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Expand Up @@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"## Introduction\n",
"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.\n",
"\n",
"This notebook demonstrates how to build a RAG system using:\n",
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3 changes: 1 addition & 2 deletions smolagents/query_based/RAG_with_Couchbase_SmolAgents.ipynb
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"vector_store.create_index(index_type=IndexType.BHIVE, index_name=\"smolagents_bhive_index\", index_description=\"IVF,SQ8\")"
]
},

{
"cell_type": "code",
"execution_count": null,
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"\n",
"- 🛠️ Tool-agnostic: you can use tools from LangChain, Anthropic's MCP, you can even use a Hub Space as a tool.\n",
"\n",
"# Building a RAG Agent using smolagents\n",
"## Building a RAG Agent using smolagents\n",
"\n",
"smolagents allows users to define their own tools for the agent to use. These tools can be of two types:\n",
"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.\n",
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