chore: Enabling Milvus for VectorIO CI

Signed-off-by: Francisco Javier Arceo <farceo@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
+{}
+
+```
+