RAG Pipeline with Pydantic AI

from pydantic_ai import Agent, RunContext

agent = Agent(
    "openai:gpt-4o",
    system_prompt="You are a document Q&A assistant. Search documents before answering. Cite sources using [1], [2] format.",
)

@agent.tool
async def search_documents(ctx: RunContext, query: str, top_k: int = 5) -> str:
    """Search uploaded documents for relevant content."""
    results = await vector_store.asimilarity_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)
    )

result = await agent.run("What does the refund policy say about digital products?")
print(result.output)