mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-06-26 22:00:19 +00:00
1939 lines
72 KiB
Python
1939 lines
72 KiB
Python
from fastapi import (
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FastAPI,
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HTTPException,
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File,
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UploadFile,
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Form,
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Request,
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BackgroundTasks,
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)
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# Backend (Python)
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# Add this to store progress globally
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from typing import Dict
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import threading
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import json
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import os
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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import logging
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import argparse
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import time
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import re
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from typing import List, Any, Optional, Union
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from lightrag import LightRAG, QueryParam
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from lightrag.api import __api_version__
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from lightrag.utils import EmbeddingFunc
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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 sys
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import configparser
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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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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Global progress tracker
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scan_progress: Dict = {
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"is_scanning": False,
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"current_file": "",
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"indexed_count": 0,
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"total_files": 0,
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"progress": 0,
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}
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# Lock for thread-safe operations
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progress_lock = threading.Lock()
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def estimate_tokens(text: str) -> int:
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"""Estimate the number of tokens in text
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Chinese characters: approximately 1.5 tokens per character
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English characters: approximately 0.25 tokens per character
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"""
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# Use regex to match Chinese and non-Chinese characters separately
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chinese_chars = len(re.findall(r"[\u4e00-\u9fff]", text))
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non_chinese_chars = len(re.findall(r"[^\u4e00-\u9fff]", text))
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# Calculate estimated token count
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tokens = chinese_chars * 1.5 + non_chinese_chars * 0.25
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return int(tokens)
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class OllamaServerInfos:
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# Constants for emulated Ollama model information
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LIGHTRAG_NAME = "lightrag"
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LIGHTRAG_TAG = os.getenv("OLLAMA_EMULATING_MODEL_TAG", "latest")
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LIGHTRAG_MODEL = f"{LIGHTRAG_NAME}:{LIGHTRAG_TAG}"
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LIGHTRAG_SIZE = 7365960935 # it's a dummy value
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LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z"
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LIGHTRAG_DIGEST = "sha256:lightrag"
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KV_STORAGE = "JsonKVStorage"
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DOC_STATUS_STORAGE = "JsonDocStatusStorage"
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GRAPH_STORAGE = "NetworkXStorage"
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VECTOR_STORAGE = "NanoVectorDBStorage"
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# Add infos
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ollama_server_infos = OllamaServerInfos()
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# read config.ini
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config = configparser.ConfigParser()
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config.read("config.ini", "utf-8")
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# Redis config
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redis_uri = config.get("redis", "uri", fallback=None)
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if redis_uri:
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os.environ["REDIS_URI"] = redis_uri
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ollama_server_infos.KV_STORAGE = "RedisKVStorage"
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ollama_server_infos.DOC_STATUS_STORAGE = "RedisKVStorage"
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# Neo4j config
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neo4j_uri = config.get("neo4j", "uri", fallback=None)
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neo4j_username = config.get("neo4j", "username", fallback=None)
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neo4j_password = config.get("neo4j", "password", fallback=None)
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if neo4j_uri:
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os.environ["NEO4J_URI"] = neo4j_uri
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os.environ["NEO4J_USERNAME"] = neo4j_username
