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Let's officially give your agent its brain.
We are going to use LangChain's @tool decorator.
This magical little wrapper takes your standard Python function,
reads the type hints (like query: str), reads the docstring, and automatically
translates the entire thing into a strict JSON schema that OpenAI and Anthropic natively understand .
"""
import os
from langchain_core.tools import tool
from src.rag.vector_store import get_vector_store
# The @tool decorator converts this Python function into an LLM-readable JSON schema
@tool
def search_documentation(query: str) -> str:
"""
Searches the internal engineering documentation for information about the OmniRouter,
LangChain, LangGraph, or general AI engineering concepts.
Use this tool WHENEVER the user asks a technical question about how the system works,
fallback protocols, or specific coding architecture. Do NOT use this for general greetings.
Args:
query: The specific search term to look up in the database.
It should be a standalone, highly descriptive phrase.
"""
print(f"\n--- [TOOL EXECUTION] Searching Vector DB for: '{query}' ---")
# In production, ensure your API key is loaded securely
api_key = os.getenv("HF_TOKEN")
try:
db = get_vector_store(api_key)
# Retrieve the top 2 most relevant chunks
results = db.similarity_search(query, k=2)
if not results:
return "No relevant information found in the documentation."
# We must return a STRING, not a list of objects, so the LLM can read it easily
combined_text = "\n\n".join([doc.page_content for doc in results])
return combined_text
except Exception as e:
# FIXED: Print the error to the server terminal so we can see it!
print(f"\n🚨 [TOOL CRASHED]: {str(e)}")
return f"Error executing search: {str(e)}"
if __name__ == "__main__":
# Print the name the LLM sees
print(f"Tool Name: {search_documentation.name}")
# Print the description the LLM reads to make its decision
print(f"\nTool Description: \n{search_documentation.description}")
# Print the strict JSON schema the LLM must follow to use the tool
print(f"\nTool Arguments Schema: \n{search_documentation.args}") |