Merge pull request #592 from danielaskdd/yangdx
Add Ollama compatible API server
This commit is contained in:
zrguo
GitHub
@@ -716,7 +716,7 @@ Output the results in the following structure:
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```
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</details>
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### Batch Eval
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### Batch Eval
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To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
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<details>
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@@ -767,6 +767,7 @@ Output your evaluation in the following JSON format:
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</details>
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### Overall Performance Table
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| | **Agriculture** | | **CS** | | **Legal** | | **Mix** | |
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|----------------------|-------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
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| | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** |
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924
lightrag/api/lightrag_ollama.py
Normal file
924
lightrag/api/lightrag_ollama.py
Normal file
@@ -0,0 +1,924 @@
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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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|
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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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|
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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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|
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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}")
|
||||
except Exception as e:
|
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trace_exception(e)
|
||||
logging.error(f"Error indexing file {file_path}: {str(e)}")
|
||||
|
||||
logging.info(f"Indexed {len(new_files)} documents from {args.input_dir}")
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"Error during startup indexing: {str(e)}")
|
||||
|
||||
@app.post("/documents/scan", dependencies=[Depends(optional_api_key)])
|
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async def scan_for_new_documents():
|
||||
"""Manually trigger scanning for new documents"""
|
||||
try:
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new_files = doc_manager.scan_directory()
|
||||
indexed_count = 0
|
||||
|
||||
for file_path in new_files:
|
||||
try:
|
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with open(file_path, "r", encoding="utf-8") as f:
|
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content = f.read()
|
||||
await rag.ainsert(content)
|
||||
doc_manager.mark_as_indexed(file_path)
|
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indexed_count += 1
|
||||
except Exception as e:
|
||||
logging.error(f"Error indexing file {file_path}: {str(e)}")
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"indexed_count": indexed_count,
|
||||
"total_documents": len(doc_manager.indexed_files),
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/documents/upload", dependencies=[Depends(optional_api_key)])
|
||||
async def upload_to_input_dir(file: UploadFile = File(...)):
|
||||
"""Upload a file to the input directory"""
|
||||
try:
|
||||
if not doc_manager.is_supported_file(file.filename):
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}",
|
||||
)
|
||||
|
||||
file_path = doc_manager.input_dir / file.filename
|
||||
with open(file_path, "wb") as buffer:
|
||||
shutil.copyfileobj(file.file, buffer)
|
||||
|
||||
# Immediately index the uploaded file
|
||||
with open(file_path, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
await rag.ainsert(content)
|
||||
doc_manager.mark_as_indexed(file_path)
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"message": f"File uploaded and indexed: {file.filename}",
|
||||
"total_documents": len(doc_manager.indexed_files),
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post(
|
||||
"/query", response_model=QueryResponse, dependencies=[Depends(optional_api_key)]
|
||||
)
|
||||
async def query_text(request: QueryRequest):
|
||||
try:
|
||||
response = await rag.aquery(
|
||||
request.query,
|
||||
param=QueryParam(
|
||||
mode=request.mode,
|
||||
stream=request.stream,
|
||||
only_need_context=request.only_need_context,
|
||||
),
|
||||
)
|
||||
|
||||
# If 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:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/query/stream", dependencies=[Depends(optional_api_key)])
|
||||
async def query_text_stream(request: QueryRequest):
|
||||
try:
|
||||
response = 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,
|
||||
),
|
||||
)
|
||||
|
||||
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:
|
||||
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()
|
||||
@@ -1,7 +1,6 @@
|
||||
aioboto3
|
||||
ascii_colors
|
||||
fastapi
|
||||
lightrag-hku
|
||||
nano_vectordb
|
||||
nest_asyncio
|
||||
numpy
|
||||
|
||||
1
setup.py
1
setup.py
@@ -101,6 +101,7 @@ setuptools.setup(
|
||||
entry_points={
|
||||
"console_scripts": [
|
||||
"lightrag-server=lightrag.api.lightrag_server:main [api]",
|
||||
"lightrag-ollama=lightrag.api.lightrag_ollama:main [api]",
|
||||
],
|
||||
},
|
||||
)
|
||||
|
||||
3
start-server.sh
Executable file
3
start-server.sh
Executable file
@@ -0,0 +1,3 @@
|
||||
. venv/bin/activate
|
||||
|
||||
lightrag-ollama --llm-binding openai --llm-model deepseek-chat --embedding-model "bge-m3:latest" --embedding-dim 1024
|
||||
572
test_lightrag_ollama_chat.py
Normal file
572
test_lightrag_ollama_chat.py
Normal file
@@ -0,0 +1,572 @@
|
||||
"""
|
||||
LightRAG Ollama Compatibility Interface Test Script
|
||||
|
||||
This script tests the LightRAG's Ollama compatibility interface, including:
|
||||
1. Basic functionality tests (streaming and non-streaming responses)
|
||||
2. Query mode tests (local, global, naive, hybrid)
|
||||
3. Error handling tests (including streaming and non-streaming scenarios)
|
||||
|
||||
All responses use the JSON Lines format, complying with the Ollama API specification.
