|
||||
skipped_count=total_skipped,
|
||||
unsupported_count=total_unsupported,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -2738,7 +2761,12 @@ async def run_dropbox_indexing(
|
|||
stage="fetching",
|
||||
)
|
||||
|
||||
total_indexed, total_skipped, error_message = await index_dropbox_files(
|
||||
(
|
||||
total_indexed,
|
||||
total_skipped,
|
||||
error_message,
|
||||
total_unsupported,
|
||||
) = await index_dropbox_files(
|
||||
session,
|
||||
connector_id,
|
||||
search_space_id,
|
||||
|
|
@ -2779,6 +2807,7 @@ async def run_dropbox_indexing(
|
|||
indexed_count=total_indexed,
|
||||
error_message=error_message,
|
||||
skipped_count=total_skipped,
|
||||
unsupported_count=total_unsupported,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -2836,6 +2865,8 @@ 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 +2919,8 @@ 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 +2971,8 @@ 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,
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ from app.db import (
|
|||
SearchSpaceMembership,
|
||||
SearchSpaceRole,
|
||||
User,
|
||||
VisionLLMConfig,
|
||||
get_async_session,
|
||||
get_default_roles_config,
|
||||
)
|
||||
|
|
@ -483,6 +484,63 @@ async def _get_image_gen_config_by_id(
|
|||
return None
|
||||
|
||||
|
||||
async def _get_vision_llm_config_by_id(
|
||||
session: AsyncSession, config_id: int | None
|
||||
) -> dict | None:
|
||||
if config_id is None:
|
||||
return None
|
||||
|
||||
if config_id == 0:
|
||||
return {
|
||||
"id": 0,
|
||||
"name": "Auto (Fastest)",
|
||||
"description": "Automatically routes requests across available vision LLM providers",
|
||||
"provider": "AUTO",
|
||||
"model_name": "auto",
|
||||
"is_global": True,
|
||||
"is_auto_mode": True,
|
||||
}
|
||||
|
||||
if config_id < 0:
|
||||
for cfg in config.GLOBAL_VISION_LLM_CONFIGS:
|
||||
if cfg.get("id") == config_id:
|
||||
return {
|
||||
"id": cfg.get("id"),
|
||||
"name": cfg.get("name"),
|
||||
"description": cfg.get("description"),
|
||||
"provider": cfg.get("provider"),
|
||||
"custom_provider": cfg.get("custom_provider"),
|
||||
"model_name": cfg.get("model_name"),
|
||||
"api_base": cfg.get("api_base") or None,
|
||||
"api_version": cfg.get("api_version") or None,
|
||||
"litellm_params": cfg.get("litellm_params", {}),
|
||||
"is_global": True,
|
||||
}
|
||||
return None
|
||||
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if db_config:
|
||||
return {
|
||||
"id": db_config.id,
|
||||
"name": db_config.name,
|
||||
"description": db_config.description,
|
||||
"provider": db_config.provider.value if db_config.provider else None,
|
||||
"custom_provider": db_config.custom_provider,
|
||||
"model_name": db_config.model_name,
|
||||
"api_base": db_config.api_base,
|
||||
"api_version": db_config.api_version,
|
||||
"litellm_params": db_config.litellm_params or {},
|
||||
"created_at": db_config.created_at.isoformat()
|
||||
if db_config.created_at
|
||||
else None,
|
||||
"search_space_id": db_config.search_space_id,
|
||||
}
|
||||
return None
|
||||
|
||||
|
||||
@router.get(
|
||||
"/search-spaces/{search_space_id}/llm-preferences",
|
||||
response_model=LLMPreferencesRead,
|
||||
|
|
@ -522,14 +580,19 @@ async def get_llm_preferences(
|
|||
image_generation_config = await _get_image_gen_config_by_id(
|
||||
session, search_space.image_generation_config_id
|
||||
)
|
||||
vision_llm_config = await _get_vision_llm_config_by_id(
|
||||
session, search_space.vision_llm_config_id
|
||||
)
|
||||
|
||||
return LLMPreferencesRead(
|
||||
agent_llm_id=search_space.agent_llm_id,
|
||||
document_summary_llm_id=search_space.document_summary_llm_id,
|
||||
image_generation_config_id=search_space.image_generation_config_id,
|
||||
vision_llm_config_id=search_space.vision_llm_config_id,
|
||||
agent_llm=agent_llm,
|
||||
document_summary_llm=document_summary_llm,
|
||||
image_generation_config=image_generation_config,
|
||||
vision_llm_config=vision_llm_config,
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
|
|
@ -589,14 +652,19 @@ async def update_llm_preferences(
|
|||
image_generation_config = await _get_image_gen_config_by_id(
|
||||
session, search_space.image_generation_config_id
|
||||
)
|
||||
vision_llm_config = await _get_vision_llm_config_by_id(
|
||||
session, search_space.vision_llm_config_id
|
||||
)
|
||||
|
||||
return LLMPreferencesRead(
|
||||
agent_llm_id=search_space.agent_llm_id,
|
||||
document_summary_llm_id=search_space.document_summary_llm_id,
|
||||
image_generation_config_id=search_space.image_generation_config_id,
|
||||
vision_llm_config_id=search_space.vision_llm_config_id,
|
||||
agent_llm=agent_llm,
|
||||
document_summary_llm=document_summary_llm,
|
||||
image_generation_config=image_generation_config,
|
||||
vision_llm_config=vision_llm_config,
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
|
|
|
|||
291
surfsense_backend/app/routes/vision_llm_routes.py
Normal file
291
surfsense_backend/app/routes/vision_llm_routes.py
Normal file
|
|
@ -0,0 +1,291 @@
|
|||
import logging
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from pydantic import BaseModel
|
||||
from sqlalchemy import select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.config import config
|
||||
from app.db import (
|
||||
Permission,
|
||||
User,
|
||||
VisionLLMConfig,
|
||||
get_async_session,
|
||||
)
|
||||
from app.schemas import (
|
||||
GlobalVisionLLMConfigRead,
|
||||
VisionLLMConfigCreate,
|
||||
VisionLLMConfigRead,
|
||||
VisionLLMConfigUpdate,
|
||||
)
|
||||
from app.services.vision_model_list_service import get_vision_model_list
|
||||
from app.users import current_active_user
|
||||
from app.utils.rbac import check_permission
|
||||
|
||||
router = APIRouter()
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Vision Model Catalogue (from OpenRouter, filtered for image-input models)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class VisionModelListItem(BaseModel):
|
||||
value: str
|
||||
label: str
|
||||
provider: str
|
||||
context_window: str | None = None
|
||||
|
||||
|
||||
@router.get("/vision-models", response_model=list[VisionModelListItem])
|
||||
async def list_vision_models(
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
"""Return vision-capable models sourced from OpenRouter (filtered by image input)."""
|
||||
try:
|
||||
return await get_vision_model_list()
|
||||
except Exception as e:
|
||||
logger.exception("Failed to fetch vision model list")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch vision model list: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Global Vision LLM Configs (from YAML)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@router.get(
|
||||
"/global-vision-llm-configs",
|
||||
response_model=list[GlobalVisionLLMConfigRead],
|
||||
)
|
||||
async def get_global_vision_llm_configs(
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
global_configs = config.GLOBAL_VISION_LLM_CONFIGS
|
||||
safe_configs = []
|
||||
|
||||
if global_configs and len(global_configs) > 0:
|
||||
safe_configs.append(
|
||||
{
|
||||
"id": 0,
|
||||
"name": "Auto (Fastest)",
|
||||
"description": "Automatically routes across available vision LLM providers.",
|
||||
"provider": "AUTO",
|
||||
"custom_provider": None,
|
||||
"model_name": "auto",
|
||||
"api_base": None,
|
||||
"api_version": None,
|
||||
"litellm_params": {},
|
||||
"is_global": True,
|
||||
"is_auto_mode": True,
|
||||
}
|
||||
)
|
||||
|
||||
for cfg in global_configs:
|
||||
safe_configs.append(
|
||||
{
|
||||
"id": cfg.get("id"),
|
||||
"name": cfg.get("name"),
|
||||
"description": cfg.get("description"),
|
||||
"provider": cfg.get("provider"),
|
||||
"custom_provider": cfg.get("custom_provider"),
|
||||
"model_name": cfg.get("model_name"),
|
||||
"api_base": cfg.get("api_base") or None,
|
||||
"api_version": cfg.get("api_version") or None,
|
||||
"litellm_params": cfg.get("litellm_params", {}),
|
||||
"is_global": True,
|
||||
}
|
||||
)
|
||||
|
||||
return safe_configs
|
||||
except Exception as e:
|
||||
logger.exception("Failed to fetch global vision LLM configs")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch configs: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# VisionLLMConfig CRUD
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@router.post("/vision-llm-configs", response_model=VisionLLMConfigRead)
|
||||
async def create_vision_llm_config(
|
||||
config_data: VisionLLMConfigCreate,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
config_data.search_space_id,
|
||||
Permission.VISION_CONFIGS_CREATE.value,
|
||||
"You don't have permission to create vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
db_config = VisionLLMConfig(**config_data.model_dump(), user_id=user.id)
|
||||
session.add(db_config)
|
||||
await session.commit()
|
||||
await session.refresh(db_config)
|
||||
return db_config
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
logger.exception("Failed to create VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to create config: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.get("/vision-llm-configs", response_model=list[VisionLLMConfigRead])
|
||||
async def list_vision_llm_configs(
|
||||
search_space_id: int,
|
||||
skip: int = 0,
|
||||
limit: int = 100,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
search_space_id,
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
"You don't have permission to view vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig)
|
||||
.filter(VisionLLMConfig.search_space_id == search_space_id)
|
||||
.order_by(VisionLLMConfig.created_at.desc())
|
||||
.offset(skip)
|
||||
.limit(limit)
|
||||
)
|
||||
return result.scalars().all()
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.exception("Failed to list VisionLLMConfigs")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch configs: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.get("/vision-llm-configs/{config_id}", response_model=VisionLLMConfigRead)
|
||||
async def get_vision_llm_config(
|
||||
config_id: int,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if not db_config:
|
||||
raise HTTPException(status_code=404, detail="Config not found")
|
||||
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
db_config.search_space_id,
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
"You don't have permission to view vision LLM configs in this search space",
|
||||
)
|
||||
return db_config
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.exception("Failed to get VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch config: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.put("/vision-llm-configs/{config_id}", response_model=VisionLLMConfigRead)
|
||||
async def update_vision_llm_config(
|
||||
config_id: int,
|
||||
update_data: VisionLLMConfigUpdate,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if not db_config:
|
||||
raise HTTPException(status_code=404, detail="Config not found")
|
||||
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
db_config.search_space_id,
|
||||
Permission.VISION_CONFIGS_CREATE.value,
|
||||
"You don't have permission to update vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
for key, value in update_data.model_dump(exclude_unset=True).items():
|
||||
setattr(db_config, key, value)
|
||||
|
||||
await session.commit()
|
||||
await session.refresh(db_config)
|
||||
return db_config
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
logger.exception("Failed to update VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to update config: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.delete("/vision-llm-configs/{config_id}", response_model=dict)
|
||||
async def delete_vision_llm_config(
|
||||
config_id: int,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if not db_config:
|
||||
raise HTTPException(status_code=404, detail="Config not found")
|
||||
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
db_config.search_space_id,
|
||||
Permission.VISION_CONFIGS_DELETE.value,
|
||||
"You don't have permission to delete vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
await session.delete(db_config)
|
||||
await session.commit()
|
||||
return {
|
||||
"message": "Vision LLM config deleted successfully",
|
||||
"id": config_id,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
logger.exception("Failed to delete VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to delete config: {e!s}"
|
||||
) from e
|
||||
|
|
@ -125,6 +125,13 @@ from .video_presentations import (
|
|||
VideoPresentationRead,
|
||||
VideoPresentationUpdate,
|
||||
)
|
||||
from .vision_llm import (
|
||||
GlobalVisionLLMConfigRead,
|
||||
VisionLLMConfigCreate,
|
||||
VisionLLMConfigPublic,
|
||||
VisionLLMConfigRead,
|
||||
VisionLLMConfigUpdate,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# Folder schemas
|
||||
|
|
@ -163,6 +170,8 @@ __all__ = [
|
|||
"FolderUpdate",
|
||||
"GlobalImageGenConfigRead",
|
||||
"GlobalNewLLMConfigRead",
|
||||
# Vision LLM Config schemas
|
||||
"GlobalVisionLLMConfigRead",
|
||||
"GoogleDriveIndexRequest",
|
||||
"GoogleDriveIndexingOptions",
|
||||
# Base schemas
|
||||
|
|
@ -264,4 +273,8 @@ __all__ = [
|
|||
"VideoPresentationCreate",
|
||||
"VideoPresentationRead",
|
||||
"VideoPresentationUpdate",
|
||||
"VisionLLMConfigCreate",
|
||||
"VisionLLMConfigPublic",
|
||||
"VisionLLMConfigRead",
|
||||
"VisionLLMConfigUpdate",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -182,6 +182,10 @@ class LLMPreferencesRead(BaseModel):
|
|||
image_generation_config_id: int | None = Field(
|
||||
None, description="ID of the image generation config to use"
|
||||
)
|
||||
vision_llm_config_id: int | None = Field(
|
||||
None,
|
||||
description="ID of the vision LLM config to use for vision/screenshot analysis",
|
||||
)
|
||||
agent_llm: dict[str, Any] | None = Field(
|
||||
None, description="Full config for agent LLM"
|
||||
)
|
||||
|
|
@ -191,6 +195,9 @@ class LLMPreferencesRead(BaseModel):
|
|||
image_generation_config: dict[str, Any] | None = Field(
|
||||
None, description="Full config for image generation"
|
||||
)
|
||||
vision_llm_config: dict[str, Any] | None = Field(
|
||||
None, description="Full config for vision LLM"
|
||||
)
|
||||
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
|
|
@ -207,3 +214,7 @@ class LLMPreferencesUpdate(BaseModel):
|
|||
image_generation_config_id: int | None = Field(
|
||||
None, description="ID of the image generation config to use"
|
||||
)
|
||||
vision_llm_config_id: int | None = Field(
|
||||
None,
|
||||
description="ID of the vision LLM config to use for vision/screenshot analysis",
|
||||
)
|
||||
|
|
|
|||
75
surfsense_backend/app/schemas/vision_llm.py
Normal file
75
surfsense_backend/app/schemas/vision_llm.py
Normal file
|
|
@ -0,0 +1,75 @@
|
|||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
from app.db import VisionProvider
|
||||
|
||||
|
||||
class VisionLLMConfigBase(BaseModel):
|
||||
name: str = Field(..., max_length=100)
|
||||
description: str | None = Field(None, max_length=500)
|
||||
provider: VisionProvider = Field(...)