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os.environ["NEO4J_PASSWORD"] = neo4j_password
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ollama_server_infos.GRAPH_STORAGE = "Neo4JStorage"
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# Milvus config
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milvus_uri = config.get("milvus", "uri", fallback=None)
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milvus_user = config.get("milvus", "user", fallback=None)
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milvus_password = config.get("milvus", "password", fallback=None)
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milvus_db_name = config.get("milvus", "db_name", fallback=None)
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if milvus_uri:
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os.environ["MILVUS_URI"] = milvus_uri
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os.environ["MILVUS_USER"] = milvus_user
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os.environ["MILVUS_PASSWORD"] = milvus_password
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os.environ["MILVUS_DB_NAME"] = milvus_db_name
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ollama_server_infos.VECTOR_STORAGE = "MilvusVectorDBStorge"
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# MongoDB config
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mongo_uri = config.get("mongodb", "uri", fallback=None)
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mongo_database = config.get("mongodb", "LightRAG", fallback=None)
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if mongo_uri:
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os.environ["MONGO_URI"] = mongo_uri
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os.environ["MONGO_DATABASE"] = mongo_database
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ollama_server_infos.KV_STORAGE = "MongoKVStorage"
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ollama_server_infos.DOC_STATUS_STORAGE = "MongoKVStorage"
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def get_default_host(binding_type: str) -> str:
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default_hosts = {
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"ollama": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"),
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"lollms": os.getenv("LLM_BINDING_HOST", "http://localhost:9600"),
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"azure_openai": os.getenv("AZURE_OPENAI_ENDPOINT", "https://api.openai.com/v1"),
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"openai": os.getenv("LLM_BINDING_HOST", "https://api.openai.com/v1"),
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}
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return default_hosts.get(
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binding_type, os.getenv("LLM_BINDING_HOST", "http://localhost:11434")
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) # fallback to ollama if unknown
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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 isinstance(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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ASCIIColors.white(" ├─ Chunk Size: ", end="")
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ASCIIColors.yellow(f"{args.chunk_size}")
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ASCIIColors.white(" ├─ Chunk Overlap Size: ", end="")
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ASCIIColors.yellow(f"{args.chunk_overlap_size}")
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ASCIIColors.white(" ├─ History Turns: ", end="")
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ASCIIColors.yellow(f"{args.history_turns}")
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ASCIIColors.white(" ├─ Cosine Threshold: ", end="")
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ASCIIColors.yellow(f"{args.cosine_threshold}")
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ASCIIColors.white(" └─ Top-K: ", end="")
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ASCIIColors.yellow(f"{args.top_k}")
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# System Configuration
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ASCIIColors.magenta("\n🛠️ System Configuration:")
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ASCIIColors.white(" ├─ Ollama Emulating Model: ", end="")
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ASCIIColors.yellow(f"{ollama_server_infos.LIGHTRAG_MODEL}")
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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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# Ensure splash output flush to system log
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sys.stdout.flush()
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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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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 configuration
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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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# 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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parser.add_argument(
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"--llm-binding-host",
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default=get_env_value("LLM_BINDING_HOST", None),
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help="LLM server host URL. If not provided, defaults based on llm-binding:\n"
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+ "- ollama: http://localhost:11434\n"
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+ "- lollms: http://localhost:9600\n"
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+ "- openai: https://api.openai.com/v1",
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)
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default_llm_api_key = get_env_value("LLM_BINDING_API_KEY", None)
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parser.add_argument(
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"--llm-binding-api-key",
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default=default_llm_api_key,
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help="llm server API key (default: from env or empty string)",
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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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parser.add_argument(
