Da 1076 smolagenst gsi tutorial

smolagents/RAG_with_Couchbase_and_SmolAgents.ipynb → smolagents/fts/RAG_with_Couchbase_and_SmolAgents.ipynb

@@ -7,7 +7,7 @@
       },
       "source": [
         "# Introduction\n",
-        "In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database, [OpenAI](https://openai.com) as the embedding and LLM provider, and [Hugging Face smolagents](https://huggingface.co/docs/smolagents/en/index) as an agent framework. Semantic search goes beyond simple keyword matching by understanding the context and meaning behind the words in a query, making it an essential tool for applications that require intelligent information retrieval. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch."
+        "In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database, [OpenAI](https://openai.com) as the embedding and LLM provider, and [Hugging Face smolagents](https://huggingface.co/docs/smolagents/en/index) as an agent framework. Semantic search goes beyond simple keyword matching by understanding the context and meaning behind the words in a query, making it an essential tool for applications that require intelligent information retrieval. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this.](https://developer.couchbase.com//tutorial-smolagents-couchbase-rag-with-global-secondary-index)"
       ]
     },
     {

smolagents/frontmatter.md → smolagents/fts/frontmatter.md

@@ -1,6 +1,6 @@
 ---
 # frontmatter
-path: "/tutorial-smolagents-couchbase-rag"
+path: "/tutorial-smolagents-couchbase-rag-with-fts"
 title: Retrieval-Augmented Generation (RAG) with Couchbase and smolagents
 short_title: RAG with Couchbase and smolagents
 description:
@@ -16,6 +16,7 @@ tags:
   - LangChain
   - OpenAI
   - smolagents
+  - FTS
 sdk_language:
   - python
 length: 30 Mins

smolagents/gsi/.env.sample

@@ -0,0 +1,7 @@
+OPENAI_API_KEY=
+CB_HOST=
+CB_USERNAME=
+CB_PASSWORD=
+CB_BUCKET_NAME=
+SCOPE_NAME=
+COLLECTION_NAME=

smolagents/gsi/frontmatter.md

@@ -0,0 +1,23 @@
+---
+# frontmatter
+path: "/tutorial-smolagents-couchbase-rag-with-global-secondary-index"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and smolagents using GSI
+short_title: RAG with Couchbase and smolagents using GSI
+description:
+  - Learn how to build a semantic search engine using Couchbase and Hugging Face smolagents using GSI.
+  - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with smolagents using GSI indexes.
+  - You'll understand how to perform Retrieval-Augmented Generation (RAG) using smolagents and Couchbase with GSI optimization.
+content_type: tutorial
+filter: sdk
+technology:
+  - vector search
+tags:
+  - Artificial Intelligence
+  - LangChain
+  - OpenAI
+  - smolagents
+  - GSI
+sdk_language:
+  - python
+length: 30 Mins
+---