mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
# Load
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from langchain_chroma import Chroma
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from langchain_community.chat_models import ChatOllama
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from langchain_community.document_loaders import WebBaseLoader
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from langchain_community.embeddings import GPT4AllEmbeddings
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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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from langchain_text_splitters import RecursiveCharacterTextSplitter
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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=GPT4AllEmbeddings(),
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)
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retriever = vectorstore.as_retriever()
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# Prompt
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# Optionally, pull from the Hub
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# from langchain import hub
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# prompt = hub.pull("rlm/rag-prompt")
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# Or, define your own:
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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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# Select the LLM that you downloaded
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ollama_llm = "llama2:7b-chat"
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model = ChatOllama(model=ollama_llm)
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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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