Added API as an option to the installation, reorganized the API and fused all documentations in README.md

2024-12-24 10:18:41 +01:00
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@@ -1019,6 +1019,294 @@ def extract_queries(file_path):
 └── test.py
 ```

+## Install with API Support
+
+LightRAG provides optional API support through FastAPI servers that add RAG capabilities to existing LLM services. You can install LightRAG with API support in two ways:
+
+### 1. Installation from PyPI
+
+```bash
+pip install "lightrag-hku[api]"
+```
+
+### 2. Installation from Source (Development)
+
+```bash
+# Clone the repository
+git clone https://github.com/ParisNeo/lightrag.git
+
+# Change to the repository directory
+cd lightrag
+
+# Install in editable mode with API support
+pip install -e ".[api]"
+```
+
+### Prerequisites
+
+Before running any of the servers, ensure you have the corresponding backend service running:
+
+#### For LoLLMs Server
+- LoLLMs must be running and accessible
+- Default connection: http://localhost:11434
+- Configure using --lollms-host if running on a different host/port
+
+#### For Ollama Server
+- Ollama must be running and accessible
+- Default connection: http://localhost:11434
+- Configure using --ollama-host if running on a different host/port
+
+#### For OpenAI Server
+- Requires valid OpenAI API credentials set in environment variables
+- OPENAI_API_KEY must be set
+
+### Configuration Options
+
+Each server has its own specific configuration options:
+
+#### LoLLMs Server Options
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | RAG server host |
+| --port | 9621 | RAG server port |
+| --model | mistral-nemo:latest | LLM model name |
+| --embedding-model | bge-m3:latest | Embedding model name |
+| --lollms-host | http://localhost:11434 | LoLLMS backend URL |
+| --working-dir | ./rag_storage | Working directory for RAG |
+| --max-async | 4 | Maximum async operations |
+| --max-tokens | 32768 | Maximum token size |
+| --embedding-dim | 1024 | Embedding dimensions |
+| --max-embed-tokens | 8192 | Maximum embedding token size |
+| --input-file | ./book.txt | Initial input file |
+| --log-level | INFO | Logging level |
+
+#### Ollama Server Options
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | RAG server host |
+| --port | 9621 | RAG server port |
+| --model | mistral-nemo:latest | LLM model name |
+| --embedding-model | bge-m3:latest | Embedding model name |
+| --ollama-host | http://localhost:11434 | Ollama backend URL |
+| --working-dir | ./rag_storage | Working directory for RAG |
+| --max-async | 4 | Maximum async operations |
+| --max-tokens | 32768 | Maximum token size |
+| --embedding-dim | 1024 | Embedding dimensions |
+| --max-embed-tokens | 8192 | Maximum embedding token size |
+| --input-file | ./book.txt | Initial input file |
+| --log-level | INFO | Logging level |
+
+#### OpenAI Server Options
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | RAG server host |
+| --port | 9621 | RAG server port |
+| --model | gpt-4 | OpenAI model name |
+| --embedding-model | text-embedding-3-large | OpenAI embedding model |
+| --working-dir | ./rag_storage | Working directory for RAG |
+| --max-tokens | 32768 | Maximum token size |
+| --max-embed-tokens | 8192 | Maximum embedding token size |
+| --input-dir | ./inputs | Input directory for documents |
+| --log-level | INFO | Logging level |
+
+### Example Usage
+
+#### LoLLMs RAG Server
+
+```bash
+# Custom configuration with specific model and working directory
+lollms-lightrag-server --model mistral-nemo --port 8080 --working-dir ./custom_rag
+
+# Using specific models (ensure they are installed in your LoLLMs instance)
+lollms-lightrag-server --model mistral-nemo:latest --embedding-model bge-m3 --embedding-dim 1024
+```
+
+#### Ollama RAG Server
+
+```bash
+# Custom configuration with specific model and working directory
+ollama-lightrag-server --model mistral-nemo:latest --port 8080 --working-dir ./custom_rag
+
+# Using specific models (ensure they are installed in your Ollama instance)
+ollama-lightrag-server --model mistral-nemo:latest --embedding-model bge-m3 --embedding-dim 1024
+```
+
+#### OpenAI RAG Server
+
+```bash
+# Using GPT-4 with text-embedding-3-large
+openai-lightrag-server --port 9624 --model gpt-4 --embedding-model text-embedding-3-large
+```
+
+**Important Notes:**
+- For LoLLMs: Make sure the specified models are installed in your LoLLMs instance
+- For Ollama: Make sure the specified models are installed in your Ollama instance
+- For OpenAI: Ensure you have set up your OPENAI_API_KEY environment variable
+
+For help on any server, use the --help flag:
+```bash
+lollms-lightrag-server --help
+ollama-lightrag-server --help
+openai-lightrag-server --help
+```
+
+Note: If you don't need the API functionality, you can install the base package without API support using:
+```bash
+pip install lightrag-hku
+```
+
+## API Endpoints
+
+All servers (LoLLMs, Ollama, and OpenAI) provide the same REST API endpoints for RAG functionality.
