mirror of
https://github.com/hwchase17/langchain
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fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
# Load
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from langchain.prompts import ChatPromptTemplate
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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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_community.vectorstores import Chroma
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from langchain_core.output_parsers import StrOutputParser
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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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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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