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"--embedding-binding-host",
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default=get_env_value("EMBEDDING_BINDING_HOST", None),
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help="Embedding server host URL. If not provided, defaults based on embedding-binding:\n"
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+ "- ollama: http://localhost:11434\n"
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+ "- lollms: http://localhost:9600\n"
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+ "- openai: https://api.openai.com/v1",
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)
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default_embedding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "")
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parser.add_argument(
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"--embedding-binding-api-key",
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default=default_embedding_api_key,
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help="embedding server API key (default: from env or empty string)",
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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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parser.add_argument(
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"--chunk_size",
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default=get_env_value("CHUNK_SIZE", 1200),
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help="chunk chunk size default 1200",
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)
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parser.add_argument(
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"--chunk_overlap_size",
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default=get_env_value("CHUNK_OVERLAP_SIZE", 100),
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help="chunk overlap size default 100",
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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,
|
|
default=get_env_value("MAX_ASYNC", 4, int),
|
|
help="Maximum async operations (default: from env or 4)",
|
|
)
|
|
parser.add_argument(
|
|
"--max-tokens",
|
|
type=int,
|
|
default=get_env_value("MAX_TOKENS", 32768, int),
|
|
help="Maximum token size (default: from env or 32768)",
|
|
)
|
|
parser.add_argument(
|
|
"--embedding-dim",
|
|
type=int,
|
|
default=get_env_value("EMBEDDING_DIM", 1024, int),
|
|
help="Embedding dimensions (default: from env or 1024)",
|
|
)
|
|
parser.add_argument(
|
|
"--max-embed-tokens",
|
|
type=int,
|
|
default=get_env_value("MAX_EMBED_TOKENS", 8192, int),
|
|
help="Maximum embedding token size (default: from env or 8192)",
|
|
)
|
|
|
|
# Logging configuration
|
|
parser.add_argument(
|
|
"--log-level",
|
|
default=get_env_value("LOG_LEVEL", "INFO"),
|
|
choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
|
|
help="Logging level (default: from env or INFO)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--key",
|
|
type=str,
|
|
default=get_env_value("LIGHTRAG_API_KEY", None),
|
|
help="API key for authentication. This protects lightrag server against unauthorized access",
|
|
)
|
|
|
|
# Optional https parameters
|
|
parser.add_argument(
|
|
"--ssl",
|
|
action="store_true",
|
|
default=get_env_value("SSL", False, bool),
|
|
help="Enable HTTPS (default: from env or False)",
|
|
)
|
|
parser.add_argument(
|
|
"--ssl-certfile",
|
|
default=get_env_value("SSL_CERTFILE", None),
|
|
help="Path to SSL certificate file (required if --ssl is enabled)",
|
|
)
|
|
parser.add_argument(
|
|
"--ssl-keyfile",
|
|
default=get_env_value("SSL_KEYFILE", None),
|
|
help="Path to SSL private key file (required if --ssl is enabled)",
|
|
)
|
|
parser.add_argument(
|
|
"--auto-scan-at-startup",
|
|
action="store_true",
|
|
default=False,
|
|
help="Enable automatic scanning when the program starts",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--history-turns",
|
|
type=int,
|
|
default=get_env_value("HISTORY_TURNS", 3, int),
|
|
help="Number of conversation history turns to include (default: from env or 3)",
|
|
)
|
|
|
|
# Search parameters
|
|
parser.add_argument(
|
|
"--top-k",
|
|
type=int,
|
|
default=get_env_value("TOP_K", 50, int),
|
|
help="Number of most similar results to return (default: from env or 50)",
|
|
)
|
|
parser.add_argument(
|
|
"--cosine-threshold",
|
|
type=float,
|
|
default=get_env_value("COSINE_THRESHOLD", 0.4, float),
|
|
help="Cosine similarity threshold (default: from env or 0.4)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--simulated-model-name",
|
|
type=str,
|
|
default=get_env_value(
|
|
"SIMULATED_MODEL_NAME", ollama_server_infos.LIGHTRAG_MODEL
|
|
),
|
|
help="Number of conversation history turns to include (default: from env or 3)",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name
|
|
|
|
return args
|
|
|
|
|
|
class DocumentManager:
|
|
"""Handles document operations and tracking"""
|
|
|
|
def __init__(
|
|
self,
|
|
input_dir: str,
|
|
supported_extensions: tuple = (".txt", ".md", ".pdf", ".docx", ".pptx", "xlsx"),
|
|
):
|
|
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_for_new_files(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 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}"):
|
|
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"
|
|
mix = "mix"
|
|
bypass = "bypass"
|
|
|
|
|
|
class OllamaMessage(BaseModel):
|
|
role: str
|
|
content: str
|
|
images: Optional[List[str]] = None
|
|
|
|
|
|
class OllamaChatRequest(BaseModel):
|
|
model: str = ollama_server_infos.LIGHTRAG_MODEL
|
|
messages: List[OllamaMessage]
|
|
stream: bool = True # Default to streaming mode
|
|
options: Optional[Dict[str, Any]] = None
|
|
system: Optional[str] = None
|
|
|
|
|
|
class OllamaChatResponse(BaseModel):
|
|
model: str
|
|
created_at: str
|
|
message: OllamaMessage
|
|
done: bool
|
|
|
|
|
|
class OllamaGenerateRequest(BaseModel):
|
|
model: str = ollama_server_infos.LIGHTRAG_MODEL
|
|
prompt: str
|
|
system: Optional[str] = None
|
|
stream: bool = False
|
|
options: Optional[Dict[str, Any]] = None
|
|
|
|
|
|
class OllamaGenerateResponse(BaseModel):
|
|
model: str
|
|
created_at: str
|
|
response: str
|
|
done: bool
|
|
context: Optional[List[int]]
|
|
total_duration: Optional[int]
|
|
load_duration: Optional[int]
|
|
prompt_eval_count: Optional[int]
|
|
prompt_eval_duration: Optional[int]
|
|
eval_count: Optional[int]
|
|
eval_duration: Optional[int]
|
|
|
|
|
|
class OllamaVersionResponse(BaseModel):
|
|
version: str
|
|
|
|
|
|
class OllamaModelDetails(BaseModel):
|
|
parent_model: str
|
|
format: str
|
|
family: str
|
|
families: List[str]
