LightRAG/lightrag/api/README.md

## 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/HKUDS/lightrag.git

# Change to the repository directory
cd lightrag

# create a Python virtual enviroment if neccesary
# 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 both llm and embedding.
The new api allows you to mix different bindings for llm/embeddings.
For example, you have the possibility to use ollama for the embedding and openai for the llm.

#### For LoLLMs Server
- LoLLMs must be running and accessible
- Default connection: http://localhost:9600
- Configure using --llm-binding-host and/or --embedding-binding-host if running on a different host/port

#### For Ollama Server
- Ollama must be running and accessible
- Requires environment variables setup or command line argument provided
- Environment variables: LLM_BINDING=ollama, LLM_BINDING_HOST, LLM_MODEL
- Command line arguments: --llm-binding=ollama, --llm-binding-host, --llm-model
- Default connection is  http://localhost:11434 if not priveded

> The default MAX_TOKENS(num_ctx) for Ollama is 32768. If your Ollama server is lacking or GPU memory, set it to a lower value.

#### For OpenAI Alike Server
- Requires environment variables setup or command line argument provided
- Environment variables: LLM_BINDING=ollama, LLM_BINDING_HOST, LLM_MODEL, LLM_BINDING_API_KEY
- Command line arguments: --llm-binding=ollama, --llm-binding-host, --llm-model, --llm-binding-api-key
- Default connection is https://api.openai.com/v1 if not priveded

#### For Azure OpenAI Server
Azure OpenAI API can be created using the following commands in Azure CLI (you need to install Azure CLI first from [https://docs.microsoft.com/en-us/cli/azure/install-azure-cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli)):
```bash
# Change the resource group name, location and OpenAI resource name as needed
RESOURCE_GROUP_NAME=LightRAG
LOCATION=swedencentral
RESOURCE_NAME=LightRAG-OpenAI

az login
az group create --name $RESOURCE_GROUP_NAME --location $LOCATION
az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME  --kind OpenAI --sku S0 --location swedencentral
az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME  --model-format OpenAI --name $RESOURCE_NAME --deployment-name gpt-4o --model-name gpt-4o --model-version "2024-08-06"  --sku-capacity 100 --sku-name "Standard"
az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME  --model-format OpenAI --name $RESOURCE_NAME --deployment-name text-embedding-3-large --model-name text-embedding-3-large --model-version "1"  --sku-capacity 80 --sku-name "Standard"
az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint"
az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME

```
The output of the last command will give you the endpoint and the key for the OpenAI API. You can use these values to set the environment variables in the `.env` file.

```
LLM_BINDING=azure_openai
LLM_BINDING_HOST=endpoint_of_azure_ai
LLM_MODEL=model_name_of_azure_ai
LLM_BINDING_API_KEY=api_key_of_azure_ai
```

### 3. Install Lightrag as a Linux Service

Create a your service file `lightrag.sevice` from the sample file : `lightrag.sevice.example`. Modified the WorkingDirectoryand EexecStart in the service file:

```text
Description=LightRAG Ollama Service
WorkingDirectory=<lightrag installed directory>
ExecStart=<lightrag installed directory>/lightrag/api/lightrag-api
```

Modify your service startup script: `lightrag-api`. Change you python virtual environment activation command as needed:

```shell
#!/bin/bash

# your python virtual environment activation
source /home/netman/lightrag-xyj/venv/bin/activate
# start lightrag api server
lightrag-server
```

Install LightRAG service. If your system is Ubuntu, the following commands will work:

```shell
sudo cp lightrag.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl start lightrag.service
sudo systemctl status lightrag.service
sudo systemctl enable lightrag.service
```



## Configuration

LightRAG can be configured using either command-line arguments or environment variables. When both are provided, command-line arguments take precedence over environment variables.

Default `TOP_K` is set to `60`. Default `COSINE_THRESHOLD` are set to `0.2`.

### Environment Variables

You can configure LightRAG using environment variables by creating a `.env` file in your project root directory. A sample file `.env.example` is provided for your convenience.

### Config.ini

Datastorage configuration can be also set by config.ini. A sample file `config.ini.example` is provided for your convenience.

### Configuration Priority

The configuration values are loaded in the following order (highest priority first):
1. Command-line arguments
2. Environment variables
3. Config.ini
4. Defaul values

For example:
```bash
# This command-line argument will override both the environment variable and default value
python lightrag.py --port 8080

# The environment variable will override the default value but not the command-line argument
PORT=7000 python lightrag.py
```

> Best practices: you can set your database setting in Config.ini while testing, and you use .env for production.

