Merge remote-tracking branch 'upstream/main'

.gitignore

@@ -17,3 +17,7 @@ gui/
 .vscode
 inputs
 rag_storage
+.env
+venv/
+examples/input/
+examples/output/

examples/.env.oai.example

@@ -0,0 +1,7 @@
+AZURE_OPENAI_API_VERSION=2024-08-01-preview
+AZURE_OPENAI_DEPLOYMENT=gpt-4o
+AZURE_OPENAI_API_KEY=myapikey
+AZURE_OPENAI_ENDPOINT=https://myendpoint.openai.azure.com
+
+AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
+AZURE_EMBEDDING_API_VERSION=2023-05-15

lightrag/api/.env.aoi.example

@@ -0,0 +1,7 @@
+AZURE_OPENAI_API_VERSION=2024-08-01-preview
+AZURE_OPENAI_DEPLOYMENT=gpt-4o
+AZURE_OPENAI_API_KEY=myapikey
+AZURE_OPENAI_ENDPOINT=https://myendpoint.openai.azure.com
+
+AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
+AZURE_EMBEDDING_API_VERSION=2023-05-15

lightrag/api/README_AZURE_OPENAI.md

@@ -0,0 +1,202 @@
+
+# LightRAG API Server
+
+A powerful FastAPI-based server for managing and querying documents using LightRAG (Light Retrieval-Augmented Generation). This server provides a REST API interface for document management and intelligent querying using OpenAI's language models.
+
+## Features
+
+- 🔍 Multiple search modes (naive, local, global, hybrid)
+- 📡 Streaming and non-streaming responses
+- 📝 Document management (insert, batch upload, clear)
+- ⚙️ Highly configurable model parameters
+- 📚 Support for text and file uploads
+- 🔧 RESTful API with automatic documentation
+- 🚀 Built with FastAPI for high performance
+
+## Prerequisites
+
+- Python 3.8+
+- Azure OpenAI API key
+- Azure OpenAI Deployments (gpt-4o, text-embedding-3-large)
+- Required Python packages:
+  - fastapi
+  - uvicorn
+  - lightrag
+  - pydantic
+  - openai
+  - nest-asyncio
+
+## Installation
+If you are using Windows, you will need to download and install visual c++ build tools from [https://visualstudio.microsoft.com/visual-cpp-build-tools/](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
+Make sure you install the VS 2022 C++ x64/x86 Build tools from individual components tab.
+
+1. Clone the repository:
+```bash
+git clone https://github.com/ParisNeo/LightRAG.git
+cd api
+```
+
+2. Install dependencies:
+```bash
+python -m venv venv
+source venv/bin/activate
+#venv\Scripts\activate for Windows
+pip install -r requirements.txt
+```
+
+3. Set up environment variables:
+   use the `.env` file to set the environment variables (you can copy the `.env.aoi.example` file and rename it to `.env`),
+   or set them manually:
+```bash
+export AZURE_OPENAI_API_VERSION='2024-08-01-preview'
+export AZURE_OPENAI_DEPLOYMENT='gpt-4o'
+export AZURE_OPENAI_API_KEY='myapikey'
+export AZURE_OPENAI_ENDPOINT='https://myendpoint.openai.azure.com'
+export AZURE_EMBEDDING_DEPLOYMENT='text-embedding-3-large'
+export AZURE_EMBEDDING_API_VERSION='2023-05-15'
+```
+
+## Configuration
+
+The server can be configured using command-line arguments:
+
+```bash
+python azure_openai_lightrag_server.py --help
+```
+
+Available options:
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | Server host |
+| --port | 9621 | 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 |
+| --enable-cache | True | Enable response cache |
+| --log-level | INFO | Logging level |
+
+## Quick Start
+
+1. Basic usage with default settings:
+```bash
+python azure_openai_lightrag_server.py
+```
+
+2. Custom configuration:
+```bash
+python azure_openai_lightrag_server.py --model gpt-4o --port 8080 --working-dir ./custom_rag
+```
+
+## API Endpoints
+
+### 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
+
+```bash
+uvicorn azure_openai_lightrag_server:app --reload --port 9621
+```
+
+### API Documentation
+
+When the server is running, visit:
+- Swagger UI: http://localhost:9621/docs
+- ReDoc: http://localhost:9621/redoc
+
+## Deployment
+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.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgments
+
+- Built with [FastAPI](https://fastapi.tiangolo.com/)
+- Uses [LightRAG](https://github.com/HKUDS/LightRAG) for document processing
+- Powered by [OpenAI](https://openai.com/) for language model inference

