RAG-Pipeline mit LangChain

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

@tool
def search_documents(query: str, top_k: int = 5) -> str:
    """Search uploaded documents for relevant content."""
    results = vector_store.similarity_search(query, k=top_k)
    return "\n\n".join(
        f"[{i+1}] {r.metadata.get('source', 'unknown')}: {r.page_content}"
        for i, r in enumerate(results)
    )

model = ChatOpenAI(model="gpt-4o")
model_with_tools = model.bind_tools([search_documents])
response = model_with_tools.invoke([
    ("system", "You are a document Q&A assistant. Search documents before answering. Cite sources using [1], [2] format."),
    ("user", "What does the refund policy say about digital products?"),
])