feat(vector-io): add OpenGauss vector database provider

Implement OpenGauss vector database integration for Llama Stack with the following features:
- Add OpenGaussVectorIOAdapter for vector storage and retrieval
- Support native vector similarity search operations
- Provide configuration template for easy setup
- Add comprehensive unit tests
- Align with the latest Llama Stack provider architecture, including KVStore and OpenAI Vector Store Mixin.

The implementation allows Llama Stack users to leverage OpenGauss as an
enterprise-grade vector database for RAG applications.

docs/source/providers/agents/index.md

@@ -4,12 +4,12 @@
 
 Agents API for creating and interacting with agentic systems.
 
-    Main functionalities provided by this API:
-    - Create agents with specific instructions and ability to use tools.
-    - Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
-    - Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
-    - Agents can be provided with various shields (see the Safety API for more details).
-    - Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
+Main functionalities provided by this API:
+- Create agents with specific instructions and ability to use tools.
+- Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
+- Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
+- Agents can be provided with various shields (see the Safety API for more details).
+- Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
 
 This section contains documentation for all available providers for the **agents** API.
 

docs/source/providers/batches/index.md

@@ -4,11 +4,11 @@
 
 Protocol for batch processing API operations.
 
-    The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
+The Batches API enables efficient processing of multiple requests in a single operation,
+particularly useful for processing large datasets, batch evaluation workflows, and
+cost-effective inference at scale.
 
-    Note: This API is currently under active development and may undergo changes.
+Note: This API is currently under active development and may undergo changes.
 
 This section contains documentation for all available providers for the **batches** API.
 

docs/source/providers/inference/index.md

@@ -4,9 +4,9 @@
 
 Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
+This API provides the raw interface to the underlying models. Two kinds of models are supported:
+- LLM models: these models generate "raw" and "chat" (conversational) completions.
+- Embedding models: these models generate embeddings to be used for semantic search.
 
 This section contains documentation for all available providers for the **inference** API.
 

docs/source/providers/vector_io/index.md

@@ -18,6 +18,7 @@ inline_sqlite-vec
 inline_sqlite_vec
 remote_chromadb
 remote_milvus
+remote_opengauss
 remote_pgvector
 remote_qdrant
 remote_weaviate

docs/source/providers/vector_io/remote_opengauss.md

@@ -0,0 +1,58 @@
+# remote::opengauss
+
+## Description
+
+
+[OpenGauss](https://opengauss.org/en/) is a remote vector database provider for Llama Stack. It
+allows you to store and query vectors directly in memory.
+That means you'll get fast and efficient vector retrieval.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use OpenGauss in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use OpenGauss.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install OpenGauss using docker:
+
+```bash
+docker pull opengauss/opengauss:latest
+```
+## Documentation
+See [OpenGauss' documentation](https://docs.opengauss.org/en/docs/5.0.0/docs/GettingStarted/understanding-opengauss.html) for more details about OpenGauss in general.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `host` | `str \| None` | No | localhost |  |
+| `port` | `int \| None` | No | 5432 |  |
+| `db` | `str \| None` | No | postgres |  |
+| `user` | `str \| None` | No | postgres |  |
+| `password` | `str \| None` | No | mysecretpassword |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No |  | Config for KV store backend (SQLite only for now) |
+
+## Sample Configuration
+
+```yaml
+host: ${env.OPENGAUSS_HOST:=localhost}
+port: ${env.OPENGAUSS_PORT:=5432}
+db: ${env.OPENGAUSS_DB}
+user: ${env.OPENGAUSS_USER}
+password: ${env.OPENGAUSS_PASSWORD}
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/opengauss_registry.db
+
+```
+