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
import argparse
|
||||
import time
|
||||
from typing import Dict, Any, Optional, List, Callable
|
||||
from dataclasses import dataclass, asdict
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
class OutputControl:
|
||||
"""Output control class, manages the verbosity of test output"""
|
||||
|
||||
_verbose: bool = False
|
||||
|
||||
@classmethod
|
||||
def set_verbose(cls, verbose: bool) -> None:
|
||||
cls._verbose = verbose
|
||||
|
||||
@classmethod
|
||||
def is_verbose(cls) -> bool:
|
||||
return cls._verbose
|
||||
|
||||
|
||||
@dataclass
|
||||
class TestResult:
|
||||
"""Test result data class"""
|
||||
|
||||
name: str
|
||||
success: bool
|
||||
duration: float
|
||||
error: Optional[str] = None
|
||||
timestamp: str = ""
|
||||
|
||||
def __post_init__(self):
|
||||
if not self.timestamp:
|
||||
self.timestamp = datetime.now().isoformat()
|
||||
|
||||
|
||||
class TestStats:
|
||||
"""Test statistics"""
|
||||
|
||||
def __init__(self):
|
||||
self.results: List[TestResult] = []
|
||||
self.start_time = datetime.now()
|
||||
|
||||
def add_result(self, result: TestResult):
|
||||
self.results.append(result)
|
||||
|
||||
def export_results(self, path: str = "test_results.json"):
|
||||
"""Export test results to a JSON file
|
||||
Args:
|
||||
path: Output file path
|
||||
"""
|
||||
results_data = {
|
||||
"start_time": self.start_time.isoformat(),
|
||||
"end_time": datetime.now().isoformat(),
|
||||
"results": [asdict(r) for r in self.results],
|
||||
"summary": {
|
||||
"total": len(self.results),
|
||||
"passed": sum(1 for r in self.results if r.success),
|
||||
"failed": sum(1 for r in self.results if not r.success),
|
||||
"total_duration": sum(r.duration for r in self.results),
|
||||
},
|
||||
}
|
||||
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
json.dump(results_data, f, ensure_ascii=False, indent=2)
|
||||
print(f"\nTest results saved to: {path}")
|
||||
|
||||
def print_summary(self):
|
||||
total = len(self.results)
|
||||
passed = sum(1 for r in self.results if r.success)
|
||||
failed = total - passed
|
||||
duration = sum(r.duration for r in self.results)
|
||||
|
||||
print("\n=== Test Summary ===")
|
||||
print(f"Start time: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print(f"Total duration: {duration:.2f} seconds")
|
||||
print(f"Total tests: {total}")
|
||||
print(f"Passed: {passed}")
|
||||
print(f"Failed: {failed}")
|
||||
|
||||
if failed > 0:
|
||||
print("\nFailed tests:")
|
||||
for result in self.results:
|
||||
if not result.success:
|
||||
print(f"- {result.name}: {result.error}")
|
||||
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
"server": {
|
||||
"host": "localhost",
|
||||
"port": 9621,
|
||||
"model": "lightrag:latest",
|
||||
"timeout": 30,
|
||||
"max_retries": 3,
|
||||
"retry_delay": 1,
|
||||
},
|
||||
"test_cases": {"basic": {"query": "唐僧有几个徒弟"}},
|
||||
}
|
||||
|
||||
|
||||
def make_request(
|
||||
url: str, data: Dict[str, Any], stream: bool = False
|
||||
) -> requests.Response:
|
||||
"""Send an HTTP request with retry mechanism
|
||||
Args:
|
||||
url: Request URL
|
||||
data: Request data
|
||||
stream: Whether to use streaming response
|
||||
Returns:
|
||||
requests.Response: Response object
|
||||
|
||||
Raises:
|
||||
requests.exceptions.RequestException: Request failed after all retries
|
||||
"""
|
||||
server_config = CONFIG["server"]
|
||||
max_retries = server_config["max_retries"]
|
||||
retry_delay = server_config["retry_delay"]
|
