mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-07-03 23:19:22 +00:00
604 lines
20 KiB
Python
604 lines
20 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.utils import EmbeddingFunc
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from typing import Optional, List
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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
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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 starlette.status import HTTP_403_FORBIDDEN
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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(binding_type, "http://localhost:11434") # fallback to ollama if unknown
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def parse_args():
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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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#Start by the bindings
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parser.add_argument(
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"--llm-binding",
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default="ollama",
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help="LLM binding to be used. Supported: lollms, ollama, openai (default: ollama)",
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)
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parser.add_argument(
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"--embedding-binding",
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default="ollama",
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help="Embedding binding to be used. Supported: lollms, ollama, openai (default: ollama)",
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)
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# Parse just these arguments first
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temp_args, _ = parser.parse_known_args()
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# Add remaining arguments with dynamic defaults for hosts
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# Server configuration
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parser.add_argument(
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"--host", default="0.0.0.0", help="Server host (default: 0.0.0.0)"
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)
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parser.add_argument(
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"--port", type=int, default=9621, help="Server port (default: 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="./rag_storage",
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help="Working directory for RAG storage (default: ./rag_storage)",
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)
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parser.add_argument(
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"--input-dir",
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default="./inputs",
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help="Directory containing input documents (default: ./inputs)",
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)
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# LLM Model configuration
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default_llm_host = get_default_host(temp_args.llm_binding)
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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: {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="mistral-nemo:latest",
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help="LLM model name (default: mistral-nemo:latest)",
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)
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# Embedding model configuration
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default_embedding_host = get_default_host(temp_args.embedding_binding)
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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: {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="bge-m3:latest",
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help="Embedding model name (default: 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=None,
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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", type=int, default=4, help="Maximum async operations (default: 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=32768,
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help="Maximum token size (default: 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=1024,
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help="Embedding dimensions (default: 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=8192,
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help="Maximum embedding token size (default: 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="INFO",
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choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
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help="Logging level (default: 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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help="API key for authentication. This protects lightrag server against unauthorized access",
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default=None,
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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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help="Enable HTTPS (default: False)"
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)
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parser.add_argument(
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"--ssl-certfile",
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default=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=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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return parser.parse_args()
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class DocumentManager:
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"""Handles document operations and tracking"""
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def __init__(self, input_dir: str, supported_extensions: tuple = (".txt", ".md")):
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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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class QueryResponse(BaseModel):
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response: str
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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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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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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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# 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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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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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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if args.embedding_binding not in ["lollms", "ollama", "openai"]:
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raise Exception("embedding binding not supported")
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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("SSL certificate and key files must be provided when SSL is enabled")
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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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# 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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# 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
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# Initialize FastAPI
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app = FastAPI(
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title="LightRAG API",
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description="API for querying text using LightRAG with separate storage and input directories"
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+ "(With authentication)"
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if api_key
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else "",
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version="1.0.1",
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openapi_tags=[{"name": "api"}],
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)
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Create the optional API key dependency
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optional_api_key = get_api_key_dependency(api_key)
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# Create working directory if it doesn't exist
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Path(args.working_dir).mkdir(parents=True, exist_ok=True)
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# Initialize document manager
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doc_manager = DocumentManager(args.input_dir)
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# Initialize RAG
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rag = LightRAG(
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working_dir=args.working_dir,
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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,
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llm_model_name=args.llm_model,
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llm_model_max_async=args.max_async,
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llm_model_max_token_size=args.max_tokens,
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llm_model_kwargs={
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"host": args.llm_binding_host,
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"timeout":args.timeout,
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"options": {"num_ctx": args.max_tokens},
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},
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embedding_func=EmbeddingFunc(
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embedding_dim=args.embedding_dim,
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max_token_size=args.max_embed_tokens,
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func=lambda texts: lollms_embed(
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texts, embed_model=args.embedding_model, host=args.embedding_binding_host
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) if args.llm_binding=="lollms" else ollama_embed(
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texts, embed_model=args.embedding_model, host=args.embedding_binding_host
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) if args.llm_binding=="ollama" else azure_openai_embedding(
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texts, model=args.embedding_model # no host is used for openai
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) if args.llm_binding=="azure_openai" else openai_embedding(
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texts, model=args.embedding_model # no host is used for openai
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)
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),
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)
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@app.on_event("startup")
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async def startup_event():
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"""Index all files in input directory during startup"""
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try:
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new_files = doc_manager.scan_directory()
