docs: auto generated documentation for providers (#2543)

# What does this PR do?

Simple approach to get some provider pages in the docs.

Add or update description fields in the provider configuration class
using Pydantic’s Field, ensuring these descriptions are clear and
complete, as they will be used to auto-generate provider documentation
via ./scripts/distro_codegen.py instead of editing the docs manually.

Signed-off-by: Sébastien Han <seb@redhat.com>

docs/source/providers/vector_io/index.md

@@ -0,0 +1,16 @@
+# Vector_Io Providers
+
+This section contains documentation for all available providers for the **vector_io** API.
+
+- [inline::chromadb](inline_chromadb.md)
+- [inline::faiss](inline_faiss.md)
+- [inline::meta-reference](inline_meta-reference.md)
+- [inline::milvus](inline_milvus.md)
+- [inline::qdrant](inline_qdrant.md)
+- [inline::sqlite-vec](inline_sqlite-vec.md)
+- [inline::sqlite_vec](inline_sqlite_vec.md)
+- [remote::chromadb](remote_chromadb.md)
+- [remote::milvus](remote_milvus.md)
+- [remote::pgvector](remote_pgvector.md)
+- [remote::qdrant](remote_qdrant.md)
+- [remote::weaviate](remote_weaviate.md)

docs/source/providers/vector_io/inline_chromadb.md

@@ -0,0 +1,52 @@
+# inline::chromadb
+
+## Description
+
+
+[Chroma](https://www.trychroma.com/) is an inline and remote vector
+database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+Chroma supports:
+- Store embeddings and their metadata
+- Vector search
+- Full-text search
+- Document storage
+- Metadata filtering
+- Multi-modal retrieval
+
+## Usage
+
+To use Chrome in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use chroma.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install chroma using pip:
+
+```bash
+pip install chromadb
+```
+
+## Documentation
+See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general.
+
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+
+## Sample Configuration
+
+```yaml
+db_path: ${env.CHROMADB_PATH}
+
+```
+

docs/source/providers/vector_io/inline_faiss.md

@@ -1,7 +1,7 @@
----
-orphan: true
----
-# Faiss
+# inline::faiss
+
+## Description
+
 
 [Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
@@ -31,3 +31,21 @@ pip install faiss-cpu
 ## Documentation
 See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for
 more details about Faiss in general.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+
+## Sample Configuration
+
+```yaml
+kvstore:
+  type: sqlite
+  namespace: null
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
+
+```
+

docs/source/providers/vector_io/inline_meta-reference.md

@@ -0,0 +1,26 @@
+# inline::meta-reference
+
+## Description
+
+Meta's reference implementation of a vector database.
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+
+## Sample Configuration
+
+```yaml
+kvstore:
+  type: sqlite
+  namespace: null
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
+
+```
+
+## Deprecation Notice
+
+⚠️ **Warning**: Please use the `inline::faiss` provider instead.
+

docs/source/providers/vector_io/inline_milvus.md

@@ -0,0 +1,26 @@
+# inline::milvus
+
+## Description
+
+
+Please refer to the remote provider documentation.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+
+## Sample Configuration
+
+```yaml
+db_path: ${env.MILVUS_DB_PATH:=~/.llama/dummy/milvus.db}
+kvstore:
+  type: sqlite
+  namespace: null
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/${env.MILVUS_KVSTORE_DB_PATH:=~/.llama/dummy/milvus_registry.db}
+
+```
+

docs/source/providers/vector_io/inline_qdrant.md

@@ -1,7 +1,7 @@
----
-orphan: true
----
-# Qdrant
+# inline::qdrant
+
+## Description
+
 