lightrag/api/azure_openai_lightrag_server.py

@@ -0,0 +1,443 @@
+from fastapi import FastAPI, HTTPException, File, UploadFile, Form
+from pydantic import BaseModel
+import asyncio
+import logging
+import argparse
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import (
+    azure_openai_complete_if_cache,
+    azure_openai_embedding,
+)
+from lightrag.utils import EmbeddingFunc
+from typing import Optional, List
+from enum import Enum
+from pathlib import Path
+import shutil
+import aiofiles
+from ascii_colors import trace_exception
+import os
+from dotenv import load_dotenv
+import inspect
+import json
+from fastapi.responses import StreamingResponse
+
+load_dotenv()
+
+AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")
+AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
+AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
+AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
+
+AZURE_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_EMBEDDING_DEPLOYMENT")
+AZURE_EMBEDDING_API_VERSION = os.getenv("AZURE_EMBEDDING_API_VERSION")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="LightRAG FastAPI Server with OpenAI integration"
+    )
+
+    # Server configuration
+    parser.add_argument(
+        "--host", default="0.0.0.0", help="Server host (default: 0.0.0.0)"
+    )
+    parser.add_argument(
+        "--port", type=int, default=9621, help="Server port (default: 9621)"
+    )
+
+    # Directory configuration
+    parser.add_argument(
+        "--working-dir",
+        default="./rag_storage",
+        help="Working directory for RAG storage (default: ./rag_storage)",
+    )
+    parser.add_argument(
+        "--input-dir",
+        default="./inputs",
+        help="Directory containing input documents (default: ./inputs)",
+    )
+
+    # Model configuration
+    parser.add_argument(
+        "--model", default="gpt-4o", help="OpenAI model name (default: gpt-4o)"
+    )
+    parser.add_argument(
+        "--embedding-model",
+        default="text-embedding-3-large",
+        help="OpenAI embedding model (default: text-embedding-3-large)",
+    )
+
+    # RAG configuration
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=32768,
+        help="Maximum token size (default: 32768)",
+    )
+    parser.add_argument(
+        "--max-embed-tokens",
+        type=int,
+        default=8192,
+        help="Maximum embedding token size (default: 8192)",
+    )
+    parser.add_argument(
+        "--enable-cache",
+        default=True,
+        help="Enable response cache (default: True)",
+    )
+    # Logging configuration
+    parser.add_argument(
+        "--log-level",
+        default="INFO",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+        help="Logging level (default: INFO)",
+    )
+
+    return parser.parse_args()
+
+
+class DocumentManager:
+    """Handles document operations and tracking"""
+
+    def __init__(self, input_dir: str, supported_extensions: tuple = (".txt", ".md")):
+        self.input_dir = Path(input_dir)
+        self.supported_extensions = supported_extensions
+        self.indexed_files = set()
+
+        # Create input directory if it doesn't exist
+        self.input_dir.mkdir(parents=True, exist_ok=True)
+
+    def scan_directory(self) -> List[Path]:
+        """Scan input directory for new files"""
+        new_files = []
+        for ext in self.supported_extensions:
+            for file_path in self.input_dir.rglob(f"*{ext}"):
+                if file_path not in self.indexed_files:
+                    new_files.append(file_path)
+        return new_files
+
+    def mark_as_indexed(self, file_path: Path):
+        """Mark a file as indexed"""
+        self.indexed_files.add(file_path)
+
+    def is_supported_file(self, filename: str) -> bool:
+        """Check if file type is supported"""
+        return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
+
+
+# Pydantic models
+class SearchMode(str, Enum):
+    naive = "naive"
+    local = "local"
+    global_ = "global"
+    hybrid = "hybrid"
+
+
+class QueryRequest(BaseModel):
+    query: str
+    mode: SearchMode = SearchMode.hybrid
+    # stream: bool = False
+
+
+class QueryResponse(BaseModel):
+    response: str
+
+
+class InsertTextRequest(BaseModel):
+    text: str
+    description: Optional[str] = None
+
+
+class InsertResponse(BaseModel):
+    status: str
+    message: str
+    document_count: int
+
+
+async def get_embedding_dim(embedding_model: str) -> int:
+    """Get embedding dimensions for the specified model"""
+    test_text = ["This is a test sentence."]
+    embedding = await azure_openai_embedding(test_text, model=embedding_model)
+    return embedding.shape[1]
+
+
+def create_app(args):
+    # Setup logging
+    logging.basicConfig(
+        format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
+    )
+
+    # Initialize FastAPI app
+    app = FastAPI(
+        title="LightRAG API",
+        description="API for querying text using LightRAG with OpenAI integration",
+    )
+
+    # Create working directory if it doesn't exist
+    Path(args.working_dir).mkdir(parents=True, exist_ok=True)
+
+    # Initialize document manager
+    doc_manager = DocumentManager(args.input_dir)
+
+    # Get embedding dimensions
+    embedding_dim = asyncio.run(get_embedding_dim(args.embedding_model))
+
+    async def async_openai_complete(
+        prompt, system_prompt=None, history_messages=[], **kwargs
+    ):
+        """Async wrapper for OpenAI completion"""
+        kwargs.pop("keyword_extraction", None)
+
+        return await azure_openai_complete_if_cache(
+            args.model,
+            prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            base_url=AZURE_OPENAI_ENDPOINT,
+            api_key=AZURE_OPENAI_API_KEY,
+            api_version=AZURE_OPENAI_API_VERSION,
+            **kwargs,
+        )
+
+    # Initialize RAG with OpenAI configuration
+    rag = LightRAG(
+        enable_llm_cache=args.enable_cache,
+        working_dir=args.working_dir,
+        llm_model_func=async_openai_complete,
+        llm_model_name=args.model,
+        llm_model_max_token_size=args.max_tokens,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=embedding_dim,
+            max_token_size=args.max_embed_tokens,
+            func=lambda texts: azure_openai_embedding(
+                texts, model=args.embedding_model
+            ),
+        ),
+    )
+
+    @app.on_event("startup")
+    async def startup_event():
+        """Index all files in input directory during startup"""
+        try:
+            new_files = doc_manager.scan_directory()
+            for file_path in new_files:
+                try:
+                    # Use async file reading
+                    async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
+                        content = await f.read()
+                        # Use the async version of insert directly
+                        await rag.ainsert(content)
+                        doc_manager.mark_as_indexed(file_path)
+                        logging.info(f"Indexed file: {file_path}")
+                except Exception as e:
+                    trace_exception(e)
+                    logging.error(f"Error indexing file {file_path}: {str(e)}")
