updated docs

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

docs/source/index.md

@@ -67,6 +67,7 @@ A number of "adapters" are available for some popular Inference and Vector Store
 |  **Provider** |  **Environments** |
 | :----: | :----: |
 |  FAISS | Single Node |
+|  SQLite-Vec| Single Node |
 |  Chroma | Hosted and Single Node |
 |  Postgres (PGVector) | Hosted and Single Node |
 |  Weaviate | Hosted |
@@ -88,6 +89,7 @@ self
 introduction/index
 getting_started/index
 concepts/index
+providers/index
 distributions/index
 distributions/selection
 building_applications/index

docs/source/providers/index.md

@@ -0,0 +1,45 @@
+# Providers Overview
+
+The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:
+- LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, etc.),
+- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, FAISS, PGVector, etc.),
+- Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
+
+Providers come in two flavors:
+- **Remote**: the provider runs as a separate service external to the Llama Stack codebase. Llama Stack contains a small amount of adapter code.
+- **Inline**: the provider is fully specified and implemented within the Llama Stack codebase. It may be a simple wrapper around an existing library, or a full fledged implementation within Llama Stack.
+
+Importantly, Llama Stack always strives to provide at least one fully "local" provider for each API so you can iterate on a fully featured environment locally.
+
+## Agents
+
+## DatasetIO
+
+## Eval
+
+## Inference
+
+## iOS
+
+## Post Training
+
+## Safety
+
+## Scoring
+
+## Telemetry
+
+## Tool Runtime
+
+## [Vector DBs](vector_db/index.md)
+
+```{toctree}
+:maxdepth: 1
+
+vector_db/chromadb
+vector_db/sqlite-vec
+vector_db/faiss
+vector_db/pgvector
+vector_db/qdrant
+vector_db/weaviate
+```

docs/source/providers/vector_db/chromadb.md

@@ -0,0 +1,33 @@
+# 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_db/faiss.md

@@ -0,0 +1,30 @@
+# 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_db/index.md

@@ -0,0 +1,12 @@
+## Vector DB Providers
+
+The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations 
+for the same Vector Database.
+
+Examples for these include:
+- [FAISS](vector_db/faiss.md) (inline)
+- [SQLite-Vec](vector_db/sqlite-vec.md) (inline)
+- [ChromaDB](vector_db/chromadb.md) (inline and remote)
+- [Weaviate](vector_db/weaviate.md) (remote)
+- [Qdrant](vector_db/qdrant.md) (remote)
+- [PGVector](vector_db/pgvector.md) (remote)

docs/source/providers/vector_db/pgvector.md

@@ -0,0 +1,28 @@
+# 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_db/qdrant.md

@@ -0,0 +1,28 @@
+# 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_db/sqlite-vec.md

@@ -0,0 +1,30 @@
+# 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.