 [Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
@@ -44,3 +44,18 @@ docker pull qdrant/qdrant
 ```
 ## Documentation
 See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `path` | `<class 'str'>` | No | PydanticUndefined |  |
+
+## Sample Configuration
+
+```yaml
+path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
+
+```
+

docs/source/providers/vector_io/inline_sqlite-vec.md

@@ -1,7 +1,7 @@
----
-orphan: true
----
-# SQLite-Vec
+# inline::sqlite-vec
+
+## Description
+
 
 [SQLite-Vec](https://github.com/asg017/sqlite-vec) is an inline vector database provider for Llama Stack. It
 allows you to store and query vectors directly within an SQLite database.
@@ -199,3 +199,18 @@ pip install sqlite-vec
 See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) for more details about sqlite-vec in general.
 
 [^1]: Cormack, G. V., Clarke, C. L., & Buettcher, S. (2009). [Reciprocal rank fusion outperforms condorcet and individual rank learning methods](https://dl.acm.org/doi/10.1145/1571941.1572114). In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 758-759).
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+
+## Sample Configuration
+
+```yaml
+db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
+
+```
+

docs/source/providers/vector_io/inline_sqlite_vec.md

@@ -0,0 +1,25 @@
+# inline::sqlite_vec
+
+## Description
+
+
+Please refer to the sqlite-vec provider documentation.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+
+## Sample Configuration
+
+```yaml
+db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
+
+```
+
+## Deprecation Notice
+
+⚠️ **Warning**: Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.
+

docs/source/providers/vector_io/remote_chromadb.md

@@ -1,7 +1,7 @@
----
-orphan: true
----
-# Chroma
+# remote::chromadb
+
+## Description
+
 
 [Chroma](https://www.trychroma.com/) is an inline and remote vector
 database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
@@ -34,3 +34,18 @@ pip install chromadb
 
 ## Documentation
 See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `url` | `str \| None` | No | PydanticUndefined |  |
+
+## Sample Configuration
+
+```yaml
+url: ${env.CHROMADB_URL}
+
+```
+

docs/source/providers/vector_io/remote_milvus.md

@@ -1,7 +1,7 @@
----
-orphan: true
----
-# Milvus
+# remote::milvus
+
+## Description
+
 
 [Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly within a Milvus database.
@@ -96,7 +96,7 @@ vector_io:
 #### Key Parameters for TLS Configuration
 
 - **`secure`**: Enables TLS encryption when set to `true`. Defaults to `false`.
-- **`server_pem_path`**: Path to the **server certificate** for verifying the server’s identity (used in one-way TLS).
+- **`server_pem_path`**: Path to the **server certificate** for verifying the server's identity (used in one-way TLS).
 - **`ca_pem_path`**: Path to the **Certificate Authority (CA) certificate** for validating the server certificate (required in mTLS).
 - **`client_pem_path`**: Path to the **client certificate** file (required for mTLS).
 - **`client_key_path`**: Path to the **client private key** file (required for mTLS).
@@ -105,3 +105,24 @@ vector_io:
 See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.
 
 For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md).
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `uri` | `<class 'str'>` | No | PydanticUndefined | The URI of the Milvus server |
+| `token` | `str \| None` | No | PydanticUndefined | The token of the Milvus server |
+| `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
+| `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |
+
+> **Note**: This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider.
+
+## Sample Configuration
+
+```yaml
+uri: ${env.MILVUS_ENDPOINT}
+token: ${env.MILVUS_TOKEN}
+
+```
+

docs/source/providers/vector_io/remote_pgvector.md

@@ -1,7 +1,7 @@
----
-orphan: true
----
-# Postgres PGVector
+# remote::pgvector
+
+## Description
+
 
 [PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
@@ -29,3 +29,26 @@ docker pull pgvector/pgvector:pg17
 ```
 ## Documentation
 See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector 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 |  |
+
+## Sample Configuration
+
+```yaml
+host: ${env.PGVECTOR_HOST:=localhost}
+port: ${env.PGVECTOR_PORT:=5432}
+db: ${env.PGVECTOR_DB}
+user: ${env.PGVECTOR_USER}
+password: ${env.PGVECTOR_PASSWORD}
+
+```
+

docs/source/providers/vector_io/remote_qdrant.md

@@ -0,0 +1,30 @@
+# remote::qdrant
+
+## Description
+
+
+Please refer to the inline provider documentation.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `location` | `str \| None` | No |  |  |
+| `url` | `str \| None` | No |  |  |
+| `port` | `int \| None` | No | 6333 |  |
+| `grpc_port` | `<class 'int'>` | No | 6334 |  |
+| `prefer_grpc` | `<class 'bool'>` | No | False |  |
+| `https` | `bool \| None` | No |  |  |
+| `api_key` | `str \| None` | No |  |  |
+| `prefix` | `str \| None` | No |  |  |
+| `timeout` | `int \| None` | No |  |  |
+| `host` | `str \| None` | No |  |  |
+
+## Sample Configuration
+
+```yaml
+api_key: ${env.QDRANT_API_KEY}
+
+```
+

docs/source/providers/vector_io/remote_weaviate.md

@@ -1,7 +1,7 @@
----
-orphan: true
----
-# Weaviate
+# remote::weaviate
+
+## Description
+
 
 [Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack.
 It allows you to store and query vectors directly within a Weaviate database.
@@ -31,3 +31,12 @@ To install Weaviate see the [Weaviate quickstart documentation](https://weaviate
 
 ## Documentation
 See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.
+
+
+## Sample Configuration
+
+```yaml
+{}
+
+```
+