mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-07-03 23:19:22 +00:00
1043 lines
36 KiB
Python
1043 lines
36 KiB
Python
from fastapi import FastAPI, HTTPException, File, UploadFile, Form
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from pydantic import BaseModel
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import logging
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import argparse
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import lollms_model_complete, lollms_embed
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from lightrag.llm import ollama_model_complete, ollama_embed
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from lightrag.llm import openai_complete_if_cache, openai_embedding
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from lightrag.llm import azure_openai_complete_if_cache, azure_openai_embedding
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from lightrag.api import __api_version__
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from lightrag.utils import EmbeddingFunc
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from typing import Optional, List, Union, Any
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from enum import Enum
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from pathlib import Path
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import shutil
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import aiofiles
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from ascii_colors import trace_exception, ASCIIColors
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import os
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from fastapi import Depends, Security
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from fastapi.security import APIKeyHeader
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from fastapi.middleware.cors import CORSMiddleware
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from contextlib import asynccontextmanager
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from starlette.status import HTTP_403_FORBIDDEN
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import pipmaster as pm
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def get_default_host(binding_type: str) -> str:
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default_hosts = {
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"ollama": "http://localhost:11434",
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"lollms": "http://localhost:9600",
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"azure_openai": "https://api.openai.com/v1",
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"openai": "https://api.openai.com/v1",
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}
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return default_hosts.get(
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binding_type, "http://localhost:11434"
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) # fallback to ollama if unknown
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from dotenv import load_dotenv
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def get_env_value(env_key: str, default: Any, value_type: type = str) -> Any:
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"""
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Get value from environment variable with type conversion
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Args:
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env_key (str): Environment variable key
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default (Any): Default value if env variable is not set
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value_type (type): Type to convert the value to
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Returns:
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Any: Converted value from environment or default
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"""
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value = os.getenv(env_key)
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if value is None:
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return default
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if value_type == bool:
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return value.lower() in ("true", "1", "yes")
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try:
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return value_type(value)
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except ValueError:
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return default
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def display_splash_screen(args: argparse.Namespace) -> None:
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"""
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Display a colorful splash screen showing LightRAG server configuration
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Args:
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args: Parsed command line arguments
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"""
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# Banner
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ASCIIColors.cyan(f"""
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╔══════════════════════════════════════════════════════════════╗
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║ 🚀 LightRAG Server v{__api_version__} ║
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║ Fast, Lightweight RAG Server Implementation ║
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╚══════════════════════════════════════════════════════════════╝
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""")
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# Server Configuration
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ASCIIColors.magenta("\n📡 Server Configuration:")
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ASCIIColors.white(" ├─ Host: ", end="")
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ASCIIColors.yellow(f"{args.host}")
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ASCIIColors.white(" ├─ Port: ", end="")
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ASCIIColors.yellow(f"{args.port}")
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ASCIIColors.white(" ├─ SSL Enabled: ", end="")
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ASCIIColors.yellow(f"{args.ssl}")
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if args.ssl:
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ASCIIColors.white(" ├─ SSL Cert: ", end="")
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ASCIIColors.yellow(f"{args.ssl_certfile}")
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ASCIIColors.white(" └─ SSL Key: ", end="")
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ASCIIColors.yellow(f"{args.ssl_keyfile}")
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# Directory Configuration
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ASCIIColors.magenta("\n📂 Directory Configuration:")
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ASCIIColors.white(" ├─ Working Directory: ", end="")
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ASCIIColors.yellow(f"{args.working_dir}")
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ASCIIColors.white(" └─ Input Directory: ", end="")
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ASCIIColors.yellow(f"{args.input_dir}")
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# LLM Configuration
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ASCIIColors.magenta("\n🤖 LLM Configuration:")
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ASCIIColors.white(" ├─ Binding: ", end="")
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ASCIIColors.yellow(f"{args.llm_binding}")
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ASCIIColors.white(" ├─ Host: ", end="")
