Rag pipeline

Skill bg-szy/TOP-SKILLS/skills/marketplace/rag-pipeline

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Install

npx -y skills add bg-szy/TOP-SKILLS --skill rag-pipeline

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What its author says it does

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

SKILL.md

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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.