Add comprehensive Claude instructions for AI Tutor App

CLAUDE.md

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+# AI Tutor App Instructions for Claude
+
+## Project Overview
+This is an AI tutor application that uses RAG (Retrieval Augmented Generation) to provide accurate responses about AI concepts by searching through multiple documentation sources. The application has a Gradio UI and uses ChromaDB for vector storage.
+
+## Key Repositories and URLs
+- Main code: https://github.com/towardsai/ai-tutor-app
+- Live demo: https://huggingface.co/spaces/towardsai-tutors/ai-tutor-chatbot
+- Vector database: https://huggingface.co/datasets/towardsai-tutors/ai-tutor-vector-db
+- Private JSONL repo: https://huggingface.co/datasets/towardsai-tutors/ai-tutor-data
+
+## Architecture Overview
+- Frontend: Gradio-based UI in `scripts/main.py`
+- Retrieval: Custom retriever using ChromaDB vector stores
+- Embedding: Cohere embeddings for vector search
+- LLM: OpenAI models (GPT-4o, etc.) for context addition and responses
+- Storage: Individual JSONL files per source + combined file for retrieval
+
+## Data Update Workflows
+
+### 1. Adding a New Course
+```bash
+python data/scraping_scripts/add_course_workflow.py --course [COURSE_NAME]
+```
+- This requires the course to be configured in `process_md_files.py` under `SOURCE_CONFIGS`
+- The workflow will pause for manual URL addition after processing markdown files
+- Only new content will have context added by default (efficient)
+- Use `--process-all-context` if you need to regenerate context for all documents
+- Both database and data files are uploaded to HuggingFace by default
+- Use `--skip-data-upload` if you don't want to upload data files
+
+### 2. Updating Documentation from GitHub
+```bash
+python data/scraping_scripts/update_docs_workflow.py
+```
+- Updates all supported documentation sources (or specify specific ones with `--sources`)
+- Downloads fresh documentation from GitHub repositories
+- Only new content will have context added by default (efficient)
+- Use `--process-all-context` if you need to regenerate context for all documents
+- Both database and data files are uploaded to HuggingFace by default
+- Use `--skip-data-upload` if you don't want to upload data files
+
+### 3. Data File Management
+```bash
+# Upload both JSONL and PKL files to private HuggingFace repository
+python data/scraping_scripts/upload_data_to_hf.py
+```
+
+## Data Flow and File Relationships
+
+### Document Processing Pipeline
+1. **Markdown Files** → `process_md_files.py` → **Individual JSONL files** (e.g., `transformers_data.jsonl`)
+2. Individual JSONL files → `combine_all_sources()` → `all_sources_data.jsonl`
+3. `all_sources_data.jsonl` → `add_context_to_nodes.py` → `all_sources_contextual_nodes.pkl`
+4. `all_sources_contextual_nodes.pkl` → `create_vector_stores.py` → ChromaDB vector stores
+
+### Important Files and Their Purpose
+- `all_sources_data.jsonl` - Combined raw document data without context
+- Source-specific JSONL files (e.g., `transformers_data.jsonl`) - Raw data for individual sources
+- `all_sources_contextual_nodes.pkl` - Processed nodes with added context
+- `chroma-db-all_sources` - Vector database directory containing embeddings
+- `document_dict_all_sources.pkl` - Dictionary mapping document IDs to full documents
+
+## Configuration Details
+
+### Adding a New Course Source
+1. Update `SOURCE_CONFIGS` in `process_md_files.py`:
+```python
+"new_course": {
+    "base_url": "",
+    "input_directory": "data/new_course",
+    "output_file": "data/new_course_data.jsonl",
+    "source_name": "new_course",
+    "use_include_list": False,
+    "included_dirs": [],
+    "excluded_dirs": [],
+    "excluded_root_files": [],
+    "included_root_files": [],
+    "url_extension": "",
+},
+```
+
+2. Update UI configurations in:
+   - `setup.py`: Add to `AVAILABLE_SOURCES` and `AVAILABLE_SOURCES_UI`
+   - `main.py`: Add mapping in `source_mapping` dictionary
+
+## Deployment and Publishing
+
+### GitHub Actions Workflow
+The application is automatically deployed to HuggingFace Spaces when changes are pushed to the main branch (excluding documentation and scraping scripts).
+
+### Manual Deployment
+```bash
+git push --force https://$HF_USERNAME:[email protected]/spaces/towardsai-tutors/ai-tutor-chatbot main:main
+```
+
+## Development Environment Setup
+
+### Required Environment Variables
+- `OPENAI_API_KEY` - For LLM processing
+- `COHERE_API_KEY` - For embeddings
+- `HF_TOKEN` - For HuggingFace uploads
+- `GITHUB_TOKEN` - For accessing documentation via the GitHub API
+
+### Running the Application Locally
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Start the Gradio UI
+python scripts/main.py
+```
+
+## Important Notes
+
+1. When adding new courses, make sure to:
+   - Place markdown files exported from Notion in the appropriate directory
+   - Add URLs manually from the live course platform 
+   - Example URL format: `https://academy.towardsai.net/courses/take/python-for-genai/multimedia/62515980-course-structure`
+   - Configure the course in `process_md_files.py`
+   - Verify it appears in the UI after deployment
+
+2. For updating documentation:
+   - The GitHub API is used to fetch the latest documentation
+   - The workflow handles updating existing sources without affecting course data
+
+3. For efficient context addition:
+   - Only new content gets processed by default
+   - Old nodes for updated sources are removed from the PKL file
+   - This ensures no duplicate content in the vector database
+
+## Technical Details for Debugging
+
+### Node Removal Logic
+- When adding context, the workflow now removes existing nodes for sources being updated
+- This prevents duplication of content in the vector database
+- The source of each node is extracted from either `node.source_node.metadata` or `node.metadata`
+
+### Performance Considerations
+- Context addition is the most time-consuming step (uses OpenAI API)
+- The new default behavior only processes new content
+- For large updates, consider running in batches