136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
import os
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import numpy as np
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from typing import Optional, List
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from loguru import logger
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from openai import AzureOpenAI
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from lightrag import LightRAG, QueryParam
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from lightrag.utils import EmbeddingFunc
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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class RAGService:
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"""Service class for RAG operations."""
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def __init__(self):
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self.rag: Optional[LightRAG] = None
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# Azure OpenAI for LLM
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async def llm_model_func(
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self, prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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client = AzureOpenAI(
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api_key=os.environ["AZURE_OPENAI_API_KEY"],
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api_version=os.environ["AZURE_OPENAI_API_VERSION"],
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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)
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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if history_messages:
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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chat_completion = client.chat.completions.create(
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model=os.environ["AZURE_OPENAI_DEPLOYMENT"],
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messages=messages,
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temperature=kwargs.get("temperature", 0),
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top_p=kwargs.get("top_p", 1),
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n=kwargs.get("n", 1),
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)
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return chat_completion.choices[0].message.content
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# vLLM for embeddings
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async def embedding_func(self, texts: List[str]) -> np.ndarray:
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try:
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return await openai_embed(
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texts,
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model=os.environ["EMBEDDING_MODEL"],
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api_key="anything",
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base_url=os.environ["VLLM_EMBED_HOST"],
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)
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except Exception as e:
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logger.error(f"Error in embedding call: {e}")
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raise
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async def get_embedding_dim(self) -> int:
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"""Get embedding dimension by testing with a sample text."""
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test_text = ["This is a test sentence."]
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embedding = await self.embedding_func(test_text)
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return embedding.shape[1]
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async def initialize(self):
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"""Initialize the RAG system."""
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try:
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knowledge_graph_path = os.environ["KNOWLEDGE_GRAPH_PATH"]
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# Get embedding dimension dynamically
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embedding_dimension = await self.get_embedding_dim()
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logger.info(f"Detected embedding dimension: {embedding_dimension}")
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self.rag = LightRAG(
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working_dir=knowledge_graph_path,
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graph_storage="NetworkXStorage",
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kv_storage="JsonKVStorage",
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vector_storage="FaissVectorDBStorage",
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vector_db_storage_cls_kwargs={
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"cosine_better_than_threshold": 0.2
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},
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embedding_func=EmbeddingFunc(
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embedding_dim=embedding_dimension,
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max_token_size=8192,
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func=self.embedding_func
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),
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llm_model_func=self.llm_model_func,
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enable_llm_cache=True,
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enable_llm_cache_for_entity_extract=False,
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embedding_cache_config={
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"enabled": False,
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"similarity_threshold": 0.95,
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"use_llm_check": False
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},
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)
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# Initialize storages
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await self.rag.initialize_storages()
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await initialize_pipeline_status()
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logger.success("RAG system initialized successfully")
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except Exception as e:
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logger.error(f"Error initializing RAG: {e}")
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raise
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async def query(
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self,
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question: str,
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mode: str = "mix",
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response_type: str = "Multiple Paragraphs"
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) -> str:
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"""Query the RAG system."""
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if not self.rag:
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raise RuntimeError("RAG system not initialized")
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try:
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response = await self.rag.aquery(
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question,
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param=QueryParam(
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mode=mode,
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response_type=response_type,
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only_need_context=False,
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)
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)
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return response
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except Exception as e:
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logger.error(f"Error processing query: {e}")
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raise
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def is_initialized(self) -> bool:
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"""Check if RAG is initialized."""
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return self.rag is not None
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rag_service = RAGService() |