### Storage Types Supported

LightRAG uses 4 types of storage for difference purposes:

* KV_STORAGE：llm response cache, text chunks, document information
* VECTOR_STORAGE：entities vectors, relation vectors, chunks vectors
* GRAPH_STORAGE：entity relation graph
* DOC_STATUS_STORAGE：documents indexing status

Each storage type have servals implementations:

* KV_STORAGE supported implement-name

```
JsonKVStorage    JsonFile(default)
MongoKVStorage   MogonDB
RedisKVStorage   Redis
TiDBKVStorage    TiDB
PGKVStorage      Postgres
OracleKVStorage  Oracle
```

* GRAPH_STORAGE supported implement-name

```
NetworkXStorage      NetworkX(defualt)
Neo4JStorage         Neo4J
MongoGraphStorage    MongoDB
TiDBGraphStorage     TiDB
AGEStorage           AGE
GremlinStorage       Gremlin
PGGraphStorage       Postgres
OracleGraphStorage   Postgres
```

* VECTOR_STORAGE supported implement-name

```
NanoVectorDBStorage         NanoVector(default)
MilvusVectorDBStorge        Milvus
ChromaVectorDBStorage       Chroma
TiDBVectorDBStorage         TiDB
PGVectorStorage             Postgres
FaissVectorDBStorage        Faiss
QdrantVectorDBStorage       Qdrant
OracleVectorDBStorage       Oracle
MongoVectorDBStorage        MongoDB
```

* DOC_STATUS_STORAGE：supported implement-name

```
JsonDocStatusStorage        JsonFile(default)
PGDocStatusStorage          Postgres
MongoDocStatusStorage       MongoDB
```

### How Select Storage Implementation

You can select storage  implementation by enviroment variables or command line arguments. You can not change storage implementation selection after you add documents to LightRAG. Data migration from one storage implementation to anthor is not supported yet. For further information please read the sample env file or config.ini file.

### LightRag API Server Comand Line Options

| Parameter | Default | Description |
|-----------|---------|-------------|
| --host | 0.0.0.0 | Server host |
| --port | 9621 | Server port |
| --llm-binding | ollama | LLM binding to be used. Supported: lollms, ollama, openai |
| --llm-binding-host | (dynamic) | LLM server host URL. Defaults based on binding: http://localhost:11434 (ollama), http://localhost:9600 (lollms), https://api.openai.com/v1 (openai) |
| --llm-model | mistral-nemo:latest | LLM model name |
| --llm-binding-api-key | None | API Key for OpenAI Alike LLM |
| --embedding-binding | ollama | Embedding binding to be used. Supported: lollms, ollama, openai |
| --embedding-binding-host | (dynamic) | Embedding server host URL. Defaults based on binding: http://localhost:11434 (ollama), http://localhost:9600 (lollms), https://api.openai.com/v1 (openai) |
| --embedding-model | bge-m3:latest | Embedding model name |
| --working-dir | ./rag_storage | Working directory for RAG storage |
| --input-dir | ./inputs | Directory containing input documents |
| --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 |
| --timeout | None | Timeout in seconds (useful when using slow AI). Use None for infinite timeout |
| --log-level | INFO | Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) |
| --key | None | API key for authentication. Protects lightrag server against unauthorized access |
| --ssl | False | Enable HTTPS |
| --ssl-certfile | None | Path to SSL certificate file (required if --ssl is enabled) |
| --ssl-keyfile | None | Path to SSL private key file (required if --ssl is enabled) |
| --top-k | 50 | Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode. |
| --cosine-threshold | 0.4 | The cossine threshold for nodes and relations retrieval, works with top-k to control the retrieval of nodes and relations. |
| --kv-storage | JsonKVStorage | implement-name of  KV_STORAGE |
| --graph-storage | NetworkXStorage | implement-name of  GRAPH_STORAGE |
| --vector-storage | NanoVectorDBStorage | implement-name of  VECTOR_STORAGE |
| --doc-status-storage | JsonDocStatusStorage | implement-name of  DOC_STATUS_STORAGE |

### Example Usage

#### Running a Lightrag server with ollama default local server as llm and embedding backends

Ollama is the default backend for both llm and embedding, so by default you can run lightrag-server with no parameters and the default ones will be used. Make sure ollama is installed and is running and default models are already installed on ollama.

```bash
# Run lightrag with ollama, mistral-nemo:latest for llm, and bge-m3:latest for embedding
lightrag-server

# Using specific models (ensure they are installed in your ollama instance)
lightrag-server --llm-model adrienbrault/nous-hermes2theta-llama3-8b:f16 --embedding-model nomic-embed-text --embedding-dim 1024

# Using an authentication key
lightrag-server --key my-key

# Using lollms for llm and ollama for embedding
lightrag-server --llm-binding lollms
```

#### Running a Lightrag server with lollms default local server as llm and embedding backends

```bash
# Run lightrag with lollms, mistral-nemo:latest for llm, and bge-m3:latest for embedding, use lollms for both llm and embedding
lightrag-server --llm-binding lollms --embedding-binding lollms

# Using specific models (ensure they are installed in your ollama instance)
lightrag-server --llm-binding lollms --llm-model adrienbrault/nous-hermes2theta-llama3-8b:f16 --embedding-binding lollms --embedding-model nomic-embed-text --embedding-dim 1024

# Using an authentication key
lightrag-server --key my-key

# Using lollms for llm and openai for embedding
lightrag-server --llm-binding lollms --embedding-binding openai --embedding-model text-embedding-3-small
```


