As a PostgreSQL-compatible database, AlloyDB integrates
seamlessly with the tools and frameworks supported by PostgreSQL, in addition to
other services from the Google Cloud environment.

AlloyDB AI provides a suite of AI and ML features that enable you to build
generative AI applications. These features allow you to build
applications with capabilities like vector search for semantic similarity,
natural language queries, and integration with machine learning models by providers, such as Google, OpenAI, and Anthropic.

To simplify the process of building AI applications, AlloyDB provides the following extensions:

- **vector** extension: the standard [`pgvector` PostgreSQL
  extension](https://github.com/pgvector/pgvector?tab=readme-ov-file#indexing) is customized for AlloyDB, and referred to as `vector`.
  It supports storing generated embeddings in a vector column. The extension also
  adds support for scalar quantization features to create `IVF` indexes. You can
  also create an `IVFFlat` index or `HNSW` index that are available with stock
  `pgvector`.

- **alloydb_scann** extension: the [`alloydb_scann` extension](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index#create-index) implements a highly efficient
  nearest-neighbor index powered by the [ScaNN
  algorithm](https://github.com/google-research/google-research/blob/master/scann/docs/algorithms.md).

  You can use the `alloydb_scann` extension with PostgreSQL 14, 15, 16, and 17.
- **google_columnar_engine** extension: ScaNN index can be loaded into the columnar engine for further accelerating the vector search.

- **google_ml_integration** extension: the `google_ml_integration` extension
  provides AI functions for generating embeddings,
  semantic ranking, and implementing AI-based filters, joins and text
  generation/summarization. This extension also provides functions to register
  metadata for AI models. The registered metadata is then used to invoke predictions from these
  models.

- **alloydb_ai_nl** extension: the `alloydb_ai_nl` extension enables developers
  to build applications that accurately and securely answer end user natural
  language questions about data in the AlloyDB database. This makes the data
  accessible to users who might not be proficient in writing SQL.

The following are some use cases that these extensions enable:

- [Vector search](https://docs.cloud.google.com/alloydb/docs/ai/run-vector-similarity-search): use AlloyDB to store vector embeddings and perform highly efficient similarity searches. You can generate a highly efficient nearest-neighbor index powered by the ScaNN algorithm.

- [Perform intelligent SQL queries using AlloyDB AI functions](https://docs.cloud.google.com/alloydb/docs/ai/evaluate-semantic-queries-ai-operators): use AI directly within your SQL queries. This allows you to re-rank search results for higher relevance, integrate natural language into your SQL queries, and generate multimodal embeddings for vector search.

- [Call models using model endpoints](https://docs.cloud.google.com/alloydb/docs/ai/model-endpoint-overview): register AI models as model endpoints and call the endpoints from within AlloyDB to generate embeddings, invoke predictions, or perform similarity searches.

- [Generate embeddings](https://docs.cloud.google.com/alloydb/docs/ai/work-with-embeddings) and [invoke predictions](https://docs.cloud.google.com/alloydb/docs/ai/invoke-predictions): use Gemini Enterprise Agent Platform text embedding models or registered model endpoints to generate text or multimodal embeddings.

- [Generate SQL statements from natural language](https://docs.cloud.google.com/alloydb/docs/ai/natural-language-overview): add natural language capabilities to your application, and interact with AlloyDB by asking questions in natural language. The natural language questions are then processed by AlloyDB AI to automatically generate an accurate SQL query that retrieve the answer.

## What's next

- [Perform vector search tutorial](https://docs.cloud.google.com/alloydb/docs/ai/perform-vector-search)

- [Integrate AlloyDB with Agent Platform](https://docs.cloud.google.com/alloydb/docs/ai/configure-vertex-ai).

- [Create a ScaNN index](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index).