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ASCIIColors.yellow(f"{args.llm_binding_host}")
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ASCIIColors.white(" └─ Model: ", end="")
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ASCIIColors.yellow(f"{args.llm_model}")
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# Embedding Configuration
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ASCIIColors.magenta("\n📊 Embedding Configuration:")
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ASCIIColors.white(" ├─ Binding: ", end="")
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ASCIIColors.yellow(f"{args.embedding_binding}")
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ASCIIColors.white(" ├─ Host: ", end="")
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ASCIIColors.yellow(f"{args.embedding_binding_host}")
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ASCIIColors.white(" ├─ Model: ", end="")
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ASCIIColors.yellow(f"{args.embedding_model}")
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ASCIIColors.white(" └─ Dimensions: ", end="")
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ASCIIColors.yellow(f"{args.embedding_dim}")
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# RAG Configuration
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ASCIIColors.magenta("\n⚙️ RAG Configuration:")
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ASCIIColors.white(" ├─ Max Async Operations: ", end="")
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ASCIIColors.yellow(f"{args.max_async}")
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ASCIIColors.white(" ├─ Max Tokens: ", end="")
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ASCIIColors.yellow(f"{args.max_tokens}")
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ASCIIColors.white(" └─ Max Embed Tokens: ", end="")
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ASCIIColors.yellow(f"{args.max_embed_tokens}")
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# System Configuration
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ASCIIColors.magenta("\n🛠️ System Configuration:")
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ASCIIColors.white(" ├─ Log Level: ", end="")
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ASCIIColors.yellow(f"{args.log_level}")
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ASCIIColors.white(" ├─ Timeout: ", end="")
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ASCIIColors.yellow(f"{args.timeout if args.timeout else 'None (infinite)'}")
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ASCIIColors.white(" └─ API Key: ", end="")
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ASCIIColors.yellow("Set" if args.key else "Not Set")
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# Server Status
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ASCIIColors.green("\n✨ Server starting up...\n")
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# Server Access Information
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protocol = "https" if args.ssl else "http"
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if args.host == "0.0.0.0":
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ASCIIColors.magenta("\n🌐 Server Access Information:")
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ASCIIColors.white(" ├─ Local Access: ", end="")
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ASCIIColors.yellow(f"{protocol}://localhost:{args.port}")
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ASCIIColors.white(" ├─ Remote Access: ", end="")
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ASCIIColors.yellow(f"{protocol}://<your-ip-address>:{args.port}")
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ASCIIColors.white(" ├─ API Documentation (local): ", end="")
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ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/docs")
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ASCIIColors.white(" └─ Alternative Documentation (local): ", end="")
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ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/redoc")
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ASCIIColors.yellow("\n📝 Note:")
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ASCIIColors.white(""" Since the server is running on 0.0.0.0:
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- Use 'localhost' or '127.0.0.1' for local access
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- Use your machine's IP address for remote access
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- To find your IP address:
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• Windows: Run 'ipconfig' in terminal
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• Linux/Mac: Run 'ifconfig' or 'ip addr' in terminal
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""")
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else:
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base_url = f"{protocol}://{args.host}:{args.port}"
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ASCIIColors.magenta("\n🌐 Server Access Information:")
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ASCIIColors.white(" ├─ Base URL: ", end="")
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ASCIIColors.yellow(f"{base_url}")
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ASCIIColors.white(" ├─ API Documentation: ", end="")
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ASCIIColors.yellow(f"{base_url}/docs")
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ASCIIColors.white(" └─ Alternative Documentation: ", end="")
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ASCIIColors.yellow(f"{base_url}/redoc")
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# Usage Examples
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ASCIIColors.magenta("\n📚 Quick Start Guide:")
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ASCIIColors.cyan("""
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1. Access the Swagger UI:
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Open your browser and navigate to the API documentation URL above
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2. API Authentication:""")
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if args.key:
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ASCIIColors.cyan(""" Add the following header to your requests:
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X-API-Key: <your-api-key>
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""")
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else:
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ASCIIColors.cyan(" No authentication required\n")
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ASCIIColors.cyan(""" 3. Basic Operations:
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- POST /upload_document: Upload new documents to RAG
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- POST /query: Query your document collection
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- GET /collections: List available collections
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4. Monitor the server:
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- Check server logs for detailed operation information
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- Use healthcheck endpoint: GET /health
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""")
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# Security Notice
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if args.key:
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ASCIIColors.yellow("\n⚠️ Security Notice:")
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ASCIIColors.white(""" API Key authentication is enabled.
