rag-infrastructure
rag-infrastructure is an engineering AI skill with a core value of Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search.
Quick Facts
mkdir -p ./skills/rag-infrastructure && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/rag-infrastructure/SKILL.md -o ./skills/rag-infrastructure/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# RAG Infrastructure
Production infrastructure for Retrieval-Augmented Generation: ingest documents, generate embeddings, store in vector databases, and serve grounded LLM responses.
When to Use This Skill
Use this skill when:
- Building a knowledge base Q&A system over internal documents
- Implementing semantic search over large document collections
- Reducing LLM hallucinations with retrieved context
- Setting up embedding pipelines and vector store infrastructure
- Deploying hybrid search (dense + sparse/BM25)
Prerequisites
- Python 3.10+ with `pip`
- A vector database (Qdrant, Weaviate, Pinecone, or pgvector)
- An embedding model (OpenAI, Cohere, or local via `sentence-transformers`)
- An LLM endpoint (OpenAI API or self-hosted vLLM)
- Docker for local vector DB deployment
Architecture Overview
Documents → Chunker → Embedder → Vector Store
↓
User Query → Embedder → Vector Store (search) → Reranker → LLM → AnswerEmbedding Pipeline
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
import uuid
# Local embedding model (no API cost)
model = SentenceTransformer("BAAI/bge-large-en-v1.5")
# Connect to Qdrant
client = QdrantClient("http://localhost:6333")
# Create collection
client.create_collection(
collection_name="knowledge-base",
vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
)
def ingest_documents(docs: list[dict]):
"""Chunk, embed, and upsert documents."""
points = []
for doc in docs:
chunks = chunk_text(doc["text"], chunk_size=512, overlap=50)
embeddings = model.encode(chunks, batch_size=32, show_progress_bar=True)
for chunk, embedding in zip(chunks, embeddings):
points.append(PointStruct(
id=str(uuid.uuid4()),
vector=embedding.tolist(),
payload={"text": chunk, "source": doc["source"], "title": doc["title"]},
))
client.upsert(collection_name="knowledge-base", points=points)
print(f"Ingested {len(points)} chunks")Chunking Strategies
from langchain.text_splitter import RecursiveCharacterTextSplitter
def chunk_text(text: str, chunk_size: int = 512, overlap: int = 50) -> list[str]:
"""Recursive character splitter — best general-purpose strategy."""
splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=overlap,
separators=["\n\n", "\n", ". ", " ", ""],
)
return splitter.split_text(text)
# For code/markdown — use language-aware splitter
from langchain.text_splitter import MarkdownHeaderTextSplitter
headers = [("#", "H1"), ("##", "H2"), ("###", "H3")]
md_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers)Hybrid Search (Dense + Sparse)
from qdrant_client.models import SparseVector, SparseVectorParams, NamedSparseVector
from fastembed import SparseTextEmbedding
# Qdrant hybrid collection (dense + BM25 sparse)
client.create_collection(
collection_name="hybrid-kb",
vectors_config={"dense": VectorParams(size=1024, distance=Distance.COSINE)},
sparse_vectors_config={"sparse": SparseVectorParams()},
)
sparse_model = SparseTextEmbedding("prithivida/Splade_PP_en_v1")
def hybrid_search(query: str, top_k: int = 10) -> list[dict]:
dense_vec = model.encode(query).tolist()
sparse_vec = list(sparse_model.embed(query))[0]
results = client.query_points(
collection_name="hybrid-kb",
prefetch=[
{"query": dense_vec, "using": "dense", "limit": 20},
{"query": SparseVector(indices=sparse_vec.indices.tolist(),
values=sparse_vec.values.tolist()),
"using": "sparse", "limit": 20},
],
query={"fusion": "rrf"}, # Reciprocal Rank Fusion
limit=top_k,
)
return [{"te🎯 Best For
- UI designers
- Product designers
- Claude users
- AI users
💡 Use Cases
- Generating component mockups
- Creating design system tokens
- Using rag-infrastructure in daily workflow
- Automating repetitive engineering tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply rag-infrastructure to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Does this work with Figma?
Some design skills integrate with Figma plugins. Check the Works With section for supported tools.
How do I install rag-infrastructure?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/rag-infrastructure/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping usability testing
AI-generated designs should be validated with real users before development.
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.