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/index.md

@@ -0,0 +1,59 @@
+# 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 inline provider for each API so you can iterate on a fully featured environment locally.
+
+## Agents
+Run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.
+
+## DatasetIO
+Interfaces with datasets and data loaders.
+
+## Eval
+Generates outputs (via Inference or Agents) and perform scoring.
+
+## Inference
+Runs inference with an LLM.
+
+## Post Training
+Fine-tunes a model.
+
+## Safety
+Applies safety policies to the output at a Systems (not only model) level.
+
+## Scoring
+Evaluates the outputs of the system.
+
+## Telemetry
+Collects telemetry data from the system.
+
+## Tool Runtime
+Is associated with the ToolGroup resouces. 
+
+## Vector IO
+
+Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents.
+Vector IO plays a crucial role in [Retreival Augmented Generation (RAG)](../..//building_applications/rag), where the vector
+io and database are used to store and retrieve documents for retrieval.
+
+#### Vector IO Providers
+The following providers (i.e., databases) are available for Vector IO:
+
+```{toctree}
+:maxdepth: 1
+
+vector_io/faiss
+vector_io/sqlite-vec
+vector_io/chromadb
+vector_io/pgvector
+vector_io/qdrant
+vector_io/weaviate
+```