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Make sure to include the X-API-Key header in all your requests.
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""")
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ASCIIColors.green("Server is ready to accept connections! 🚀\n")
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def parse_args() -> argparse.Namespace:
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"""
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Parse command line arguments with environment variable fallback
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Returns:
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argparse.Namespace: Parsed arguments
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"""
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# Load environment variables from .env file
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load_dotenv()
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parser = argparse.ArgumentParser(
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description="LightRAG FastAPI Server with separate working and input directories"
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)
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# Bindings (with env var support)
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parser.add_argument(
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"--llm-binding",
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default=get_env_value("LLM_BINDING", "ollama"),
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help="LLM binding to be used. Supported: lollms, ollama, openai (default: from env or ollama)",
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)
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parser.add_argument(
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"--embedding-binding",
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default=get_env_value("EMBEDDING_BINDING", "ollama"),
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help="Embedding binding to be used. Supported: lollms, ollama, openai (default: from env or ollama)",
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)
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# Parse temporary args for host defaults
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temp_args, _ = parser.parse_known_args()
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# Server configuration
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parser.add_argument(
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"--host",
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default=get_env_value("HOST", "0.0.0.0"),
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help="Server host (default: from env or 0.0.0.0)",
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)
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parser.add_argument(
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"--port",
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type=int,
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default=get_env_value("PORT", 9621, int),
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help="Server port (default: from env or 9621)",
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)
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# Directory configuration
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parser.add_argument(
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"--working-dir",
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default=get_env_value("WORKING_DIR", "./rag_storage"),
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help="Working directory for RAG storage (default: from env or ./rag_storage)",
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)
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parser.add_argument(
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"--input-dir",
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default=get_env_value("INPUT_DIR", "./inputs"),
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help="Directory containing input documents (default: from env or ./inputs)",
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)
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# LLM Model configuration
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default_llm_host = get_env_value(
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"LLM_BINDING_HOST", get_default_host(temp_args.llm_binding)
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)
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parser.add_argument(
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"--llm-binding-host",
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default=default_llm_host,
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help=f"llm server host URL (default: from env or {default_llm_host})",
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)
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parser.add_argument(
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"--llm-model",
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default=get_env_value("LLM_MODEL", "mistral-nemo:latest"),
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help="LLM model name (default: from env or mistral-nemo:latest)",
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)
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# Embedding model configuration
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default_embedding_host = get_env_value(