||||
timeout = server_config["timeout"]
|
||||
|
||||
for attempt in range(max_retries):
|
||||
try:
|
||||
response = requests.post(url, json=data, stream=stream, timeout=timeout)
|
||||
return response
|
||||
except requests.exceptions.RequestException as e:
|
||||
if attempt == max_retries - 1: # Last retry
|
||||
raise
|
||||
print(f"\nRequest failed, retrying in {retry_delay} seconds: {str(e)}")
|
||||
time.sleep(retry_delay)
|
||||
|
||||
|
||||
def load_config() -> Dict[str, Any]:
|
||||
"""Load configuration file
|
||||
|
||||
First try to load from config.json in the current directory,
|
||||
if it doesn't exist, use the default configuration
|
||||
Returns:
|
||||
Configuration dictionary
|
||||
"""
|
||||
config_path = Path("config.json")
|
||||
if config_path.exists():
|
||||
with open(config_path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
return DEFAULT_CONFIG
|
||||
|
||||
|
||||
def print_json_response(data: Dict[str, Any], title: str = "", indent: int = 2) -> None:
|
||||
"""Format and print JSON response data
|
||||
Args:
|
||||
data: Data dictionary to print
|
||||
title: Title to print
|
||||
indent: Number of spaces for JSON indentation
|
||||
"""
|
||||
if OutputControl.is_verbose():
|
||||
if title:
|
||||
print(f"\n=== {title} ===")
|
||||
print(json.dumps(data, ensure_ascii=False, indent=indent))
|
||||
|
||||
|
||||
# Global configuration
|
||||
CONFIG = load_config()
|
||||
|
||||
|
||||
def get_base_url() -> str:
|
||||
"""Return the base URL"""
|
||||
server = CONFIG["server"]
|
||||
return f"http://{server['host']}:{server['port']}/api/chat"
|
||||
|
||||
|
||||
def create_request_data(
|
||||
content: str, stream: bool = False, model: str = None
|
||||
) -> Dict[str, Any]:
|
||||
"""Create basic request data
|
||||
Args:
|
||||
content: User message content
|
||||
stream: Whether to use streaming response
|
||||
model: Model name
|
||||
Returns:
|
||||
Dictionary containing complete request data
|
||||
"""
|
||||
return {
|
||||
"model": model or CONFIG["server"]["model"],
|
||||
"messages": [{"role": "user", "content": content}],
|
||||
"stream": stream,
|
||||
}
|
||||
|
||||
|
||||
# Global test statistics
|
||||
STATS = TestStats()
|
||||
|
||||
|
||||
def run_test(func: Callable, name: str) -> None:
|
||||
"""Run a test and record the results
|
||||
Args:
|
||||
func: Test function
|
||||
name: Test name
|
||||
"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
func()
|
||||
duration = time.time() - start_time
|
||||
STATS.add_result(TestResult(name, True, duration))
|
||||
except Exception as e:
|
||||
duration = time.time() - start_time
|
||||
STATS.add_result(TestResult(name, False, duration, str(e)))
|
||||
raise
|
||||
|
||||
|
||||
def test_non_stream_chat():
|
||||
"""Test non-streaming call to /api/chat endpoint"""
|
||||
url = get_base_url()
|
||||
data = create_request_data(CONFIG["test_cases"]["basic"]["query"], stream=False)
|
||||
|
||||
# Send request
|
||||
response = make_request(url, data)
|
||||
|
||||
# Print response
|
||||
if OutputControl.is_verbose():
|
||||
print("\n=== Non-streaming call response ===")
|
||||
response_json = response.json()
|
||||
|
||||
# Print response content
|
||||
print_json_response(
|
||||
{"model": response_json["model"], "message": response_json["message"]},
|
||||
"Response content",
|
||||
)
|
||||
|
||||
|
||||
def test_stream_chat():
|
||||
"""Test streaming call to /api/chat endpoint
|
||||
|
||||
Use JSON Lines format to process streaming responses, each line is a complete JSON object.
