Agent skill
RAG Pipeline
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/rag-abdulsamad94-hackhaton-specskitpl-c3aba4a1
SKILL.md
RAG Pipeline Logic
Ingestion
- Script:
backend/ingest.py - Process:
- Scans
docs/. - Cleans MDX (removes frontmatter/imports).
- Chunks text (1000 chars, 100 overlap).
- Embeds using
models/text-embedding-004. - Upserts to Qdrant collection
physical_ai_book.
- Scans
- 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.pycreates system instructions based onsoftware_backgroundandhardware_backgroundof 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.
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