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115 lines
2.9 KiB
Markdown
115 lines
2.9 KiB
Markdown
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## API Server Implementation
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LightRAG also provides a FastAPI-based server implementation for RESTful API access to RAG operations. This allows you to run LightRAG as a service and interact with it through HTTP requests.
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### Setting up the API Server
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<details>
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<summary>Click to expand setup instructions</summary>
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1. First, ensure you have the required dependencies:
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```bash
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pip install fastapi uvicorn pydantic
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```
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2. Set up your environment variables:
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```bash
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export RAG_DIR="your_index_directory" # Optional: Defaults to "index_default"
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export OPENAI_BASE_URL="Your OpenAI API base URL" # Optional: Defaults to "https://api.openai.com/v1"
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export OPENAI_API_KEY="Your OpenAI API key" # Required
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export LLM_MODEL="Your LLM model" # Optional: Defaults to "gpt-4o-mini"
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export EMBEDDING_MODEL="Your embedding model" # Optional: Defaults to "text-embedding-3-large"
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```
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3. Run the API server:
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```bash
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python examples/lightrag_api_openai_compatible_demo.py
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```
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The server will start on `http://0.0.0.0:8020`.
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</details>
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### API Endpoints
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The API server provides the following endpoints:
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#### 1. Query Endpoint
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<details>
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<summary>Click to view Query endpoint details</summary>
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- **URL:** `/query`
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- **Method:** POST
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- **Body:**
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```json
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{
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"query": "Your question here",
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"mode": "hybrid", // Can be "naive", "local", "global", or "hybrid"
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"only_need_context": true // Optional: Defaults to false, if true, only the referenced context will be returned, otherwise the llm answer will be returned
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}
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```
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- **Example:**
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```bash
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curl -X POST "http://127.0.0.1:8020/query" \
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-H "Content-Type: application/json" \
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-d '{"query": "What are the main themes?", "mode": "hybrid"}'
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```
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</details>
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#### 2. Insert Text Endpoint
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<details>
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<summary>Click to view Insert Text endpoint details</summary>
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- **URL:** `/insert`
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- **Method:** POST
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- **Body:**
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```json
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{
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"text": "Your text content here"
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}
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```
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- **Example:**
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```bash
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curl -X POST "http://127.0.0.1:8020/insert" \
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-H "Content-Type: application/json" \
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-d '{"text": "Content to be inserted into RAG"}'
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```
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</details>
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#### 3. Insert File Endpoint
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<details>
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<summary>Click to view Insert File endpoint details</summary>
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- **URL:** `/insert_file`
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- **Method:** POST
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- **Body:**
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```json
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{
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"file_path": "path/to/your/file.txt"
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}
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```
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- **Example:**
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```bash
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curl -X POST "http://127.0.0.1:8020/insert_file" \
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-H "Content-Type: application/json" \
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-d '{"file_path": "./book.txt"}'
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```
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</details>
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#### 4. Health Check Endpoint
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<details>
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<summary>Click to view Health Check endpoint details</summary>
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- **URL:** `/health`
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- **Method:** GET
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- **Example:**
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```bash
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curl -X GET "http://127.0.0.1:8020/health"
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```
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</details>
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### Configuration
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The API server can be configured using environment variables:
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- `RAG_DIR`: Directory for storing the RAG index (default: "index_default")
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- API keys and base URLs should be configured in the code for your specific LLM and embedding model providers
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