name: rag-pipeline description: Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.

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.