|
|
parameter_size: str
|
|
quantization_level: str
|
|
|
|
|
|
class OllamaModel(BaseModel):
|
|
name: str
|
|
model: str
|
|
size: int
|
|
digest: str
|
|
modified_at: str
|
|
details: OllamaModelDetails
|
|
|
|
|
|
class OllamaTagResponse(BaseModel):
|
|
models: List[OllamaModel]
|
|
|
|
|
|
class QueryRequest(BaseModel):
|
|
query: str
|
|
mode: SearchMode = SearchMode.hybrid
|
|
stream: bool = False
|
|
only_need_context: 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
|
|
|
|
|
|
def get_api_key_dependency(api_key: Optional[str]):
|
|
if not api_key:
|
|
# If no API key is configured, return a dummy dependency that always succeeds
|
|
async def no_auth():
|
|
return None
|
|
|
|
return no_auth
|
|
|
|
# If API key is configured, use proper authentication
|
|
api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
|
|
|
|
async def api_key_auth(api_key_header_value: str | None = Security(api_key_header)):
|
|
if not api_key_header_value:
|
|
raise HTTPException(
|
|
status_code=HTTP_403_FORBIDDEN, detail="API Key required"
|
|
)
|
|
if api_key_header_value != api_key:
|
|
raise HTTPException(
|
|
status_code=HTTP_403_FORBIDDEN, detail="Invalid API Key"
|
|
)
|
|
return api_key_header_value
|
|
|
|
return api_key_auth
|
|
|
|
|
|
def create_app(args):
|
|
# Verify that bindings are correctly setup
|
|
if args.llm_binding not in [
|
|
"lollms",
|
|
"ollama",
|
|
"openai",
|
|
"openai-ollama",
|
|
"azure_openai",
|
|
]:
|
|
raise Exception("llm binding not supported")
|
|
|
|
if args.embedding_binding not in ["lollms", "ollama", "openai", "azure_openai"]:
|
|
raise Exception("embedding binding not supported")
|
|
|
|
# Set default hosts if not provided
|
|
if args.llm_binding_host is None:
|
|
args.llm_binding_host = get_default_host(args.llm_binding)
|
|
|
|
if args.embedding_binding_host is None:
|
|
args.embedding_binding_host = get_default_host(args.embedding_binding)
|
|
|
|
# Add SSL validation
|
|
if args.ssl:
|
|
if not args.ssl_certfile or not args.ssl_keyfile:
|
|
raise Exception(
|
|
"SSL certificate and key files must be provided when SSL is enabled"
|
|
)
|
|
if not os.path.exists(args.ssl_certfile):
|
|
raise Exception(f"SSL certificate file not found: {args.ssl_certfile}")
|
|
if not os.path.exists(args.ssl_keyfile):
|
|
raise Exception(f"SSL key file not found: {args.ssl_keyfile}")
|
|
|
|
# Setup logging
|
|
logging.basicConfig(
|
|
format="%(levelname)s:%(message)s", level=getattr(logging, args.log_level)
|
|
)
|
|
|
|
# Check if API key is provided either through env var or args
|
|
api_key = os.getenv("LIGHTRAG_API_KEY") or args.key
|
|
|
|
# Initialize document manager
|
|
doc_manager = DocumentManager(args.input_dir)
|
|
|
|
@asynccontextmanager
|
|
async def lifespan(app: FastAPI):
|
|
"""Lifespan context manager for startup and shutdown events"""
|
|
# Startup logic
|
|
if args.auto_scan_at_startup:
|
|
try:
|
|
new_files = doc_manager.scan_directory_for_new_files()
|
|
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)}")
|
|
|
|
ASCIIColors.info(
|
|
f"Indexed {len(new_files)} documents from {args.input_dir}"
|
|
)
|
|
except Exception as e:
|
|
logging.error(f"Error during startup indexing: {str(e)}")
|
|
yield
|
|
# Cleanup logic (if needed)
|
|
pass
|
|
|
|
# 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=__api_version__,
|
|
openapi_tags=[{"name": "api"}],
|
|
lifespan=lifespan,
|
|
)
|
|
|
|
# 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)
|
|
if args.llm_binding == "lollms" or args.embedding_binding == "lollms":
|
|
from lightrag.llm.lollms import lollms_model_complete, lollms_embed
|
|
if args.llm_binding == "ollama" or args.embedding_binding == "ollama":
|
|
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
|
|
if args.llm_binding == "openai" or args.embedding_binding == "openai":
|
|
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
|
|
if args.llm_binding == "azure_openai" or args.embedding_binding == "azure_openai":
|
|
from lightrag.llm.azure_openai import (
|
|
azure_openai_complete_if_cache,
|
|
azure_openai_embed,
|
|
)
|
|
if args.llm_binding_host == "openai-ollama" or args.embedding_binding == "ollama":
|
|
from lightrag.llm.openai import openai_complete_if_cache
|
|
from lightrag.llm.ollama import ollama_embed
|
|
|
|
async def openai_alike_model_complete(
|
|
prompt,
|
|
system_prompt=None,
|
|
history_messages=[],
|
|
keyword_extraction=False,
|
|
**kwargs,
|
|
) -> str:
|
|
return await openai_complete_if_cache(
|
|
args.llm_model,
|
|
prompt,
|
|
system_prompt=system_prompt,
|
|
history_messages=history_messages,
|
|
base_url=args.llm_binding_host,
|
|
api_key=args.llm_binding_api_key,
|
|
**kwargs,
|
|
)
|
|
|
|
async def azure_openai_model_complete(
|
|
prompt,
|
|
system_prompt=None,
|
|
history_messages=[],
|
|
keyword_extraction=False,
|
|
**kwargs,
|
|
) -> str:
|
|
return await azure_openai_complete_if_cache(
|
|
args.llm_model,
|
|
prompt,
|
|
system_prompt=system_prompt,
|
|
history_messages=history_messages,
|
|
base_url=args.llm_binding_host,
|
|
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
|
api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview"),
|
|
**kwargs,
|
|
)
|
|
|
|
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,
|
|
api_key=args.embedding_binding_api_key,
|
|
)
|
|
if args.embedding_binding == "lollms"
|
|
else ollama_embed(
|
|
texts,
|
|
embed_model=args.embedding_model,
|
|
host=args.embedding_binding_host,
|
|
api_key=args.embedding_binding_api_key,
|
|
)
|
|
if args.embedding_binding == "ollama"
|
|
else azure_openai_embed(
|
|
texts,
|
|
model=args.embedding_model, # no host is used for openai,
|
|
api_key=args.embedding_binding_api_key,
|
|
)
|
|
if args.embedding_binding == "azure_openai"
|
|
else openai_embed(
|
|
texts,
|
|
model=args.embedding_model, # no host is used for openai,
|
|
api_key=args.embedding_binding_api_key,
|
|
),
|
|
)
|
|
|
|
# Initialize RAG
|
|
if args.llm_binding in ["lollms", "ollama", "openai-ollama"]:
|
|
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 openai_alike_model_complete,
|
|
llm_model_name=args.llm_model,
|
|
llm_model_max_async=args.max_async,
|
|
llm_model_max_token_size=args.max_tokens,
|
|
chunk_token_size=int(args.chunk_size),
|
|
chunk_overlap_token_size=int(args.chunk_overlap_size),
|
|
llm_model_kwargs={
|
|
"host": args.llm_binding_host,
|
|
"timeout": args.timeout,
|
|
"options": {"num_ctx": args.max_tokens},
|
|
"api_key": args.llm_binding_api_key,
|
|
}
|
|
if args.llm_binding == "lollms" or args.llm_binding == "ollama"
|
|
else {},
|
|
embedding_func=embedding_func,
|
|
kv_storage=ollama_server_infos.KV_STORAGE,
|
|
graph_storage=ollama_server_infos.GRAPH_STORAGE,
|
|
vector_storage=ollama_server_infos.VECTOR_STORAGE,
