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18 changes: 10 additions & 8 deletions README.md
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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 Search-based vector search capabilities with pre-created search indices (in `search_based/` directories)
- **Hyperscale & Composite Vector Indexes**: Leverages Couchbase's native SQL++ queries with vector similarity functions (in `query_based/` directories)

For guidance on choosing the right vector index for your use case, see the [Couchbase documentation](https://docs.couchbase.com/server/current/vector-search/choose-the-right-vector-index.html).

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.

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### 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 & Composite Vector Index Examples:
No additional setup required. Vector indexes will be created in each model's example in the `query_based/` directory.

### 3. Run the notebook file

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The system implements caching functionality using `CouchbaseCache` to improve performance for repeated queries.

## Couchbase Vector Search Index (FTS Approach Only)
## Couchbase Search Vector Index (Search-based Approach Only)

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
{
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