Add rag_agent.py

rag_agent.py

@@ -0,0 +1,32 @@
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
+import os
+
+def build_vector_store(directory: str):
+    """Load text files from directory, split into chunks, and build FAISS store."""
+    loader = TextLoader(directory, glob='**/*.txt')
+    documents = loader.load()
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    docs = splitter.split_documents(documents)
+    embeddings = OpenAIEmbeddings()
+    vector_store = FAISS.from_documents(docs, embeddings)
+    return vector_store
+
+def main():
+    # Load documents and build vector store
+    vector_store = build_vector_store('data')
+    # Create RetrievalQA chain
+    llm = OpenAI(temperature=0)
+    qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_store.as_retriever())
+    # Sample query
+    query = "What is the capital of France?"
+    result = qa_chain.run(query)
+    print("Query:", query)
+    print("Answer:", result)
+
+if __name__ == "__main__":
+    main()