|
|
doc_status_storage=ollama_server_infos.DOC_STATUS_STORAGE,
|
|
vector_db_storage_cls_kwargs={
|
|
"cosine_better_than_threshold": args.cosine_threshold
|
|
},
|
|
enable_llm_cache_for_entity_extract=False, # set to True for debuging to reduce llm fee
|
|
embedding_cache_config={
|
|
"enabled": True,
|
|
"similarity_threshold": 0.95,
|
|
"use_llm_check": False,
|
|
},
|
|
)
|
|
else:
|
|
rag = LightRAG(
|
|
working_dir=args.working_dir,
|
|
llm_model_func=azure_openai_model_complete
|
|
if args.llm_binding == "azure_openai"
|
|
else openai_alike_model_complete,
|
|
chunk_token_size=int(args.chunk_size),
|
|
chunk_overlap_token_size=int(args.chunk_overlap_size),
|
|
llm_model_kwargs={
|
|
"timeout": args.timeout,
|
|
},
|
|
llm_model_name=args.llm_model,
|
|
llm_model_max_async=args.max_async,
|
|
llm_model_max_token_size=args.max_tokens,
|
|
embedding_func=embedding_func,
|
|
kv_storage=ollama_server_infos.KV_STORAGE,
|
|
graph_storage=ollama_server_infos.GRAPH_STORAGE,
|
|
vector_storage=ollama_server_infos.VECTOR_STORAGE,
|
|
doc_status_storage=ollama_server_infos.DOC_STATUS_STORAGE,
|
|
vector_db_storage_cls_kwargs={
|
|
"cosine_better_than_threshold": args.cosine_threshold
|
|
},
|
|
enable_llm_cache_for_entity_extract=False, # set to True for debuging to reduce llm fee
|
|
embedding_cache_config={
|
|
"enabled": True,
|
|
"similarity_threshold": 0.95,
|
|
"use_llm_check": False,
|
|
},
|
|
)
|
|
|
|
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" | ".docx" | ".pptx" | ".xlsx":
|
|
if not pm.is_installed("docling"):
|
|
pm.install("docling")
|
|
from docling.document_converter import DocumentConverter
|
|
|
|
converter = DocumentConverter()
|
|
result = converter.convert(file_path)
|
|
content = result.document.export_to_markdown()
|
|
|
|
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}")
|
|
|
|
@app.post("/documents/scan", dependencies=[Depends(optional_api_key)])
|
|
async def scan_for_new_documents(background_tasks: BackgroundTasks):
|
|
"""Trigger the scanning process"""
|
|
global scan_progress
|
|
|
|
with progress_lock:
|
|
if scan_progress["is_scanning"]:
|
|
return {"status": "already_scanning"}
|
|
|
|
scan_progress["is_scanning"] = True
|
|
scan_progress["indexed_count"] = 0
|
|
scan_progress["progress"] = 0
|
|
|
|
# Start the scanning process in the background
|
|
background_tasks.add_task(run_scanning_process)
|
|
|
|
return {"status": "scanning_started"}
|
|
|
|
async def run_scanning_process():
|
|
"""Background task to scan and index documents"""
|
|
global scan_progress
|
|
|
|
try:
|
|
new_files = doc_manager.scan_directory_for_new_files()
|
|
scan_progress["total_files"] = len(new_files)
|
|
|
|
for file_path in new_files:
|
|
try:
|
|
with progress_lock:
|
|
scan_progress["current_file"] = os.path.basename(file_path)
|
|
|
|
await index_file(file_path)
|
|
|
|
with progress_lock:
|
|
scan_progress["indexed_count"] += 1
|
|
scan_progress["progress"] = (
|
|
scan_progress["indexed_count"]
|
|
/ scan_progress["total_files"]
|
|
) * 100
|
|
|
|
except Exception as e:
|
|
logging.error(f"Error indexing file {file_path}: {str(e)}")
|
|
|
|
except Exception as e:
|
|
logging.error(f"Error during scanning process: {str(e)}")
|
|
finally:
|
|
with progress_lock:
|
|
scan_progress["is_scanning"] = False
|
|
|
|
@app.get("/documents/scan-progress")
|
|
async def get_scan_progress():
|
|
"""Get the current scanning progress"""
|
|
with progress_lock:
|
|
return scan_progress
|
|
|
|
@app.post("/documents/upload", dependencies=[Depends(optional_api_key)])
|
|
async def upload_to_input_dir(file: UploadFile = File(...)):
|
|
"""
|
|
Endpoint for uploading a file to the input directory and indexing it.
|
|
|
|
This API endpoint accepts a file through an HTTP POST request, checks if the
|
|
uploaded file is of a supported type, saves it in the specified input directory,
|
|
indexes it for retrieval, and returns a success status with relevant details.
|
|
|
|
Parameters:
|
|
file (UploadFile): The file to be uploaded. It must have an allowed extension as per
|
|
`doc_manager.supported_extensions`.
|
|
|
|
Returns:
|
|
dict: A dictionary containing the upload status ("success"),
|
|
a message detailing the operation result, and
|
|
the total number of indexed documents.
|
|
|
|
Raises:
|
|
HTTPException: If the file type is not supported, it raises a 400 Bad Request error.
|
|
If any other exception occurs during the file handling or indexing,
|
|
it raises a 500 Internal Server Error with details about the exception.
|
|
"""
|
|
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):
|
|
"""
|
|
Handle a POST request at the /query endpoint to process user queries using RAG capabilities.
|
|
|
|
Parameters:
|
|
request (QueryRequest): A Pydantic model containing the following fields:
|
|
- query (str): The text of the user's query.
|
|
- mode (ModeEnum): Optional. Specifies the mode of retrieval augmentation.
|
|
- stream (bool): Optional. Determines if the response should be streamed.
|
|
- only_need_context (bool): Optional. If true, returns only the context without further processing.
|
|
|
|
Returns:
|
|
QueryResponse: A Pydantic model containing the result of the query processing.
|
|
If a string is returned (e.g., cache hit), it's directly returned.
|
|
Otherwise, an async generator may be used to build the response.
|
|
|
|
Raises:
|
|
HTTPException: Raised when an error occurs during the request handling process,
|
|
with status code 500 and detail containing the exception message.
|
|
"""
|
|
try:
|
|
response = await rag.aquery(
|
|
request.query,
|
|
param=QueryParam(
|
|
mode=request.mode,
|
|
stream=request.stream,
|
|
only_need_context=request.only_need_context,
|
|
top_k=args.top_k,
|
|
),
|
|
)
|
|
|
|
# If response is a string (e.g. cache hit), return directly
|
|
if isinstance(response, str):
|
|
return QueryResponse(response=response)
|
|
|
|
# If it's an async generator, decide whether to stream based on stream parameter
|
|
if request.stream:
|
|
result = ""
|
|
async for chunk in response:
|
|
result += chunk
|
|
return QueryResponse(response=result)
|
|
else:
|
|
result = ""
|
|
async for chunk in response:
|
|
result += chunk
|
|
return QueryResponse(response=result)
|
|
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):
|
|
"""
|
|
This endpoint performs a retrieval-augmented generation (RAG) query and streams the response.
|
|
|
|
Args:
|
|
request (QueryRequest): The request object containing the query parameters.
|
|
optional_api_key (Optional[str], optional): An optional API key for authentication. Defaults to None.
|
|
|
|
Returns:
|
|
StreamingResponse: A streaming response containing the RAG query results.
