Generated markdown tutorials from Jupyter Notebooks

Generated from: couchbase-examples/vector-search-cookbook

Author: actions-user

tutorial/markdown/generated/vector-search-cookbook/capella-model-services-langchain-query_based-RAG_with_Capella_Model_Services_and_LangChain.md

@@ -29,7 +29,7 @@ alt_paths:
 
 [View Source](https://github.com/couchbase-examples/vector-search-cookbook/tree/main/capella-model-services/langchain/query_based/RAG_with_Capella_Model_Services_and_LangChain.ipynb)
 
-# Introduction
+## Introduction
 In this guide, we will walk you through building a Retrieval Augmented Generation (RAG) application using Couchbase Capella as the database, [Mistral-7B-Instruct-v0.3](https://build.nvidia.com/mistralai/mistral-7b-instruct-v03/modelcard) model as the large language model provided by Capella Model Services. We will use the [NVIDIA NeMo Retriever Llama3.2](https://build.nvidia.com/nvidia/llama-3_2-nv-embedqa-1b-v2/modelcard) model for generating embeddings via Capella Model Services.
 
 This notebook demonstrates how to build a RAG system using:

tutorial/markdown/generated/vector-search-cookbook/smolagents-query_based-RAG_with_Couchbase_SmolAgents.md

@@ -740,7 +740,7 @@ Some of the features of smolagents are:
 
 - 🛠️ Tool-agnostic: you can use tools from LangChain, Anthropic's MCP, you can even use a Hub Space as a tool.
 
-# Building a RAG Agent using smolagents
+## Building a RAG Agent using smolagents
 
 smolagents allows users to define their own tools for the agent to use. These tools can be of two types:
 1. Tools defined as classes: These tools are subclassed from the `Tool` class and must override the `forward` method, which is called when the tool is used.