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"EMBEDDING_BINDING_HOST", get_default_host(temp_args.embedding_binding)
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)
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parser.add_argument(
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"--embedding-binding-host",
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default=default_embedding_host,
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help=f"embedding server host URL (default: from env or {default_embedding_host})",
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)
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parser.add_argument(
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"--embedding-model",
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default=get_env_value("EMBEDDING_MODEL", "bge-m3:latest"),
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help="Embedding model name (default: from env or bge-m3:latest)",
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)
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def timeout_type(value):
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if value is None or value == "None":
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return None
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return int(value)
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parser.add_argument(
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"--timeout",
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default=get_env_value("TIMEOUT", None, timeout_type),
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type=timeout_type,
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help="Timeout in seconds (useful when using slow AI). Use None for infinite timeout",
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)
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# RAG configuration
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parser.add_argument(
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"--max-async",
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type=int,
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default=get_env_value("MAX_ASYNC", 4, int),
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help="Maximum async operations (default: from env or 4)",
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)
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parser.add_argument(
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"--max-tokens",
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type=int,
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default=get_env_value("MAX_TOKENS", 32768, int),
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help="Maximum token size (default: from env or 32768)",
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)
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parser.add_argument(
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"--embedding-dim",
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type=int,
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default=get_env_value("EMBEDDING_DIM", 1024, int),
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help="Embedding dimensions (default: from env or 1024)",
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)
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parser.add_argument(
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"--max-embed-tokens",
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type=int,
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default=get_env_value("MAX_EMBED_TOKENS", 8192, int),
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help="Maximum embedding token size (default: from env or 8192)",
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)
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# Logging configuration
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parser.add_argument(
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"--log-level",
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default=get_env_value("LOG_LEVEL", "INFO"),
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choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
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help="Logging level (default: from env or INFO)",
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)
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parser.add_argument(
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"--key",
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type=str,
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default=get_env_value("LIGHTRAG_API_KEY", None),
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help="API key for authentication. This protects lightrag server against unauthorized access",
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)
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# Optional https parameters
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parser.add_argument(
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"--ssl",
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action="store_true",
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default=get_env_value("SSL", False, bool),
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help="Enable HTTPS (default: from env or False)",
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)
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parser.add_argument(
