app.py

# /// script
# dependencies = [
#     "gradio[mcp]",
#     "langchain_community",
#     "chromadb",
#     "huggingface_hub",
#     "langchain_community",
#     "sentence_transformers",
# ]
# ///

import gradio as gr
from query_interface import AristaDocumentQuery

def search_docs(query: str, k: int = 5) -> str:
   """
    Search the Arista AVD documentation vector database.
    Args:
         query (str): The search query.
         k (int): Number of results to return.
    Returns:
         str: Formatted string of search results.
   """
   query_interface = AristaDocumentQuery()
   results = query_interface.similarity_search(query, k=k)
   return query_interface.format_results(results)

# Create a standard Gradio interface
demo = gr.Interface(
    fn=search_docs,
    inputs=["textbox", "number"],
    outputs="text",
    title="Document Search",
    description="Enter a search query and the number of results to return."
)

# Launch both the Gradio web interface and the MCP server
if __name__ == "__main__":
    demo.launch(mcp_server=True)