+
+            logging.info(f"Indexed {len(new_files)} documents from {args.input_dir}")
+
+        except Exception as e:
+            logging.error(f"Error during startup indexing: {str(e)}")
+
+    @app.post("/documents/scan")
+    async def scan_for_new_documents():
+        """Manually trigger scanning for new documents"""
+        try:
+            new_files = doc_manager.scan_directory()
+            indexed_count = 0
+
+            for file_path in new_files:
+                try:
+                    with open(file_path, "r", encoding="utf-8") as f:
+                        content = f.read()
+                        await rag.ainsert(content)
+                        doc_manager.mark_as_indexed(file_path)
+                        indexed_count += 1
+                except Exception as e:
+                    logging.error(f"Error indexing file {file_path}: {str(e)}")
+
+            return {
+                "status": "success",
+                "indexed_count": indexed_count,
+                "total_documents": len(doc_manager.indexed_files),
+            }
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/resetcache")
+    async def reset_cache():
+        """Manually reset cache"""
+        try:
+            cachefile = args.working_dir + "/kv_store_llm_response_cache.json"
+            if os.path.exists(cachefile):
+                with open(cachefile, "w") as f:
+                    f.write("{}")
+            return {"status": "success"}
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/documents/upload")
+    async def upload_to_input_dir(file: UploadFile = File(...)):
+        """Upload a file to the input directory"""
+        try:
+            if not doc_manager.is_supported_file(file.filename):
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
+                )
+
+            file_path = doc_manager.input_dir / file.filename
+            with open(file_path, "wb") as buffer:
+                shutil.copyfileobj(file.file, buffer)
+
+            # Immediately index the uploaded file
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+                await rag.ainsert(content)
+                doc_manager.mark_as_indexed(file_path)
+
+            return {
+                "status": "success",
+                "message": f"File uploaded and indexed: {file.filename}",
+                "total_documents": len(doc_manager.indexed_files),
+            }
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/query", response_model=QueryResponse)
+    async def query_text(request: QueryRequest):
+        try:
+            response = await rag.aquery(
+                request.query,
+                param=QueryParam(mode=request.mode, stream=False),
+            )
+            return QueryResponse(response=response)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/query/stream")
+    async def query_text_stream(request: QueryRequest):
+        try:
+            response = await rag.aquery(
+                request.query,
+                param=QueryParam(mode=request.mode, stream=True),
+            )
+            if inspect.isasyncgen(response):
+
+                async def stream_generator():
+                    async for chunk in response:
+                        yield json.dumps({"data": chunk}) + "\n"
+
+                return StreamingResponse(
+                    stream_generator(), media_type="application/json"
+                )
+            else:
+                return QueryResponse(response=response)
+
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/documents/text", response_model=InsertResponse)
+    async def insert_text(request: InsertTextRequest):
+        try:
+            rag.insert(request.text)
+            return InsertResponse(
+                status="success",
+                message="Text successfully inserted",
+                document_count=len(rag),
+            )
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/documents/file", response_model=InsertResponse)
+    async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
+        try:
+            content = await file.read()
+
+            if file.filename.endswith((".txt", ".md")):
+                text = content.decode("utf-8")
+                rag.insert(text)
+            else:
+                raise HTTPException(
+                    status_code=400,
+                    detail="Unsupported file type. Only .txt and .md files are supported",
+                )
+
+            return InsertResponse(
+                status="success",
+                message=f"File '{file.filename}' successfully inserted",
+                document_count=len(rag),
+            )
+        except UnicodeDecodeError:
+            raise HTTPException(status_code=400, detail="File encoding not supported")
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post("/documents/batch", response_model=InsertResponse)
+    async def insert_batch(files: List[UploadFile] = File(...)):
+        try:
+            inserted_count = 0
+            failed_files = []
+
+            for file in files:
+                try:
+                    content = await file.read()
+                    if file.filename.endswith((".txt", ".md")):
+                        text = content.decode("utf-8")
+                        rag.insert(text)
+                        inserted_count += 1
+                    else:
+                        failed_files.append(f"{file.filename} (unsupported type)")
+                except Exception as e:
+                    failed_files.append(f"{file.filename} ({str(e)})")
+
+            status_message = f"Successfully inserted {inserted_count} documents"
+            if failed_files:
+                status_message += f". Failed files: {', '.join(failed_files)}"
+
+            return InsertResponse(
+                status="success" if inserted_count > 0 else "partial_success",
+                message=status_message,
+                document_count=len(rag),
+            )
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.delete("/documents", response_model=InsertResponse)
+    async def clear_documents():
+        try:
+            rag.text_chunks = []
+            rag.entities_vdb = None
+            rag.relationships_vdb = None
+            return InsertResponse(
+                status="success",
+                message="All documents cleared successfully",
+                document_count=0,
+            )
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.get("/health")
+    async def get_status():
+        """Get current system status"""
+        return {
+            "status": "healthy",
+            "working_directory": str(args.working_dir),
+            "input_directory": str(args.input_dir),
+            "indexed_files": len(doc_manager.indexed_files),
+            "configuration": {
+                "model": args.model,
+                "embedding_model": args.embedding_model,
+                "max_tokens": args.max_tokens,
+                "embedding_dim": embedding_dim,
+            },
+        }
+
+    return app
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    import uvicorn
+
+    app = create_app(args)
+    uvicorn.run(app, host=args.host, port=args.port)