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"--ssl-certfile",
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default=get_env_value("SSL_CERTFILE", None),
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help="Path to SSL certificate file (required if --ssl is enabled)",
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)
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parser.add_argument(
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"--ssl-keyfile",
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default=get_env_value("SSL_KEYFILE", None),
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help="Path to SSL private key file (required if --ssl is enabled)",
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)
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args = parser.parse_args()
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display_splash_screen(args)
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return args
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class DocumentManager:
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"""Handles document operations and tracking"""
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def __init__(
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self,
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input_dir: str,
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supported_extensions: tuple = (".txt", ".md", ".pdf", ".docx", ".pptx"),
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):
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self.input_dir = Path(input_dir)
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self.supported_extensions = supported_extensions
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self.indexed_files = set()
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# Create input directory if it doesn't exist
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self.input_dir.mkdir(parents=True, exist_ok=True)
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def scan_directory(self) -> List[Path]:
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"""Scan input directory for new files"""
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new_files = []
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for ext in self.supported_extensions:
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for file_path in self.input_dir.rglob(f"*{ext}"):
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if file_path not in self.indexed_files:
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new_files.append(file_path)
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return new_files
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def mark_as_indexed(self, file_path: Path):
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"""Mark a file as indexed"""
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self.indexed_files.add(file_path)
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def is_supported_file(self, filename: str) -> bool:
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"""Check if file type is supported"""
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return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
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# Pydantic models
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class SearchMode(str, Enum):
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naive = "naive"
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local = "local"
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global_ = "global"
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hybrid = "hybrid"
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class QueryRequest(BaseModel):
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query: str
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mode: SearchMode = SearchMode.hybrid
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stream: bool = False
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only_need_context: bool = False
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|
|
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class QueryResponse(BaseModel):
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response: str
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|
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class InsertTextRequest(BaseModel):
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text: str
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description: Optional[str] = None
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|
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class InsertResponse(BaseModel):
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status: str
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message: str
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document_count: int
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|
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def get_api_key_dependency(api_key: Optional[str]):
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if not api_key:
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# If no API key is configured, return a dummy dependency that always succeeds
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async def no_auth():
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return None
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return no_auth
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|