|
|
"""
|
|
try:
|
|
response = await rag.aquery( # Use aquery instead of query, and add await
|
|
request.query,
|
|
param=QueryParam(
|
|
mode=request.mode,
|
|
stream=True,
|
|
only_need_context=request.only_need_context,
|
|
top_k=args.top_k,
|
|
),
|
|
)
|
|
|
|
from fastapi.responses import StreamingResponse
|
|
|
|
async def stream_generator():
|
|
if isinstance(response, str):
|
|
# If it's a string, send it all at once
|
|
yield f"{json.dumps({'response': response})}\n"
|
|
else:
|
|
# If it's an async generator, send chunks one by one
|
|
try:
|
|
async for chunk in response:
|
|
if chunk: # Only send non-empty content
|
|
yield f"{json.dumps({'response': chunk})}\n"
|
|
except Exception as e:
|
|
logging.error(f"Streaming error: {str(e)}")
|
|
yield f"{json.dumps({'error': str(e)})}\n"
|
|
|
|
return StreamingResponse(
|
|
stream_generator(),
|
|
media_type="application/x-ndjson",
|
|
headers={
|
|
"Cache-Control": "no-cache",
|
|
"Connection": "keep-alive",
|
|
"Content-Type": "application/x-ndjson",
|
|
"Access-Control-Allow-Origin": "*",
|
|
"Access-Control-Allow-Methods": "POST, OPTIONS",
|
|
"Access-Control-Allow-Headers": "Content-Type",
|
|
"X-Accel-Buffering": "no", # Disable Nginx buffering
|
|
},
|
|
)
|
|
except Exception as e:
|
|
trace_exception(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):
|
|
"""
|
|
Insert text into the Retrieval-Augmented Generation (RAG) system.
|
|
|
|
This endpoint allows you to insert text data into the RAG system for later retrieval and use in generating responses.
|
|
|
|
Args:
|
|
request (InsertTextRequest): The request body containing the text to be inserted.
|
|
|
|
Returns:
|
|
InsertResponse: A response object containing the status of the operation, a message, and the number of documents inserted.
|
|
"""
|
|
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" | ".docx" | ".pptx" | ".xlsx":
|
|
if not pm.is_installed("docling"):
|
|
pm.install("docling")
|
|
from docling.document_converter import DocumentConverter
|
|
|
|
# Create a temporary file to save the uploaded content
|
|
temp_path = Path("temp") / file.filename
|
|
temp_path.parent.mkdir(exist_ok=True)
|
|
|
|
# Save the uploaded file
|
|
with temp_path.open("wb") as f:
|
|
f.write(await file.read())
|
|
|
|
try:
|
|
converter = DocumentConverter()
|
|
result = converter.convert(str(temp_path))
|
|
content = result.document.export_to_markdown()
|
|
finally:
|
|
# Clean up the temporary file
|
|
temp_path.unlink()
|
|
|
|
# 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 # type: ignore
|
|
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():
|
|
"""
|
|
Clear all documents from the LightRAG system.
|
|
|
|
This endpoint deletes all text chunks, entities vector database, and relationships vector database,
|
|
effectively clearing all documents from the LightRAG system.
|
|
|
|
Returns:
|
|
InsertResponse: A response object containing the status, message, and the new document count (0 in this case).
|
|
"""
|
|
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))
|
|
|
|
# query all graph labels
|
|
@app.get("/graph/label/list")
|
|
async def get_graph_labels():
|
|
return await rag.get_graph_labels()
|
|
|
|
# query all graph
|
|
@app.get("/graphs")
|
|
async def get_graphs(label: str):
|
|
return await rag.get_graps(nodel_label=label, max_depth=100)
|
|
|
|
# Ollama compatible API endpoints
|
|
# -------------------------------------------------
|
|
@app.get("/api/version")
|
|
async def get_version():
|
|
"""Get Ollama version information"""
|
|
return OllamaVersionResponse(version="0.5.4")
|
|
|
|
@app.get("/api/tags")
|
|
async def get_tags():
|
|
"""Retrun available models acting as an Ollama server"""
|
|
return OllamaTagResponse(
|
|
models=[
|
|
{
|
|
"name": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"size": ollama_server_infos.LIGHTRAG_SIZE,
|
|
"digest": ollama_server_infos.LIGHTRAG_DIGEST,
|
|
"modified_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"details": {
|
|
"parent_model": "",
|
|
"format": "gguf",
|
|
"family": ollama_server_infos.LIGHTRAG_NAME,
|
|
"families": [ollama_server_infos.LIGHTRAG_NAME],
|
|
"parameter_size": "13B",
|
|
"quantization_level": "Q4_0",
|
|
},
|
|
}
|
|
]
|
|
)
|
|
|
|
def parse_query_mode(query: str) -> tuple[str, SearchMode]:
|
|
"""Parse query prefix to determine search mode
|
|
Returns tuple of (cleaned_query, search_mode)
|
|
"""
|
|
mode_map = {
|
|
"/local ": SearchMode.local,
|
|
"/global ": SearchMode.global_, # global_ is used because 'global' is a Python keyword
|
|
"/naive ": SearchMode.naive,
|
|
"/hybrid ": SearchMode.hybrid,
|
|
"/mix ": SearchMode.mix,
|
|
"/bypass ": SearchMode.bypass,
|
|
}
|
|
|
|
for prefix, mode in mode_map.items():
|
|
if query.startswith(prefix):
|
|
# After removing prefix an leading spaces
|
|
cleaned_query = query[len(prefix) :].lstrip()
|
|
return cleaned_query, mode
|
|
|
|
return query, SearchMode.hybrid
|
|
|
|
@app.post("/api/generate")
|
|
async def generate(raw_request: Request, request: OllamaGenerateRequest):
|
|
"""Handle generate completion requests acting as an Ollama model
|
|
For compatiblity purpuse, the request is not processed by LightRAG,
|
|
and will be handled by underlying LLM model.