|
||||
custom_provider: str | None = Field(None, max_length=100)
|
||||
model_name: str = Field(..., max_length=100)
|
||||
api_key: str = Field(...)
|
||||
api_base: str | None = Field(None, max_length=500)
|
||||
api_version: str | None = Field(None, max_length=50)
|
||||
litellm_params: dict[str, Any] | None = Field(default=None)
|
||||
|
||||
|
||||
class VisionLLMConfigCreate(VisionLLMConfigBase):
|
||||
search_space_id: int = Field(...)
|
||||
|
||||
|
||||
class VisionLLMConfigUpdate(BaseModel):
|
||||
name: str | None = Field(None, max_length=100)
|
||||
description: str | None = Field(None, max_length=500)
|
||||
provider: VisionProvider | None = None
|
||||
custom_provider: str | None = Field(None, max_length=100)
|
||||
model_name: str | None = Field(None, max_length=100)
|
||||
api_key: str | None = None
|
||||
api_base: str | None = Field(None, max_length=500)
|
||||
api_version: str | None = Field(None, max_length=50)
|
||||
litellm_params: dict[str, Any] | None = None
|
||||
|
||||
|
||||
class VisionLLMConfigRead(VisionLLMConfigBase):
|
||||
id: int
|
||||
created_at: datetime
|
||||
search_space_id: int
|
||||
user_id: uuid.UUID
|
||||
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
|
||||
class VisionLLMConfigPublic(BaseModel):
|
||||
id: int
|
||||
name: str
|
||||
description: str | None = None
|
||||
provider: VisionProvider
|
||||
custom_provider: str | None = None
|
||||
model_name: str
|
||||
api_base: str | None = None
|
||||
api_version: str | None = None
|
||||
litellm_params: dict[str, Any] | None = None
|
||||
created_at: datetime
|
||||
search_space_id: int
|
||||
user_id: uuid.UUID
|
||||
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
|
||||
class GlobalVisionLLMConfigRead(BaseModel):
|
||||
id: int = Field(...)
|
||||
name: str
|
||||
description: str | None = None
|
||||
provider: str
|
||||
custom_provider: str | None = None
|
||||
model_name: str
|
||||
api_base: str | None = None
|
||||
api_version: str | None = None
|
||||
litellm_params: dict[str, Any] | None = None
|
||||
is_global: bool = True
|
||||
is_auto_mode: bool = False
|
||||
|
|
@ -111,9 +111,8 @@ class DoclingService:
|
|||
pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend
|
||||
)
|
||||
|
||||
# Initialize DocumentConverter
|
||||
self.converter = DocumentConverter(
|
||||
format_options={InputFormat.PDF: pdf_format_option}
|
||||
format_options={InputFormat.PDF: pdf_format_option},
|
||||
)
|
||||
|
||||
acceleration_type = "GPU (WSL2)" if self.use_gpu else "CPU"
|
||||
|
|
|
|||
|
|
@ -405,6 +405,121 @@ async def get_document_summary_llm(
|
|||
)
|
||||
|
||||
|
||||
async def get_vision_llm(
|
||||
session: AsyncSession, search_space_id: int
|
||||
) -> ChatLiteLLM | ChatLiteLLMRouter | None:
|
||||
"""Get the search space's vision LLM instance for screenshot analysis.
|
||||
|
||||
Resolves from the dedicated VisionLLMConfig system:
|
||||
- Auto mode (ID 0): VisionLLMRouterService
|
||||
- Global (negative ID): YAML configs
|
||||
- DB (positive ID): VisionLLMConfig table
|
||||
"""
|
||||
from app.db import VisionLLMConfig
|
||||
from app.services.vision_llm_router_service import (
|
||||
VISION_PROVIDER_MAP,
|
||||
VisionLLMRouterService,
|
||||
get_global_vision_llm_config,
|
||||
is_vision_auto_mode,
|
||||
)
|
||||
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(SearchSpace).where(SearchSpace.id == search_space_id)
|
||||
)
|
||||
search_space = result.scalars().first()
|
||||
if not search_space:
|
||||
logger.error(f"Search space {search_space_id} not found")
|
||||
return None
|
||||
|
||||
config_id = search_space.vision_llm_config_id
|
||||
if config_id is None:
|
||||
logger.error(f"No vision LLM configured for search space {search_space_id}")
|
||||
return None
|
||||
|
||||
if is_vision_auto_mode(config_id):
|
||||
if not VisionLLMRouterService.is_initialized():
|
||||
logger.error(
|
||||
"Vision Auto mode requested but Vision LLM Router not initialized"
|
||||
)
|
||||
return None
|
||||
try:
|
||||
return ChatLiteLLMRouter(
|
||||
router=VisionLLMRouterService.get_router(),
|
||||
streaming=True,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create vision ChatLiteLLMRouter: {e}")
|
||||
return None
|
||||
|
||||
if config_id < 0:
|
||||
global_cfg = get_global_vision_llm_config(config_id)
|
||||
if not global_cfg:
|
||||
logger.error(f"Global vision LLM config {config_id} not found")
|
||||
return None
|
||||
|
||||
if global_cfg.get("custom_provider"):
|
||||
model_string = (
|
||||
f"{global_cfg['custom_provider']}/{global_cfg['model_name']}"
|
||||
)
|
||||
else:
|
||||
prefix = VISION_PROVIDER_MAP.get(
|
||||
global_cfg["provider"].upper(),
|
||||
global_cfg["provider"].lower(),
|
||||
)
|
||||
model_string = f"{prefix}/{global_cfg['model_name']}"
|
||||
|
||||
litellm_kwargs = {
|
||||
"model": model_string,
|
||||
"api_key": global_cfg["api_key"],
|
||||
}
|
||||
if global_cfg.get("api_base"):
|
||||
litellm_kwargs["api_base"] = global_cfg["api_base"]
|
||||
if global_cfg.get("litellm_params"):
|
||||
litellm_kwargs.update(global_cfg["litellm_params"])
|
||||
|
||||
return ChatLiteLLM(**litellm_kwargs)
|
||||
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).where(
|
||||
VisionLLMConfig.id == config_id,
|
||||
VisionLLMConfig.search_space_id == search_space_id,
|
||||
)
|
||||
)
|
||||
vision_cfg = result.scalars().first()
|
||||
if not vision_cfg:
|
||||
logger.error(
|
||||
f"Vision LLM config {config_id} not found in search space {search_space_id}"
|
||||
)
|
||||
return None
|
||||
|
||||
if vision_cfg.custom_provider:
|
||||
model_string = f"{vision_cfg.custom_provider}/{vision_cfg.model_name}"
|
||||
else:
|
||||
prefix = VISION_PROVIDER_MAP.get(
|
||||
vision_cfg.provider.value.upper(),
|
||||
vision_cfg.provider.value.lower(),
|
||||
)
|
||||
model_string = f"{prefix}/{vision_cfg.model_name}"
|
||||
|
||||
litellm_kwargs = {
|
||||
"model": model_string,
|
||||
"api_key": vision_cfg.api_key,
|
||||
}
|
||||
if vision_cfg.api_base:
|
||||
litellm_kwargs["api_base"] = vision_cfg.api_base
|
||||
if vision_cfg.litellm_params:
|
||||
litellm_kwargs.update(vision_cfg.litellm_params)
|
||||
|
||||
return ChatLiteLLM(**litellm_kwargs)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Error getting vision LLM for search space {search_space_id}: {e!s}"
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
# Backward-compatible alias (LLM preferences are now per-search-space, not per-user)
|
||||
async def get_user_long_context_llm(
|
||||
session: AsyncSession,
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
|
|
|||
|
|
@ -421,6 +421,7 @@ class ConnectorIndexingNotificationHandler(BaseNotificationHandler):
|
|||
error_message: str | None = None,
|
||||
is_warning: bool = False,
|
||||
skipped_count: int | None = None,
|
||||
unsupported_count: int | None = None,
|
||||
) -> Notification:
|
||||
"""
|
||||
Update notification when connector indexing completes.
|
||||
|
|
@ -428,10 +429,11 @@ class ConnectorIndexingNotificationHandler(BaseNotificationHandler):
|
|||
Args:
|
||||
session: Database session
|
||||
notification: Notification to update
|
||||
indexed_count: Total number of items indexed
|
||||
indexed_count: Total number of files indexed
|
||||
error_message: Error message if indexing failed, or warning message (optional)
|
||||
is_warning: If True, treat error_message as a warning (success case) rather than an error
|
||||
skipped_count: Number of items skipped (e.g., duplicates) - optional
|
||||
skipped_count: Number of files skipped (e.g., unchanged) - optional
|
||||
unsupported_count: Number of files skipped because the ETL parser doesn't support them
|
||||
|
||||
Returns:
|
||||
Updated notification
|
||||
|
|
@ -440,52 +442,45 @@ class ConnectorIndexingNotificationHandler(BaseNotificationHandler):
|
|||
"connector_name", "Connector"
|
||||
)
|
||||
|
||||
# Build the skipped text if there are skipped items
|
||||
skipped_text = ""
|
||||
if skipped_count and skipped_count > 0:
|
||||
skipped_item_text = "item" if skipped_count == 1 else "items"
|
||||
skipped_text = (
|
||||
f" ({skipped_count} {skipped_item_text} skipped - already indexed)"
|
||||
)
|
||||
unsupported_text = ""
|
||||
if unsupported_count and unsupported_count > 0:
|
||||
file_word = "file was" if unsupported_count == 1 else "files were"
|
||||
unsupported_text = f" {unsupported_count} {file_word} not supported."
|
||||
|
||||
# If there's an error message but items were indexed, treat it as a warning (partial success)
|
||||
# If is_warning is True, treat it as success even with 0 items (e.g., duplicates found)
|
||||
# Otherwise, treat it as a failure
|
||||
if error_message:
|
||||
if indexed_count > 0:
|
||||
# Partial success with warnings (e.g., duplicate content from other connectors)
|
||||
title = f"Ready: {connector_name}"
|
||||
item_text = "item" if indexed_count == 1 else "items"
|
||||
message = f"Now searchable! {indexed_count} {item_text} synced{skipped_text}. Note: {error_message}"
|
||||
file_text = "file" if indexed_count == 1 else "files"
|
||||
message = f"Now searchable! {indexed_count} {file_text} synced.{unsupported_text} Note: {error_message}"
|
||||
status = "completed"
|
||||
elif is_warning:
|
||||
# Warning case (e.g., duplicates found) - treat as success
|
||||
title = f"Ready: {connector_name}"
|
||||
message = f"Sync completed{skipped_text}. {error_message}"
|
||||
message = f"Sync complete.{unsupported_text} {error_message}"
|
||||
status = "completed"
|
||||
else:
|
||||
# Complete failure
|
||||
title = f"Failed: {connector_name}"
|
||||
message = f"Sync failed: {error_message}"
|
||||
if unsupported_text:
|
||||
message += unsupported_text
|
||||
status = "failed"
|
||||
else:
|
||||
title = f"Ready: {connector_name}"
|
||||
if indexed_count == 0:
|
||||
if skipped_count and skipped_count > 0:
|
||||
skipped_item_text = "item" if skipped_count == 1 else "items"
|
||||
message = f"Already up to date! {skipped_count} {skipped_item_text} skipped (already indexed)."
|
||||
if unsupported_count and unsupported_count > 0:
|
||||
message = f"Sync complete.{unsupported_text}"
|
||||
else:
|
||||
message = "Already up to date! No new items to sync."
|
||||
message = "Already up to date!"
|
||||
else:
|
||||
item_text = "item" if indexed_count == 1 else "items"
|
||||
message = (
|
||||
f"Now searchable! {indexed_count} {item_text} synced{skipped_text}."
|
||||
)
|
||||
file_text = "file" if indexed_count == 1 else "files"
|
||||
message = f"Now searchable! {indexed_count} {file_text} synced."
|
||||
if unsupported_text:
|
||||
message += unsupported_text
|
||||
status = "completed"
|
||||
|
||||
metadata_updates = {
|
||||
"indexed_count": indexed_count,
|
||||
"skipped_count": skipped_count or 0,
|
||||
"unsupported_count": unsupported_count or 0,
|
||||
"sync_stage": "completed"
|
||||
if (not error_message or is_warning or indexed_count > 0)
|
||||
else "failed",
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ Service for managing user page limits for ETL services.
|
|||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
from pathlib import Path, PurePosixPath
|
||||
|
||||
from sqlalchemy import select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
|
@ -223,10 +223,155 @@ class PageLimitService:
|
|||
# Estimate ~2000 characters per page
|
||||
return max(1, content_length // 2000)
|
||||
|
||||
@staticmethod
|
||||
def estimate_pages_from_metadata(
|
||||
file_name_or_ext: str, file_size: int | str | None = None
|
||||
) -> int:
|
||||
"""Size-based page estimation from file name/extension and byte size.
|
||||
|
||||
Pure function — no file I/O, no database access. Used by cloud
|
||||
connectors (which only have API metadata) and as the internal
|
||||
fallback for :meth:`estimate_pages_before_processing`.
|
||||
|
||||
``file_name_or_ext`` can be a full filename (``"report.pdf"``) or
|
||||
a bare extension (``".pdf"``). ``file_size`` may be an int, a
|
||||
stringified int from a cloud API, or *None*.
