Retrieval Rag And Data Pipelines

Organization

VectorSpaceLab

retrieval-rag-and-data-pipelines

Document preprocessing, LocalDB persistence, retriever indexing, and RAG context assembly.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`retrieval-rag-and-data-pipelines`
Stars	341
Forks	26
Bundled files	6
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
6 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Retrieval Rag And Data Pipelines AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adalflow/sub-skills/retrieval-rag-and-data-pipelines
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adalflow/sub-skills/retrieval-rag-and-data-pipelines

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/adalflow/sub-skills/retrieval-rag-and-data-pipelines .claude/skills/retrieval-rag-and-data-pipelines

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Retrieval Rag And Data Pipelines in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Retrieval Rag And Data Pipelines on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Retrieval Rag And Data Pipelines is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Retrieval, RAG, and Data Pipelines

Use this sub-skill when the task is to prepare documents, split text, persist local data, build retriever indexes, convert retrieval output into context, or assemble a retrieval-first RAG flow.

Route here for

Document modeling for text corpora and chunked passages.
TextSplitter chunking rules, separator choices, overlap settings, and invalid chunk-shape troubleshooting.
ToEmbeddings and RetrieverOutputToContextStr as retrieval-side data transforms.
LocalDB load/extend/transform/save/load flows for documents and chunked corpora.
Retriever base contracts and concrete retrieval backends: BM25Retriever, FAISSRetriever, LanceDBRetriever, QdrantRetriever, and PostgresRetriever.
Retrieval-oriented RAG assembly patterns: split, embed, persist, retrieve, deduplicate, and pass context downstream.

Do not handle here

Provider/client selection, embedding model setup, or generator configuration: route to the model-client-and-generator-workflows sub-skill.
Metrics, evaluator loops, prompt optimization, or retriever scoring analysis: route to the evaluation-and-optimization sub-skill.
Agentic RAG, tool use, runner orchestration, or streaming event handling: route to the agents-tools-and-streaming sub-skill.
Model training or optimization of retriever/generator parameters: route to the evaluation-and-optimization sub-skill.

Operating workflow

Normalize inputs into Document objects and keep raw metadata with the corpus.
Choose the lightest valid preprocessing path:
- TextSplitter for chunking plain text.
- ToEmbeddings when chunks need vectors.
- LocalDB when you need persistence, transform reuse, or staged filtering.
Pick the retrieval backend:
- BM25Retriever for lexical or keyword-first retrieval.
- FAISSRetriever for local vector search.
- LanceDBRetriever, QdrantRetriever, or PostgresRetriever when the index lives in an optional store.
Use RetrieverOutputToContextStr to assemble retrieved chunks into a downstream context string.
Build the RAG boundary with a retrieval step, a context builder, and a downstream answer component from another sub-skill.
When retrieval fails, consult troubleshooting before changing the corpus or the retrieval contract.

Bundled scripts

scripts/text_splitter_smoke.py: deterministic TextSplitter smoke check with a tiny Document.
scripts/localdb_smoke.py: LocalDB load/transform/save/load smoke using a safe bundled transform.

Start with data pipelines for document and transform contracts, retrievers for backend selection, and RAG recipes for assembly patterns.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Retrieval Rag And Data Pipelines AI skill do?

Document preprocessing, LocalDB persistence, retriever indexing, and RAG context assembly.

Why use Retrieval Rag And Data Pipelines on TypingMind?

Because you install it once and use it with any model. Retrieval Rag And Data Pipelines is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Retrieval Rag And Data Pipelines in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adalflow/sub-skills/retrieval-rag-and-data-pipelines. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Retrieval Rag And Data Pipelines?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Retrieval Rag And Data Pipelines?

As many as you like. As long as a model supports skills, you can use Retrieval Rag And Data Pipelines with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Retrieval Rag And Data Pipelines AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Retrieval, RAG, and Data Pipelines

Route here for

Do not handle here

Operating workflow

Bundled scripts

Bundled files

Frequently asked questions

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