docs: update full list of providers with matched APIs and dockerhub images (#2452)

# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- add model_type in example
- change "Memory" to "VectorIO" as column name
- update index.md and README.md

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<!-- Closes #[issue-number] -->

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
run pre-commit to catch changes.

---------

Signed-off-by: Wen Zhou <wenzhou@redhat.com>
Co-authored-by: Sébastien Han <seb@redhat.com>

docs/source/providers/index.md

@@ -1,9 +1,10 @@
 # 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., Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.),
-- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, Milvus, FAISS, PGVector, SQLite-Vec, etc.),
-- Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
+- LLM inference providers (e.g., Meta Reference, Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, OpenAI, Anthropic, Gemini, WatsonX, etc.),
+- Vector databases (e.g., FAISS, SQLite-Vec, ChromaDB, Weaviate, Qdrant, Milvus, PGVector, etc.),
+- Safety providers (e.g., Meta's Llama Guard, Prompt Guard, Code Scanner, AWS Bedrock Guardrails, etc.),
+- Tool Runtime providers (e.g., RAG Runtime, Brave Search, 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.