mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
from langchain_community.chat_models import ChatOpenAI
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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
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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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# Example for document loading (from url), splitting, and creating vectostore
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"""
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# Load
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from langchain_community.document_loaders import WebBaseLoader
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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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from langchain.text_splitter import RecursiveCharacterTextSplitter
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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(documents=all_splits,
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collection_name="rag-chroma",
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embedding=OpenAIEmbeddings(),
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)
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retriever = vectorstore.as_retriever()
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"""
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# Embed a single document as a test
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vectorstore = Chroma.from_texts(
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["harrison worked at kensho"],
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collection_name="rag-chroma",
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embedding=OpenAIEmbeddings(),
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)
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retriever = vectorstore.as_retriever()
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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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# LLM
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model = ChatOpenAI()
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# RAG chain
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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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