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for file_path in new_files:
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try:
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# Use async file reading
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async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
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content = await f.read()
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# Use the async version of insert directly
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await rag.ainsert(content)
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doc_manager.mark_as_indexed(file_path)
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logging.info(f"Indexed file: {file_path}")
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except Exception as e:
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trace_exception(e)
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logging.error(f"Error indexing file {file_path}: {str(e)}")
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logging.info(f"Indexed {len(new_files)} documents from {args.input_dir}")
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except Exception as e:
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logging.error(f"Error during startup indexing: {str(e)}")
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@app.post("/documents/scan", dependencies=[Depends(optional_api_key)])
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async def scan_for_new_documents():
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"""Manually trigger scanning for new documents"""
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try:
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new_files = doc_manager.scan_directory()
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indexed_count = 0
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for file_path in new_files:
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try:
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with open(file_path, "r", encoding="utf-8") as f:
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content = f.read()
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await rag.ainsert(content)
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doc_manager.mark_as_indexed(file_path)
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indexed_count += 1
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except Exception as e:
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logging.error(f"Error indexing file {file_path}: {str(e)}")
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return {
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"status": "success",
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"indexed_count": indexed_count,
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"total_documents": len(doc_manager.indexed_files),
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/documents/upload", dependencies=[Depends(optional_api_key)])
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async def upload_to_input_dir(file: UploadFile = File(...)):
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"""Upload a file to the input directory"""
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try:
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if not doc_manager.is_supported_file(file.filename):
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raise HTTPException(
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status_code=400,
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detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
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)
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file_path = doc_manager.input_dir / file.filename
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with open(file_path, "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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# Immediately index the uploaded file
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with open(file_path, "r", encoding="utf-8") as f:
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content = f.read()
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await rag.ainsert(content)
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doc_manager.mark_as_indexed(file_path)
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return {
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"status": "success",
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"message": f"File uploaded and indexed: {file.filename}",
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"total_documents": len(doc_manager.indexed_files),
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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"/query", response_model=QueryResponse, dependencies=[Depends(optional_api_key)]
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)
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async def query_text(request: QueryRequest):
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try:
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response = await rag.aquery(
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request.query,
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param=QueryParam(
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mode=request.mode,
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stream=request.stream,
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only_need_context=request.only_need_context,
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),
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)
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if request.stream:
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result = ""
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async for chunk in response:
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result += chunk
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return QueryResponse(response=result)
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else:
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return QueryResponse(response=response)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/query/stream", dependencies=[Depends(optional_api_key)])
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async def query_text_stream(request: QueryRequest):
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try:
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response = rag.query(
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request.query,
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param=QueryParam(
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mode=request.mode,
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stream=True,
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only_need_context=request.only_need_context,
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),
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)
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async def stream_generator():
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async for chunk in response:
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yield chunk
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return stream_generator()
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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"/documents/text",
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response_model=InsertResponse,
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dependencies=[Depends(optional_api_key)],
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)
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async def insert_text(request: InsertTextRequest):
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try:
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rag.insert(request.text)
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return InsertResponse(
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status="success",
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message="Text successfully inserted",
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document_count=len(rag),
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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"/documents/file",
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response_model=InsertResponse,
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dependencies=[Depends(optional_api_key)],
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)
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async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
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try:
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content = await file.read()
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if file.filename.endswith((".txt", ".md")):
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text = content.decode("utf-8")
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await rag.ainsert(text)
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else:
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raise HTTPException(
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status_code=400,
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detail="Unsupported file type. Only .txt and .md files are supported",
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)
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return InsertResponse(
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status="success",
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message=f"File '{file.filename}' successfully inserted",
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document_count=1,
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)
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except UnicodeDecodeError:
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raise HTTPException(status_code=400, detail="File encoding not supported")
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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"/documents/batch",
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response_model=InsertResponse,
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dependencies=[Depends(optional_api_key)],
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)
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async def insert_batch(files: List[UploadFile] = File(...)):
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try:
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inserted_count = 0
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failed_files = []
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for file in files:
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try:
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content = await file.read()
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if file.filename.endswith((".txt", ".md")):
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text = content.decode("utf-8")
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await rag.ainsert(text)
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inserted_count += 1
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else:
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failed_files.append(f"{file.filename} (unsupported type)")
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except Exception as e:
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failed_files.append(f"{file.filename} ({str(e)})")
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status_message = f"Successfully inserted {inserted_count} documents"
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if failed_files:
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|
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(files),
|
|
)
|
|
except Exception as 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()
|