feat: pre-download and cache embedding models in Docker image for faster startup

Dockerfile

@@ -13,13 +13,22 @@ WORKDIR /app
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
+# --- Pre-download Embedding Models to cache them in the Docker image ---
+# This ensures the app boots instantly and doesn't get stuck in 'Starting...'
+RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2'); SentenceTransformer('intfloat/e5-small-v2')"
+
 # Copy the rest of the application
 COPY . .
 
+# Environment variables for performance and avoiding telemetry issues
+ENV CHROMA_TELEMETRY_NO_ANALYTICS=True
+ENV ANONYMIZED_TELEMETRY=False
+ENV STREAMLIT_SERVER_HEADLESS=true
+
 # Create dummy db_store for local testing during build if needed
 RUN mkdir -p db_store
 
-# Set permissions for Hugging Face
+# Set permissions for Hugging Face (any user can write)
 RUN chmod -R 777 /app
 
 # Expose Streamlit's default port