mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
from langchain.retrievers.multi_query import MultiQueryRetriever
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.chat_models import ChatOllama, ChatOpenAI
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from langchain_community.document_loaders import WebBaseLoader
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import Chroma
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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# Load
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loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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data = loader.load()
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# Split
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
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all_splits = text_splitter.split_documents(data)
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# Add to vectorDB
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vectorstore = Chroma.from_documents(
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documents=all_splits,
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collection_name="rag-private",
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embedding=OpenAIEmbeddings(),
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)
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QUERY_PROMPT = PromptTemplate(
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input_variables=["question"],
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template="""You are an AI language model assistant. Your task is to generate five
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different versions of the given user question to retrieve relevant documents from
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a vector database. By generating multiple perspectives on the user question, your
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goal is to help the user overcome some of the limitations of the distance-based
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similarity search. Provide these alternative questions separated by newlines.
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Original question: {question}""",
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)
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# Add the LLM downloaded from Ollama
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ollama_llm = "zephyr"
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llm = ChatOllama(model=ollama_llm)
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# Run
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retriever = MultiQueryRetriever.from_llm(
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vectorstore.as_retriever(), llm, prompt=QUERY_PROMPT
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) # "lines" is the key (attribute name) of the parsed output
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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model = ChatOpenAI()
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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