聊天机器人仅使用检索器中的信息，仅此而已

Question

我需要一些帮助，我正在用 langchain 和 Pinecone 构建一个聊天机器人；我尝试使用我发现的不同链，但它们实际上都不能正常工作。创建带有内存、检索器、map_reduce 和指定机器人应仅回答知识的提示的链的最佳方法是什么在猎犬中？ 我还有一个附加问题，检索器是否找到与问题高度相似的块/文档？有多少？

llm = ChatOpenAI(
openai_api_key=OPENAI_API_KEY,
model_name='gpt-3.5-turbo',
temperature=0.0
)
# conversational memory
conversational_memory = ConversationSummaryBufferMemory(
    llm=llm,
    memory_key='chat_history',
    max_token_limit=1000,
    return_messages=True
)
# retrieval qa chain
from langchain.chains import RetrievalQAWithSourcesChain
qa_with_sources = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="map_reduce",
    retriever=vectorstore.as_retriever()
)

query = input("Ask me anything: ")

from langchain.agents import Tool

tools = [
    Tool(
        name='Knowledge Base',
        func=qa_with_sources,
        description=(
            'use this tool to answer with the text retrieved only'
        )
    )
]

from langchain.agents import initialize_agent

agent = initialize_agent(
    agent='chat-conversational-react-description',
    tools=tools,
    llm=llm,
    verbose=True,
    max_iterations=3,
    early_stopping_method='generate',
    memory=conversational_memory
)       

Answer 1

您可以根据您的代码尝试以下 LangChain 代码，其中使用：

• pipecone

  vectorDB（您需要设置项目/索引，然后获取 API 密钥作为先决条件）

• VectorStoreRetrieverMemory

  ：通过记忆检索

• sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

  作为嵌入模型

• ConversationChain

  有记忆

from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
    model_name='gpt-3.5-turbo',
    temperature=0.0
)

from langchain_community.embeddings import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(
    model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")

from langchain_pinecone import PineconeVectorStore
from langchain.memory import VectorStoreRetrieverMemory
vectorstore = PineconeVectorStore.from_texts(
    ["Harry Potter's owl is in the castle."], embedding=embedding,
    index_name="langchain-test-index")
retriever = vectorstore.as_retriever(search_kwargs=dict(k=2))
memory = VectorStoreRetrieverMemory(retriever=retriever)

from langchain.prompts import PromptTemplate
TEMPLATE = """You're a helpful assistant, aiming at solving the problem.

Relevant pieces of previous conversation:
{history}

(You do not need to use these pieces of information if not relevant)

Answer my question: {input}
"""
PROMPT = PromptTemplate(
   input_variables=["history", "input"], template=TEMPLATE
)

from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
    llm=llm,
    prompt=PROMPT,
    memory=memory,
    verbose=True
)

while True:
    user_input = input("Enter your question: ")
    if user_input == "exit":
        break
    else:
        result = conversation_with_summary.predict(input=user_input)
        print(result)