lightrag/api/requirements.txt

@@ -1,4 +1,17 @@
+aioboto3
 ascii_colors
 fastapi
+lightrag-hku
+nano_vectordb
+nest_asyncio
+numpy
+ollama
+openai
+python-dotenv
 python-multipart
+tenacity
+tiktoken
+torch
+tqdm
+transformers
 uvicorn

lightrag/llm.py

@@ -140,12 +140,34 @@ async def azure_openai_complete_if_cache(
     if prompt is not None:
         messages.append({"role": "user", "content": prompt})
 
-    response = await openai_async_client.chat.completions.create(
-        model=model, messages=messages, **kwargs
-    )
-    content = response.choices[0].message.content
+    if "response_format" in kwargs:
+        response = await openai_async_client.beta.chat.completions.parse(
+            model=model, messages=messages, **kwargs
+        )
+    else:
+        response = await openai_async_client.chat.completions.create(
+            model=model, messages=messages, **kwargs
+        )
+
+    if hasattr(response, "__aiter__"):
 
-    return content
+        async def inner():
+            async for chunk in response:
+                if len(chunk.choices) == 0:
+                    continue
+                content = chunk.choices[0].delta.content
+                if content is None:
+                    continue
+                if r"\u" in content:
+                    content = safe_unicode_decode(content.encode("utf-8"))
+                yield content
+
+        return inner()
+    else:
+        content = response.choices[0].message.content
+        if r"\u" in content:
+            content = safe_unicode_decode(content.encode("utf-8"))
+        return content
 
 
 class BedrockError(Exception):