|
|
"""
|
|
try:
|
|
query = request.prompt
|
|
start_time = time.time_ns()
|
|
prompt_tokens = estimate_tokens(query)
|
|
|
|
if request.system:
|
|
rag.llm_model_kwargs["system_prompt"] = request.system
|
|
|
|
if request.stream:
|
|
from fastapi.responses import StreamingResponse
|
|
|
|
response = await rag.llm_model_func(
|
|
query, stream=True, **rag.llm_model_kwargs
|
|
)
|
|
|
|
async def stream_generator():
|
|
try:
|
|
first_chunk_time = None
|
|
last_chunk_time = None
|
|
total_response = ""
|
|
|
|
# Ensure response is an async generator
|
|
if isinstance(response, str):
|
|
# If it's a string, send in two parts
|
|
first_chunk_time = time.time_ns()
|
|
last_chunk_time = first_chunk_time
|
|
total_response = response
|
|
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"response": response,
|
|
"done": False,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
|
|
completion_tokens = estimate_tokens(total_response)
|
|
total_time = last_chunk_time - start_time
|
|
prompt_eval_time = first_chunk_time - start_time
|
|
eval_time = last_chunk_time - first_chunk_time
|
|
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"done": True,
|
|
"total_duration": total_time,
|
|
"load_duration": 0,
|
|
"prompt_eval_count": prompt_tokens,
|
|
"prompt_eval_duration": prompt_eval_time,
|
|
"eval_count": completion_tokens,
|
|
"eval_duration": eval_time,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
else:
|
|
async for chunk in response:
|
|
if chunk:
|
|
if first_chunk_time is None:
|
|
first_chunk_time = time.time_ns()
|
|
|
|
last_chunk_time = time.time_ns()
|
|
|
|
total_response += chunk
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"response": chunk,
|
|
"done": False,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
|
|
completion_tokens = estimate_tokens(total_response)
|
|
total_time = last_chunk_time - start_time
|
|
prompt_eval_time = first_chunk_time - start_time
|
|
eval_time = last_chunk_time - first_chunk_time
|
|
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"done": True,
|
|
"total_duration": total_time,
|
|
"load_duration": 0,
|
|
"prompt_eval_count": prompt_tokens,
|
|
"prompt_eval_duration": prompt_eval_time,
|
|
"eval_count": completion_tokens,
|
|
"eval_duration": eval_time,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
return
|
|
|
|
except Exception as e:
|
|
logging.error(f"Error in stream_generator: {str(e)}")
|
|
raise
|
|
|
|
return StreamingResponse(
|
|
stream_generator(),
|
|
media_type="application/x-ndjson",
|
|
headers={
|
|
"Cache-Control": "no-cache",
|
|
"Connection": "keep-alive",
|
|
"Content-Type": "application/x-ndjson",
|
|
"Access-Control-Allow-Origin": "*",
|
|
"Access-Control-Allow-Methods": "POST, OPTIONS",
|
|
"Access-Control-Allow-Headers": "Content-Type",
|
|
},
|
|
)
|
|
else:
|
|
first_chunk_time = time.time_ns()
|
|
response_text = await rag.llm_model_func(
|
|
query, stream=False, **rag.llm_model_kwargs
|
|
)
|
|
last_chunk_time = time.time_ns()
|
|
|
|
if not response_text:
|
|
response_text = "No response generated"
|
|
|
|
completion_tokens = estimate_tokens(str(response_text))
|
|
total_time = last_chunk_time - start_time
|
|
prompt_eval_time = first_chunk_time - start_time
|
|
eval_time = last_chunk_time - first_chunk_time
|
|
|
|
return {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"response": str(response_text),
|
|
"done": True,
|
|
"total_duration": total_time,
|
|
"load_duration": 0,
|
|
"prompt_eval_count": prompt_tokens,
|
|
"prompt_eval_duration": prompt_eval_time,
|
|
"eval_count": completion_tokens,
|
|
"eval_duration": eval_time,
|
|
}
|
|
except Exception as e:
|
|
trace_exception(e)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post("/api/chat")
|
|
async def chat(raw_request: Request, request: OllamaChatRequest):
|
|
"""Process chat completion requests acting as an Ollama model
|
|
Routes user queries through LightRAG by selecting query mode based on prefix indicators.
|
|
Detects and forwards OpenWebUI session-related requests (for meta data generation task) directly to LLM.
|
|
"""
|
|
try:
|
|
# Get all messages
|
|
messages = request.messages
|
|
if not messages:
|
|
raise HTTPException(status_code=400, detail="No messages provided")
|
|
|
|
# Get the last message as query and previous messages as history
|
|
query = messages[-1].content
|
|
# Convert OllamaMessage objects to dictionaries
|
|
conversation_history = [
|
|
{"role": msg.role, "content": msg.content} for msg in messages[:-1]
|
|
]
|
|
|
|
# Check for query prefix
|
|
cleaned_query, mode = parse_query_mode(query)
|
|
|
|
start_time = time.time_ns()
|
|
prompt_tokens = estimate_tokens(cleaned_query)
|
|
|
|
param_dict = {
|
|
"mode": mode,
|
|
"stream": request.stream,
|
|
"only_need_context": False,
|
|
"conversation_history": conversation_history,
|
|
"top_k": args.top_k,
|
|
}
|
|
|
|
if args.history_turns is not None:
|
|
param_dict["history_turns"] = args.history_turns
|
|
|
|
query_param = QueryParam(**param_dict)
|
|
|
|
if request.stream:
|
|
from fastapi.responses import StreamingResponse
|
|
|
|
# Determine if the request is prefix with "/bypass"
|
|
if mode == SearchMode.bypass:
|
|
if request.system:
|
|
rag.llm_model_kwargs["system_prompt"] = request.system
|
|
response = await rag.llm_model_func(
|
|
cleaned_query,
|
|
stream=True,
|
|
history_messages=conversation_history,
|
|
**rag.llm_model_kwargs,
|
|
)
|
|
else:
|
|
response = await rag.aquery( # Need await to get async generator
|
|
cleaned_query, param=query_param
|
|
)
|
|
|
|
