Merged back the original features and added more progress output

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

docs/source/providers/index.md

@@ -31,15 +31,16 @@ Importantly, Llama Stack always strives to provide at least one fully "local" pr
 
 ## Tool Runtime
 
-## Vector DBs
+## 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.
+
+The following providers (i.e., databases) are available for Vector IO:
 
 ```{toctree}
 :maxdepth: 1
 
-vector_db/chromadb
-vector_db/sqlite-vec
-vector_db/faiss
-vector_db/pgvector
-vector_db/qdrant
-vector_db/weaviate
+vector_io/index
 ```

docs/source/providers/vector_io/index.md

@@ -0,0 +1,10 @@
+```{toctree}
+:maxdepth: 2
+
+chromadb
+sqlite-vec
+faiss
+pgvector
+qdrant
+weaviate
+```

docs/source/providers/vector_io/weaviate.md

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