54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import openai
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from langchain.embeddings.openai import OpenAIEmbeddings
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# from langchain.output_parsers import CommaSeparatedListOutputParser
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# from langchain.prompts import PromptTemplate
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from langchain.vectorstores import Milvus
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from langchain.chains.qa_with_sources import load_qa_with_sources_chain
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from langchain.llms import OpenAI
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question = input("请输入问题：")
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question += " reply in spoken language "
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# question = "这个 yarn 为什么会发生错误，该怎么解决？使用中文回复"
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MILVUS_HOST = "127.0.0.1"
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MILVUS_PORT = "19530"
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# 准备嵌入模型
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embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
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vector_db: Milvus = Milvus(
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embedding_function=embeddings,
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connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT},
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collection_name="todos",
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)
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print("正在从向量数据库中搜索...")
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docs = vector_db.similarity_search(query=question)
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f = open("question_docs.txt", "w")
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f.write(str(docs))
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f.close()
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# print(docs)
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# exit(0)
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# load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True, verbose=True)
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# print("正在调用 LLM...")
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# chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True, verbose=True)
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print("正在调用 LLM...")
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# # load_qa_with_sources_chain with custom prompt
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chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=False,
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verbose=False)
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output = chain({"input_documents": docs, "question": question}, return_only_outputs=True)
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print("回复：" + output["output_text"])
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#
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#
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