+
+### Query Endpoints
+
+#### POST /query
+Query the RAG system with options for different search modes.
+
+```bash
+curl -X POST "http://localhost:9621/query" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "Your question here", "mode": "hybrid"}'
+```
+
+#### POST /query/stream
+Stream responses from the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/query/stream" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "Your question here", "mode": "hybrid"}'
+```
+
+### Document Management Endpoints
+
+#### POST /documents/text
+Insert text directly into the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/documents/text" \
+    -H "Content-Type: application/json" \
+    -d '{"text": "Your text content here", "description": "Optional description"}'
+```
+
+#### POST /documents/file
+Upload a single file to the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/documents/file" \
+    -F "file=@/path/to/your/document.txt" \
+    -F "description=Optional description"
+```
+
+#### POST /documents/batch
+Upload multiple files at once.
+
+```bash
+curl -X POST "http://localhost:9621/documents/batch" \
+    -F "files=@/path/to/doc1.txt" \
+    -F "files=@/path/to/doc2.txt"
+```
+
+#### DELETE /documents
+Clear all documents from the RAG system.
+
+```bash
+curl -X DELETE "http://localhost:9621/documents"
+```
+
+### Utility Endpoints
+
+#### GET /health
+Check server health and configuration.
+
+```bash
+curl "http://localhost:9621/health"
+```
+
+## Development
+
+### Running in Development Mode
+
+For LoLLMs:
+```bash
+uvicorn lollms_lightrag_server:app --reload --port 9621
+```
+
+For Ollama:
+```bash
+uvicorn ollama_lightrag_server:app --reload --port 9621
+```
+
+For OpenAI:
+```bash
+uvicorn openai_lightrag_server:app --reload --port 9621
+```
+
+### API Documentation
+
+When any server is running, visit:
+- Swagger UI: http://localhost:9621/docs
+- ReDoc: http://localhost:9621/redoc
+
+### Testing API Endpoints
+
+You can test the API endpoints using the provided curl commands or through the Swagger UI interface. Make sure to:
+1. Start the appropriate backend service (LoLLMs, Ollama, or OpenAI)
+2. Start the RAG server
+3. Upload some documents using the document management endpoints
+4. Query the system using the query endpoints
+
+### Important Features
+
+#### Automatic Document Vectorization
+When starting any of the servers with the `--input-dir` parameter, the system will automatically:
+1. Scan the specified directory for documents
+2. Check for existing vectorized content in the database
+3. Only vectorize new documents that aren't already in the database
+4. Make all content immediately available for RAG queries
+
+This intelligent caching mechanism:
+- Prevents unnecessary re-vectorization of existing documents
+- Reduces startup time for subsequent runs
+- Preserves system resources
+- Maintains consistency across restarts
+
+### Example Usage
+
+#### LoLLMs RAG Server
+
+```bash
+# Start server with automatic document vectorization
+# Only new documents will be vectorized, existing ones will be loaded from cache
+lollms-lightrag-server --input-dir ./my_documents --port 8080
+```
+
+#### Ollama RAG Server
+
+```bash
+# Start server with automatic document vectorization
+# Previously vectorized documents will be loaded from the database
+ollama-lightrag-server --input-dir ./my_documents --port 8080
+```
+
+#### OpenAI RAG Server
+
+```bash
+# Start server with automatic document vectorization
+# Existing documents are retrieved from cache, only new ones are processed
+openai-lightrag-server --input-dir ./my_documents --port 9624
+```
+
+**Important Notes:**
+- The `--input-dir` parameter enables automatic document processing at startup
+- Documents already in the database are not re-vectorized
+- Only new documents in the input directory will be processed
+- This optimization significantly reduces startup time for subsequent runs
+- The working directory (`--working-dir`) stores the vectorized documents database

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