86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
from fastapi import Body
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from fastapi.responses import StreamingResponse
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from configs.model_config import (llm_model_dict, LLM_MODEL, PROMPT_TEMPLATE,
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CACHED_VS_NUM, VECTOR_SEARCH_TOP_K,
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embedding_model_dict, EMBEDDING_MODEL, EMBEDDING_DEVICE)
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from server.chat.utils import wrap_done
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from langchain.chat_models import ChatOpenAI
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from langchain import LLMChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from typing import AsyncIterable
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import asyncio
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from langchain.prompts import PromptTemplate
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from langchain.vectorstores import FAISS
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from server.knowledge_base.utils import get_vs_path
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from functools import lru_cache
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@lru_cache(1)
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def load_embeddings(model: str, device: str):
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embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model],
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model_kwargs={'device': device})
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return embeddings
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@lru_cache(CACHED_VS_NUM)
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def load_vector_store(
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knowledge_base_name: str,
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embedding_model: str,
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embedding_device: str,
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):
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embeddings = load_embeddings(embedding_model, embedding_device)
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vs_path = get_vs_path(knowledge_base_name)
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search_index = FAISS.load_local(vs_path, embeddings)
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return search_index
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def lookup_vs(
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query: str,
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knowledge_base_name: str,
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top_k: int = VECTOR_SEARCH_TOP_K,
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embedding_model: str = EMBEDDING_MODEL,
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embedding_device: str = EMBEDDING_DEVICE,
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):
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search_index = load_vector_store(knowledge_base_name, embedding_model, embedding_device)
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docs = search_index.similarity_search(query, k=top_k)
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return docs
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def knowledge_base_chat(query: str = Body(..., description="用户输入", example="你好"),
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knowledge_base_name: str = Body(..., description="知识库名称", example="samples"),
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top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
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):
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async def knowledge_base_chat_iterator(query: str,
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knowledge_base_name: str,
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top_k: int,
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) -> AsyncIterable[str]:
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callback = AsyncIteratorCallbackHandler()
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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callbacks=[callback],
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openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
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openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
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model_name=LLM_MODEL
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)
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docs = lookup_vs(query, knowledge_base_name, top_k)
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context = "\n".join([doc.page_content for doc in docs])
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prompt = PromptTemplate(template=PROMPT_TEMPLATE, input_variables=["context", "question"])
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chain = LLMChain(prompt=prompt, llm=model)
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# Begin a task that runs in the background.
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task = asyncio.create_task(wrap_done(
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chain.acall({"context": context, "question": query}),
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callback.done),
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)
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async for token in callback.aiter():
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# Use server-sent-events to stream the response
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yield token
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await task
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return StreamingResponse(knowledge_base_chat_iterator(query, knowledge_base_name, top_k),
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media_type="text/event-stream")
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