|
||||
"""
|
||||
if file_size is not None:
|
||||
try:
|
||||
file_size = int(file_size)
|
||||
except (ValueError, TypeError):
|
||||
file_size = 0
|
||||
else:
|
||||
file_size = 0
|
||||
|
||||
if file_size <= 0:
|
||||
return 1
|
||||
|
||||
ext = PurePosixPath(file_name_or_ext).suffix.lower() if file_name_or_ext else ""
|
||||
if not ext and file_name_or_ext.startswith("."):
|
||||
ext = file_name_or_ext.lower()
|
||||
file_ext = ext
|
||||
|
||||
if file_ext == ".pdf":
|
||||
return max(1, file_size // (100 * 1024))
|
||||
|
||||
if file_ext in {
|
||||
".doc",
|
||||
".docx",
|
||||
".docm",
|
||||
".dot",
|
||||
".dotm",
|
||||
".odt",
|
||||
".ott",
|
||||
".sxw",
|
||||
".stw",
|
||||
".uot",
|
||||
".rtf",
|
||||
".pages",
|
||||
".wpd",
|
||||
".wps",
|
||||
".abw",
|
||||
".zabw",
|
||||
".cwk",
|
||||
".hwp",
|
||||
".lwp",
|
||||
".mcw",
|
||||
".mw",
|
||||
".sdw",
|
||||
".vor",
|
||||
}:
|
||||
return max(1, file_size // (50 * 1024))
|
||||
|
||||
if file_ext in {
|
||||
".ppt",
|
||||
".pptx",
|
||||
".pptm",
|
||||
".pot",
|
||||
".potx",
|
||||
".odp",
|
||||
".otp",
|
||||
".sxi",
|
||||
".sti",
|
||||
".uop",
|
||||
".key",
|
||||
".sda",
|
||||
".sdd",
|
||||
".sdp",
|
||||
}:
|
||||
return max(1, file_size // (200 * 1024))
|
||||
|
||||
if file_ext in {
|
||||
".xls",
|
||||
".xlsx",
|
||||
".xlsm",
|
||||
".xlsb",
|
||||
".xlw",
|
||||
".xlr",
|
||||
".ods",
|
||||
".ots",
|
||||
".fods",
|
||||
".numbers",
|
||||
".123",
|
||||
".wk1",
|
||||
".wk2",
|
||||
".wk3",
|
||||
".wk4",
|
||||
".wks",
|
||||
".wb1",
|
||||
".wb2",
|
||||
".wb3",
|
||||
".wq1",
|
||||
".wq2",
|
||||
".csv",
|
||||
".tsv",
|
||||
".slk",
|
||||
".sylk",
|
||||
".dif",
|
||||
".dbf",
|
||||
".prn",
|
||||
".qpw",
|
||||
".602",
|
||||
".et",
|
||||
".eth",
|
||||
}:
|
||||
return max(1, file_size // (100 * 1024))
|
||||
|
||||
if file_ext in {".epub"}:
|
||||
return max(1, file_size // (50 * 1024))
|
||||
|
||||
if file_ext in {".txt", ".log", ".md", ".markdown", ".htm", ".html", ".xml"}:
|
||||
return max(1, file_size // 3000)
|
||||
|
||||
if file_ext in {
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".png",
|
||||
".gif",
|
||||
".bmp",
|
||||
".tiff",
|
||||
".webp",
|
||||
".svg",
|
||||
".cgm",
|
||||
".odg",
|
||||
".pbd",
|
||||
}:
|
||||
return 1
|
||||
|
||||
if file_ext in {".mp3", ".m4a", ".wav", ".mpga"}:
|
||||
return max(1, file_size // (1024 * 1024))
|
||||
|
||||
if file_ext in {".mp4", ".mpeg", ".webm"}:
|
||||
return max(1, file_size // (5 * 1024 * 1024))
|
||||
|
||||
return max(1, file_size // (80 * 1024))
|
||||
|
||||
def estimate_pages_before_processing(self, file_path: str) -> int:
|
||||
"""
|
||||
Estimate page count from file before processing (to avoid unnecessary API calls).
|
||||
This is called BEFORE sending to ETL services to prevent cost on rejected files.
|
||||
Estimate page count from a local file before processing.
|
||||
|
||||
For PDFs, attempts to read the actual page count via pypdf.
|
||||
For everything else, delegates to :meth:`estimate_pages_from_metadata`.
|
||||
|
||||
Args:
|
||||
file_path: Path to the file
|
||||
|
|
@ -240,7 +385,6 @@ class PageLimitService:
|
|||
file_ext = Path(file_path).suffix.lower()
|
||||
file_size = os.path.getsize(file_path)
|
||||
|
||||
# PDF files - try to get actual page count
|
||||
if file_ext == ".pdf":
|
||||
try:
|
||||
import pypdf
|
||||
|
|
@ -249,153 +393,6 @@ class PageLimitService:
|
|||
pdf_reader = pypdf.PdfReader(f)
|
||||
return len(pdf_reader.pages)
|
||||
except Exception:
|
||||
# If PDF reading fails, fall back to size estimation
|
||||
# Typical PDF: ~100KB per page (conservative estimate)
|
||||
return max(1, file_size // (100 * 1024))
|
||||
pass # fall through to size-based estimation
|
||||
|
||||
# Word Processing Documents
|
||||
# Microsoft Word, LibreOffice Writer, WordPerfect, Pages, etc.
|
||||
elif file_ext in [
|
||||
".doc",
|
||||
".docx",
|
||||
".docm",
|
||||
".dot",
|
||||
".dotm", # Microsoft Word
|
||||
".odt",
|
||||
".ott",
|
||||
".sxw",
|
||||
".stw",
|
||||
".uot", # OpenDocument/StarOffice Writer
|
||||
".rtf", # Rich Text Format
|
||||
".pages", # Apple Pages
|
||||
".wpd",
|
||||
".wps", # WordPerfect, Microsoft Works
|
||||
".abw",
|
||||
".zabw", # AbiWord
|
||||
".cwk",
|
||||
".hwp",
|
||||
".lwp",
|
||||
".mcw",
|
||||
".mw",
|
||||
".sdw",
|
||||
".vor", # Other word processors
|
||||
]:
|
||||
# Typical word document: ~50KB per page (conservative)
|
||||
return max(1, file_size // (50 * 1024))
|
||||
|
||||
# Presentation Documents
|
||||
# PowerPoint, Impress, Keynote, etc.
|
||||
elif file_ext in [
|
||||
".ppt",
|
||||
".pptx",
|
||||
".pptm",
|
||||
".pot",
|
||||
".potx", # Microsoft PowerPoint
|
||||
".odp",
|
||||
".otp",
|
||||
".sxi",
|
||||
".sti",
|
||||
".uop", # OpenDocument/StarOffice Impress
|
||||
".key", # Apple Keynote
|
||||
".sda",
|
||||
".sdd",
|
||||
".sdp", # StarOffice Draw/Impress
|
||||
]:
|
||||
# Typical presentation: ~200KB per slide (conservative)
|
||||
return max(1, file_size // (200 * 1024))
|
||||
|
||||
# Spreadsheet Documents
|
||||
# Excel, Calc, Numbers, Lotus, etc.
|
||||
elif file_ext in [
|
||||
".xls",
|
||||
".xlsx",
|
||||
".xlsm",
|
||||
".xlsb",
|
||||
".xlw",
|
||||
".xlr", # Microsoft Excel
|
||||
".ods",
|
||||
".ots",
|
||||
".fods", # OpenDocument Spreadsheet
|
||||
".numbers", # Apple Numbers
|
||||
".123",
|
||||
".wk1",
|
||||
".wk2",
|
||||
".wk3",
|
||||
".wk4",
|
||||
".wks", # Lotus 1-2-3
|
||||
".wb1",
|
||||
".wb2",
|
||||
".wb3",
|
||||
".wq1",
|
||||
".wq2", # Quattro Pro
|
||||
".csv",
|
||||
".tsv",
|
||||
".slk",
|
||||
".sylk",
|
||||
".dif",
|
||||
".dbf",
|
||||
".prn",
|
||||
".qpw", # Data formats
|
||||
".602",
|
||||
".et",
|
||||
".eth", # Other spreadsheets
|
||||
]:
|
||||
# Spreadsheets typically have 1 sheet = 1 page for ETL
|
||||
# Conservative: ~100KB per sheet
|
||||
return max(1, file_size // (100 * 1024))
|
||||
|
||||
# E-books
|
||||
elif file_ext in [".epub"]:
|
||||
# E-books vary widely, estimate by size
|
||||
# Typical e-book: ~50KB per page
|
||||
return max(1, file_size // (50 * 1024))
|
||||
|
||||
# Plain Text and Markup Files
|
||||
elif file_ext in [
|
||||
".txt",
|
||||
".log", # Plain text
|
||||
".md",
|
||||
".markdown", # Markdown
|
||||
".htm",
|
||||
".html",
|
||||
".xml", # Markup
|
||||
]:
|
||||
# Plain text: ~3000 bytes per page
|
||||
return max(1, file_size // 3000)
|
||||
|
||||
# Image Files
|
||||
# Each image is typically processed as 1 page
|
||||
elif file_ext in [
|
||||
".jpg",
|
||||
".jpeg", # JPEG
|
||||
".png", # PNG
|
||||
".gif", # GIF
|
||||
".bmp", # Bitmap
|
||||
".tiff", # TIFF
|
||||
".webp", # WebP
|
||||
".svg", # SVG
|
||||
".cgm", # Computer Graphics Metafile
|
||||
".odg",
|
||||
".pbd", # OpenDocument Graphics
|
||||
]:
|
||||
# Each image = 1 page
|
||||
return 1
|
||||
|
||||
# Audio Files (transcription = typically 1 page per minute)
|
||||
# Note: These should be handled by audio transcription flow, not ETL
|
||||
elif file_ext in [".mp3", ".m4a", ".wav", ".mpga"]:
|
||||
# Audio files: estimate based on duration
|
||||
# Fallback: ~1MB per minute of audio, 1 page per minute transcript
|
||||
return max(1, file_size // (1024 * 1024))
|
||||
|
||||
# Video Files (typically not processed for pages, but just in case)
|
||||
elif file_ext in [".mp4", ".mpeg", ".webm"]:
|
||||
# Video files: very rough estimate
|
||||
# Typically wouldn't be page-based, but use conservative estimate
|
||||
return max(1, file_size // (5 * 1024 * 1024))
|
||||
|
||||
# Other/Unknown Document Types
|
||||
else:
|
||||
# Conservative estimate: ~80KB per page
|
||||
# This catches: .sgl, .sxg, .uof, .uos1, .uos2, .web, and any future formats
|
||||
return max(1, file_size // (80 * 1024))
|
||||
return self.estimate_pages_from_metadata(file_ext, file_size)
|
||||
|
|
|
|||
158
surfsense_backend/app/services/vision_autocomplete_service.py
Normal file
158
surfsense_backend/app/services/vision_autocomplete_service.py
Normal file
|
|
@ -0,0 +1,158 @@
|
|||
"""Vision autocomplete service — agent-based with scoped filesystem.
|
||||
|
||||
Optimized pipeline:
|
||||
1. Start the SSE stream immediately so the UI shows progress.
|
||||
2. Derive a KB search query from window_title (no separate LLM call).
|
||||
3. Run KB filesystem pre-computation and agent graph compilation in PARALLEL.
|
||||
4. Inject pre-computed KB files as initial state and stream the agent.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from collections.abc import AsyncGenerator
|
||||
|
||||
from langchain_core.messages import HumanMessage
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.agents.autocomplete import create_autocomplete_agent, stream_autocomplete_agent
|
||||
from app.services.llm_service import get_vision_llm
|
||||
from app.services.new_streaming_service import VercelStreamingService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
PREP_STEP_ID = "autocomplete-prep"
|
||||
|
||||
|
||||
def _derive_kb_query(app_name: str, window_title: str) -> str:
|
||||
parts = [p for p in (window_title, app_name) if p]
|
||||
return " ".join(parts)
|
||||
|
||||
|
||||
def _is_vision_unsupported_error(e: Exception) -> bool:
|
||||
msg = str(e).lower()
|
||||
return "content must be a string" in msg or "does not support image" in msg
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main entry point
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def stream_vision_autocomplete(
|
||||
screenshot_data_url: str,
|
||||
search_space_id: int,
|
||||
session: AsyncSession,
|
||||
*,
|
||||
app_name: str = "",
|
||||
window_title: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Analyze a screenshot with a vision-LLM agent and stream a text completion."""
|
||||
streaming = VercelStreamingService()
|
||||
vision_error_msg = (
|
||||
"The selected model does not support vision. "
|
||||
"Please set a vision-capable model (e.g. GPT-4o, Gemini) in your search space settings."
|
||||
)
|
||||
|
||||
llm = await get_vision_llm(session, search_space_id)
|
||||
if not llm:
|
||||
yield streaming.format_message_start()
|
||||
yield streaming.format_error("No Vision LLM configured for this search space")
|
||||
yield streaming.format_done()
|
||||
return
|
||||
|
||||
# Start SSE stream immediately so the UI has something to show
|
||||
yield streaming.format_message_start()
|
||||
|
||||
kb_query = _derive_kb_query(app_name, window_title)
|
||||
|
||||
# Show a preparation step while KB search + agent compile run
|
||||
yield streaming.format_thinking_step(
|
||||
step_id=PREP_STEP_ID,
|
||||
title="Searching knowledge base",
|
||||
status="in_progress",
|
||||
items=[kb_query] if kb_query else [],
|
||||
)
|
||||
|
||||
try:
|
||||
agent, kb = await create_autocomplete_agent(
|
||||
llm,
|
||||
search_space_id=search_space_id,
|
||||
kb_query=kb_query,
|
||||
app_name=app_name,
|
||||
window_title=window_title,
|
||||
)
|
||||
except Exception as e:
|
||||
if _is_vision_unsupported_error(e):
|
||||
logger.warning("Vision autocomplete: model does not support vision: %s", e)
|
||||
yield streaming.format_error(vision_error_msg)
|
||||
yield streaming.format_done()
|
||||
return
|
||||
logger.error("Failed to create autocomplete agent: %s", e, exc_info=True)
|
||||
yield streaming.format_error("Autocomplete failed. Please try again.")
|
||||
yield streaming.format_done()
|
||||
return
|
||||
|
||||
has_kb = kb.has_documents
|
||||
doc_count = len(kb.files) if has_kb else 0 # type: ignore[arg-type]
|
||||
|
||||
yield streaming.format_thinking_step(
|
||||
step_id=PREP_STEP_ID,
|
||||
title="Searching knowledge base",
|
||||
status="complete",
|
||||
items=[f"Found {doc_count} document{'s' if doc_count != 1 else ''}"]
|
||||
if kb_query
|
||||
else ["Skipped"],
|
||||
)
|
||||
|
||||
# Build agent input with pre-computed KB as initial state
|
||||
if has_kb:
|
||||
instruction = (
|
||||
"Analyze this screenshot, then explore the knowledge base documents "
|
||||
"listed above — read the chunk index of any document whose title "
|
||||
"looks relevant and check matched chunks for useful facts. "
|
||||
"Finally, generate a concise autocomplete for the active text area, "
|
||||
"enhanced with any relevant KB information you found."
|
||||
)
|
||||
else:
|
||||
instruction = (
|
||||
"Analyze this screenshot and generate a concise autocomplete "
|
||||
"for the active text area based on what you see."