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# If API key is configured, use proper authentication
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api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
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|
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async def api_key_auth(api_key_header_value: str | None = Security(api_key_header)):
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if not api_key_header_value:
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raise HTTPException(
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status_code=HTTP_403_FORBIDDEN, detail="API Key required"
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)
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if api_key_header_value != api_key:
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raise HTTPException(
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status_code=HTTP_403_FORBIDDEN, detail="Invalid API Key"
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)
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return api_key_header_value
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return api_key_auth
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|
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def create_app(args):
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# Verify that bindings arer correctly setup
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|
if args.llm_binding not in ["lollms", "ollama", "openai"]:
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raise Exception("llm binding not supported")
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|
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if args.embedding_binding not in ["lollms", "ollama", "openai"]:
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raise Exception("embedding binding not supported")
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|
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# Add SSL validation
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|
if args.ssl:
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if not args.ssl_certfile or not args.ssl_keyfile:
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raise Exception(
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|
"SSL certificate and key files must be provided when SSL is enabled"
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)
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if not os.path.exists(args.ssl_certfile):
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raise Exception(f"SSL certificate file not found: {args.ssl_certfile}")
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if not os.path.exists(args.ssl_keyfile):
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raise Exception(f"SSL key file not found: {args.ssl_keyfile}")
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|
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# Setup logging
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|
logging.basicConfig(
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format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
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)
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|
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# Check if API key is provided either through env var or args
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|
api_key = os.getenv("LIGHTRAG_API_KEY") or args.key
|
|
|
|
# Initialize FastAPI
|
|
app = FastAPI(
|
|
title="LightRAG API",
|
|
description="API for querying text using LightRAG with separate storage and input directories"
|
|
+ "(With authentication)"
|
|
if api_key
|
|
else "",
|
|
version="1.0.2",
|
|
openapi_tags=[{"name": "api"}],
|
|
)
|
|
|
|
# Add CORS middleware
|
|
app.add_middleware(
|
|
CORSMiddleware,
|
|
allow_origins=["*"],
|
|
allow_credentials=True,
|
|
allow_methods=["*"],
|
|
allow_headers=["*"],
|
|
)
|
|
|
|
# Create the optional API key dependency
|
|
optional_api_key = get_api_key_dependency(api_key)
|
|
|
|
# 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)
|
|
|
|
# Initialize RAG
|
|
rag = LightRAG(
|
|
working_dir=args.working_dir,
|
|
llm_model_func=lollms_model_complete
|
|
if args.llm_binding == "lollms"
|
|
else ollama_model_complete
|
|
if args.llm_binding == "ollama"
|
|
else azure_openai_complete_if_cache
|
|
if args.llm_binding == "azure_openai"
|
|
else openai_complete_if_cache,
|
|
llm_model_name=args.llm_model,
|
|
llm_model_max_async=args.max_async,
|
|
llm_model_max_token_size=args.max_tokens,
|
|
llm_model_kwargs={
|
|
"host": args.llm_binding_host,
|
|
"timeout": args.timeout,
|
|
"options": {"num_ctx": args.max_tokens},
|
|
},
|
|
embedding_func=EmbeddingFunc(
|
|
embedding_dim=args.embedding_dim,
|
|
max_token_size=args.max_embed_tokens,
|
|
func=lambda texts: lollms_embed(
|
|
texts,
|
|
embed_model=args.embedding_model,
|
|
host=args.embedding_binding_host,
|
|
)
|
|
if args.llm_binding == "lollms"
|
|
else ollama_embed(
|
|
texts,
|
|
embed_model=args.embedding_model,
|
|
host=args.embedding_binding_host,
|
|
)
|
|
if args.llm_binding == "ollama"
|
|
else azure_openai_embedding(
|
|
texts,
|
|
model=args.embedding_model, # no host is used for openai
|
|
)
|
|
if args.llm_binding == "azure_openai"
|
|
else openai_embedding(
|
|
texts,
|
|
model=args.embedding_model, # no host is used for openai
|
|
),
|
|
),
|
|
)
|
|
|
|
async def index_file(file_path: Union[str, Path]) -> None:
|
|
