Agent-Skills.md

Agent Skills: RAG Pipeline Logic

Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.

UncategorizedID: aiskillstore/marketplace/rag-pipeline

Repository

aiskillstore/marketplace
aiskillstore
23014

Install this agent skill to your local

pnpm dlx add-skill https://github.com/aiskillstore/marketplace/tree/HEAD/skills/abdulsamad94/rag-pipeline

Skill Files

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skills/abdulsamad94/rag-pipeline/SKILL.md

Skill Metadata

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.