#### Running a Lightrag server with openai server as llm and embedding backends

```bash
# Run lightrag with lollms, GPT-4o-mini  for llm, and text-embedding-3-small for embedding, use openai for both llm and embedding
lightrag-server --llm-binding openai --llm-model GPT-4o-mini --embedding-binding openai --embedding-model text-embedding-3-small

# Using an authentication key
lightrag-server --llm-binding openai --llm-model GPT-4o-mini --embedding-binding openai --embedding-model text-embedding-3-small --key my-key

# Using lollms for llm and openai for embedding
lightrag-server --llm-binding lollms --embedding-binding openai --embedding-model text-embedding-3-small
```

#### Running a Lightrag server with azure openai server as llm and embedding backends

```bash
# Run lightrag with lollms, GPT-4o-mini  for llm, and text-embedding-3-small for embedding, use openai for both llm and embedding
lightrag-server --llm-binding azure_openai --llm-model GPT-4o-mini --embedding-binding openai --embedding-model text-embedding-3-small

# Using an authentication key
lightrag-server --llm-binding azure_openai --llm-model GPT-4o-mini --embedding-binding azure_openai --embedding-model text-embedding-3-small --key my-key

# Using lollms for llm and azure_openai for embedding
lightrag-server --llm-binding lollms --embedding-binding azure_openai --embedding-model text-embedding-3-small
```

**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 Azure OpenAI: Build and configure your server as stated in the Prequisites section

For help on any server, use the --help flag:
```bash
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, OpenAI and Azure 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"
```

#### POST /documents/scan

Trigger document scan for new files in the Input directory.

```bash
curl -X POST "http://localhost:9621/documents/scan" --max-time 1800
```

> Ajust max-time according to the estimated index time  for all new files.

#### DELETE /documents

Clear all documents from the RAG system.

```bash
curl -X DELETE "http://localhost:9621/documents"
```

### Ollama Emulation Endpoints

#### GET /api/version

Get Ollama version information.

```bash
curl http://localhost:9621/api/version
```

#### GET /api/tags

Get Ollama available models.

```bash
curl http://localhost:9621/api/tags
```

#### POST /api/chat

Handle chat completion requests. Routes user queries through LightRAG by selecting query mode based on query prefix. Detects and forwards OpenWebUI session-related requests (for meta data generation task) directly to underlying LLM.

```shell
curl -N -X POST http://localhost:9621/api/chat -H "Content-Type: application/json" -d \
  '{"model":"lightrag:latest","messages":[{"role":"user","content":"猪八戒是谁"}],"stream":true}'
```

> For more information about Ollama API pls. visit :  [Ollama API documentation](https://github.com/ollama/ollama/blob/main/docs/api.md)

#### POST /api/generate

Handle generate completion requests. For compatibility purpose, the request is not processed by LightRAG, and will be handled by underlying LLM model.

### Utility Endpoints

#### GET /health
Check server health and configuration.

```bash
curl "http://localhost:9621/health"
```

## Ollama Emulation

We provide an Ollama-compatible interfaces for LightRAG, aiming to emulate LightRAG as an Ollama chat model. This allows AI chat frontends supporting Ollama, such as Open WebUI, to access LightRAG easily.

### Connect Open WebUI to LightRAG

After starting the lightrag-server, you can add an Ollama-type connection in the Open WebUI admin pannel. And then a model named lightrag:latest will appear in Open WebUI's model management interface. Users can then send queries to LightRAG through the chat interface. You'd better install LightRAG as service for this use case.

Open WebUI's use LLM to do the session title and session keyword generation task. So the Ollama chat chat completion API detects and forwards OpenWebUI session-related requests directly to underlying LLM.

### Choose Query mode in chat

A query prefix in the query string can determines which LightRAG query mode is used to generate the respond for the query. The supported prefixes include:

```
/local
/global
/hybrid
/naive
/mix
/bypass
```

For example, chat message "/mix 唐僧有几个徒弟" will trigger a mix mode query for LighRAG. A chat message without query prefix will trigger a hybrid mode query by default。

"/bypass" is not a LightRAG query mode, it will tell API Server to pass the query directly to the underlying LLM with chat history. So user can use LLM to answer question base on the chat history. If you are using Open WebUI as front end, you can just switch the model to a normal LLM instead of using /bypass prefix.

## Development

Contribute to the project: [Guide](contributor-readme.MD)

### 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
```
For Azure OpenAI:
```bash
uvicorn azure_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
5. Trigger document scan if new files is put into inputs directory

### Important Features

#### Automatic Document Vectorization
When starting any of the servers with the `--input-dir` parameter, the system will automatically:
1. Check for existing vectorized content in the database
2. Only vectorize new documents that aren't already in the database
3. 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

**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