|
||||
)
|
||||
|
||||
user_message = HumanMessage(
|
||||
content=[
|
||||
{"type": "text", "text": instruction},
|
||||
{"type": "image_url", "image_url": {"url": screenshot_data_url}},
|
||||
]
|
||||
)
|
||||
|
||||
input_data: dict = {"messages": [user_message]}
|
||||
|
||||
if has_kb:
|
||||
input_data["files"] = kb.files
|
||||
input_data["messages"] = [kb.ls_ai_msg, kb.ls_tool_msg, user_message]
|
||||
logger.info(
|
||||
"Autocomplete: injected %d KB files into agent initial state", doc_count
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
"Autocomplete: no KB documents found, proceeding with screenshot only"
|
||||
)
|
||||
|
||||
# Stream the agent (message_start already sent above)
|
||||
try:
|
||||
async for sse in stream_autocomplete_agent(
|
||||
agent,
|
||||
input_data,
|
||||
streaming,
|
||||
emit_message_start=False,
|
||||
):
|
||||
yield sse
|
||||
except Exception as e:
|
||||
if _is_vision_unsupported_error(e):
|
||||
logger.warning("Vision autocomplete: model does not support vision: %s", e)
|
||||
yield streaming.format_error(vision_error_msg)
|
||||
yield streaming.format_done()
|
||||
else:
|
||||
logger.error("Vision autocomplete streaming error: %s", e, exc_info=True)
|
||||
yield streaming.format_error("Autocomplete failed. Please try again.")
|
||||
yield streaming.format_done()
|
||||
193
surfsense_backend/app/services/vision_llm_router_service.py
Normal file
193
surfsense_backend/app/services/vision_llm_router_service.py
Normal file
|
|
@ -0,0 +1,193 @@
|
|||
import logging
|
||||
from typing import Any
|
||||
|
||||
from litellm import Router
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
VISION_AUTO_MODE_ID = 0
|
||||
|
||||
VISION_PROVIDER_MAP = {
|
||||
"OPENAI": "openai",
|
||||
"ANTHROPIC": "anthropic",
|
||||
"GOOGLE": "gemini",
|
||||
"AZURE_OPENAI": "azure",
|
||||
"VERTEX_AI": "vertex_ai",
|
||||
"BEDROCK": "bedrock",
|
||||
"XAI": "xai",
|
||||
"OPENROUTER": "openrouter",
|
||||
"OLLAMA": "ollama_chat",
|
||||
"GROQ": "groq",
|
||||
"TOGETHER_AI": "together_ai",
|
||||
"FIREWORKS_AI": "fireworks_ai",
|
||||
"DEEPSEEK": "openai",
|
||||
"MISTRAL": "mistral",
|
||||
"CUSTOM": "custom",
|
||||
}
|
||||
|
||||
|
||||
class VisionLLMRouterService:
|
||||
_instance = None
|
||||
_router: Router | None = None
|
||||
_model_list: list[dict] = []
|
||||
_router_settings: dict = {}
|
||||
_initialized: bool = False
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls) -> "VisionLLMRouterService":
|
||||
if cls._instance is None:
|
||||
cls._instance = cls()
|
||||
return cls._instance
|
||||
|
||||
@classmethod
|
||||
def initialize(
|
||||
cls,
|
||||
global_configs: list[dict],
|
||||
router_settings: dict | None = None,
|
||||
) -> None:
|
||||
instance = cls.get_instance()
|
||||
|
||||
if instance._initialized:
|
||||
logger.debug("Vision LLM Router already initialized, skipping")
|
||||
return
|
||||
|
||||
model_list = []
|
||||
for config in global_configs:
|
||||
deployment = cls._config_to_deployment(config)
|
||||
if deployment:
|
||||
model_list.append(deployment)
|
||||
|
||||
if not model_list:
|
||||
logger.warning(
|
||||
"No valid vision LLM configs found for router initialization"
|
||||
)
|
||||
return
|
||||
|
||||
instance._model_list = model_list
|
||||
instance._router_settings = router_settings or {}
|
||||
|
||||
default_settings = {
|
||||
"routing_strategy": "usage-based-routing",
|
||||
"num_retries": 3,
|
||||
"allowed_fails": 3,
|
||||
"cooldown_time": 60,
|
||||
"retry_after": 5,
|
||||
}
|
||||
|
||||
final_settings = {**default_settings, **instance._router_settings}
|
||||
|
||||
try:
|
||||
instance._router = Router(
|
||||
model_list=model_list,
|
||||
routing_strategy=final_settings.get(
|
||||
"routing_strategy", "usage-based-routing"
|
||||
),
|
||||
num_retries=final_settings.get("num_retries", 3),
|
||||
allowed_fails=final_settings.get("allowed_fails", 3),
|
||||
cooldown_time=final_settings.get("cooldown_time", 60),
|
||||
set_verbose=False,
|
||||
)
|
||||
instance._initialized = True
|
||||
logger.info(
|
||||
"Vision LLM Router initialized with %d deployments, strategy: %s",
|
||||
len(model_list),
|
||||
final_settings.get("routing_strategy"),
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to initialize Vision LLM Router: {e}")
|
||||
instance._router = None
|
||||
|
||||
@classmethod
|
||||
def _config_to_deployment(cls, config: dict) -> dict | None:
|
||||
try:
|
||||
if not config.get("model_name") or not config.get("api_key"):
|
||||
return None
|
||||
|
||||
if config.get("custom_provider"):
|
||||
model_string = f"{config['custom_provider']}/{config['model_name']}"
|
||||
else:
|
||||
provider = config.get("provider", "").upper()
|
||||
provider_prefix = VISION_PROVIDER_MAP.get(provider, provider.lower())
|
||||
model_string = f"{provider_prefix}/{config['model_name']}"
|
||||
|
||||
litellm_params: dict[str, Any] = {
|
||||
"model": model_string,
|
||||
"api_key": config.get("api_key"),
|
||||
}
|
||||
|
||||
if config.get("api_base"):
|
||||
litellm_params["api_base"] = config["api_base"]
|
||||
|
||||
if config.get("api_version"):
|
||||
litellm_params["api_version"] = config["api_version"]
|
||||
|
||||
if config.get("litellm_params"):
|
||||
litellm_params.update(config["litellm_params"])
|
||||
|
||||
deployment: dict[str, Any] = {
|
||||
"model_name": "auto",
|
||||
"litellm_params": litellm_params,
|
||||
}
|
||||
|
||||
if config.get("rpm"):
|
||||
deployment["rpm"] = config["rpm"]
|
||||
if config.get("tpm"):
|
||||
deployment["tpm"] = config["tpm"]
|
||||
|
||||
return deployment
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to convert vision config to deployment: {e}")
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def get_router(cls) -> Router | None:
|
||||
instance = cls.get_instance()
|
||||
return instance._router
|
||||
|
||||
@classmethod
|
||||
def is_initialized(cls) -> bool:
|
||||
instance = cls.get_instance()
|
||||
return instance._initialized and instance._router is not None
|
||||
|
||||
@classmethod
|
||||
def get_model_count(cls) -> int:
|
||||
instance = cls.get_instance()
|
||||
return len(instance._model_list)
|
||||
|
||||
|
||||
def is_vision_auto_mode(config_id: int | None) -> bool:
|
||||
return config_id == VISION_AUTO_MODE_ID
|
||||
|
||||
|
||||
def build_vision_model_string(
|
||||
provider: str, model_name: str, custom_provider: str | None
|
||||
) -> str:
|
||||
if custom_provider:
|
||||
return f"{custom_provider}/{model_name}"
|
||||
prefix = VISION_PROVIDER_MAP.get(provider.upper(), provider.lower())
|
||||
return f"{prefix}/{model_name}"
|
||||
|
||||
|
||||
def get_global_vision_llm_config(config_id: int) -> dict | None:
|
||||
from app.config import config
|
||||
|
||||
if config_id == VISION_AUTO_MODE_ID:
|
||||
return {
|
||||
"id": VISION_AUTO_MODE_ID,
|
||||
"name": "Auto (Fastest)",
|
||||
"provider": "AUTO",
|
||||
"model_name": "auto",
|
||||
"is_auto_mode": True,
|
||||
}
|
||||
if config_id > 0:
|
||||
return None
|
||||
for cfg in config.GLOBAL_VISION_LLM_CONFIGS:
|
||||
if cfg.get("id") == config_id:
|
||||
return cfg
|
||||
return None
|
||||
134
surfsense_backend/app/services/vision_model_list_service.py
Normal file
134
surfsense_backend/app/services/vision_model_list_service.py
Normal file
|
|
@ -0,0 +1,134 @@
|
|||
"""
|
||||
Service for fetching and caching the vision-capable model list.
|
||||
|
||||
Reuses the same OpenRouter public API and local fallback as the LLM model
|
||||
list service, but filters for models that accept image input.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import httpx
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
OPENROUTER_API_URL = "https://openrouter.ai/api/v1/models"
|
||||
FALLBACK_FILE = (
|
||||
Path(__file__).parent.parent / "config" / "vision_model_list_fallback.json"
|
||||
)
|
||||
CACHE_TTL_SECONDS = 86400 # 24 hours
|
||||
|
||||
_cache: list[dict] | None = None
|
||||
_cache_timestamp: float = 0
|
||||
|
||||
OPENROUTER_SLUG_TO_VISION_PROVIDER: dict[str, str] = {
|
||||
"openai": "OPENAI",
|
||||
"anthropic": "ANTHROPIC",
|
||||
"google": "GOOGLE",
|
||||
"mistralai": "MISTRAL",
|
||||
"x-ai": "XAI",
|
||||
}
|
||||
|
||||
|
||||
def _format_context_length(length: int | None) -> str | None:
|
||||
if not length:
|
||||
return None
|
||||
if length >= 1_000_000:
|
||||
return f"{length / 1_000_000:g}M"
|
||||
if length >= 1_000:
|
||||
return f"{length / 1_000:g}K"
|
||||
return str(length)
|
||||
|
||||
|
||||
async def _fetch_from_openrouter() -> list[dict] | None:
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=15) as client:
|
||||
response = await client.get(OPENROUTER_API_URL)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
return data.get("data", [])
|
||||
except Exception as e:
|
||||
logger.warning("Failed to fetch from OpenRouter API for vision models: %s", e)
|
||||
return None
|
||||
|
||||
|
||||
def _load_fallback() -> list[dict]:
|
||||
try:
|
||||
with open(FALLBACK_FILE, encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
logger.error("Failed to load vision model fallback list: %s", e)
|
||||
return []
|
||||
|
||||
|
||||
def _is_vision_model(model: dict) -> bool:
|
||||
"""Return True if the model accepts image input and outputs text."""
|
||||
arch = model.get("architecture", {})
|
||||
input_mods = arch.get("input_modalities", [])
|
||||
output_mods = arch.get("output_modalities", [])
|
||||
return "image" in input_mods and "text" in output_mods
|
||||
|
||||
|
||||
def _process_vision_models(raw_models: list[dict]) -> list[dict]:
|
||||
processed: list[dict] = []
|
||||
|
||||
for model in raw_models:
|
||||
model_id: str = model.get("id", "")
|
||||
name: str = model.get("name", "")
|
||||
context_length = model.get("context_length")
|
||||
|
||||
if "/" not in model_id:
|
||||
continue
|
||||
|
||||
if not _is_vision_model(model):
|
||||
continue
|
||||
|
||||
provider_slug, model_name = model_id.split("/", 1)
|
||||
context_window = _format_context_length(context_length)
|
||||
|
||||
processed.append(
|
||||
{
|
||||
"value": model_id,
|
||||
"label": name,
|
||||
"provider": "OPENROUTER",
|
||||
"context_window": context_window,
|
||||
}
|
||||
)
|
||||
|
||||
native_provider = OPENROUTER_SLUG_TO_VISION_PROVIDER.get(provider_slug)
|
||||
if native_provider:
|
||||
if native_provider == "GOOGLE" and not model_name.startswith("gemini-"):
|
||||
continue
|
||||
|
||||
processed.append(
|
||||
{
|
||||
"value": model_name,
|
||||
"label": name,
|
||||
"provider": native_provider,
|
||||
"context_window": context_window,
|
||||
}
|
||||
)
|
||||
|
||||
return processed
|
||||
|
||||
|
||||
async def get_vision_model_list() -> list[dict]:
|
||||
global _cache, _cache_timestamp
|
||||
|
||||
if _cache is not None and (time.time() - _cache_timestamp) < CACHE_TTL_SECONDS:
|
||||
return _cache
|
||||
|
||||
raw_models = await _fetch_from_openrouter()
|
||||
|
||||
if raw_models is None:
|
||||
logger.info("Using fallback vision model list")
|
||||
return _load_fallback()
|
||||
|
||||
processed = _process_vision_models(raw_models)
|
||||
|
||||
_cache = processed
|
||||
_cache_timestamp = time.time()
|
||||
|
||||
return processed
|
||||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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 = ["<mentioned_documents>"]
|
||||
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("<document>")
|
||||
context_parts.append("<document_metadata>")
|
||||
context_parts.append(f" <document_id>{doc.id}</document_id>")
|
||||
context_parts.append(
|
||||
f" <document_type>{doc.document_type.value}</document_type>"
|
||||
)
|
||||
context_parts.append(f" <title><![CDATA[{doc.title}]]></title>")
|
||||
context_parts.append(f" <url><![CDATA[{url}]]></url>")
|
||||
context_parts.append(
|
||||
f" <metadata_json><![CDATA[{metadata_json}]]></metadata_json>"
|
||||
)
|
||||
context_parts.append("</document_metadata>")
|
||||
context_parts.append("")
|
||||
context_parts.append("<document_content>")
|
||||
|
||||
# Use chunks if available (preferred for proper citations)
|
||||
if hasattr(doc, "chunks") and doc.chunks:
|
||||
for chunk in doc.chunks:
|
||||
context_parts.append(
|
||||
f" <chunk id='{chunk.id}'><![CDATA[{chunk.content}]]></chunk>"
|
||||
)
|
||||
else:
|
||||
# Fallback to document content if chunks not loaded
|
||||
# Use document ID as chunk ID prefix for consistency
|
||||
context_parts.append(
|
||||
f" <chunk id='{doc.id}'><![CDATA[{doc.content}]]></chunk>"
|
||||
)
|
||||
|
||||
context_parts.append("</document_content>")
|
||||
context_parts.append("</document>")
|
||||
context_parts.append("")
|
||||
|
||||
context_parts.append("</mentioned_documents>")
|
||||
|
||||
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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -28,6 +28,7 @@ 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 PageLimitService
|
||||
from app.services.task_logging_service import TaskLoggingService
|
||||
from app.tasks.connector_indexers.base import (
|
||||
check_document_by_unique_identifier,
|
||||
|
|
@ -50,7 +51,10 @@ async def _should_skip_file(
|
|||
file_id = file.get("id", "")
|
||||
file_name = file.get("name", "Unknown")
|
||||
|
||||
if skip_item(file):
|
||||
skip, unsup_ext = skip_item(file)
|
||||
if skip:
|
||||
if unsup_ext:
|
||||
return True, f"unsupported:{unsup_ext}"
|
||||
return True, "folder/non-downloadable"
|
||||
if not file_id:
|
||||
return True, "missing file_id"
|
||||
|
|
@ -250,6 +254,121 @@ async def _download_and_index(
|
|||
return batch_indexed, download_failed + batch_failed
|
||||
|
||||
|
||||
async def _remove_document(session: AsyncSession, file_id: str, search_space_id: int):
|
||||
"""Remove a document that was deleted in Dropbox."""
