Migrate Ollama API to lightrag_server.py
This commit is contained in:
@@ -25,9 +25,9 @@ EMBEDDING_BINDING_HOST=http://host.docker.internal:11434
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EMBEDDING_MODEL=bge-m3:latest
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# Lollms example
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EMBEDDING_BINDING=lollms
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EMBEDDING_BINDING_HOST=http://host.docker.internal:9600
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EMBEDDING_MODEL=bge-m3:latest
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# EMBEDDING_BINDING=lollms
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# EMBEDDING_BINDING_HOST=http://host.docker.internal:9600
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# EMBEDDING_MODEL=bge-m3:latest
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# RAG Configuration
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MAX_ASYNC=4
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@@ -1,7 +1,11 @@
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from fastapi import FastAPI, HTTPException, File, UploadFile, Form
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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, Union
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import lollms_model_complete, lollms_embed
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from lightrag.llm import ollama_model_complete, ollama_embed
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@@ -10,7 +14,6 @@ from lightrag.llm import azure_openai_complete_if_cache, azure_openai_embedding
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from lightrag.api import __api_version__
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from lightrag.utils import EmbeddingFunc
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from typing import Optional, List, Union, Any
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from enum import Enum
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from pathlib import Path
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import shutil
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@@ -28,16 +31,41 @@ import pipmaster as pm
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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 emulated Ollama 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 # it's a dummy value
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LIGHTRAG_CREATED_AT = "2024-01-15T00:00:00Z"
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LIGHTRAG_DIGEST = "sha256:lightrag"
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def get_default_host(binding_type: str) -> str:
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default_hosts = {
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"ollama": "http://localhost:11434",
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"lollms": "http://localhost:9600",
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"azure_openai": "https://api.openai.com/v1",
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"openai": "https://api.openai.com/v1",
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"ollama": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"),
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"lollms": os.getenv("LLM_BINDING_HOST", "http://localhost:9600"),
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"azure_openai": os.getenv("AZURE_OPENAI_ENDPOINT", "https://api.openai.com/v1"),
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"openai": os.getenv("LLM_BINDING_HOST", "https://api.openai.com/v1"),
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}
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return default_hosts.get(
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binding_type, "http://localhost:11434"
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binding_type, os.getenv("LLM_BINDING_HOST", "http://localhost:11434")
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) # fallback to ollama if unknown
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@@ -214,9 +242,7 @@ def parse_args() -> argparse.Namespace:
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Returns:
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argparse.Namespace: Parsed arguments
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"""
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# Load environment variables from .env file
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load_dotenv()
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parser = argparse.ArgumentParser(
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description="LightRAG FastAPI Server with separate working and input directories"
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)
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@@ -409,6 +435,53 @@ class SearchMode(str, Enum):
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local = "local"
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global_ = "global"
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hybrid = "hybrid"
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mix = "mix"
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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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class QueryRequest(BaseModel):
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@@ -514,50 +587,107 @@ def create_app(args):
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# Initialize document manager
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doc_manager = DocumentManager(args.input_dir)
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async def openai_alike_model_complete(
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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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args.llm_model,
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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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base_url=args.llm_binding_host,
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api_key=os.getenv("OPENAI_API_KEY"),
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**kwargs,
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)
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async def azure_openai_model_complete(
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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 azure_openai_complete_if_cache(
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args.llm_model,
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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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base_url=args.llm_binding_host,
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api_key=os.getenv("AZURE_OPENAI_API_KEY"),
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api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview"),
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**kwargs,
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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=lollms_model_complete
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if args.llm_binding == "lollms"
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else ollama_model_complete
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if args.llm_binding == "ollama"
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else azure_openai_complete_if_cache
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if args.llm_binding == "azure_openai"
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else openai_complete_if_cache,
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llm_model_name=args.llm_model,
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llm_model_max_async=args.max_async,
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llm_model_max_token_size=args.max_tokens,
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llm_model_kwargs={
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"host": args.llm_binding_host,
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"timeout": args.timeout,
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"options": {"num_ctx": args.max_tokens},
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},
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embedding_func=EmbeddingFunc(
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embedding_dim=args.embedding_dim,
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max_token_size=args.max_embed_tokens,
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func=lambda texts: lollms_embed(
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texts,
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embed_model=args.embedding_model,
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host=args.embedding_binding_host,
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)
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if args.llm_binding in ["lollms", "ollama"] :
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rag = LightRAG(
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working_dir=args.working_dir,
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llm_model_func=lollms_model_complete
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if args.llm_binding == "lollms"
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else ollama_embed(
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texts,
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embed_model=args.embedding_model,
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host=args.embedding_binding_host,
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)
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if args.llm_binding == "ollama"
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else azure_openai_embedding(
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texts,
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model=args.embedding_model, # no host is used for openai
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)
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if args.llm_binding == "azure_openai"
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else openai_embedding(
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texts,
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model=args.embedding_model, # no host is used for openai
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else ollama_model_complete,
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llm_model_name=args.llm_model,
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llm_model_max_async=args.max_async,
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llm_model_max_token_size=args.max_tokens,
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llm_model_kwargs={
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"host": args.llm_binding_host,
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"timeout": args.timeout,
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"options": {"num_ctx": args.max_tokens},
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},
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embedding_func=EmbeddingFunc(
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embedding_dim=args.embedding_dim,
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max_token_size=args.max_embed_tokens,
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func=lambda texts: lollms_embed(
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texts,
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embed_model=args.embedding_model,
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host=args.embedding_binding_host,
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)
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if args.embedding_binding == "lollms"
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else ollama_embed(
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texts,
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embed_model=args.embedding_model,
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host=args.embedding_binding_host,
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)
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if args.embedding_binding == "ollama"
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else azure_openai_embedding(
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texts,
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model=args.embedding_model, # no host is used for openai
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)
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if args.embedding_binding == "azure_openai"
