apunkt/SurfSense
1
0
Fork 0
mirror of https://github.com/MODSetter/SurfSense.git synced 2026-04-26 09:16:22 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix: Added API_BASE param for LiteLLM.

Browse source
This commit is contained in:
DESKTOP-RTLN3BA\$punk 2025-05-08 19:31:47 -07:00
parent cae5f835af
commit 4a2be4b98e
7 changed files with 151 additions and 73 deletions
Download patch file Download diff file Expand all files Collapse all files

76
surfsense_web/content/docs/manual-installation.mdx
View file

@ -27,18 +27,21 @@ The backend is the core of SurfSense. Follow these steps to set it up:
First, create and configure your environment variables by copying the example file:
**Linux/macOS:**
```bash
cd surfsense_backend
cp .env.example .env
```
**Windows (Command Prompt):**
```cmd
cd surfsense_backend
copy .env.example .env
```
**Windows (PowerShell):**
```powershell
cd surfsense_backend
Copy-Item -Path .env.example -Destination .env
@ -46,33 +49,50 @@ Copy-Item -Path .env.example -Destination .env
Edit the `.env` file and set the following variables:
| ENV VARIABLE | DESCRIPTION |
|--------------|-------------|
| DATABASE_URL | PostgreSQL connection string (e.g., `postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense`) |
| SECRET_KEY | JWT Secret key for authentication (should be a secure random string) |
| GOOGLE_OAUTH_CLIENT_ID | Google OAuth client ID |
| GOOGLE_OAUTH_CLIENT_SECRET | Google OAuth client secret |
| NEXT_FRONTEND_URL | Frontend application URL (e.g., `http://localhost:3000`) |
| EMBEDDING_MODEL | Name of the embedding model (e.g., `openai://text-embedding-ada-002`, `anthropic://claude-v1`, `mixedbread-ai/mxbai-embed-large-v1`) |
| RERANKERS_MODEL_NAME | Name of the reranker model (e.g., `ms-marco-MiniLM-L-12-v2`) |
| RERANKERS_MODEL_TYPE | Type of reranker model (e.g., `flashrank`) |
| FAST_LLM | LiteLLM routed faster LLM (e.g., `openai/gpt-4o-mini`, `ollama/deepseek-r1:8b`) |
| STRATEGIC_LLM | LiteLLM routed advanced LLM (e.g., `openai/gpt-4o`, `ollama/gemma3:12b`) |
| LONG_CONTEXT_LLM | LiteLLM routed long-context LLM (e.g., `gemini/gemini-2.0-flash`, `ollama/deepseek-r1:8b`) |
| UNSTRUCTURED_API_KEY | API key for Unstructured.io service |
| FIRECRAWL_API_KEY | API key for Firecrawl service (if using crawler) |
| TTS_SERVICE | Text-to-Speech API provider for Podcasts (e.g., `openai/tts-1`, `azure/neural`, `vertex_ai/`). See [supported providers](https://docs.litellm.ai/docs/text_to_speech#supported-providers) |
| ENV VARIABLE | DESCRIPTION |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DATABASE_URL | PostgreSQL connection string (e.g., `postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense`) |
| SECRET_KEY | JWT Secret key for authentication (should be a secure random string) |
| GOOGLE_OAUTH_CLIENT_ID | Google OAuth client ID |
| GOOGLE_OAUTH_CLIENT_SECRET | Google OAuth client secret |
| NEXT_FRONTEND_URL | Frontend application URL (e.g., `http://localhost:3000`) |
| EMBEDDING_MODEL | Name of the embedding model (e.g., `openai://text-embedding-ada-002`, `anthropic://claude-v1`, `mixedbread-ai/mxbai-embed-large-v1`) |
| RERANKERS_MODEL_NAME | Name of the reranker model (e.g., `ms-marco-MiniLM-L-12-v2`) |
| RERANKERS_MODEL_TYPE | Type of reranker model (e.g., `flashrank`) |
| FAST_LLM | LiteLLM routed faster LLM (e.g., `openai/gpt-4o-mini`, `ollama/deepseek-r1:8b`) |
| STRATEGIC_LLM | LiteLLM routed advanced LLM (e.g., `openai/gpt-4o`, `ollama/gemma3:12b`) |
| LONG_CONTEXT_LLM | LiteLLM routed long-context LLM (e.g., `gemini/gemini-2.0-flash`, `ollama/deepseek-r1:8b`) |
| UNSTRUCTURED_API_KEY | API key for Unstructured.io service |
| FIRECRAWL_API_KEY | API key for Firecrawl service (if using crawler) |
| TTS_SERVICE | Text-to-Speech API provider for Podcasts (e.g., `openai/tts-1`, `azure/neural`, `vertex_ai/`). See [supported providers](https://docs.litellm.ai/docs/text_to_speech#supported-providers) |
**Important**: Since LLM calls are routed through LiteLLM, include API keys for the LLM providers you're using:
- For OpenAI models: `OPENAI_API_KEY`