|
||||
primary_hash = compute_identifier_hash(
|
||||
DocumentType.DROPBOX_FILE.value, file_id, search_space_id
|
||||
)
|
||||
existing = await check_document_by_unique_identifier(session, primary_hash)
|
||||
|
||||
if not existing:
|
||||
result = await session.execute(
|
||||
select(Document).where(
|
||||
Document.search_space_id == search_space_id,
|
||||
Document.document_type == DocumentType.DROPBOX_FILE,
|
||||
cast(Document.document_metadata["dropbox_file_id"], String) == file_id,
|
||||
)
|
||||
)
|
||||
existing = result.scalar_one_or_none()
|
||||
|
||||
if existing:
|
||||
await session.delete(existing)
|
||||
|
||||
|
||||
async def _index_with_delta_sync(
|
||||
dropbox_client: DropboxClient,
|
||||
session: AsyncSession,
|
||||
connector_id: int,
|
||||
search_space_id: int,
|
||||
user_id: str,
|
||||
cursor: str,
|
||||
task_logger: TaskLoggingService,
|
||||
log_entry: object,
|
||||
max_files: int,
|
||||
on_heartbeat_callback: HeartbeatCallbackType | None = None,
|
||||
enable_summary: bool = True,
|
||||
) -> tuple[int, int, int, str]:
|
||||
"""Delta sync using Dropbox cursor-based change tracking.
|
||||
|
||||
Returns (indexed_count, skipped_count, new_cursor).
|
||||
"""
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
f"Starting delta sync from cursor: {cursor[:20]}...",
|
||||
{"stage": "delta_sync", "cursor_prefix": cursor[:20]},
|
||||
)
|
||||
|
||||
entries, new_cursor, error = await dropbox_client.get_changes(cursor)
|
||||
if error:
|
||||
err_lower = error.lower()
|
||||
if "401" in error or "authentication expired" in err_lower:
|
||||
raise Exception(
|
||||
f"Dropbox authentication failed. Please re-authenticate. (Error: {error})"
|
||||
)
|
||||
raise Exception(f"Failed to fetch Dropbox changes: {error}")
|
||||
|
||||
if not entries:
|
||||
logger.info("No changes detected since last sync")
|
||||
return 0, 0, 0, new_cursor or cursor
|
||||
|
||||
logger.info(f"Processing {len(entries)} change entries")
|
||||
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
files_to_download: list[dict] = []
|
||||
files_processed = 0
|
||||
|
||||
for entry in entries:
|
||||
if files_processed >= max_files:
|
||||
break
|
||||
files_processed += 1
|
||||
|
||||
tag = entry.get(".tag")
|
||||
|
||||
if tag == "deleted":
|
||||
path_lower = entry.get("path_lower", "")
|
||||
name = entry.get("name", "")
|
||||
file_id = entry.get("id", "")
|
||||
if file_id:
|
||||
await _remove_document(session, file_id, search_space_id)
|
||||
logger.debug(f"Processed deletion: {name or path_lower}")
|
||||
continue
|
||||
|
||||
if tag != "file":
|
||||
continue
|
||||
|
||||
skip, msg = await _should_skip_file(session, entry, search_space_id)
|
||||
if skip:
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
files_to_download.append(entry)
|
||||
|
||||
batch_indexed, failed = await _download_and_index(
|
||||
dropbox_client,
|
||||
session,
|
||||
files_to_download,
|
||||
connector_id=connector_id,
|
||||
search_space_id=search_space_id,
|
||||
user_id=user_id,
|
||||
enable_summary=enable_summary,
|
||||
on_heartbeat=on_heartbeat_callback,
|
||||
)
|
||||
|
||||
indexed = renamed_count + batch_indexed
|
||||
logger.info(
|
||||
f"Delta sync complete: {indexed} indexed, {skipped} skipped, "
|
||||
f"{unsupported_count} unsupported, {failed} failed"
|
||||
)
|
||||
return indexed, skipped, unsupported_count, new_cursor or cursor
|
||||
|
||||
|
||||
async def _index_full_scan(
|
||||
dropbox_client: DropboxClient,
|
||||
session: AsyncSession,
|
||||
|
|
@ -265,8 +384,11 @@ async def _index_full_scan(
|
|||
incremental_sync: bool = True,
|
||||
on_heartbeat_callback: HeartbeatCallbackType | None = None,
|
||||
enable_summary: bool = True,
|
||||
) -> tuple[int, int]:
|
||||
"""Full scan indexing of a folder."""
|
||||
) -> tuple[int, int, int]:
|
||||
"""Full scan indexing of a folder.
|
||||
|
||||
Returns (indexed, skipped, unsupported_count).
|
||||
"""
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
f"Starting full scan of folder: {folder_name}",
|
||||
|
|
@ -278,8 +400,15 @@ async def _index_full_scan(
|
|||
},
|
||||
)
|
||||
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
page_limit_reached = False
|
||||
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
files_to_download: list[dict] = []
|
||||
|
||||
all_files, error = await get_files_in_folder(
|
||||
|
|
@ -299,14 +428,36 @@ async def _index_full_scan(
|
|||
if incremental_sync:
|
||||
skip, msg = await _should_skip_file(session, file, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
elif skip_item(file):
|
||||
else:
|
||||
item_skip, item_unsup = skip_item(file)
|
||||
if item_skip:
|
||||
if item_unsup:
|
||||
unsupported_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file.get("name", ""), file.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
if not page_limit_reached:
|
||||
logger.warning(
|
||||
"Page limit reached during Dropbox full scan, "
|
||||
"skipping remaining files"
|
||||
)
|
||||
page_limit_reached = True
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(file)
|
||||
|
||||
batch_indexed, failed = await _download_and_index(
|
||||
|
|
@ -320,11 +471,20 @@ async def _index_full_scan(
|
|||
on_heartbeat=on_heartbeat_callback,
|
||||
)
|
||||
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
indexed = renamed_count + batch_indexed
|
||||
logger.info(
|
||||
f"Full scan complete: {indexed} indexed, {skipped} skipped, {failed} failed"
|
||||
f"Full scan complete: {indexed} indexed, {skipped} skipped, "
|
||||
f"{unsupported_count} unsupported, {failed} failed"
|
||||
)
|
||||
return indexed, skipped
|
||||
return indexed, skipped, unsupported_count
|
||||
|
||||
|
||||
async def _index_selected_files(
|
||||
|
|
@ -338,12 +498,18 @@ async def _index_selected_files(
|
|||
enable_summary: bool,
|
||||
incremental_sync: bool = True,
|
||||
on_heartbeat: HeartbeatCallbackType | None = None,
|
||||
) -> tuple[int, int, list[str]]:
|
||||
) -> tuple[int, int, int, list[str]]:
|
||||
"""Index user-selected files using the parallel pipeline."""
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
|
||||
files_to_download: list[dict] = []
|
||||
errors: list[str] = []
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
|
||||
for file_path, file_name in file_paths:
|
||||
file, error = await get_file_by_path(dropbox_client, file_path)
|
||||
|
|
@ -355,15 +521,31 @@ async def _index_selected_files(
|
|||
if incremental_sync:
|
||||
skip, msg = await _should_skip_file(session, file, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
elif skip_item(file):
|
||||
skipped += 1
|
||||
else:
|
||||
item_skip, item_unsup = skip_item(file)
|
||||
if item_skip:
|
||||
if item_unsup:
|
||||
unsupported_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file.get("name", ""), file.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
display = file_name or file_path
|
||||
errors.append(f"File '{display}': page limit would be exceeded")
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(file)
|
||||
|
||||
batch_indexed, _failed = await _download_and_index(
|
||||
|
|
@ -377,7 +559,15 @@ async def _index_selected_files(
|
|||
on_heartbeat=on_heartbeat,
|
||||
)
|
||||
|
||||
return renamed_count + batch_indexed, skipped, errors
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
return renamed_count + batch_indexed, skipped, unsupported_count, errors
|
||||
|
||||
|
||||
async def index_dropbox_files(
|
||||
|
|
@ -386,7 +576,7 @@ async def index_dropbox_files(
|
|||
search_space_id: int,
|
||||
user_id: str,
|
||||
items_dict: dict,
|
||||
) -> tuple[int, int, str | None]:
|
||||
) -> tuple[int, int, str | None, int]:
|
||||
"""Index Dropbox files for a specific connector.
|
||||
|
||||
items_dict format:
|
||||
|
|
@ -417,7 +607,7 @@ async def index_dropbox_files(
|
|||
await task_logger.log_task_failure(
|
||||
log_entry, error_msg, None, {"error_type": "ConnectorNotFound"}
|
||||
)
|
||||
return 0, 0, error_msg
|
||||
return 0, 0, error_msg, 0
|
||||
|
||||
token_encrypted = connector.config.get("_token_encrypted", False)
|
||||
if token_encrypted and not config.SECRET_KEY:
|
||||
|
|
@ -428,7 +618,7 @@ async def index_dropbox_files(
|
|||
"Missing SECRET_KEY",
|
||||
{"error_type": "MissingSecretKey"},
|
||||
)
|
||||
return 0, 0, error_msg
|
||||
return 0, 0, error_msg, 0
|
||||
|
||||
connector_enable_summary = getattr(connector, "enable_summary", True)
|
||||
dropbox_client = DropboxClient(session, connector_id)
|
||||
|
|
@ -437,9 +627,13 @@ async def index_dropbox_files(
|
|||
max_files = indexing_options.get("max_files", 500)
|
||||
incremental_sync = indexing_options.get("incremental_sync", True)
|
||||
include_subfolders = indexing_options.get("include_subfolders", True)
|
||||
use_delta_sync = indexing_options.get("use_delta_sync", True)
|
||||
|
||||
folder_cursors: dict = connector.config.get("folder_cursors", {})
|
||||
|
||||
total_indexed = 0
|
||||
total_skipped = 0
|
||||
total_unsupported = 0
|
||||
|
||||
selected_files = items_dict.get("files", [])
|
||||
if selected_files:
|
||||
|
|
@ -447,7 +641,7 @@ async def index_dropbox_files(
|
|||
(f.get("path", f.get("path_lower", f.get("id", ""))), f.get("name"))
|
||||
for f in selected_files
|
||||
]
|
||||
indexed, skipped, file_errors = await _index_selected_files(
|
||||
indexed, skipped, unsupported, file_errors = await _index_selected_files(
|
||||
dropbox_client,
|
||||
session,
|
||||
file_tuples,
|
||||
|
|
@ -459,6 +653,7 @@ async def index_dropbox_files(
|
|||
)
|
||||
total_indexed += indexed
|
||||
total_skipped += skipped
|
||||
total_unsupported += unsupported
|
||||
if file_errors:
|
||||
logger.warning(
|
||||
f"File indexing errors for connector {connector_id}: {file_errors}"
|
||||
|
|
@ -471,25 +666,66 @@ async def index_dropbox_files(
|
|||
)
|
||||
folder_name = folder.get("name", "Root")
|
||||
|
||||
logger.info(f"Using full scan for folder {folder_name}")
|
||||
indexed, skipped = await _index_full_scan(
|
||||
dropbox_client,
|
||||
session,
|
||||
connector_id,
|
||||
search_space_id,
|
||||
user_id,
|
||||
folder_path,
|
||||
folder_name,
|
||||
task_logger,
|
||||
log_entry,
|
||||
max_files,
|
||||
include_subfolders,
|
||||
incremental_sync=incremental_sync,
|
||||
enable_summary=connector_enable_summary,
|
||||
saved_cursor = folder_cursors.get(folder_path)
|
||||
can_use_delta = (
|
||||
use_delta_sync and saved_cursor and connector.last_indexed_at
|
||||
)
|
||||
|
||||
if can_use_delta:
|
||||
logger.info(f"Using delta sync for folder {folder_name}")
|
||||
indexed, skipped, unsup, new_cursor = await _index_with_delta_sync(
|
||||
dropbox_client,
|
||||
session,
|
||||
connector_id,
|
||||
search_space_id,
|
||||
user_id,
|
||||
saved_cursor,
|
||||
task_logger,
|
||||
log_entry,
|
||||
max_files,
|
||||
enable_summary=connector_enable_summary,
|
||||
)
|
||||
folder_cursors[folder_path] = new_cursor
|
||||
total_unsupported += unsup
|
||||
else:
|
||||
logger.info(f"Using full scan for folder {folder_name}")
|
||||
indexed, skipped, unsup = await _index_full_scan(
|
||||
dropbox_client,
|
||||
session,
|
||||
connector_id,
|
||||
search_space_id,
|
||||
user_id,
|
||||
folder_path,
|
||||
folder_name,
|
||||
task_logger,
|
||||
log_entry,
|
||||
max_files,
|
||||
include_subfolders,
|
||||
incremental_sync=incremental_sync,
|
||||
enable_summary=connector_enable_summary,
|
||||
)
|
||||
total_unsupported += unsup
|
||||
|
||||
total_indexed += indexed
|
||||
total_skipped += skipped
|
||||
|
||||
# Persist latest cursor for this folder
|
||||
try:
|
||||
latest_cursor, cursor_err = await dropbox_client.get_latest_cursor(
|
||||
folder_path
|
||||
)
|
||||
if latest_cursor and not cursor_err:
|
||||
folder_cursors[folder_path] = latest_cursor
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to get latest cursor for {folder_path}: {e}")
|
||||
|
||||
# Persist folder cursors to connector config
|
||||
if folders:
|
||||
cfg = dict(connector.config)
|
||||
cfg["folder_cursors"] = folder_cursors
|
||||
connector.config = cfg
|
||||
flag_modified(connector, "config")
|
||||
|
||||
if total_indexed > 0 or folders:
|
||||
await update_connector_last_indexed(session, connector, True)
|
||||
|
||||
|
|
@ -498,12 +734,18 @@ async def index_dropbox_files(
|
|||
await task_logger.log_task_success(
|
||||
log_entry,
|
||||
f"Successfully completed Dropbox indexing for connector {connector_id}",
|
||||
{"files_processed": total_indexed, "files_skipped": total_skipped},
|
||||
{
|
||||
"files_processed": total_indexed,
|
||||
"files_skipped": total_skipped,
|
||||
"files_unsupported": total_unsupported,
|
||||
},
|
||||
)
|
||||
logger.info(
|
||||
f"Dropbox indexing completed: {total_indexed} indexed, {total_skipped} skipped"
|
||||
f"Dropbox indexing completed: {total_indexed} indexed, "
|
||||
f"{total_skipped} skipped, {total_unsupported} unsupported"
|
||||
)
|
||||
return total_indexed, total_skipped, None
|
||||
|
||||
return total_indexed, total_skipped, None, total_unsupported
|
||||
|
||||
except SQLAlchemyError as db_error:
|
||||
await session.rollback()
|
||||
|
|
@ -514,7 +756,7 @@ async def index_dropbox_files(
|
|||
{"error_type": "SQLAlchemyError"},
|
||||
)
|
||||
logger.error(f"Database error: {db_error!s}", exc_info=True)
|
||||
return 0, 0, f"Database error: {db_error!s}"
|
||||
return 0, 0, f"Database error: {db_error!s}", 0
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
await task_logger.log_task_failure(
|
||||
|
|
@ -524,4 +766,4 @@ async def index_dropbox_files(
|
|||
{"error_type": type(e).__name__},
|
||||
)
|
||||
logger.error(f"Failed to index Dropbox files: {e!s}", exc_info=True)
|
||||
return 0, 0, f"Failed to index Dropbox files: {e!s}"
|
||||
return 0, 0, f"Failed to index Dropbox files: {e!s}", 0
|
||||
|
|
|
|||
|
|
@ -25,7 +25,11 @@ from app.connectors.google_drive import (
|
|||
get_files_in_folder,
|
||||
get_start_page_token,
|
||||
)
|
||||
from app.connectors.google_drive.file_types import should_skip_file as skip_mime
|
||||
from app.connectors.google_drive.file_types import (
|
||||
is_google_workspace_file,
|
||||
should_skip_by_extension,
|
||||
should_skip_file as skip_mime,
|
||||
)
|
||||
from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
|
||||
from app.indexing_pipeline.connector_document import ConnectorDocument
|
||||
from app.indexing_pipeline.document_hashing import compute_identifier_hash
|
||||
|
|
@ -34,6 +38,7 @@ from app.indexing_pipeline.indexing_pipeline_service import (
|
|||
PlaceholderInfo,
|
||||
)
|
||||
from app.services.llm_service import get_user_long_context_llm
|
||||
from app.services.page_limit_service import PageLimitService
|
||||
from app.services.task_logging_service import TaskLoggingService
|
||||
from app.tasks.connector_indexers.base import (
|
||||
check_document_by_unique_identifier,
|
||||
|
|
@ -77,6 +82,10 @@ async def _should_skip_file(
|
|||
|
||||
if skip_mime(mime_type):
|
||||
return True, "folder/shortcut"
|
||||
if not is_google_workspace_file(mime_type):
|
||||
ext_skip, unsup_ext = should_skip_by_extension(file_name)
|
||||
if ext_skip:
|
||||
return True, f"unsupported:{unsup_ext}"
|
||||
if not file_id:
|
||||
return True, "missing file_id"
|
||||
|
||||
|
|
@ -327,6 +336,12 @@ async def _process_single_file(
|
|||
return 1, 0, 0
|
||||
return 0, 1, 0
|
||||
|
||||
page_limit_service = PageLimitService(session)
|
||||
estimated_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file_name, file.get("size")
|
||||
)
|
||||
await page_limit_service.check_page_limit(user_id, estimated_pages)
|
||||
|
||||
markdown, drive_metadata, error = await download_and_extract_content(
|
||||
drive_client, file
|
||||
)
|
||||
|
|
@ -363,6 +378,9 @@ async def _process_single_file(
|
|||
)
|
||||
await pipeline.index(document, connector_doc, user_llm)
|
||||
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, estimated_pages, allow_exceed=True
|
||||
)
|
||||
logger.info(f"Successfully indexed Google Drive file: {file_name}")
|
||||
return 1, 0, 0
|
||||
|
||||
|
|
@ -458,18 +476,24 @@ async def _index_selected_files(
|
|||
user_id: str,
|
||||
enable_summary: bool,
|
||||
on_heartbeat: HeartbeatCallbackType | None = None,
|
||||
) -> tuple[int, int, list[str]]:
|
||||
) -> tuple[int, int, int, list[str]]:
|
||||
"""Index user-selected files using the parallel pipeline.