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else openai_embedding(
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texts,
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model=args.embedding_model, # no host is used for openai
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),
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),
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),
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)
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)
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else :
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rag = LightRAG(
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working_dir=args.working_dir,
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llm_model_func=azure_openai_model_complete
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if args.llm_binding == "azure_openai"
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else openai_alike_model_complete,
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embedding_func=EmbeddingFunc(
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embedding_dim=args.embedding_dim,
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max_token_size=args.max_embed_tokens,
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func=lambda texts: lollms_embed(
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texts,
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embed_model=args.embedding_model,
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host=args.embedding_binding_host,
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)
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if args.embedding_binding == "lollms"
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else ollama_embed(
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texts,
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embed_model=args.embedding_model,
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host=args.embedding_binding_host,
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)
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if args.embedding_binding == "ollama"
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else azure_openai_embedding(
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texts,
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model=args.embedding_model, # no host is used for openai
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)
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if args.embedding_binding == "azure_openai"
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else openai_embedding(
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texts,
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model=args.embedding_model, # no host is used for openai
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),
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),
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)
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async def index_file(file_path: Union[str, Path]) -> None:
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"""Index all files inside the folder with support for multiple file formats
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@@ -592,7 +722,7 @@ def create_app(args):
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case ".pdf":
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if not pm.is_installed("pypdf2"):
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pm.install("pypdf2")
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from pypdf2 import PdfReader
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from PyPDF2 import PdfReader
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# PDF handling
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reader = PdfReader(str(file_path))
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@@ -711,13 +841,21 @@ def create_app(args):
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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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return QueryResponse(response=response)
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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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trace_exception(e)
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raise HTTPException(status_code=500, detail=str(e))
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@@ -725,7 +863,7 @@ def create_app(args):
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@app.post("/query/stream", dependencies=[Depends(optional_api_key)])
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async def query_text_stream(request: QueryRequest):
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try:
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response = rag.query(
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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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@@ -734,12 +872,37 @@ def create_app(args):
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),
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)
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async def stream_generator():
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async for chunk in response:
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yield chunk
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from fastapi.responses import StreamingResponse
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return stream_generator()
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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
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try:
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async for chunk in response:
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if chunk: # Only send non-empty content
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yield f"{json.dumps({'response': chunk})}\n"
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except Exception as e:
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logging.error(f"Streaming error: {str(e)}")
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yield f"{json.dumps({'error': str(e)})}\n"
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return StreamingResponse(
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stream_generator(),
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media_type="application/x-ndjson",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"Content-Type": "application/x-ndjson",
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "POST, OPTIONS",
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"Access-Control-Allow-Headers": "Content-Type",
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"X-Accel-Buffering": "no", # Disable Nginx buffering
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},
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)
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except Exception as e:
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trace_exception(e)
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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@@ -790,7 +953,7 @@ def create_app(args):
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case ".pdf":
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if not pm.is_installed("pypdf2"):
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pm.install("pypdf2")
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from pypdf2 import PdfReader
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from PyPDF2 import PdfReader
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from io import BytesIO
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# Read PDF from memory
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@@ -897,7 +1060,7 @@ def create_app(args):
|
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case ".pdf":
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if not pm.is_installed("pypdf2"):
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pm.install("pypdf2")
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from pypdf2 import PdfReader
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from PyPDF2 import PdfReader
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from io import BytesIO
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pdf_content = await file.read()
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@@ -993,6 +1156,218 @@ def create_app(args):
|
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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# Ollama compatible API endpoints
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@app.get("/api/version")
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async def get_version():
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"""Get Ollama version information"""
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return OllamaVersionResponse(version="0.5.4")
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@app.get("/api/tags")
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async def get_tags():
|
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"""Get available models"""
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return OllamaTagResponse(
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models=[
|
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{
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"name": LIGHTRAG_MODEL,
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"model": LIGHTRAG_MODEL,
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"size": LIGHTRAG_SIZE,
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"digest": LIGHTRAG_DIGEST,
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"modified_at": LIGHTRAG_CREATED_AT,
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"details": {
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"parent_model": "",
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"format": "gguf",
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"family": LIGHTRAG_NAME,
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||||
"families": [LIGHTRAG_NAME],
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||||
"parameter_size": "13B",
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||||
"quantization_level": "Q4_0",
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||||
},
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||||
}
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||||
]
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||||
)
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||||
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def parse_query_mode(query: str) -> tuple[str, SearchMode]:
|
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"""Parse query prefix to determine search mode
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Returns tuple of (cleaned_query, search_mode)
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"""
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||||
mode_map = {
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"/local ": SearchMode.local,
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"/global ": SearchMode.global_, # global_ is used because 'global' is a Python keyword
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"/naive ": SearchMode.naive,
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"/hybrid ": SearchMode.hybrid,
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"/mix ": SearchMode.mix,
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}
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for prefix, mode in mode_map.items():
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if query.startswith(prefix):
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# After removing prefix an leading spaces
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cleaned_query = query[len(prefix) :].lstrip()
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return cleaned_query, mode
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return query, SearchMode.hybrid
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|
||||
@app.post("/api/chat")
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||||
async def chat(raw_request: Request, request: OllamaChatRequest):
|
||||
"""Handle chat completion requests"""
|
||||
try:
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||||
# Get all messages
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||||
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:
|
||||
trace_exception(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"""
|
||||
|
Reference in New Issue
Block a user