Refactor README section headers for consistency

Updated section headers to use consistent formatting and improved clarity in the README.

Author: SanjanaBoggaramJ

README.md

@@ -1,4 +1,4 @@
-## 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.
 
@@ -8,45 +8,45 @@ Each example provides two distinct approaches:
 
 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)
+- Couchbase Cluster (Self Managed or Capella) version 8.0+ 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 Search Vector Index Examples:
+#### 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 Hyperscale Vector Index, Composite Vector Index Examples:
+#### 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:
 
@@ -59,23 +59,23 @@ 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.
+Each notebook implements a semantic search function that performs similarity search 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 Search Vector Index
+## Couchbase Search Vector Index
 
 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.