Add LLM provider switch, markdown chat UI, and update README

- Dual RAG instances (Ollama + OpenAI) for on-the-fly provider switching
- Provider dropdown in chat UI, /api/providers endpoint
- Markdown rendering for assistant responses
- Server logs include provider and model name for each LLM response
- README: OpenAI setup, add_pdfs, API docs, provider switch

Co-authored-by: Cursor <cursoragent@cursor.com>

README.md

@@ -1,78 +1,90 @@
 # Local RAG Setup
 
-Minimal RAG implementation with LangChain, Ollama, and FAISS.
+Minimal RAG implementation with LangChain, FAISS, and support for either Ollama or OpenAI (API-key needed).
 
 ## Dependencies
 
 - `langchain` - Core framework
-- `langchain-community` - Community integrations (loaders, vectorstores)
+- `langchain-community` - Loaders, vectorstores
 - `langchain-ollama` - Ollama integration
-- `langchain-text-splitters` - Text splitting utilities
+- `langchain-openai` - OpenAI integration
+- `langchain-text-splitters` - Text splitting
 - `langchain-huggingface` - HuggingFace embeddings
 - `faiss-cpu` - Vector search
 - `sentence-transformers` - Embeddings
 - `pypdf` - PDF loading
-- `fastapi` - Web server and API
+- `fastapi` - Web server
 - `uvicorn` - ASGI server
 
 ## Installation
 
 ```bash
-# Create conda environment
 conda create -n local_rag python=3.10 -y
 conda activate local_rag
-
-# Install dependencies
 pip install -r requirements.txt
 ```
 
-## Setup Ollama
+## Setup
+
+### Ollama (optional)
 
 ```bash
-# Make sure Ollama is running
 ollama serve
-
-# Pull a model (in another terminal)
-ollama pull llama2
+ollama pull mistral
 ```
 
-## Usage
+### OpenAI (optional)
 
-Edit `local_rag.py` and uncomment the example code:
+Set the API key when using OpenAI:
+
+```bash
+export OPENAI_API_KEY="your-key"
+```
+
+## Add Documents
+
+**Option 1:** Add PDFs from a folder via script. Edit `DATA_ROOT` in [add_pdfs.py](add_pdfs.py) to point at your folder, then run:
+
+```bash
+python add_pdfs.py
+```
+
+The script clears the existing vector store and indexes all PDFs recursively. Supports `.pdf`, `.txt`, `.md`.
+
+**Option 2:** Use `local_rag.py` programmatically:
 
 ```python
-# Add documents
-rag.add_documents([
-    "path/to/document1.pdf",
-    "path/to/document2.txt"
-])
-
-# Query
-question = "What is this document about?"
-answer = rag.query(question)
-print(f"Answer: {answer}")
+from local_rag import LocalRAG
+rag = LocalRAG()
+rag.add_documents(["path/to/doc1.pdf", "path/to/doc2.txt"])
 ```
 
-Run:
-```bash
-python local_rag.py
-```
+## Chat GUI
 
-## Chat GUI (FastAPI)
-
-A simple web chat interface is included. Start the server:
+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.
+Open [http://localhost:8000](http://localhost:8000). The chat UI provides:
+
+- **Provider switch** – Toggle between Ollama and OpenAI without restart (OpenAI requires `OPENAI_API_KEY`)
+- **Conversation history** – Multi-turn chat with context
+- **Markdown** – Assistant replies rendered as markdown (headings, code, lists, links)
+
+Ensure the vector store is populated and at least one provider (Ollama or OpenAI) is configured.
+
+## API
+
+- `POST /api/chat` – `{ "message": "...", "history": [...], "llm_provider": "ollama"|"openai" }`
+- `GET /api/providers` – `{ "ollama": true, "openai": true|false }`
+- `GET /api/health` – Health and vectorstore status
 
 ## How it works
 
-1. **Load documents** - PDFs or text files
-2. **Split into chunks** - 1000 chars with 200 overlap
-3. **Create embeddings** - Using sentence-transformers
-4. **Store in FAISS** - Fast similarity search
-5. **Query** - Retrieve relevant chunks and generate answer with Ollama
-
+1. **Load documents** – PDFs or text via PyPDFLoader / TextLoader
+2. **Chunk** – RecursiveCharacterTextSplitter (2000 chars, 400 overlap)
+3. **Embed** – sentence-transformers/all-MiniLM-L6-v2
+4. **Store** – FAISS vector store (similarity search with scores)
+5. **Query** – Retrieve chunks, optionally rephrase with conversation history, generate answer with selected LLM