rag-systems

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Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization

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Sourcepluginagentmarketplace/custom-plugin-ai-agents

Added on2026-02-14

NPX Install

npx skill4agent add pluginagentmarketplace/custom-plugin-ai-agents rag-systems

SKILL.md Content

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RAG Systems

Build Retrieval-Augmented Generation systems for grounded responses.

When to Use This Skill

Invoke this skill when:

Building Q&A over custom documents
Implementing semantic search
Setting up vector databases
Optimizing retrieval quality

Parameter Schema

Parameter	Type	Required	Description	Default
`task`	string	Yes	RAG goal	-
`vector_db`	enum	No	`pinecone` , `weaviate` , `chroma` , `pgvector`	`chroma`
`embedding_model`	string	No	Embedding model	`text-embedding-3-small`
`chunk_size`	int	No	Chunk size in chars	`1000`

Quick Start

python

from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter

# 1. Split documents
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)

# 2. Create vector store
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_documents(chunks, embeddings)

# 3. Retrieve
docs = vectorstore.similarity_search("query", k=5)

Chunking Strategy

Content Type	Size	Overlap	Rationale
Technical docs	500-800	100	Preserve code
Legal docs	1000-1500	200	Keep clauses
Q&A/FAQ	200-400	50	Atomic answers

Embedding Costs

Model	Cost/1M tokens
text-embedding-3-small	$0.02
text-embedding-3-large	$0.13
Cohere embed-v3	$0.10

Troubleshooting

Issue	Solution
Irrelevant results	Improve chunking, add reranking
Missing context	Increase k, use parent retriever
Hallucinations	Add "only use context" prompt
Slow retrieval	Add caching, reduce k