|
||||
Response format:
|
||||
{
|
||||
"model": "lightrag:latest",
|
||||
"created_at": "2024-01-15T00:00:00Z",
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "Partial response content",
|
||||
"images": null
|
||||
},
|
||||
"done": false
|
||||
}
|
||||
|
||||
The last message will contain performance statistics, with done set to true.
|
||||
"""
|
||||
url = get_base_url()
|
||||
data = create_request_data(CONFIG["test_cases"]["basic"]["query"], stream=True)
|
||||
|
||||
# Send request and get streaming response
|
||||
response = make_request(url, data, stream=True)
|
||||
|
||||
if OutputControl.is_verbose():
|
||||
print("\n=== Streaming call response ===")
|
||||
output_buffer = []
|
||||
try:
|
||||
for line in response.iter_lines():
|
||||
if line: # Skip empty lines
|
||||
try:
|
||||
# Decode and parse JSON
|
||||
data = json.loads(line.decode("utf-8"))
|
||||
if data.get("done", True): # If it's the completion marker
|
||||
if (
|
||||
"total_duration" in data
|
||||
): # Final performance statistics message
|
||||
# print_json_response(data, "Performance statistics")
|
||||
break
|
||||
else: # Normal content message
|
||||
message = data.get("message", {})
|
||||
content = message.get("content", "")
|
||||
if content: # Only collect non-empty content
|
||||
output_buffer.append(content)
|
||||
print(
|
||||
content, end="", flush=True
|
||||
) # Print content in real-time
|
||||
except json.JSONDecodeError:
|
||||
print("Error decoding JSON from response line")
|
||||
finally:
|
||||
response.close() # Ensure the response connection is closed
|
||||
|
||||
# Print a newline
|
||||
print()
|
||||
|
||||
|
||||
def test_query_modes():
|
||||
"""Test different query mode prefixes
|
||||
|
||||
Supported query modes:
|
||||
- /local: Local retrieval mode, searches only in highly relevant documents
|
||||
- /global: Global retrieval mode, searches across all documents
|
||||
- /naive: Naive mode, does not use any optimization strategies
|
||||
- /hybrid: Hybrid mode (default), combines multiple strategies
|
||||
- /mix: Mix mode
|
||||
|
||||
Each mode will return responses in the same format, but with different retrieval strategies.
|
||||
"""
|
||||
url = get_base_url()
|
||||
modes = ["local", "global", "naive", "hybrid", "mix"]
|
||||
|
||||
for mode in modes:
|
||||
if OutputControl.is_verbose():
|
||||
print(f"\n=== Testing /{mode} mode ===")
|
||||
data = create_request_data(
|
||||
f"/{mode} {CONFIG['test_cases']['basic']['query']}", stream=False
|
||||
)
|
||||
|
||||
# Send request
|
||||
response = make_request(url, data)
|
||||
response_json = response.json()
|
||||
|
||||
# Print response content
|
||||
print_json_response(
|
||||
{"model": response_json["model"], "message": response_json["message"]}
|
||||
)
|
||||
|
||||
|
||||
def create_error_test_data(error_type: str) -> Dict[str, Any]:
|
||||
"""Create request data for error testing
|
||||
Args:
|
||||
error_type: Error type, supported:
|
||||
- empty_messages: Empty message list
|
||||
- invalid_role: Invalid role field
|
||||
- missing_content: Missing content field
|
||||
|
||||
Returns:
|
||||
Request dictionary containing error data
|
||||
"""
|
||||
error_data = {
|
||||
"empty_messages": {"model": "lightrag:latest", "messages": [], "stream": True},
|
||||
"invalid_role": {
|
||||
"model": "lightrag:latest",
|
||||
"messages": [{"invalid_role": "user", "content": "Test message"}],
|
||||
"stream": True,
|
||||
},
|
||||
"missing_content": {
|
||||
"model": "lightrag:latest",
|
||||
"messages": [{"role": "user"}],
|
||||
"stream": True,
|
||||
},
|
||||
}
|
||||
return error_data.get(error_type, error_data["empty_messages"])
|
||||
|
||||
|
||||
def test_stream_error_handling():
|
||||
"""Test error handling for streaming responses
|
||||
|
||||
Test scenarios:
|
||||
1. Empty message list
|
||||
2. Message format error (missing required fields)
|
||||
|
||||
Error responses should be returned immediately without establishing a streaming connection.
