Update documentation

docs/source/concepts/index.md

@@ -23,21 +23,23 @@ We are working on adding a few more APIs to complete the application lifecycle.
 
 ## API Providers
 
-The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Obvious examples for these include
-- LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, SambaNova, etc.),
-- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, etc.),
+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.
 
+Most 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.
 ## Resources
 
 Some of these APIs are associated with a set of **Resources**. Here is the mapping of APIs to resources:
 
 - **Inference**, **Eval** and **Post Training** are associated with `Model` resources.
 - **Safety** is associated with `Shield` resources.
+- **Tool Runtime** is associated with `ToolGroup` resources.
 - **DatasetIO** is associated with `Dataset` resources.
 - **Scoring** is associated with `ScoringFunction` resources.
 - **Eval** is associated with `Model` and `EvalTask` resources.
@@ -56,7 +58,7 @@ While there is a lot of flexibility to mix-and-match providers, often users will
 
 **Remotely Hosted Distro**: These are the simplest to consume from a user perspective. You can simply obtain the API key for these providers, point to a URL and have _all_ Llama Stack APIs working out of the box. Currently, [Fireworks](https://fireworks.ai/) and [Together](https://together.xyz/) provide such easy-to-consume Llama Stack distributions.
 
-**Locally Hosted Distro**: You may want to run Llama Stack on your own hardware. Typically though, you still need to use Inference via an external service. You can use providers like HuggingFace TGI, Cerebras, Fireworks, Together, etc. for this purpose. Or you may have access to GPUs and can run a [vLLM](https://github.com/vllm-project/vllm) or [NVIDIA NIM](https://build.nvidia.com/nim?filters=nimType%3Anim_type_run_anywhere&q=llama) instance. If you "just" have a regular desktop machine, you can use [Ollama](https://ollama.com/) for inference. To provide convenient quick access to these options, we provide a number of such pre-configured locally-hosted Distros.
+**Locally Hosted Distro**: You may want to run Llama Stack on your own hardware. Typically though, you still need to use Inference via an external service. You can use providers like HuggingFace TGI, Fireworks, Together, etc. for this purpose. Or you may have access to GPUs and can run a [vLLM](https://github.com/vllm-project/vllm) or [NVIDIA NIM](https://build.nvidia.com/nim?filters=nimType%3Anim_type_run_anywhere&q=llama) instance. If you "just" have a regular desktop machine, you can use [Ollama](https://ollama.com/) for inference. To provide convenient quick access to these options, we provide a number of such pre-configured locally-hosted Distros.
 
 
 **On-device Distro**: Finally, you may want to run Llama Stack directly on an edge device (mobile phone or a tablet.) We provide Distros for iOS and Android (coming soon.)