Why Use This
This skill provides specialized capabilities for aiskillstore's codebase.
Use Cases
- Developing new features in the aiskillstore repository
- Refactoring existing code to follow aiskillstore standards
- Understanding and working with aiskillstore's codebase structure
Install Guide
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https://github.com/aiskillstore/marketplace/tree/main/skills/abdulsamad94/rag-pipeline
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Updated At Jan 19, 2026, 04:39 AM
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License NOASSERTION
---
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.
