rag-pipelinelisted

# RAG Pipeline Logic ## Ingestion - **Script**: `backend/ingest.py` - **Process**: 1. Scans `docs/`. 2. Cleans MDX (removes frontmatter/imports). 3. Chunks text (1000 chars, 100 overlap). 4. Embeds using `models/text-embedding-004`. 5. Upserts to Qdrant collection `physical_ai_book`. - **Run**: `python backend/ingest.py` ## Vector Search (Qdrant) - **Client**: `qdrant-client` - **Collection**: `physical_ai_book` - **Vector Size**: 768 (Gecko-004) - **Similarity**: Cosine ## Prompt Engineering - **File**: `backend/utils/helpers.py`. - **RAG Prompt**: Constructs a prompt containing retrieved context chunks. - **Personalization**: `backend/personalization.py` creates system instructions based on `software_background` and `hardware_background` of the user. ## Agentic Flow We use a custom `Agent` class (`backend/agents.py`) that wraps the LLM calls, allowing for future expansion into multi-agent workflows.