"""Index all files inside the folder with support for multiple file formats
|
|
|
|
Args:
|
|
file_path: Path to the file to be indexed (str or Path object)
|
|
|
|
Raises:
|
|
ValueError: If file format is not supported
|
|
FileNotFoundError: If file doesn't exist
|
|
"""
|
|
if not pm.is_installed("aiofiles"):
|
|
pm.install("aiofiles")
|
|
|
|
# Convert to Path object if string
|
|
file_path = Path(file_path)
|
|
|
|
# Check if file exists
|
|
if not file_path.exists():
|
|
raise FileNotFoundError(f"File not found: {file_path}")
|
|
|
|
content = ""
|
|
# Get file extension in lowercase
|
|
ext = file_path.suffix.lower()
|
|
|
|
match ext:
|
|
case ".txt" | ".md":
|
|
# Text files handling
|
|
async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
|
|
content = await f.read()
|
|
|
|
case ".pdf":
|
|
if not pm.is_installed("pypdf2"):
|
|
pm.install("pypdf2")
|
|
from pypdf2 import PdfReader
|
|
|
|
# PDF handling
|
|
reader = PdfReader(str(file_path))
|
|
content = ""
|
|
for page in reader.pages:
|
|
content += page.extract_text() + "\n"
|
|
|
|
case ".docx":
|
|
if not pm.is_installed("docx"):
|
|
pm.install("docx")
|
|
from docx import Document
|
|
|
|
# Word document handling
|
|
doc = Document(file_path)
|
|
content = "\n".join([paragraph.text for paragraph in doc.paragraphs])
|
|
|
|
case ".pptx":
|
|
if not pm.is_installed("pptx"):
|
|
pm.install("pptx")
|
|
from pptx import Presentation
|
|
|
|
# PowerPoint handling
|
|
prs = Presentation(file_path)
|
|
content = ""
|
|
for slide in prs.slides:
|
|
for shape in slide.shapes:
|
|
if hasattr(shape, "text"):
|
|
content += shape.text + "\n"
|
|
|
|
case _:
|
|
raise ValueError(f"Unsupported file format: {ext}")
|
|
|
|
# Insert content into RAG system
|
|
if content:
|
|
await rag.ainsert(content)
|
|
doc_manager.mark_as_indexed(file_path)
|
|
logging.info(f"Successfully indexed file: {file_path}")
|
|
else:
|
|
logging.warning(f"No content extracted from file: {file_path}")
|
|
|
|
@asynccontextmanager
|
|
async def lifespan(app: FastAPI):
|
|
"""Lifespan context manager for startup and shutdown events"""
|
|
# Startup logic
|
|
try:
|
|
new_files = doc_manager.scan_directory()
|
|
for file_path in new_files:
|
|
try:
|
|
await index_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", dependencies=[Depends(optional_api_key)])
|
|
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:
|
|
await index_file(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("/documents/upload", dependencies=[Depends(optional_api_key)])
|
|
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
|
|
await index_file(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, dependencies=[Depends(optional_api_key)]
|
|
)
|
|
async def query_text(request: QueryRequest):
|
|
try:
|
|
response = await rag.aquery(
|
|
request.query,
|
|
param=QueryParam(
|
|
mode=request.mode,
|
|
stream=request.stream,
|
|
only_need_context=request.only_need_context,
|
|
),
|
|
)
|
|
|
|
if request.stream:
|
|
result = ""
|
|
async for chunk in response:
|
|
result += chunk
|
|
return QueryResponse(response=result)
|
|
else:
|
|
return QueryResponse(response=response)
|
|
except Exception as e:
|
|
trace_exception(e)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post("/query/stream", dependencies=[Depends(optional_api_key)])
|
|
async def query_text_stream(request: QueryRequest):
|
|
try:
|
|
response = rag.query(
|
|
request.query,
|
|
param=QueryParam(
|
|
mode=request.mode,
|
|
stream=True,
|
|
only_need_context=request.only_need_context,
|
|
),
|
|
)
|
|
|
|
async def stream_generator():
|
|
async for chunk in response:
|
|
yield chunk
|
|
|
|
return stream_generator()
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post(
|
|
"/documents/text",
|
|
response_model=InsertResponse,
|
|
dependencies=[Depends(optional_api_key)],
|
|
)
|
|
async def insert_text(request: InsertTextRequest):
|
|
try:
|
|
await rag.ainsert(request.text)
|
|
return InsertResponse(
|
|
status="success",
|
|
message="Text successfully inserted",
|
|
document_count=1,
|
|
)
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post(
|
|
"/documents/file",
|
|
response_model=InsertResponse,
|
|
dependencies=[Depends(optional_api_key)],
|
|
)
|
|
async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
|
|
"""Insert a file directly into the RAG system
|
|
|
|
Args:
|
|
file: Uploaded file
|
|
description: Optional description of the file
|
|
|
|
Returns:
|
|
InsertResponse: Status of the insertion operation
|
|
|
|
Raises:
|
|
HTTPException: For unsupported file types or processing errors
|
|
"""
|
|
try:
|
|
content = ""
|
|
# Get file extension in lowercase
|
|
ext = Path(file.filename).suffix.lower()
|
|
|
|
match ext:
|
|
case ".txt" | ".md":
|
|
# Text files handling
|
|
text_content = await file.read()
|
|
content = text_content.decode("utf-8")
|
|
|
|
case ".pdf":
|
|
if not pm.is_installed("pypdf2"):
|
|
pm.install("pypdf2")
|
|
from pypdf2 import PdfReader
|
|
from io import BytesIO
|
|
|
|
# Read PDF from memory
|
|
pdf_content = await file.read()
|
|
pdf_file = BytesIO(pdf_content)
|
|
reader = PdfReader(pdf_file)
|
|
content = ""
|
|
for page in reader.pages:
|
|
content += page.extract_text() + "\n"
|
|
|
|
case ".docx":
|
|
if not pm.is_installed("docx"):
|
|
pm.install("docx")
|
|
from docx import Document
|
|
from io import BytesIO
|
|
|
|
# Read DOCX from memory
|
|
docx_content = await file.read()
|
|
docx_file = BytesIO(docx_content)
|
|
doc = Document(docx_file)
|
|
content = "\n".join(
|
|
[paragraph.text for paragraph in doc.paragraphs]
|
|
)
|
|
|
|
case ".pptx":
|
|