|
||||
|
||||
Phase 1 (serial): fetch metadata + skip checks.
|
||||
Phase 2+3 (parallel): download, ETL, index via _download_and_index.
|
||||
|
||||
Returns (indexed_count, skipped_count, errors).
|
||||
Returns (indexed_count, skipped_count, unsupported_count, errors).
|
||||
"""
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
|
||||
files_to_download: list[dict] = []
|
||||
errors: list[str] = []
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
|
||||
for file_id, file_name in file_ids:
|
||||
file, error = await get_file_by_id(drive_client, file_id)
|
||||
|
|
@ -480,12 +504,23 @@ async def _index_selected_files(
|
|||
|
||||
skip, msg = await _should_skip_file(session, file, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file.get("name", ""), file.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
display = file_name or file_id
|
||||
errors.append(f"File '{display}': page limit would be exceeded")
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(file)
|
||||
|
||||
await _create_drive_placeholders(
|
||||
|
|
@ -507,7 +542,15 @@ async def _index_selected_files(
|
|||
on_heartbeat=on_heartbeat,
|
||||
)
|
||||
|
||||
return renamed_count + batch_indexed, skipped, errors
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
return renamed_count + batch_indexed, skipped, unsupported_count, errors
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
|
@ -530,8 +573,11 @@ async def _index_full_scan(
|
|||
include_subfolders: bool = False,
|
||||
on_heartbeat_callback: HeartbeatCallbackType | None = None,
|
||||
enable_summary: bool = True,
|
||||
) -> tuple[int, int]:
|
||||
"""Full scan indexing of a folder."""
|
||||
) -> tuple[int, int, int]:
|
||||
"""Full scan indexing of a folder.
|
||||
|
||||
Returns (indexed, skipped, unsupported_count).
|
||||
"""
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
f"Starting full scan of folder: {folder_name} (include_subfolders={include_subfolders})",
|
||||
|
|
@ -545,8 +591,15 @@ async def _index_full_scan(
|
|||
# ------------------------------------------------------------------
|
||||
# Phase 1 (serial): collect files, run skip checks, track renames
|
||||
# ------------------------------------------------------------------
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
page_limit_reached = False
|
||||
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
files_processed = 0
|
||||
files_to_download: list[dict] = []
|
||||
folders_to_process = [(folder_id, folder_name)]
|
||||
|
|
@ -587,12 +640,28 @@ async def _index_full_scan(
|
|||
|
||||
skip, msg = await _should_skip_file(session, file, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file.get("name", ""), file.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
if not page_limit_reached:
|
||||
logger.warning(
|
||||
"Page limit reached during Google Drive full scan, "
|
||||
"skipping remaining files"
|
||||
)
|
||||
page_limit_reached = True
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(file)
|
||||
|
||||
page_token = next_token
|
||||
|
|
@ -636,11 +705,20 @@ async def _index_full_scan(
|
|||
on_heartbeat=on_heartbeat_callback,
|
||||
)
|
||||
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
indexed = renamed_count + batch_indexed
|
||||
logger.info(
|
||||
f"Full scan complete: {indexed} indexed, {skipped} skipped, {failed} failed"
|
||||
f"Full scan complete: {indexed} indexed, {skipped} skipped, "
|
||||
f"{unsupported_count} unsupported, {failed} failed"
|
||||
)
|
||||
return indexed, skipped
|
||||
return indexed, skipped, unsupported_count
|
||||
|
||||
|
||||
async def _index_with_delta_sync(
|
||||
|
|
@ -658,8 +736,11 @@ async def _index_with_delta_sync(
|
|||
include_subfolders: bool = False,
|
||||
on_heartbeat_callback: HeartbeatCallbackType | None = None,
|
||||
enable_summary: bool = True,
|
||||
) -> tuple[int, int]:
|
||||
"""Delta sync using change tracking."""
|
||||
) -> tuple[int, int, int]:
|
||||
"""Delta sync using change tracking.
|
||||
|
||||
Returns (indexed, skipped, unsupported_count).
|
||||
"""
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
f"Starting delta sync from token: {start_page_token[:20]}...",
|
||||
|
|
@ -679,15 +760,22 @@ async def _index_with_delta_sync(
|
|||
|
||||
if not changes:
|
||||
logger.info("No changes detected since last sync")
|
||||
return 0, 0
|
||||
return 0, 0, 0
|
||||
|
||||
logger.info(f"Processing {len(changes)} changes")
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Phase 1 (serial): handle removals, collect files for download
|
||||
# ------------------------------------------------------------------
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
page_limit_reached = False
|
||||
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
files_to_download: list[dict] = []
|
||||
files_processed = 0
|
||||
|
||||
|
|
@ -709,12 +797,28 @@ async def _index_with_delta_sync(
|
|||
|
||||
skip, msg = await _should_skip_file(session, file, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file.get("name", ""), file.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
if not page_limit_reached:
|
||||
logger.warning(
|
||||
"Page limit reached during Google Drive delta sync, "
|
||||
"skipping remaining files"
|
||||
)
|
||||
page_limit_reached = True
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(file)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
|
|
@ -742,11 +846,20 @@ async def _index_with_delta_sync(
|
|||
on_heartbeat=on_heartbeat_callback,
|
||||
)
|
||||
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
indexed = renamed_count + batch_indexed
|
||||
logger.info(
|
||||
f"Delta sync complete: {indexed} indexed, {skipped} skipped, {failed} failed"
|
||||
f"Delta sync complete: {indexed} indexed, {skipped} skipped, "
|
||||
f"{unsupported_count} unsupported, {failed} failed"
|
||||
)
|
||||
return indexed, skipped
|
||||
return indexed, skipped, unsupported_count
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
|
@ -766,8 +879,11 @@ async def index_google_drive_files(
|
|||
max_files: int = 500,
|
||||
include_subfolders: bool = False,
|
||||
on_heartbeat_callback: HeartbeatCallbackType | None = None,
|
||||
) -> tuple[int, int, str | None]:
|
||||
"""Index Google Drive files for a specific connector."""
|
||||
) -> tuple[int, int, str | None, int]:
|
||||
"""Index Google Drive files for a specific connector.
|
||||
|
||||
Returns (indexed, skipped, error_or_none, unsupported_count).
|
||||
"""
|
||||
task_logger = TaskLoggingService(session, search_space_id)
|
||||
log_entry = await task_logger.log_task_start(
|
||||
task_name="google_drive_files_indexing",
|
||||
|
|
@ -793,7 +909,7 @@ async def index_google_drive_files(
|
|||
await task_logger.log_task_failure(
|
||||
log_entry, error_msg, None, {"error_type": "ConnectorNotFound"}
|
||||
)
|
||||
return 0, 0, error_msg
|
||||
return 0, 0, error_msg, 0
|
||||
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
|
|
@ -812,7 +928,7 @@ async def index_google_drive_files(
|
|||
"Missing Composio account",
|
||||
{"error_type": "MissingComposioAccount"},
|
||||
)
|
||||
return 0, 0, error_msg
|
||||
return 0, 0, error_msg, 0
|
||||
pre_built_credentials = build_composio_credentials(connected_account_id)
|
||||
else:
|
||||
token_encrypted = connector.config.get("_token_encrypted", False)
|
||||
|
|
@ -827,6 +943,7 @@ async def index_google_drive_files(
|
|||
0,
|
||||
0,
|
||||
"SECRET_KEY not configured but credentials are marked as encrypted",
|
||||
0,
|
||||
)
|
||||
|
||||
connector_enable_summary = getattr(connector, "enable_summary", True)
|
||||
|
|
@ -839,7 +956,7 @@ async def index_google_drive_files(
|
|||
await task_logger.log_task_failure(
|
||||
log_entry, error_msg, {"error_type": "MissingParameter"}
|
||||
)
|
||||
return 0, 0, error_msg
|
||||
return 0, 0, error_msg, 0
|
||||
|
||||
target_folder_id = folder_id
|
||||
target_folder_name = folder_name or "Selected Folder"
|
||||
|
|
@ -850,9 +967,11 @@ async def index_google_drive_files(
|
|||
use_delta_sync and start_page_token and connector.last_indexed_at
|
||||
)
|
||||
|
||||
documents_unsupported = 0
|
||||
|
||||
if can_use_delta:
|
||||
logger.info(f"Using delta sync for connector {connector_id}")
|
||||
documents_indexed, documents_skipped = await _index_with_delta_sync(
|
||||
documents_indexed, documents_skipped, du = await _index_with_delta_sync(
|
||||
drive_client,
|
||||
session,
|
||||
connector,
|
||||
|
|
@ -868,8 +987,9 @@ async def index_google_drive_files(
|
|||
on_heartbeat_callback,
|
||||
connector_enable_summary,
|
||||
)
|
||||
documents_unsupported += du
|
||||
logger.info("Running reconciliation scan after delta sync")
|
||||
ri, rs = await _index_full_scan(
|
||||
ri, rs, ru = await _index_full_scan(
|
||||
drive_client,
|
||||
session,
|
||||
connector,
|
||||
|
|
@ -887,9 +1007,14 @@ async def index_google_drive_files(
|
|||
)
|
||||
documents_indexed += ri
|
||||
documents_skipped += rs
|
||||
documents_unsupported += ru
|
||||
else:
|
||||
logger.info(f"Using full scan for connector {connector_id}")
|
||||
documents_indexed, documents_skipped = await _index_full_scan(
|
||||
(
|
||||
documents_indexed,
|
||||
documents_skipped,
|
||||
documents_unsupported,
|
||||
) = await _index_full_scan(
|
||||
drive_client,
|
||||
session,
|
||||
connector,
|
||||
|
|
@ -924,14 +1049,17 @@ async def index_google_drive_files(
|
|||
{
|
||||
"files_processed": documents_indexed,
|
||||
"files_skipped": documents_skipped,
|
||||
"files_unsupported": documents_unsupported,
|
||||
"sync_type": "delta" if can_use_delta else "full",
|
||||
"folder": target_folder_name,
|
||||
},
|
||||
)
|
||||
logger.info(
|
||||
f"Google Drive indexing completed: {documents_indexed} indexed, {documents_skipped} skipped"
|
||||
f"Google Drive indexing completed: {documents_indexed} indexed, "
|
||||
f"{documents_skipped} skipped, {documents_unsupported} unsupported"
|
||||
)
|
||||
return documents_indexed, documents_skipped, None
|
||||
|
||||
return documents_indexed, documents_skipped, None, documents_unsupported
|
||||
|
||||
except SQLAlchemyError as db_error:
|
||||
await session.rollback()
|
||||
|
|
@ -942,7 +1070,7 @@ async def index_google_drive_files(
|
|||
{"error_type": "SQLAlchemyError"},
|
||||
)
|
||||
logger.error(f"Database error: {db_error!s}", exc_info=True)
|
||||
return 0, 0, f"Database error: {db_error!s}"
|
||||
return 0, 0, f"Database error: {db_error!s}", 0
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
await task_logger.log_task_failure(
|
||||
|
|
@ -952,7 +1080,7 @@ async def index_google_drive_files(
|
|||
{"error_type": type(e).__name__},
|
||||
)
|
||||
logger.error(f"Failed to index Google Drive files: {e!s}", exc_info=True)
|
||||
return 0, 0, f"Failed to index Google Drive files: {e!s}"
|
||||
return 0, 0, f"Failed to index Google Drive files: {e!s}", 0
|
||||
|
||||
|
||||
async def index_google_drive_single_file(
|
||||
|
|
@ -1154,7 +1282,7 @@ async def index_google_drive_selected_files(
|
|||
session, connector_id, credentials=pre_built_credentials
|
||||
)
|
||||
|
||||
indexed, skipped, errors = await _index_selected_files(
|
||||
indexed, skipped, unsupported, errors = await _index_selected_files(
|
||||
drive_client,
|
||||
session,
|
||||
files,
|
||||
|
|
@ -1165,6 +1293,11 @@ async def index_google_drive_selected_files(
|
|||
on_heartbeat=on_heartbeat_callback,
|
||||
)
|
||||
|
||||
if unsupported > 0:
|
||||
file_text = "file was" if unsupported == 1 else "files were"
|
||||
unsup_msg = f"{unsupported} {file_text} not supported"
|
||||
errors.append(unsup_msg)
|
||||
|
||||
await session.commit()
|
||||
|
||||
if errors:
|
||||
|
|
@ -1172,7 +1305,12 @@ async def index_google_drive_selected_files(
|
|||
log_entry,
|
||||
f"Batch file indexing completed with {len(errors)} error(s)",
|
||||
"; ".join(errors),
|
||||
{"indexed": indexed, "skipped": skipped, "error_count": len(errors)},
|
||||
{
|
||||
"indexed": indexed,
|
||||
"skipped": skipped,
|
||||
"unsupported": unsupported,
|
||||
"error_count": len(errors),
|
||||
},
|
||||
)
|
||||
else:
|
||||
await task_logger.log_task_success(
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
|
|
@ -28,6 +28,7 @@ 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 PageLimitService
|
||||
from app.services.task_logging_service import TaskLoggingService
|
||||
from app.tasks.connector_indexers.base import (
|
||||
check_document_by_unique_identifier,
|
||||
|
|
@ -55,7 +56,10 @@ async def _should_skip_file(
|
|||
file_id = file.get("id")
|
||||
file_name = file.get("name", "Unknown")
|
||||
|
||||
if skip_item(file):
|
||||
skip, unsup_ext = skip_item(file)
|
||||
if skip:
|
||||
if unsup_ext:
|
||||
return True, f"unsupported:{unsup_ext}"
|
||||
return True, "folder/onenote/remote"
|
||||
if not file_id:
|
||||
return True, "missing file_id"
|
||||
|
|
@ -289,12 +293,18 @@ async def _index_selected_files(
|
|||
user_id: str,
|
||||
enable_summary: bool,
|
||||
on_heartbeat: HeartbeatCallbackType | None = None,
|
||||
) -> tuple[int, int, list[str]]:
|
||||
) -> tuple[int, int, int, list[str]]:
|
||||
"""Index user-selected files using the parallel pipeline."""
