DA-1073 mistral ai gsi tutorial

mistralai/fts/.env.sample

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

mistralai/frontmatter.md → mistralai/fts/frontmatter.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
-path: "/tutorial-mistralai-couchbase-vector-search"
-title: Using Mistral AI Embeddings with Couchbase Vector Search
-short_title: Mistral AI with Couchbase Vector Search
+path: "/tutorial-mistralai-couchbase-vector-search-with-fts"
+title: Using Mistral AI Embeddings with Couchbase Vector Search using FTS service
+short_title: Mistral AI with Couchbase Vector Search using FTS service
 description:
-  - Learn how to generate embeddings using Mistral AI and store them in Couchbase.
+  - Learn how to generate embeddings using Mistral AI and store them in Couchbase using FTS service.
   - This tutorial demonstrates how to use Couchbase's vector search capabilities with Mistral AI embeddings.
   - You'll understand how to perform vector search to find relevant documents based on similarity.
 content_type: tutorial
@@ -14,6 +14,7 @@ technology:
 tags:
   - Artificial Intelligence
   - Mistral AI
+  - FTS
 sdk_language:
   - python
 length: 30 Mins

mistralai/mistralai.ipynb → mistralai/fts/mistralai.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Introduction\n",
     "\n",
+    "In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database, [Mistral AI](https://mistral.ai/) as the AI-powered embedding Model. 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-mistralai-couchbase-vector-search-using-global-search-indexes)\n",
+    "\n",
     "Couchbase is a NoSQL distributed document database (JSON) with many of the best features of a relational DBMS: SQL, distributed ACID transactions, and much more. [Couchbase Capella™](https://cloud.couchbase.com/sign-up) is the easiest way to get started, but you can also download and run [Couchbase Server](http://couchbase.com/downloads) on-premises.\n",
     "\n",
     "Mistral AI is a research lab building the best open source models in the world. La Plateforme enables developers and enterprises to build new products and applications, powered by Mistral’s open source and commercial LLMs. \n",
@@ -380,7 +382,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -394,7 +396,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,

mistralai/gsi/.env.sample

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

mistralai/gsi/frontmatter.md

@@ -0,0 +1,21 @@
+---
+# frontmatter
+path: "/tutorial-mistralai-couchbase-vector-search-using-global-search-indexes"
+title: Using Mistral AI Embeddings using GSI Index
+short_title: Mistral AI with Couchbase GSI Index
+description:
+  - Learn how to generate embeddings using Mistral AI and store them in Couchbase using GSI.
+  - This tutorial demonstrates how to use Couchbase's GSI index capabilities with Mistral AI embeddings.
+  - You'll understand how to perform optimized vector search using Global Secondary Index for better performance.
+content_type: tutorial
+filter: sdk
+technology:
+  - vector search
+tags:
+  - Artificial Intelligence
+  - Mistral AI
+  - GSI
+sdk_language:
+  - python
+length: 30 Mins
+---