Pydantic Tutorial

README.md

@@ -2,6 +2,10 @@
 
 This repository demonstrates how to build a powerful semantic search engine using Couchbase as the backend database, combined with various AI-powered embedding and language model providers such as OpenAI, Azure OpenAI, Anthropic (Claude), Cohere, Hugging Face, Jina AI, Mistral AI, and Voyage AI.
 
+Each example provides two distinct approaches:
+- **FTS (Full Text Search)**: Uses Couchbase's vector search capabilities with pre-created search indices
+- **GSI (Global Secondary Index)**: Leverages Couchbase's native SQL++ queries with vector similarity functions
+
 Semantic search goes beyond simple keyword matching by understanding the context and meaning behind the words in a query, making it essential for applications that require intelligent information retrieval.
 
 ## Features
@@ -27,10 +31,13 @@ Semantic search goes beyond simple keyword matching by understanding the context
    cd vector-search-cookbook
    ```
 
-### 2. Set up the Couchbase Vector Search Index:
+### 2. Choose Your Approach:
+
+#### For FTS (Full Text Search) Examples:
+Use the provided `{model}_index.json` index definition file in each model's `fts/` directory to create a new vector search index in your Couchbase cluster.
 
-Use the provided `{model}_index.json` index definition file in each model's directory to create a new index in your Couchbase cluster.
-The index supports separate properties for each embedding model.
+#### For GSI (Global Secondary Index) Examples:
+No additional setup required. GSI index will be created in each model's example.
 
 ### 3. Run the notebook file
 
@@ -68,9 +75,9 @@ Each notebook implements a semantic search function that performs similarity sea
 
 The system implements caching functionality using `CouchbaseCache` to improve performance for repeated queries.
 
-## Couchbase Vector Search Index
+## Couchbase Vector Search Index (FTS Approach Only)
 
-For more information on creating a vector search index, please follow the [instructions](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html). The following is an example for Azure OpenAI Model.
+For FTS examples, you'll need to create a vector search index using the provided JSON configuration files. For more information on creating a vector search index, please follow the [instructions](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html). The following is an example for Azure OpenAI Model.
 
 ```json
 {
@@ -146,12 +153,4 @@ For more information on creating a vector search index, please follow the [instr
     },
     "sourceParams": {}
   }
-```
-
-## Contributing
-
-Contributions are welcome! Please feel free to submit a pull request or open an issue for any bugs or feature requests.
-
-## License
-
-This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+```

awsbedrock/RAG_with_Couchbase_and_Bedrock.ipynb → awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Introduction\n",
     "\n",
-    "In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database and [Amazon Bedrock](https://aws.amazon.com/bedrock/) as both the embedding and language model provider. 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 and [Amazon Bedrock](https://aws.amazon.com/bedrock/) as both the embedding and language model provider. 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 using the FTS service 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-aws-bedrock-couchbase-rag-with-global-secondary-index/)"
    ]
   },
   {

awsbedrock/frontmatter.md → awsbedrock/fts/frontmatter.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
-path: "/tutorial-aws-bedrock-couchbase-rag"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock
-short_title: RAG with Couchbase and Amazon Bedrock
+path: "/tutorial-aws-bedrock-couchbase-rag-with-fts"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock using FTS service
+short_title: RAG with Couchbase and Amazon Bedrock using FTS service
 description:
-  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock.
+  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using FTS service.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Amazon Bedrock's Titan embeddings and Claude language model.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial

awsbedrock/gsi/.env.sample

@@ -0,0 +1,14 @@
+# AWS Credentials
+AWS_ACCESS_KEY_ID=
+AWS_SECRET_ACCESS_KEY=
+AWS_REGION=
+
+# Couchbase Settings
+CB_HOST=
+CB_USERNAME=
+CB_PASSWORD=
+CB_BUCKET_NAME=
+
+SCOPE_NAME=
+COLLECTION_NAME=
+CACHE_COLLECTION=