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Rag pipeline

Skill ComeOnOliver/skillshub/skills/aiskillstore/marketplace/abdulsamad94/rag-pipeline

Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.From its SKILL.md

Install
npx -y skills add ComeOnOliver/skillshub --skill rag-pipeline

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

1.0 KB, 253 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it

9.7 KB alongside SKILL.md

GitHub clipped this repository’s file list, so this is at least 1 file and may be more.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.