# Tool name: search_code_examples | Parameters: {"endpoints":["chat"],"programming_language":"py","query":"..."}
```

```mdx
QUESTION:
Python examples of using the chat endpoint.
==================================================
TOOL PLAN:
I will search for Python examples of using the chat endpoint. 

TOOL CALLS:
Tool name: search_code_examples_detailed | Parameters: {"query":"chat endpoint","programming_language":"py","endpoints":["chat"]}
==================================================
RESPONSE:
Here are some Python examples of using the chat endpoint:
- Calendar Agent with Native Multi Step Tool
- RAG With Chat Embed and Rerank via Pinecone
- Build Chatbots That Know Your Business with MongoDB and Cohere
==================================================
CITATIONS:

Start: 60| End:102| Text:'Calendar Agent with Native Multi Step Tool' 
Sources:
1. search_code_examples_detailed_79er2w6sycvr:0


Start: 105| End:148| Text:'RAG With Chat Embed and Rerank via Pinecone' 
Sources:
1. search_code_examples_detailed_79er2w6sycvr:2


Start: 151| End:213| Text:'Build Chatbots That Know Your Business with MongoDB and Cohere' 
Sources:
1. search_code_examples_detailed_79er2w6sycvr:3
```

## Summary

In this tutorial, we learned about:

* How to define the function for data querying
* How to create the tool for generating multi-faceted queries
* How to build an agent for performing multi-faceted queries
* How to run the agent

By implementing multi-faceted queries over semi-structured data, we've enhanced our RAG system to handle more specific and targeted searches. This approach allows for better utilization of metadata and more precise filtering of results, which is particularly useful when dealing with large collections of code examples and tutorials.

While this tutorial demonstrates how to work with semi-structured data, the agentic RAG approach can be applied to structured data as well. That means we can build agents that can translate natural language queries into queries for tables or relational databases.

In Part 5, we'll learn how to perform RAG over structured data (tables).