Update vector search cookbook md with new terminology

README.md

@@ -1,52 +1,52 @@
-# Semantic Search with Couchbase Vector Store and LLM Integration
+## Semantic Search with Couchbase Vector Store and LLM Integration
 
 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
+- **Search Vector Index**: Uses Couchbase's vector search capabilities with pre-created search indices
+- **Hyperscale Vector Index, Composite Vector 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
+### Features
 
 - **Multiple Embedding Models**: Support for embeddings from OpenAI, Azure OpenAI, Anthropic (Claude), Cohere, Hugging Face, Jina AI, Mistral AI, and Voyage AI.
 - **Couchbase Vector Store**: Utilizes Couchbase's vector storage capabilities for efficient similarity search.
 - **Retrieval-Augmented Generation (RAG)**: Integrates with advanced language models like GPT-4 for generating contextually relevant responses.
 - **Scalable and Flexible**: Easy to switch between different embedding models and adjust the index structure accordingly.
 - **Caching Mechanism**: Implements `CouchbaseCache` for improved performance on repeated queries.
 
-## Prerequisites
+### Prerequisites
 
 - Python 3.8+
 - Couchbase Cluster (Self Managed or Capella) version 7.6+ with [Search Service](https://docs.couchbase.com/server/current/search/search.html)
 
 - API keys for the respective AI providers (e.g., OpenAI, Azure OpenAI, Anthropic, Cohere, etc.)
 
-## Setup
+### Setup
 
-### 1. Clone the repository:
+#### 1. Clone the repository:
    ```bash
    git clone https://github.com/your-username/vector-search-cookbook.git
    cd vector-search-cookbook
    ```
 
-### 2. Choose Your Approach:
+#### 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.
+##### For Search Vector Index Examples:
+Use the provided `{model}_index.json` index definition file in each model's `search_based/` directory to create a new vector search index in your Couchbase cluster.
 
-#### For GSI (Global Secondary Index) Examples:
-No additional setup required. GSI index will be created in each model's example.
+##### For Hyperscale Vector Index, Composite Vector Index Examples:
+No additional setup required. Hyperscale and Composite Vector Indexes will be created in each model's example.
 
-### 3. Run the notebook file
+#### 3. Run the notebook file
 
 You can either run the notebook file on [Google Colab](https://colab.research.google.com/) or run it on your system by setting up the Python environment.
 
 
-## Components
+### Components
 
-### 1. Multiple Embedding Models
+#### 1. Multiple Embedding Models
 
 The system supports embeddings from various AI providers:
 
@@ -59,25 +59,25 @@ The system supports embeddings from various AI providers:
 * Mistral AI
 * Voyage AI
 
-### 2. Couchbase Vector Store
+#### 2. Couchbase Vector Store
 
 Couchbase is used to store document embeddings and metadata. The index structure allows for efficient retrieval across different embedding types.
 
-### 3. Retrieval-Augmented Generation (RAG)
+#### 3. Retrieval-Augmented Generation (RAG)
 
 The RAG pipeline integrates with language models like GPT-4 to generate contextually relevant answers based on retrieved documents.
 
-### 4. Semantic Search
+#### 4. Semantic Search
 
 Each notebook implements a semantic search function that performs similarity searche using the appropriate embedding type and retrieves the top-k most similar documents.
 
-### 5. Caching
+#### 5. Caching
 
 The system implements caching functionality using `CouchbaseCache` to improve performance for repeated queries.
 
-## Couchbase Vector Search Index (FTS Approach Only)
+### Couchbase Search Vector Index
 
-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.
+For Search Vector Index 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
 {
@@ -153,4 +153,4 @@ For FTS examples, you'll need to create a vector search index using the provided
     },
     "sourceParams": {}
   }
-```
+```