- For Google Gemini models: `GEMINI_API_KEY`
- For other providers, refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/providers)
**Optional LangSmith Observability:**
| ENV VARIABLE | DESCRIPTION |
|--------------|-------------|
| LANGSMITH_TRACING | Enable LangSmith tracing (e.g., `true`) |
| LANGSMITH_ENDPOINT | LangSmith API endpoint (e.g., `https://api.smith.langchain.com`) |
| LANGSMITH_API_KEY | Your LangSmith API key |
| LANGSMITH_PROJECT | LangSmith project name (e.g., `surfsense`) |
**Optional LiteLLM API Base URLs:**
| ENV VARIABLE | DESCRIPTION |
|--------------|-------------|
| FAST_LLM_API_BASE | Custom API base URL for the fast LLM |
| STRATEGIC_LLM_API_BASE | Custom API base URL for the strategic LLM |
| LONG_CONTEXT_LLM_API_BASE | Custom API base URL for the long context LLM |
### 2. Install Dependencies
Install the backend dependencies using `uv`:
**Linux/macOS:**
```bash
# Install uv if you don't have it
curl -fsSL https://astral.sh/uv/install.sh | bash
@ -82,6 +102,7 @@ uv sync
```
**Windows (PowerShell):**
```powershell
# Install uv if you don't have it
iwr -useb https://astral.sh/uv/install.ps1 | iex
@ -91,6 +112,7 @@ uv sync
```
**Windows (Command Prompt):**
```cmd
# Install dependencies with uv (after installing uv)
uv sync
@ -101,6 +123,7 @@ uv sync
Start the backend server:
**Linux/macOS/Windows:**
```bash
# Run without hot reloading
uv run main.py
@ -118,18 +141,21 @@ If everything is set up correctly, you should see output indicating the server i
Set up the frontend environment:
**Linux/macOS:**
```bash
cd surfsense_web
cp .env.example .env
```
**Windows (Command Prompt):**
```cmd
cd surfsense_web
copy .env.example .env
```
**Windows (PowerShell):**
```powershell
cd surfsense_web
Copy-Item -Path .env.example -Destination .env
@ -137,8 +163,8 @@ Copy-Item -Path .env.example -Destination .env
Edit the `.env` file and set:
| ENV VARIABLE | DESCRIPTION |
|--------------|-------------|
| ENV VARIABLE | DESCRIPTION |
| ------------------------------- | ------------------------------------------- |
| NEXT_PUBLIC_FASTAPI_BACKEND_URL | Backend URL (e.g., `http://localhost:8000`) |
### 2. Install Dependencies
@ -146,6 +172,7 @@ Edit the `.env` file and set:
Install the frontend dependencies:
**Linux/macOS:**
```bash
# Install pnpm if you don't have it
npm install -g pnpm
@ -155,6 +182,7 @@ pnpm install
```
**Windows:**
```powershell
# Install pnpm if you don't have it
npm install -g pnpm
@ -168,6 +196,7 @@ pnpm install
Start the Next.js development server:
**Linux/macOS/Windows:**
```bash
pnpm run dev
```
@ -181,18 +210,21 @@ The SurfSense browser extension allows you to save any webpage, including those
### 1. Environment Configuration
**Linux/macOS:**
```bash
cd surfsense_browser_extension
cp .env.example .env
```
**Windows (Command Prompt):**
```cmd
cd surfsense_browser_extension
copy .env.example .env
```
**Windows (PowerShell):**
```powershell
cd surfsense_browser_extension
Copy-Item -Path .env.example -Destination .env
@ -200,8 +232,8 @@ Copy-Item -Path .env.example -Destination .env
Edit the `.env` file:
| ENV VARIABLE | DESCRIPTION |
|--------------|-------------|
| ENV VARIABLE | DESCRIPTION |
| ------------------------- | ----------------------------------------------------- |
| PLASMO_PUBLIC_BACKEND_URL | SurfSense Backend URL (e.g., `http://127.0.0.1:8000`) |
### 2. Build the Extension
@ -209,6 +241,7 @@ Edit the `.env` file:
Build the extension for your browser using the [Plasmo framework](https://docs.plasmo.com/framework/workflows/build#with-a-specific-target).
**Linux/macOS/Windows:**
```bash
# Install dependencies
pnpm install
@ -253,7 +286,8 @@ Now that you have SurfSense running locally, you can explore its features:
- Explore the advanced RAG capabilities
For production deployments, consider setting up:
- A reverse proxy like Nginx
- SSL certificates for secure connections
- Proper database backups
- User access controls
- User access controls