Agent skill · software engineering · aiskillstore
rag-pipeline
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Why this skill is useful
Adds executable scripts for ingestion and vector search that enhance the AI's capabilities in managing and querying data.
What it needs
Requires qdrant-client installed locally. About 1k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 physicalaibook. Run: python backend/ingest.py Vector Search (Qdrant) Client: qdrant-client Collection: physicalaibook 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 softwarebackground and hardwarebackground 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.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/rag-pipeline