|
||||
The status code should be 4xx, and detailed error information should be returned.
|
||||
"""
|
||||
url = get_base_url()
|
||||
|
||||
if OutputControl.is_verbose():
|
||||
print("\n=== Testing streaming response error handling ===")
|
||||
|
||||
# Test empty message list
|
||||
if OutputControl.is_verbose():
|
||||
print("\n--- Testing empty message list (streaming) ---")
|
||||
data = create_error_test_data("empty_messages")
|
||||
response = make_request(url, data, stream=True)
|
||||
print(f"Status code: {response.status_code}")
|
||||
if response.status_code != 200:
|
||||
print_json_response(response.json(), "Error message")
|
||||
response.close()
|
||||
|
||||
# Test invalid role field
|
||||
if OutputControl.is_verbose():
|
||||
print("\n--- Testing invalid role field (streaming) ---")
|
||||
data = create_error_test_data("invalid_role")
|
||||
response = make_request(url, data, stream=True)
|
||||
print(f"Status code: {response.status_code}")
|
||||
if response.status_code != 200:
|
||||
print_json_response(response.json(), "Error message")
|
||||
response.close()
|
||||
|
||||
# Test missing content field
|
||||
if OutputControl.is_verbose():
|
||||
print("\n--- Testing missing content field (streaming) ---")
|
||||
data = create_error_test_data("missing_content")
|
||||
response = make_request(url, data, stream=True)
|
||||
print(f"Status code: {response.status_code}")
|
||||
if response.status_code != 200:
|
||||
print_json_response(response.json(), "Error message")
|
||||
response.close()
|
||||
|
||||
|
||||
def test_error_handling():
|
||||
"""Test error handling for non-streaming responses
|
||||
|
||||
Test scenarios:
|
||||
1. Empty message list
|
||||
2. Message format error (missing required fields)
|
||||
|
||||
Error response format:
|
||||
{
|
||||
"detail": "Error description"
|
||||
}
|
||||
|
||||
All errors should return appropriate HTTP status codes and clear error messages.
