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https://github.com/The-Art-of-Hacking/h4cker.git
synced 2024-07-05 10:51:20 +00:00
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
from langchain.document_loaders import TextLoader
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.embeddings import SentenceTransformerEmbeddings
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from langchain.vectorstores import Chroma
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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 the document and split it into chunks
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loader = TextLoader("path/to/document.txt")
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documents = loader.load()
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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chunks = text_splitter.split_documents(documents)
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# Step 2: Create embeddings
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embedding_model = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
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embeddings = embedding_model.embed(chunks)
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# Step 3: Store embeddings in ChromaDB
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db = Chroma.from_embeddings(embeddings)
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# Step 4: Create a retriever
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retriever = SemanticRetriever(db)
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# Step 5: 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 6: Create the language model
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model = ChatOpenAI()
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# Step 7: Define the output parser
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output_parser = StrOutputParser()
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# Step 8: 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 9: Invoke the RAG pipeline with a question
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question = "What is the main theme of the document?"
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answer = pipeline.invoke({"question": question})
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# Step 10: Print the answer
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print(answer)
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