Improved README.md

README.md

@@ -14,3 +14,28 @@ short_description: Conversational space enhanced with Viquipedia RAG
 # Wiki Tools
 
 An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+
+This space demonstrates how to build a conversational AI application enhanced with Retrieval-Augmented Generation (RAG) using a Vector Database (VectorDB) built from Viquipedia articles.
+
+
+### Current resources
+- **VectorDB mRoBERTA**: 9.9GB / 2400k vectors [langtech-innovation/mRoberta_experimental_ViquipediaVectorStore](https://huggingface.co/langtech-innovation/mRoberta_experimental_ViquipediaVectorStore)
+- **Embedding model mRoBERTA**: [langtech-innovation/sentence-mRoBERTa-v0](https://huggingface.co/langtech-innovation/sentence-mRoBERTa-v0)
+- **LLM endpoint model** Salamandra-7B-Instruct-Tools-16k
+
+<!-- Disclaimer -->
+> [!WARNING]
+> **DISCLAIMER:** This model is an **experimental version** and is provided for **research purposes only**.
+> Access is **not public**.
+> Please do not share.
+
+
+### Alternative resources 
+
+Configure other available alternative resources for embeddings and VectorDB.
+
+Setup `VS_HF_PATH=langtech-innovation/vdb-cawiki-v3` and `EMBEDDINGS_MODEL=BAAI/bge-m3` variables to switch between resources. In this case, the system will use:
+
+- **VectorDB BGE-M3**: 12.3GB / 2400k vectors [langtech-innovation/vdb-cawiki-v3](https://huggingface.co/langtech-innovation/vdb-cawiki-v3)
+- **Embedding model BGE-M3**: [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
+