docs: Adding Provider sections to docs (#1195)

# What does this PR do?
Adding Provider sections to docs (some of these will be empty and need
updating).


This PR is still a draft while I seek feedback from other contributors.
I opened it to make the structure visible in the linked GitHub Issue.

# Closes https://github.com/meta-llama/llama-stack/issues/1189

- Providers Overview Page
![Screenshot 2025-02-21 at 12 15
09 PM](https://github.com/user-attachments/assets/e83e5a17-0d96-4de0-8251-68161799a054)

- SQLite-Vec specific page
![Screenshot 2025-02-21 at 12 15
34 PM](https://github.com/user-attachments/assets/14773900-fc8f-49e9-832a-b060b7ca010a)

## Test Plan
N/A

[//]: # (## Documentation)

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

docs/source/providers/vector_io/chromadb.md

@@ -0,0 +1,36 @@
+---
+orphan: true
+---
+# Chroma 
+
+[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.

docs/source/providers/vector_io/faiss.md

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+---
+orphan: true
+---
+# Faiss
+
+[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.
+That means you'll get fast and efficient vector retrieval.
+
+## Features
+
+- Lightweight and easy to use
+- Fully integrated with Llama Stack
+- GPU support
+
+## Usage
+
+To use Faiss in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Faiss.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Faiss using pip:
+
+```bash
+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.

docs/source/providers/vector_io/pgvector.md

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+---
+orphan: true
+---
+# Postgres PGVector
+
+[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.
+That means you'll get fast and efficient vector retrieval.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use PGVector in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Faiss.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install PGVector using docker:
+
+```bash
+docker pull pgvector/pgvector:pg17
+```
+## Documentation
+See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general.

docs/source/providers/vector_io/qdrant.md

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+---
+orphan: true
+---
+# Qdrant
+
+[Qdrant](https://qdrant.tech/documentation/) 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 Qdrant in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Faiss.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Qdrant using docker:
+
+```bash
+docker pull qdrant/qdrant
+```
+## Documentation
+See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.

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

@@ -0,0 +1,33 @@
+---
+orphan: true
+---
+# SQLite-Vec
+
+[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. 
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+
+- Lightweight and easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use SQLite-Vec in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use SQLite-Vec.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install SQLite-Vec using pip:
+
+```bash
+pip install sqlite-vec
+```
+
+## Documentation
+
+See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) for more details about sqlite-vec in general.

docs/source/providers/vector_io/weaviate.md

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+---
+orphan: true
+---
+# Weaviate 
+
+[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. 
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+Weaviate supports:
+- Store embeddings and their metadata
+- Vector search
+- Full-text search
+- Hybrid search
+- Document storage
+- Metadata filtering
+- Multi-modal retrieval
+
+## Usage
+
+To use Weaviate 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
+
+To install Weaviate see the [Weaviate quickstart documentation](https://weaviate.io/developers/weaviate/quickstart). 
+
+## Documentation
+See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.