rag-pipeline
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Best use case
rag-pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "rag-pipeline" skill to help with this workflow task. Context: Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/rag-pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rag-pipeline Compares
| Feature / Agent | rag-pipeline | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.Related Skills
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