if not pm.is_installed("pptx"):
|
|
pm.install("pptx")
|
|
from pptx import Presentation
|
|
from io import BytesIO
|
|
|
|
# Read PPTX from memory
|
|
pptx_content = await file.read()
|
|
pptx_file = BytesIO(pptx_content)
|
|
prs = Presentation(pptx_file)
|
|
content = ""
|
|
for slide in prs.slides:
|
|
for shape in slide.shapes:
|
|
if hasattr(shape, "text"):
|
|
content += shape.text + "\n"
|
|
|
|
case _:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
|
|
)
|
|
|
|
# Insert content into RAG system
|
|
if content:
|
|
# Add description if provided
|
|
if description:
|
|
content = f"{description}\n\n{content}"
|
|
|
|
await rag.ainsert(content)
|
|
logging.info(f"Successfully indexed file: {file.filename}")
|
|
|
|
return InsertResponse(
|
|
status="success",
|
|
message=f"File '{file.filename}' successfully inserted",
|
|
document_count=1,
|
|
)
|
|
else:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="No content could be extracted from the file",
|
|
)
|
|
|
|
except UnicodeDecodeError:
|
|
raise HTTPException(status_code=400, detail="File encoding not supported")
|
|
except Exception as e:
|
|
logging.error(f"Error processing file {file.filename}: {str(e)}")
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post(
|
|
"/documents/batch",
|
|
response_model=InsertResponse,
|
|
dependencies=[Depends(optional_api_key)],
|
|
)
|
|
async def insert_batch(files: List[UploadFile] = File(...)):
|
|
"""Process multiple files in batch mode
|
|
|
|
Args:
|
|
files: List of files to process
|
|
|
|
Returns:
|
|
InsertResponse: Status of the batch insertion operation
|
|
|
|
Raises:
|
|
HTTPException: For processing errors
|
|
"""
|
|
try:
|
|
inserted_count = 0
|
|
failed_files = []
|
|
|
|
for file in files:
|
|
try:
|
|
content = ""
|
|
ext = Path(file.filename).suffix.lower()
|
|
|
|
match ext:
|
|
case ".txt" | ".md":
|
|
text_content = await file.read()
|
|
content = text_content.decode("utf-8")
|
|
|
|
case ".pdf":
|
|
if not pm.is_installed("pypdf2"):
|
|
pm.install("pypdf2")
|
|
from pypdf2 import PdfReader
|
|
from io import BytesIO
|
|
|
|
pdf_content = await file.read()
|
|
pdf_file = BytesIO(pdf_content)
|
|
reader = PdfReader(pdf_file)
|
|
for page in reader.pages:
|
|
content += page.extract_text() + "\n"
|
|
|
|
case ".docx":
|
|
if not pm.is_installed("docx"):
|
|
pm.install("docx")
|
|
from docx import Document
|
|
from io import BytesIO
|
|
|
|
docx_content = await file.read()
|
|
docx_file = BytesIO(docx_content)
|
|
doc = Document(docx_file)
|
|
content = "\n".join(
|
|
[paragraph.text for paragraph in doc.paragraphs]
|
|
)
|
|
|
|
case ".pptx":
|
|
if not pm.is_installed("pptx"):
|
|
pm.install("pptx")
|
|
from pptx import Presentation
|
|
from io import BytesIO
|
|
|
|
pptx_content = await file.read()
|
|
pptx_file = BytesIO(pptx_content)
|
|
prs = Presentation(pptx_file)
|
|
for slide in prs.slides:
|
|
for shape in slide.shapes:
|
|
if hasattr(shape, "text"):
|
|
content += shape.text + "\n"
|
|
|
|
case _:
|
|
failed_files.append(f"{file.filename} (unsupported type)")
|
|
continue
|
|
|
|
if content:
|
|
await rag.ainsert(content)
|
|
inserted_count += 1
|
|
logging.info(f"Successfully indexed file: {file.filename}")
|
|
else:
|
|
failed_files.append(f"{file.filename} (no content extracted)")
|
|
|
|
except UnicodeDecodeError:
|
|
failed_files.append(f"{file.filename} (encoding error)")
|
|
except Exception as e:
|
|
failed_files.append(f"{file.filename} ({str(e)})")
|
|
logging.error(f"Error processing file {file.filename}: {str(e)}")
|
|
|
|
# Prepare status message
|
|
if inserted_count == len(files):
|
|
status = "success"
|
|
status_message = f"Successfully inserted all {inserted_count} documents"
|
|
elif inserted_count > 0:
|
|
status = "partial_success"
|
|
status_message = f"Successfully inserted {inserted_count} out of {len(files)} documents"
|
|
if failed_files:
|
|
status_message += f". Failed files: {', '.join(failed_files)}"
|
|
else:
|
|
status = "failure"
|
|
status_message = "No documents were successfully inserted"
|
|
if failed_files:
|
|
status_message += f". Failed files: {', '.join(failed_files)}"
|
|
|
|
return InsertResponse(
|
|
status=status,
|
|
message=status_message,
|
|
document_count=inserted_count,
|
|
)
|
|
|
|
except Exception as e:
|
|
logging.error(f"Batch processing error: {str(e)}")
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.delete(
|
|
"/documents",
|
|
response_model=InsertResponse,
|
|
dependencies=[Depends(optional_api_key)],
|
|
)
|
|
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", dependencies=[Depends(optional_api_key)])
|
|
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": {
|
|
# LLM configuration binding/host address (if applicable)/model (if applicable)
|
|
"llm_binding": args.llm_binding,
|
|
"llm_binding_host": args.llm_binding_host,
|
|
"llm_model": args.llm_model,
|
|
# embedding model configuration binding/host address (if applicable)/model (if applicable)
|
|
"embedding_binding": args.embedding_binding,
|
|
"embedding_binding_host": args.embedding_binding_host,
|
|
"embedding_model": args.embedding_model,
|
|
"max_tokens": args.max_tokens,
|
|
},
|
|
}
|
|
|
|
return app
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
import uvicorn
|
|
|
|
app = create_app(args)
|
|
uvicorn_config = {
|
|
"app": app,
|
|
"host": args.host,
|
|
"port": args.port,
|
|
}
|
|
if args.ssl:
|
|
uvicorn_config.update(
|
|
{
|
|
"ssl_certfile": args.ssl_certfile,
|
|
"ssl_keyfile": args.ssl_keyfile,
|
|
}
|
|
)
|
|
uvicorn.run(**uvicorn_config)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|