ollama + langchain + FAISS 向量数据库，给定知识上下文的问答

from langchain_core.output_parsers import StrOutputParser from langchain_community.llms import Ollama from langchain_community.document_loaders import WebBaseLoader from langchain_community.embeddings import OllamaEmbeddings from langchain_community.vectorstores import FAISS from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_core.prompts import ChatPromptTemplate from langchain.chains import create_retrieval_chain # 从url导入知识作为聊天背景上下文 loader = WebBaseLoader("https://docs.smith.langchain.com/user_guide") #加载 docs = loader.load() # 文本分词器 text_splitter = RecursiveCharacterTextSplitter() documents = text_splitter.split_documents(docs) # ollama嵌入层 embeddings = OllamaEmbeddings() # 文档向量化 vector = FAISS.from_documents(documents, embeddings) # 创建ollama 模型 llama2 llm = Ollama(model="llama2") output_parser = StrOutputParser() # 创建提示词模版 prompt = ChatPromptTemplate.from_template( """Answer the following question based only on the provided context: <context> {context} </context> Question: {input}""" ) # 生成chain ： prompt | llm document_chain = create_stuff_documents_chain(llm, prompt) # 向量数据库检索器 retriever = vector.as_retriever() #向量数据库检索chain : vector | prompt | llm retrieval_chain = create_retrieval_chain(retriever, document_chain) # 调用上面的 (向量数据库检索chain) response = retrieval_chain.invoke({"input": "how can langsmith help with testing?"}) # 打印结果 print(response["answer"])