mirror of
https://github.com/langchain-ai/learning-langchain.git
synced 2026-07-19 21:43:35 -04:00
mayo
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
"""
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1. Ensure docker is installed and running (https://docs.docker.com/get-docker/)
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2. pip install -qU langchain_postgres
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3. Run the following command to start the postgres container:
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docker run \
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--name pgvector-container \
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-e POSTGRES_USER=langchain \
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-e POSTGRES_PASSWORD=langchain \
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-e POSTGRES_DB=langchain \
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-p 6024:5432 \
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-d pgvector/pgvector:pg16
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4. Use the connection string below for the postgres container
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"""
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from langchain_community.document_loaders import TextLoader
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from langchain_openai import OpenAIEmbeddings
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_postgres.vectorstores import PGVector
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import chain
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# See docker command above to launch a postgres instance with pgvector enabled.
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connection = "postgresql+psycopg://langchain:langchain@localhost:6024/langchain"
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# Load the document, split it into chunks
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raw_documents = TextLoader('./test.txt', encoding='utf-8').load()
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=1000, chunk_overlap=200)
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documents = text_splitter.split_documents(raw_documents)
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# Create embeddings for the documents
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embeddings_model = OpenAIEmbeddings()
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db = PGVector.from_documents(
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documents, embeddings_model, connection=connection)
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# create retriever to retrieve 2 relevant documents
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retriever = db.as_retriever(search_kwargs={"k": 2})
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query = 'Who are the key figures in the ancient greek history of philosophy?'
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# fetch relevant documents
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docs = retriever.invoke(query)
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print(docs[0].page_content)
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prompt = ChatPromptTemplate.from_template(
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"""Answer the question based only on the following context: {context} Question: {question} """
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)
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
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llm_chain = prompt | llm
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# answer the question based on relevant documents
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result = llm_chain.invoke({"context": docs, "question": query})
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print(result)
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print("\n\n")
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# Run again but this time encapsulate the logic for efficiency
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# @chain decorator transforms this function into a LangChain runnable,
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# making it compatible with LangChain's chain operations and pipeline
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print("Running again but this time encapsulate the logic for efficiency\n")
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@chain
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def qa(input):
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# fetch relevant documents
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docs = retriever.invoke(input)
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# format prompt
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formatted = prompt.invoke({"context": docs, "question": input})
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# generate answer
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answer = llm.invoke(formatted)
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return answer
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# run it
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result = qa.invoke(query)
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print(result.content)
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