|
||||
"""
|
||||
url = get_base_url()
|
||||
|
||||
if OutputControl.is_verbose():
|
||||
print("\n=== Testing error handling ===")
|
||||
|
||||
# Test empty message list
|
||||
if OutputControl.is_verbose():
|
||||
print("\n--- Testing empty message list ---")
|
||||
data = create_error_test_data("empty_messages")
|
||||
data["stream"] = False # Change to non-streaming mode
|
||||
response = make_request(url, data)
|
||||
print(f"Status code: {response.status_code}")
|
||||
print_json_response(response.json(), "Error message")
|
||||
|
||||
# Test invalid role field
|
||||
if OutputControl.is_verbose():
|
||||
print("\n--- Testing invalid role field ---")
|
||||
data = create_error_test_data("invalid_role")
|
||||
data["stream"] = False # Change to non-streaming mode
|
||||
response = make_request(url, data)
|
||||
print(f"Status code: {response.status_code}")
|
||||
print_json_response(response.json(), "Error message")
|
||||
|
||||
# Test missing content field
|
||||
if OutputControl.is_verbose():
|
||||
print("\n--- Testing missing content field ---")
|
||||
data = create_error_test_data("missing_content")
|
||||
data["stream"] = False # Change to non-streaming mode
|
||||
response = make_request(url, data)
|
||||
print(f"Status code: {response.status_code}")
|
||||
print_json_response(response.json(), "Error message")
|
||||
|
||||
|
||||
def get_test_cases() -> Dict[str, Callable]:
|
||||
"""Get all available test cases
|
||||
Returns:
|
||||
A dictionary mapping test names to test functions
|
||||
"""
|
||||
return {
|
||||
"non_stream": test_non_stream_chat,
|
||||
"stream": test_stream_chat,
|
||||
"modes": test_query_modes,
|
||||
"errors": test_error_handling,
|
||||
"stream_errors": test_stream_error_handling,
|
||||
}
|
||||
|
||||
|
||||
def create_default_config():
|
||||
"""Create a default configuration file"""
|
||||
config_path = Path("config.json")
|
||||
if not config_path.exists():
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
json.dump(DEFAULT_CONFIG, f, ensure_ascii=False, indent=2)
|
||||
print(f"Default configuration file created: {config_path}")
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command line arguments"""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="LightRAG Ollama Compatibility Interface Testing",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Configuration file (config.json):
|
||||
{
|
||||
"server": {
|
||||
"host": "localhost", # Server address
|
||||
"port": 9621, # Server port
|
||||
"model": "lightrag:latest" # Default model name
|
||||
},
|
||||
"test_cases": {
|
||||
"basic": {
|
||||
"query": "Test query", # Basic query text
|
||||
"stream_query": "Stream query" # Stream query text
|
||||
}
|
||||
}
|
||||
}
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-q",
|
||||
"--quiet",
|
||||
action="store_true",
|
||||
help="Silent mode, only display test result summary",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-a",
|
||||
"--ask",
|
||||
type=str,
|
||||
help="Specify query content, which will override the query settings in the configuration file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--init-config", action="store_true", help="Create default configuration file"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
type=str,
|
||||
default="",
|
||||
help="Test result output file path, default is not to output to a file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tests",
|
||||
nargs="+",
|
||||
choices=list(get_test_cases().keys()) + ["all"],
|
||||
default=["all"],
|
||||
help="Test cases to run, options: %(choices)s. Use 'all' to run all tests",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parse_args()
|
||||
|
||||
# Set output mode
|
||||
OutputControl.set_verbose(not args.quiet)
|
||||
|
||||
# If query content is specified, update the configuration
|
||||
if args.ask:
|
||||
CONFIG["test_cases"]["basic"]["query"] = args.ask
|
||||
|
||||
# If specified to create a configuration file
|
||||
if args.init_config:
|
||||
create_default_config()
|
||||
exit(0)
|
||||
|
||||
test_cases = get_test_cases()
|
||||
|
||||
try:
|
||||
if "all" in args.tests:
|
||||
# Run all tests
|
||||
if OutputControl.is_verbose():
|
||||
print("\n【Basic Functionality Tests】")
|
||||
run_test(test_non_stream_chat, "Non-streaming Call Test")
|
||||
run_test(test_stream_chat, "Streaming Call Test")
|
||||
|
||||
if OutputControl.is_verbose():
|
||||
print("\n【Query Mode Tests】")
|
||||
run_test(test_query_modes, "Query Mode Test")
|
||||
|
||||
if OutputControl.is_verbose():
|
||||
print("\n【Error Handling Tests】")
|
||||
run_test(test_error_handling, "Error Handling Test")
|
||||
run_test(test_stream_error_handling, "Streaming Error Handling Test")
|
||||
else:
|
||||
# Run specified tests
|
||||
for test_name in args.tests:
|
||||
if OutputControl.is_verbose():
|
||||
print(f"\n【Running Test: {test_name}】")
|
||||
run_test(test_cases[test_name], test_name)
|
||||
except Exception as e:
|
||||
print(f"\nAn error occurred: {str(e)}")
|
||||
finally:
|
||||
# Print test statistics
|
||||
STATS.print_summary()
|
||||
# If an output file path is specified, export the results
|
||||
if args.output:
|
||||
STATS.export_results(args.output)
|
||||
Reference in New Issue
Block a user