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
|
||||
files_to_download: list[dict] = []
|
||||
errors: list[str] = []
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
|
||||
for file_id, file_name in file_ids:
|
||||
file, error = await get_file_by_id(onedrive_client, file_id)
|
||||
|
|
@ -305,12 +315,23 @@ async def _index_selected_files(
|
|||
|
||||
skip, msg = await _should_skip_file(session, file, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file.get("name", ""), file.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
display = file_name or file_id
|
||||
errors.append(f"File '{display}': page limit would be exceeded")
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(file)
|
||||
|
||||
batch_indexed, _failed = await _download_and_index(
|
||||
|
|
@ -324,7 +345,15 @@ async def _index_selected_files(
|
|||
on_heartbeat=on_heartbeat,
|
||||
)
|
||||
|
||||
return renamed_count + batch_indexed, skipped, errors
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
return renamed_count + batch_indexed, skipped, unsupported_count, errors
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
|
@ -346,8 +375,11 @@ async def _index_full_scan(
|
|||
include_subfolders: bool = True,
|
||||
on_heartbeat_callback: HeartbeatCallbackType | None = None,
|
||||
enable_summary: bool = True,
|
||||
) -> tuple[int, int]:
|
||||
"""Full scan indexing of a folder."""
|
||||
) -> tuple[int, int, int]:
|
||||
"""Full scan indexing of a folder.
|
||||
|
||||
Returns (indexed, skipped, unsupported_count).
|
||||
"""
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
f"Starting full scan of folder: {folder_name}",
|
||||
|
|
@ -358,8 +390,15 @@ async def _index_full_scan(
|
|||
},
|
||||
)
|
||||
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
page_limit_reached = False
|
||||
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
files_to_download: list[dict] = []
|
||||
|
||||
all_files, error = await get_files_in_folder(
|
||||
|
|
@ -378,11 +417,28 @@ async def _index_full_scan(
|
|||
for file in all_files[:max_files]:
|
||||
skip, msg = await _should_skip_file(session, file, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
file.get("name", ""), file.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
if not page_limit_reached:
|
||||
logger.warning(
|
||||
"Page limit reached during OneDrive full scan, "
|
||||
"skipping remaining files"
|
||||
)
|
||||
page_limit_reached = True
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(file)
|
||||
|
||||
batch_indexed, failed = await _download_and_index(
|
||||
|
|
@ -396,11 +452,20 @@ async def _index_full_scan(
|
|||
on_heartbeat=on_heartbeat_callback,
|
||||
)
|
||||
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
indexed = renamed_count + batch_indexed
|
||||
logger.info(
|
||||
f"Full scan complete: {indexed} indexed, {skipped} skipped, {failed} failed"
|
||||
f"Full scan complete: {indexed} indexed, {skipped} skipped, "
|
||||
f"{unsupported_count} unsupported, {failed} failed"
|
||||
)
|
||||
return indexed, skipped
|
||||
return indexed, skipped, unsupported_count
|
||||
|
||||
|
||||
async def _index_with_delta_sync(
|
||||
|
|
@ -416,8 +481,11 @@ async def _index_with_delta_sync(
|
|||
max_files: int,
|
||||
on_heartbeat_callback: HeartbeatCallbackType | None = None,
|
||||
enable_summary: bool = True,
|
||||
) -> tuple[int, int, str | None]:
|
||||
"""Delta sync using OneDrive change tracking. Returns (indexed, skipped, new_delta_link)."""
|
||||
) -> tuple[int, int, int, str | None]:
|
||||
"""Delta sync using OneDrive change tracking.
|
||||
|
||||
Returns (indexed, skipped, unsupported_count, new_delta_link).
|
||||
"""
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
"Starting delta sync",
|
||||
|
|
@ -437,12 +505,19 @@ async def _index_with_delta_sync(
|
|||
|
||||
if not changes:
|
||||
logger.info("No changes detected since last sync")
|
||||
return 0, 0, new_delta_link
|
||||
return 0, 0, 0, new_delta_link
|
||||
|
||||
logger.info(f"Processing {len(changes)} delta changes")
|
||||
|
||||
page_limit_service = PageLimitService(session)
|
||||
pages_used, pages_limit = await page_limit_service.get_page_usage(user_id)
|
||||
remaining_quota = pages_limit - pages_used
|
||||
batch_estimated_pages = 0
|
||||
page_limit_reached = False
|
||||
|
||||
renamed_count = 0
|
||||
skipped = 0
|
||||
unsupported_count = 0
|
||||
files_to_download: list[dict] = []
|
||||
files_processed = 0
|
||||
|
||||
|
|
@ -465,12 +540,28 @@ async def _index_with_delta_sync(
|
|||
|
||||
skip, msg = await _should_skip_file(session, change, search_space_id)
|
||||
if skip:
|
||||
if msg and "renamed" in msg.lower():
|
||||
if msg and msg.startswith("unsupported:"):
|
||||
unsupported_count += 1
|
||||
elif msg and "renamed" in msg.lower():
|
||||
renamed_count += 1
|
||||
else:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
file_pages = PageLimitService.estimate_pages_from_metadata(
|
||||
change.get("name", ""), change.get("size")
|
||||
)
|
||||
if batch_estimated_pages + file_pages > remaining_quota:
|
||||
if not page_limit_reached:
|
||||
logger.warning(
|
||||
"Page limit reached during OneDrive delta sync, "
|
||||
"skipping remaining files"
|
||||
)
|
||||
page_limit_reached = True
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
batch_estimated_pages += file_pages
|
||||
files_to_download.append(change)
|
||||
|
||||
batch_indexed, failed = await _download_and_index(
|
||||
|
|
@ -484,11 +575,20 @@ async def _index_with_delta_sync(
|
|||
on_heartbeat=on_heartbeat_callback,
|
||||
)
|
||||
|
||||
if batch_indexed > 0 and files_to_download and batch_estimated_pages > 0:
|
||||
pages_to_deduct = max(
|
||||
1, batch_estimated_pages * batch_indexed // len(files_to_download)
|
||||
)
|
||||
await page_limit_service.update_page_usage(
|
||||
user_id, pages_to_deduct, allow_exceed=True
|
||||
)
|
||||
|
||||
indexed = renamed_count + batch_indexed
|
||||
logger.info(
|
||||
f"Delta sync complete: {indexed} indexed, {skipped} skipped, {failed} failed"
|
||||
f"Delta sync complete: {indexed} indexed, {skipped} skipped, "
|
||||
f"{unsupported_count} unsupported, {failed} failed"
|
||||
)
|
||||
return indexed, skipped, new_delta_link
|
||||
return indexed, skipped, unsupported_count, new_delta_link
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
|
@ -502,7 +602,7 @@ async def index_onedrive_files(
|
|||
search_space_id: int,
|
||||
user_id: str,
|
||||
items_dict: dict,
|
||||
) -> tuple[int, int, str | None]:
|
||||
) -> tuple[int, int, str | None, int]:
|
||||
"""Index OneDrive files for a specific connector.
|
||||
|
||||
items_dict format:
|
||||
|
|
@ -529,7 +629,7 @@ async def index_onedrive_files(
|
|||
await task_logger.log_task_failure(
|
||||
log_entry, error_msg, None, {"error_type": "ConnectorNotFound"}
|
||||
)
|
||||
return 0, 0, error_msg
|
||||
return 0, 0, error_msg, 0
|
||||
|
||||
token_encrypted = connector.config.get("_token_encrypted", False)
|
||||
if token_encrypted and not config.SECRET_KEY:
|
||||
|
|
@ -540,7 +640,7 @@ async def index_onedrive_files(
|
|||
"Missing SECRET_KEY",
|
||||
{"error_type": "MissingSecretKey"},
|
||||
)
|
||||
return 0, 0, error_msg
|
||||
return 0, 0, error_msg, 0
|
||||
|
||||
connector_enable_summary = getattr(connector, "enable_summary", True)
|
||||
onedrive_client = OneDriveClient(session, connector_id)
|
||||
|
|
@ -552,12 +652,13 @@ async def index_onedrive_files(
|
|||
|
||||
total_indexed = 0
|
||||
total_skipped = 0
|
||||
total_unsupported = 0
|
||||
|
||||
# Index selected individual files
|
||||
selected_files = items_dict.get("files", [])
|
||||
if selected_files:
|
||||
file_tuples = [(f["id"], f.get("name")) for f in selected_files]
|
||||
indexed, skipped, _errors = await _index_selected_files(
|
||||
indexed, skipped, unsupported, _errors = await _index_selected_files(
|
||||
onedrive_client,
|
||||
session,
|
||||
file_tuples,
|
||||
|
|
@ -568,6 +669,7 @@ async def index_onedrive_files(
|
|||
)
|
||||
total_indexed += indexed
|
||||
total_skipped += skipped
|
||||
total_unsupported += unsupported
|
||||
|
||||
# Index selected folders
|
||||
folders = items_dict.get("folders", [])
|
||||
|
|
@ -581,7 +683,7 @@ async def index_onedrive_files(
|
|||
|
||||
if can_use_delta:
|
||||
logger.info(f"Using delta sync for folder {folder_name}")
|
||||
indexed, skipped, new_delta_link = await _index_with_delta_sync(
|
||||
indexed, skipped, unsup, new_delta_link = await _index_with_delta_sync(
|
||||
onedrive_client,
|
||||
session,
|
||||
connector_id,
|
||||
|
|
@ -596,6 +698,7 @@ async def index_onedrive_files(
|
|||
)
|
||||
total_indexed += indexed
|
||||
total_skipped += skipped
|
||||
total_unsupported += unsup
|
||||
|
||||
if new_delta_link:
|
||||
await session.refresh(connector)
|
||||
|
|
@ -605,7 +708,7 @@ async def index_onedrive_files(
|
|||
flag_modified(connector, "config")
|
||||
|
||||
# Reconciliation full scan
|
||||
ri, rs = await _index_full_scan(
|
||||
ri, rs, ru = await _index_full_scan(
|
||||
onedrive_client,
|
||||
session,
|
||||
connector_id,
|
||||
|
|
@ -621,9 +724,10 @@ async def index_onedrive_files(
|
|||
)
|
||||
total_indexed += ri
|
||||
total_skipped += rs
|
||||
total_unsupported += ru
|
||||
else:
|
||||
logger.info(f"Using full scan for folder {folder_name}")
|
||||
indexed, skipped = await _index_full_scan(
|
||||
indexed, skipped, unsup = await _index_full_scan(
|
||||
onedrive_client,
|
||||
session,
|
||||
connector_id,
|
||||
|
|
@ -639,6 +743,7 @@ async def index_onedrive_files(
|
|||
)
|
||||
total_indexed += indexed
|
||||
total_skipped += skipped
|
||||
total_unsupported += unsup
|
||||
|
||||
# Store new delta link for this folder
|
||||
_, new_delta_link, _ = await onedrive_client.get_delta(folder_id=folder_id)
|
||||
|
|
@ -657,12 +762,18 @@ async def index_onedrive_files(
|
|||
await task_logger.log_task_success(
|
||||
log_entry,
|
||||
f"Successfully completed OneDrive indexing for connector {connector_id}",
|
||||
{"files_processed": total_indexed, "files_skipped": total_skipped},
|
||||
{
|
||||
"files_processed": total_indexed,
|
||||
"files_skipped": total_skipped,
|
||||
"files_unsupported": total_unsupported,
|
||||
},
|
||||
)
|
||||
logger.info(
|
||||
f"OneDrive indexing completed: {total_indexed} indexed, {total_skipped} skipped"
|
||||
f"OneDrive indexing completed: {total_indexed} indexed, "
|
||||
f"{total_skipped} skipped, {total_unsupported} unsupported"
|
||||
)
|
||||
return total_indexed, total_skipped, None
|
||||
|
||||
return total_indexed, total_skipped, None, total_unsupported
|
||||
|
||||
except SQLAlchemyError as db_error:
|
||||
await session.rollback()
|
||||
|
|
@ -673,7 +784,7 @@ async def index_onedrive_files(
|
|||
{"error_type": "SQLAlchemyError"},
|
||||
)
|
||||
logger.error(f"Database error: {db_error!s}", exc_info=True)
|
||||
return 0, 0, f"Database error: {db_error!s}"
|
||||
return 0, 0, f"Database error: {db_error!s}", 0
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
await task_logger.log_task_failure(
|
||||
|
|
@ -683,4 +794,4 @@ async def index_onedrive_files(
|
|||
{"error_type": type(e).__name__},
|
||||
)
|
||||
logger.error(f"Failed to index OneDrive files: {e!s}", exc_info=True)
|
||||
return 0, 0, f"Failed to index OneDrive files: {e!s}"
|
||||
return 0, 0, f"Failed to index OneDrive files: {e!s}", 0
|
||||
|
|
|
|||
|
|
@ -1,43 +1,17 @@
|
|||
"""
|
||||
Document processors module for background tasks.
