- added three iwm articles as test data

- added simple webview and FastAPI server

README.md

@@ -4,12 +4,16 @@ Minimal RAG implementation with LangChain, Ollama, and FAISS.
 
 ## Dependencies
 
-Only 5 packages:
 - `langchain` - Core framework
+- `langchain-community` - Community integrations (loaders, vectorstores)
 - `langchain-ollama` - Ollama integration
+- `langchain-text-splitters` - Text splitting utilities
+- `langchain-huggingface` - HuggingFace embeddings
 - `faiss-cpu` - Vector search
 - `sentence-transformers` - Embeddings
 - `pypdf` - PDF loading
+- `fastapi` - Web server and API
+- `uvicorn` - ASGI server
 
 ## Installation
 
@@ -54,6 +58,16 @@ Run:
 python local_rag.py
 ```
 
+## Chat GUI (FastAPI)
+
+A simple web chat interface is included. Start the server:
+
+```bash
+uvicorn server:app --reload
+```
+
+Then open [http://localhost:8000](http://localhost:8000) in your browser. The chat view uses the same RAG system: your messages are answered using the vector store and Ollama. Ensure your vector store is populated (e.g. by running the document-add steps in `local_rag.py` once) and that Ollama is running.
+
 ## How it works
 
 1. **Load documents** - PDFs or text files