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55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
from langchain.document_loaders import WebBaseLoader
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from langchain.document_transformers import ChunkTransformer
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.retrievers import SemanticRetriever
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from langchain.prompts import ChatPromptTemplate
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from langchain.chat_models import ChatOpenAI
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnablePassthrough
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# Step 1: Load documents
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loader = WebBaseLoader("https://example.com")
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documents = loader.load()
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# Step 2: Transform documents
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transformer = ChunkTransformer(chunk_size=512)
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transformed_documents = transformer.transform(documents)
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# Step 3: Create embeddings
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embedding_model = OpenAIEmbeddings()
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embeddings = embedding_model.embed(transformed_documents)
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# Step 4: Store embeddings in a vector store
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vector_store = FAISS.from_embeddings(embeddings)
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# Step 5: Create a retriever
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retriever = SemanticRetriever(vector_store)
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# Step 6: Define the prompt template
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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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# Step 7: Create the language model
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model = ChatOpenAI()
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# Step 8: Define the output parser
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output_parser = StrOutputParser()
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# Step 9: Define the RAG pipeline
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pipeline = {
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"context": retriever,
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"question": RunnablePassthrough(),
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} | prompt | model | output_parser
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# Step 10: Invoke the RAG pipeline with a question
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question = "What is the capital of France?"
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answer = pipeline.invoke({"question": question})
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# Step 11: Print the answer
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print(answer)
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