mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-07-03 23:19:22 +00:00
925 lines
32 KiB
Python
925 lines
32 KiB
Python
from fastapi import FastAPI, HTTPException, File, UploadFile, Form, Request
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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 json
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import time
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import re
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from typing import List, Dict, Any, Optional
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import openai_complete_if_cache, ollama_embedding
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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
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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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from dotenv import load_dotenv
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load_dotenv()
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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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# Constants for model information
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LIGHTRAG_NAME = "lightrag"
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LIGHTRAG_TAG = "latest"
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LIGHTRAG_MODEL = "lightrag:latest"
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LIGHTRAG_SIZE = 7365960935
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LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z"
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LIGHTRAG_DIGEST = "sha256:lightrag"
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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"deepseek-chat",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key=os.getenv("DEEPSEEK_API_KEY"),
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base_url=os.getenv("DEEPSEEK_ENDPOINT"),
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**kwargs,
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)
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def get_default_host(binding_type: str) -> str:
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default_hosts = {
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"ollama": "http://m4.lan.znipower.com: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": os.getenv("DEEPSEEK_ENDPOINT"),
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}
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return default_hosts.get(
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binding_type, "http://localhost:11434"
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) # fallback to ollama if unknown
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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", action="store_true", 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" # Using global_ because global is a Python reserved keyword, but enum value will be converted to string "global"
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hybrid = "hybrid"
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mix = "mix"
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# Ollama API compatible models
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class OllamaMessage(BaseModel):
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role: str
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content: str
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images: Optional[List[str]] = None
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class OllamaChatRequest(BaseModel):
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model: str = LIGHTRAG_MODEL
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messages: List[OllamaMessage]
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stream: bool = True # Default to streaming mode
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options: Optional[Dict[str, Any]] = None
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class OllamaChatResponse(BaseModel):
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model: str
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created_at: str
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message: OllamaMessage
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done: bool
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class OllamaVersionResponse(BaseModel):
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version: str
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class OllamaModelDetails(BaseModel):
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parent_model: str
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format: str
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family: str
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families: List[str]
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parameter_size: str
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quantization_level: str
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class OllamaModel(BaseModel):
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name: str
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model: str
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size: int
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digest: str
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modified_at: str
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details: OllamaModelDetails
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class OllamaTagResponse(BaseModel):
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models: List[OllamaModel]
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# Original LightRAG models
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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(
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"SSL certificate and key files must be provided when SSL is enabled"
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)
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if not os.path.exists(args.ssl_certfile):
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raise Exception(f"SSL certificate file not found: {args.ssl_certfile}")
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if not os.path.exists(args.ssl_keyfile):
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raise Exception(f"SSL key file not found: {args.ssl_keyfile}")
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# 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=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=8192,
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func=lambda texts: ollama_embedding(
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texts,
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embed_model="bge-m3:latest",
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host="http://m4.lan.znipower.com:11434",
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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 response is a string (e.g. cache hit), return directly
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if isinstance(response, str):
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return QueryResponse(response=response)
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# If it's an async generator, decide whether to stream based on stream parameter
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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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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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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 = await rag.aquery( # Use aquery instead of query, and add await
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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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from fastapi.responses import StreamingResponse
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async def stream_generator():
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if isinstance(response, str):
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# If it's a string, send it all at once
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yield f"{json.dumps({'response': response})}\n"
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else:
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|
# 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:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post(
|
|
"/documents/text",
|
|
response_model=InsertResponse,
|
|
dependencies=[Depends(optional_api_key)],
|
|
)
|
|
async def insert_text(request: InsertTextRequest):
|
|
try:
|
|
await rag.ainsert(request.text)
|
|
return InsertResponse(
|
|
status="success",
|
|
message="Text successfully inserted",
|
|
document_count=1,
|
|
)
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post(
|
|
"/documents/file",
|
|
response_model=InsertResponse,
|
|
dependencies=[Depends(optional_api_key)],
|
|
)
|
|
async def insert_file(file: UploadFile = File(...), description: str = Form(None)):
|
|
try:
|
|
content = await file.read()
|
|
|
|
if file.filename.endswith((".txt", ".md")):
|
|
text = content.decode("utf-8")
|
|
await rag.ainsert(text)
|
|
else:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="Unsupported file type. Only .txt and .md files are supported",
|
|
)
|
|
|
|
return InsertResponse(
|
|
status="success",
|
|
message=f"File '{file.filename}' successfully inserted",
|
|
document_count=1,
|
|
)
|
|
except UnicodeDecodeError:
|
|
raise HTTPException(status_code=400, detail="File encoding not supported")
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post(
|
|
"/documents/batch",
|
|
response_model=InsertResponse,
|
|
dependencies=[Depends(optional_api_key)],
|
|
)
|
|
async def insert_batch(files: List[UploadFile] = File(...)):
|
|
try:
|
|
inserted_count = 0
|
|
failed_files = []
|
|
|
|
for file in files:
|
|
try:
|
|
content = await file.read()
|
|
if file.filename.endswith((".txt", ".md")):
|
|
text = content.decode("utf-8")
|
|
await rag.ainsert(text)
|
|
inserted_count += 1
|
|
else:
|
|
failed_files.append(f"{file.filename} (unsupported type)")
|
|
except Exception as e:
|
|
failed_files.append(f"{file.filename} ({str(e)})")
|
|
|
|
status_message = f"Successfully inserted {inserted_count} documents"
|
|
if failed_files:
|
|
status_message += f". Failed files: {', '.join(failed_files)}"
|
|
|
|
return InsertResponse(
|
|
status="success" if inserted_count > 0 else "partial_success",
|
|
message=status_message,
|
|
document_count=len(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))
|
|
|
|
# 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():
|
|
"""Get available models"""
|
|
return OllamaTagResponse(
|
|
models=[
|
|
{
|
|
"name": LIGHTRAG_MODEL,
|
|
"model": LIGHTRAG_MODEL,
|
|
"size": LIGHTRAG_SIZE,
|
|
"digest": LIGHTRAG_DIGEST,
|
|
"modified_at": LIGHTRAG_CREATED_AT,
|
|
"details": {
|
|
"parent_model": "",
|
|
"format": "gguf",
|
|
"family": LIGHTRAG_NAME,
|
|
"families": [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,
|
|
}
|
|
|
|
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/chat")
|
|
async def chat(raw_request: Request, request: OllamaChatRequest):
|
|
"""Handle chat completion requests"""
|
|
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
|
|
query = messages[-1].content
|
|
|
|
# 解析查询模式
|
|
cleaned_query, mode = parse_query_mode(query)
|
|
|
|
# 开始计时
|
|
start_time = time.time_ns()
|
|
|
|
# 计算输入token数量
|
|
prompt_tokens = estimate_tokens(cleaned_query)
|
|
|
|
# 调用RAG进行查询
|
|
query_param = QueryParam(
|
|
mode=mode, stream=request.stream, only_need_context=False
|
|
)
|
|
|
|
if request.stream:
|
|
from fastapi.responses import StreamingResponse
|
|
|
|
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": LIGHTRAG_MODEL,
|
|
"created_at": 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": LIGHTRAG_MODEL,
|
|
"created_at": 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": LIGHTRAG_MODEL,
|
|
"created_at": 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": LIGHTRAG_MODEL,
|
|
"created_at": 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()
|
|
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": LIGHTRAG_MODEL,
|
|
"created_at": 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:
|
|
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()
|