async def stream_generator():
|
|
try:
|
|
first_chunk_time = None
|
|
last_chunk_time = None
|
|
total_response = ""
|
|
|
|
# Ensure response is an async generator
|
|
if isinstance(response, str):
|
|
# If it's a string, send in two parts
|
|
first_chunk_time = time.time_ns()
|
|
last_chunk_time = first_chunk_time
|
|
total_response = response
|
|
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": response,
|
|
"images": None,
|
|
},
|
|
"done": False,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
|
|
completion_tokens = estimate_tokens(total_response)
|
|
total_time = last_chunk_time - start_time
|
|
prompt_eval_time = first_chunk_time - start_time
|
|
eval_time = last_chunk_time - first_chunk_time
|
|
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"done": True,
|
|
"total_duration": total_time,
|
|
"load_duration": 0,
|
|
"prompt_eval_count": prompt_tokens,
|
|
"prompt_eval_duration": prompt_eval_time,
|
|
"eval_count": completion_tokens,
|
|
"eval_duration": eval_time,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
else:
|
|
async for chunk in response:
|
|
if chunk:
|
|
if first_chunk_time is None:
|
|
first_chunk_time = time.time_ns()
|
|
|
|
last_chunk_time = time.time_ns()
|
|
|
|
total_response += chunk
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": chunk,
|
|
"images": None,
|
|
},
|
|
"done": False,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
|
|
completion_tokens = estimate_tokens(total_response)
|
|
total_time = last_chunk_time - start_time
|
|
prompt_eval_time = first_chunk_time - start_time
|
|
eval_time = last_chunk_time - first_chunk_time
|
|
|
|
data = {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"done": True,
|
|
"total_duration": total_time,
|
|
"load_duration": 0,
|
|
"prompt_eval_count": prompt_tokens,
|
|
"prompt_eval_duration": prompt_eval_time,
|
|
"eval_count": completion_tokens,
|
|
"eval_duration": eval_time,
|
|
}
|
|
yield f"{json.dumps(data, ensure_ascii=False)}\n"
|
|
return # Ensure the generator ends immediately after sending the completion marker
|
|
except Exception as e:
|
|
logging.error(f"Error in stream_generator: {str(e)}")
|
|
raise
|
|
|
|
return StreamingResponse(
|
|
stream_generator(),
|
|
media_type="application/x-ndjson",
|
|
headers={
|
|
"Cache-Control": "no-cache",
|
|
"Connection": "keep-alive",
|
|
"Content-Type": "application/x-ndjson",
|
|
"Access-Control-Allow-Origin": "*",
|
|
"Access-Control-Allow-Methods": "POST, OPTIONS",
|
|
"Access-Control-Allow-Headers": "Content-Type",
|
|
},
|
|
)
|
|
else:
|
|
first_chunk_time = time.time_ns()
|
|
|
|
# Determine if the request is prefix with "/bypass" or from Open WebUI's session title and session keyword generation task
|
|
match_result = re.search(
|
|
r"\n<chat_history>\nUSER:", cleaned_query, re.MULTILINE
|
|
)
|
|
if match_result or mode == SearchMode.bypass:
|
|
if request.system:
|
|
rag.llm_model_kwargs["system_prompt"] = request.system
|
|
|
|
response_text = await rag.llm_model_func(
|
|
cleaned_query,
|
|
stream=False,
|
|
history_messages=conversation_history,
|
|
**rag.llm_model_kwargs,
|
|
)
|
|
else:
|
|
response_text = await rag.aquery(cleaned_query, param=query_param)
|
|
|
|
last_chunk_time = time.time_ns()
|
|
|
|
if not response_text:
|
|
response_text = "No response generated"
|
|
|
|
completion_tokens = estimate_tokens(str(response_text))
|
|
total_time = last_chunk_time - start_time
|
|
prompt_eval_time = first_chunk_time - start_time
|
|
eval_time = last_chunk_time - first_chunk_time
|
|
|
|
return {
|
|
"model": ollama_server_infos.LIGHTRAG_MODEL,
|
|
"created_at": ollama_server_infos.LIGHTRAG_CREATED_AT,
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": str(response_text),
|
|
"images": None,
|
|
},
|
|
"done": True,
|
|
"total_duration": total_time,
|
|
"load_duration": 0,
|
|
"prompt_eval_count": prompt_tokens,
|
|
"prompt_eval_duration": prompt_eval_time,
|
|
"eval_count": completion_tokens,
|
|
"eval_duration": eval_time,
|
|
}
|
|
except Exception as e:
|
|
trace_exception(e)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.get("/documents", dependencies=[Depends(optional_api_key)])
|
|
async def documents():
|
|
"""Get current system status"""
|
|
return doc_manager.indexed_files
|
|
|
|
@app.get("/health", dependencies=[Depends(optional_api_key)])
|
|
async def get_status():
|
|
"""Get current system status"""
|
|
files = doc_manager.scan_directory()
|
|
return {
|
|
"status": "healthy",
|
|
"working_directory": str(args.working_dir),
|
|
"input_directory": str(args.input_dir),
|
|
"indexed_files": [str(f) for f in files],
|
|
"indexed_files_count": len(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,
|
|
"kv_storage": ollama_server_infos.KV_STORAGE,
|
|
"doc_status_storage": ollama_server_infos.DOC_STATUS_STORAGE,
|
|
"graph_storage": ollama_server_infos.GRAPH_STORAGE,
|
|
"vector_storage": ollama_server_infos.VECTOR_STORAGE,
|
|
},
|
|
}
|
|
|
|
# webui mount /webui/index.html
|
|
# app.mount(
|
|
# "/webui",
|
|
# StaticFiles(
|
|
# directory=Path(__file__).resolve().parent / "webui" / "static", html=True
|
|
# ),
|
|
# name="webui_static",
|
|
# )
|
|
|
|
# Serve the static files
|
|
static_dir = Path(__file__).parent / "static"
|
|
static_dir.mkdir(exist_ok=True)
|
|
app.mount("/", StaticFiles(directory=static_dir, html=True), name="static")
|
|
|
|
return app
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
import uvicorn
|
|
|
|
app = create_app(args)
|
|
display_splash_screen(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()
|