|
||||
|
||||
This module provides a collection of document processors for different content types
|
||||
and sources. Each processor is responsible for handling a specific type of document
|
||||
processing task in the background.
|
||||
|
||||
Available processors:
|
||||
- Extension processor: Handle documents from browser extension
|
||||
- Markdown processor: Process markdown files
|
||||
- File processors: Handle files using different ETL services (Unstructured, LlamaCloud, Docling)
|
||||
- YouTube processor: Process YouTube videos and extract transcripts
|
||||
Content extraction is handled by ``app.etl_pipeline.EtlPipelineService``.
|
||||
This package keeps orchestration (save, notify, page-limit) and
|
||||
non-ETL processors (extension, markdown, youtube).
|
||||
"""
|
||||
|
||||
# URL crawler
|
||||
# Extension processor
|
||||
from .extension_processor import add_extension_received_document
|
||||
|
||||
# File processors
|
||||
from .file_processors import (
|
||||
add_received_file_document_using_docling,
|
||||
add_received_file_document_using_llamacloud,
|
||||
add_received_file_document_using_unstructured,
|
||||
)
|
||||
|
||||
# Markdown processor
|
||||
from .markdown_processor import add_received_markdown_file_document
|
||||
|
||||
# YouTube processor
|
||||
from .youtube_processor import add_youtube_video_document
|
||||
|
||||
__all__ = [
|
||||
# Extension processing
|
||||
"add_extension_received_document",
|
||||
"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",
|
||||
# YouTube video processing
|
||||
"add_youtube_video_document",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,91 @@
|
|||
"""
|
||||
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 / XHTML → 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,
|
||||
".xhtml": 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)
|
||||
193
surfsense_backend/app/tasks/document_processors/_helpers.py
Normal file
193
surfsense_backend/app/tasks/document_processors/_helpers.py
Normal file
|
|
@ -0,0 +1,193 @@
|
|||
"""
|
||||
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 .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()
|
||||
204
surfsense_backend/app/tasks/document_processors/_save.py
Normal file
204
surfsense_backend/app/tasks/document_processors/_save.py
Normal file
|
|
@ -0,0 +1,204 @@
|
|||
"""
|
||||
Unified document save/update logic for file processors.
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
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
|
||||
File diff suppressed because it is too large
Load diff
|
|
@ -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
|
||||
)
|
||||
|
||||
|
|
|
|||
107
surfsense_backend/app/utils/document_versioning.py
Normal file
107
surfsense_backend/app/utils/document_versioning.py
Normal file
|
|
@ -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
|
||||
153
surfsense_backend/app/utils/file_extensions.py
Normal file
153
surfsense_backend/app/utils/file_extensions.py
Normal file
|
|
@ -0,0 +1,153 @@
|
|||
"""Per-parser document extension sets for the ETL pipeline.
|
||||
|
||||
Every consumer (file_classifier, connector-level skip checks, ETL pipeline
|
||||
validation) imports from here so there is a single source of truth.
|
||||
|
||||
Extensions already covered by PLAINTEXT_EXTENSIONS, AUDIO_EXTENSIONS, or
|
||||
DIRECT_CONVERT_EXTENSIONS in file_classifier are NOT repeated here -- these
|
||||
sets are exclusively for the "document" ETL path (Docling / LlamaParse /
|
||||
Unstructured).
|
||||
"""
|
||||
|
||||
from pathlib import PurePosixPath
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Per-parser document extension sets (from official documentation)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
DOCLING_DOCUMENT_EXTENSIONS: frozenset[str] = frozenset(
|
||||
{
|
||||
".pdf",
|
||||
".docx",
|
||||
".xlsx",
|
||||
".pptx",
|
||||
".png",
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".tiff",
|
||||
".tif",
|
||||
".bmp",
|
||||
".webp",
|
||||
}
|
||||
)
|
||||
|
||||
LLAMAPARSE_DOCUMENT_EXTENSIONS: frozenset[str] = frozenset(
|
||||
{
|
||||
".pdf",
|
||||
".docx",
|
||||
".doc",
|
||||
".xlsx",
|
||||
".xls",
|
||||
".pptx",
|
||||
".ppt",
|
||||
".docm",
|
||||
".dot",
|
||||
".dotm",
|
||||
".pptm",
|
||||
".pot",
|
||||
".potx",
|
||||
".xlsm",
|
||||
".xlsb",
|
||||
".xlw",
|
||||
".rtf",
|
||||
".epub",
|
||||
".png",
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".gif",
|
||||
".bmp",
|
||||
".tiff",
|
||||
".tif",
|
||||
".webp",
|
||||
".svg",
|
||||
".odt",
|
||||
".ods",
|
||||
".odp",
|
||||
".hwp",
|
||||
".hwpx",
|
||||
}
|
||||
)
|
||||
|
||||
UNSTRUCTURED_DOCUMENT_EXTENSIONS: frozenset[str] = frozenset(
|
||||
{
|
||||
".pdf",
|
||||
".docx",
|
||||
".doc",
|
||||
".xlsx",
|
||||
".xls",
|
||||
".pptx",
|
||||
".ppt",
|
||||
".png",
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".bmp",
|
||||
".tiff",
|
||||
".tif",
|
||||
".heic",
|
||||
".rtf",
|
||||
".epub",
|
||||
".odt",
|
||||
".eml",
|
||||
".msg",
|
||||
".p7s",
|
||||
}
|
||||
)
|
||||
|
||||
AZURE_DI_DOCUMENT_EXTENSIONS: frozenset[str] = frozenset(
|
||||
{
|
||||
".pdf",
|
||||
".docx",
|
||||
".xlsx",
|
||||
".pptx",
|
||||
".png",
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".bmp",
|
||||
".tiff",
|
||||
".tif",
|
||||
".heif",
|
||||
}
|
||||
)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Union (used by classify_file for routing) + service lookup
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
DOCUMENT_EXTENSIONS: frozenset[str] = (
|
||||
DOCLING_DOCUMENT_EXTENSIONS
|
||||
| LLAMAPARSE_DOCUMENT_EXTENSIONS
|
||||
| UNSTRUCTURED_DOCUMENT_EXTENSIONS
|
||||
| AZURE_DI_DOCUMENT_EXTENSIONS
|
||||
)
|
||||
|
||||
_SERVICE_MAP: dict[str, frozenset[str]] = {
|
||||
"DOCLING": DOCLING_DOCUMENT_EXTENSIONS,
|
||||
"LLAMACLOUD": LLAMAPARSE_DOCUMENT_EXTENSIONS,
|
||||
"UNSTRUCTURED": UNSTRUCTURED_DOCUMENT_EXTENSIONS,
|
||||
}
|
||||
|
||||
|
||||
def get_document_extensions_for_service(etl_service: str | None) -> frozenset[str]:
|
||||
"""Return the document extensions supported by *etl_service*.
|
||||
|
||||
When *etl_service* is ``LLAMACLOUD`` and Azure Document Intelligence
|
||||
credentials are configured, the set is dynamically expanded to include
|
||||
Azure DI's supported extensions (e.g. ``.heif``).
|
||||
|
||||
Falls back to the full union when the service is ``None`` or unknown.
|
||||
"""
|
||||
extensions = _SERVICE_MAP.get(etl_service or "", DOCUMENT_EXTENSIONS)
|
||||
if etl_service == "LLAMACLOUD":
|
||||
from app.config import config as app_config
|
||||
|
||||
if getattr(app_config, "AZURE_DI_ENDPOINT", None) and getattr(
|
||||
app_config, "AZURE_DI_KEY", None
|
||||
):
|
||||
extensions = extensions | AZURE_DI_DOCUMENT_EXTENSIONS
|
||||
return extensions
|
||||
|
||||
|
||||
def is_supported_document_extension(filename: str) -> bool:
|
||||
"""Return True if the file's extension is in the supported document set."""
|
||||
suffix = PurePosixPath(filename).suffix.lower()
|
||||
return suffix in DOCUMENT_EXTENSIONS
|
||||
|
|
@ -11,6 +11,8 @@ import hmac
|
|||
import json
|
||||
import logging
|
||||
import time
|
||||
from random import SystemRandom
|
||||
from string import ascii_letters, digits
|
||||
from uuid import UUID
|
||||
|
||||
from cryptography.fernet import Fernet
|
||||
|
|
@ -18,6 +20,25 @@ from fastapi import HTTPException
|
|||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_PKCE_CHARS = ascii_letters + digits + "-._~"
|
||||
_PKCE_RNG = SystemRandom()
|
||||
|
||||
|
||||
def generate_code_verifier(length: int = 128) -> str:
|
||||
"""Generate a PKCE code_verifier (RFC 7636, 43-128 unreserved chars)."""
|
||||
return "".join(_PKCE_RNG.choice(_PKCE_CHARS) for _ in range(length))
|
||||
|
||||
|
||||
def generate_pkce_pair(length: int = 128) -> tuple[str, str]:
|
||||
"""Generate a PKCE code_verifier and its S256 code_challenge."""
|
||||
verifier = generate_code_verifier(length)
|
||||
challenge = (
|
||||
base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest())
|
||||
.decode()
|
||||
.rstrip("=")
|
||||
)
|
||||
return verifier, challenge
|
||||
|
||||
|
||||
class OAuthStateManager:
|
||||
"""Manages secure OAuth state parameters with HMAC signatures."""
|
||||
|
|
|
|||
|
|
@ -46,8 +46,6 @@ dependencies = [
|
|||
"redis>=5.2.1",
|
||||
"firecrawl-py>=4.9.0",
|
||||
"boto3>=1.35.0",
|
||||
"litellm>=1.80.10",
|
||||
"langchain-litellm>=0.3.5",
|
||||
"fake-useragent>=2.2.0",
|
||||
"trafilatura>=2.0.0",
|
||||
"fastapi-users[oauth,sqlalchemy]>=15.0.3",
|
||||
|
|
@ -75,6 +73,9 @@ dependencies = [
|
|||
"langchain-community>=0.4.1",
|
||||
"deepagents>=0.4.12",
|
||||
"stripe>=15.0.0",
|
||||
"azure-ai-documentintelligence>=1.0.2",
|
||||
"litellm>=1.83.0",
|
||||
"langchain-litellm>=0.6.4",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@
|
|||
Prerequisites: PostgreSQL + pgvector only.
|
||||
|
||||
External system boundaries are mocked:
|
||||
- ETL parsing — LlamaParse (external API) and Docling (heavy library)
|
||||
- LLM summarization, text embedding, text chunking (external APIs)
|
||||
- Redis heartbeat (external infrastructure)
|
||||
- Task dispatch is swapped via DI (InlineTaskDispatcher)
|
||||
|
|
@ -11,6 +12,7 @@ External system boundaries are mocked:
|
|||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import os
|
||||
from collections.abc import AsyncGenerator
|
||||
from unittest.mock import AsyncMock, MagicMock
|
||||
|
||||
|
|
@ -298,3 +300,59 @@ def _mock_redis_heartbeat(monkeypatch):
|
|||
"app.tasks.celery_tasks.document_tasks._run_heartbeat_loop",
|
||||
AsyncMock(),
|
||||
)
|
||||
|
||||
|
||||
_MOCK_ETL_MARKDOWN = "# Mocked Document\n\nThis is mocked ETL content."
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _mock_etl_parsing(monkeypatch):
|
||||
"""Mock ETL parsing services — LlamaParse and Docling are external boundaries.
|
||||
|
||||
Preserves the real contract: empty/corrupt files raise an error just like
|
||||
the actual services would, so tests covering failure paths keep working.
|
||||
"""
|
||||
|
||||
def _reject_empty(file_path: str) -> None:
|
||||
if os.path.getsize(file_path) == 0:
|
||||
raise RuntimeError(f"Cannot parse empty file: {file_path}")
|
||||
|
||||
# -- LlamaParse mock (external API) --------------------------------
|
||||
|
||||
async def _fake_llamacloud_parse(file_path: str, estimated_pages: int) -> str:
|
||||
_reject_empty(file_path)
|
||||
return _MOCK_ETL_MARKDOWN
|
||||
|
||||
monkeypatch.setattr(
|
||||
"app.etl_pipeline.parsers.llamacloud.parse_with_llamacloud",
|
||||
_fake_llamacloud_parse,
|
||||
)
|
||||
|
||||
# -- Docling mock (heavy library boundary) -------------------------
|
||||
|
||||
async def _fake_docling_parse(file_path: str, filename: str) -> str:
|
||||
_reject_empty(file_path)
|
||||
return _MOCK_ETL_MARKDOWN
|
||||
|
||||
monkeypatch.setattr(
|
||||
"app.etl_pipeline.parsers.docling.parse_with_docling",
|
||||
_fake_docling_parse,
|
||||
)
|
||||
|
||||
class _FakeDoclingResult:
|
||||
class Document:
|
||||
@staticmethod
|
||||
def export_to_markdown():
|
||||
return _MOCK_ETL_MARKDOWN
|
||||
|
||||
document = Document()
|
||||
|
||||
class _FakeDocumentConverter:
|
||||
def convert(self, file_path):
|
||||
_reject_empty(file_path)
|
||||
return _FakeDoclingResult()
|
||||
|
||||
monkeypatch.setattr(
|
||||
"docling.document_converter.DocumentConverter",
|
||||
_FakeDocumentConverter,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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", []))
|
||||
|
|
|
|||
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