Add files via upload

README.md

@@ -1,2 +1,104 @@
-# TAI_Rag_project
-This is used for LLM project submission 
+# AI Tutor App Data Workflows
+
+This directory contains scripts for managing the AI Tutor App's data pipeline.
+
+## Workflow Scripts
+
+### 1. Adding a New Course
+
+To add a new course to the AI Tutor:
+
+```bash
+python add_course_workflow.py --course [COURSE_NAME]
+```
+
+This will guide you through the complete process:
+
+1. Process markdown files from Notion exports
+2. Prompt you to manually add URLs to the course content
+3. Merge the course data into the main dataset
+4. Add contextual information to document nodes
+5. Create vector stores
+6. Upload databases to HuggingFace
+7. Update UI configuration
+
+**Requirements before running:**
+
+- The course name must be properly configured in `process_md_files.py` under `SOURCE_CONFIGS`
+- Course markdown files must be placed in the directory specified in the configuration
+- You must have access to the live course platform to add URLs
+
+### 2. Updating Documentation via GitHub API
+
+To update library documentation from GitHub repositories:
+
+```bash
+python update_docs_workflow.py
+```
+
+This will update all supported documentation sources. You can also specify specific sources:
+
+```bash
+python update_docs_workflow.py --sources transformers peft
+```
+
+The workflow includes:
+
+1. Downloading documentation from GitHub using the API
+2. Processing markdown files to create JSONL data
+3. Adding contextual information to document nodes
+4. Creating vector stores
+5. Uploading databases to HuggingFace
+
+### 3. Uploading JSONL to HuggingFace
+
+To upload the main JSONL file to a private HuggingFace repository:
+
+```bash
+python upload_jsonl_to_hf.py
+```
+
+This is useful for sharing the latest data with team members.
+
+## Individual Components
+
+If you need to run specific steps individually:
+
+- **GitHub to Markdown**: `github_to_markdown_ai_docs.py`
+- **Process Markdown**: `process_md_files.py`
+- **Add Context**: `add_context_to_nodes.py` 
+- **Create Vector Stores**: `create_vector_stores.py`
+- **Upload to HuggingFace**: `upload_dbs_to_hf.py`
+
+## Tips for New Team Members
+
+1. To update the AI Tutor with new content:
+   - For new courses, use `add_course_workflow.py`
+   - For updated documentation, use `update_docs_workflow.py`
+
+2. When adding URLs to course content:
+   - Get the URLs from the live course platform
+   - Add them to the generated JSONL file in the `url` field
+   - Example URL format: `https://academy.towardsai.net/courses/take/python-for-genai/multimedia/62515980-course-structure`
+   - Make sure every document has a valid URL
+
+3. By default, only new content will have context added to save time and resources. Use `--process-all-context` only if you need to regenerate context for all documents. Use `--skip-data-upload` if you don't want to upload data files to the private HuggingFace repo (they're uploaded by default).
+
+4. When adding a new course, verify that it appears in the Gradio UI:
+   - The workflow automatically updates `main.py` and `setup.py` to include the new source
+   - Check that the new source appears in the dropdown menu in the UI
+   - Make sure it's properly included in the default selected sources
+   - Restart the Gradio app to see the changes
+
+5. First time setup or missing files:
+   - Both workflows automatically check for and download required data files:
+     - `all_sources_data.jsonl` - Contains the raw document data
+     - `all_sources_contextual_nodes.pkl` - Contains the processed nodes with added context
+   - If the PKL file exists, the `--new-context-only` flag will only process new content
+   - You must have proper HuggingFace credentials with access to the private repository
+
+6. Make sure you have the required environment variables set:
+   - `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