RAG Pipeline with Deep Agents

from deepagents import create_deep_agent
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)
    )

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    tools=[search_documents],
    system_prompt="You are a document Q&A assistant. Search documents before answering. Cite sources using [1], [2] format.",
)

result = agent.invoke({
    "messages": [("user", "What does the refund policy say about digital products?")]
})
print(result["messages"][-1].content)