rag-architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jeffallan/claude-skills --skill rag-architect- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads rag-architect automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
RAG Architect
Core Workflow
- Requirements Analysis — Identify retrieval needs, latency constraints, accuracy requirements, and scale
- Vector Store Design — Select database, schema design, indexing strategy, sharding approach
- Chunking Strategy — Document splitting, overlap, semantic boundaries, metadata enrichment
- Retrieval Pipeline — Embedding selection, query transformation, hybrid search, reranking
- Evaluation & Iteration — Metrics tracking, retrieval debugging, continuous optimization
For each step, validate before moving on (see checkpoints below).
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Vector Databases | references/vector-databases.md | Comparing Pinecone, Weaviate, Chroma, pgvector, Qdrant |
| Embedding Models | references/embedding-models.md | Selecting embeddings, fine-tuning, dimension trade-offs |
| Chunking Strategies | references/chunking-strategies.md | Document splitting, overlap, semantic chunking |
| Retrieval Optimization | references/retrieval-optimization.md | Hybrid search, reranking, query expansion, filtering |
| RAG Evaluation | references/rag-evaluation.md | Metrics, evaluation frameworks, debugging retrieval |
Implementation Examples
1. Chunking Documents
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Evaluate chunk_size on your domain data — never use 512 blindly
splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
chunk_overlap=100,
separators=["\n\n", "\n", ". ", " "],
)
chunks = splitter.create_documents(
texts=[doc.page_content for doc in raw_docs],
metadatas=[{"source": doc.metadata["source"], "timestamp": doc.metadata.get("timestamp")} for doc in raw_docs],
)
Checkpoint: assert all(c.metadata.get("source") for c in chunks), "Missing source metadata"
2. Generating Embeddings & Indexing
from openai import OpenAI
import qdrant_client
from qdrant_client.models import VectorParams, Distance, PointStruct
client = OpenAI()
qdrant = qdrant_client.QdrantClient("localhost", port=6333)
# Create collection
qdrant.recreate_collection(
collection_name="knowledge_base",
vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)
def embed_chunks(chunks: list[str], model: str = "text-embedding-3-small") -> list[list[float]]:
response = client.embeddings.create(input=chunks, model=model)
return [r.embedding for r in response.data]
# Idempotent upsert with deduplication via deterministic IDs
import hashlib, uuid
points = []
for i, chunk in enumerate(chunks):
doc_id = str(uuid.UUID(hashlib.md5(chunk.page_content.encode()).hexdigest()))
embedding = embed_chunks([chunk.page_content])[0]
points.append(PointStruct(id=doc_id, vector=embedding, payload=chunk.metadata))
qdrant.upsert(collection_name="knowledge_base", points=points)
Checkpoint: assert qdrant.count("knowledge_base").count == len(set(p.id for p in points)), "Deduplication failed"
3. Hybrid Search (Vector + BM25)
from qdrant_client.models import Filter, FieldCondition, MatchValue, SparseVector
from rank_bm25 import BM25Okapi
def hybrid_search(query: str, tenant_id: str, top_k: int = 20) -> list:
# Dense retrieval
query_embedding = embed_chunks([query])[0]
tenant_filter = Filter(must=[FieldCondition(key="tenant_id", match=MatchValue(value=tenant_id))])
dense_results = qdrant.search(
collection_name="knowledge_base",
query_vector=query_embedding,
query_filter=tenant_filter,
limit=top_k,
)
# Sparse retrieval (BM25)
corpus = [r.payload.get("text", "") for r in dense_results]
bm25 = BM25Okapi([doc.split() for doc in corpus])
bm25_scores = bm25.get_scores(query.split())
# Reciprocal Rank Fusion
ranked = sorted(
zip(dense_results, bm25_scores),
key=lambda x: 0.6 * x[0].score + 0.4 * x[1],
reverse=True,
)
return [r for r, _ in ranked[:top_k]]
Checkpoint: assert len(hybrid_search("test query", tenant_id="demo")) > 0, "Hybrid search returned no results"
4. Reranking Top-K Results
Load provider API keys from environment variables or a secrets manager; never commit them to source code.
import os
import cohere
co = cohere.Client(os.environ["COHERE_API_KEY"])
def rerank(query: str, results: list, top_n: int = 5) -> list:
docs = [r.payload.get("text", "") for r in results]
reranked = co.rerank(query=query, documents=docs, top_n=top_n, model="rerank-english-v3.0")
return [results[r.index] for r in reranked.results]
5. Retrieval Evaluation
# Run precision@k and recall@k against a labeled evaluation set
# python evaluate.py --metrics precision@10 recall@10 mrr --collection knowledge_base
from ragas import evaluate
from ragas.metrics import context_precision, context_recall, faithfulness, answer_relevancy
from datasets import Dataset
eval_dataset = Dataset.from_dict({
"question": questions,
"contexts": retrieved_contexts,
"answer": generated_answers,
"ground_truth": ground_truth_answers,
})
results = evaluate(eval_dataset, metrics=[context_precision, context_recall, faithfulness, answer_relevancy])
print(results)
Checkpoint: Target context_precision >= 0.7 and context_recall >= 0.6 before moving to LLM integration.
Constraints
MUST DO
- Evaluate multiple embedding models on your domain data before committing
- Implement hybrid search (vector + keyword) for production systems
- Add metadata filters for multi-tenant or domain-specific retrieval
- Measure retrieval metrics (precision@k, recall@k, MRR, NDCG)
- Use reranking for top-k results before passing context to LLM
- Implement idempotent ingestion with deduplication (deterministic IDs)
- Monitor retrieval latency and quality over time
- Version embeddings and plan for model migration
MUST NOT DO
- Use default chunk size (512) without evaluation on your domain data
- Skip metadata enrichment (source, timestamp, section)
- Ignore retrieval quality metrics in favor of only LLM output quality
- Store raw documents without preprocessing/cleaning
- Use cosine similarity alone for complex multi-domain retrieval
- Deploy without testing on production-like data volumes
- Forget to handle edge cases (empty results, malformed docs)
- Couple the embedding model tightly to application code
Output Templates
When designing RAG architecture, deliver:
- System architecture diagram (ingestion + retrieval pipelines)
- Vector database selection with trade-off analysis
- Chunking strategy with examples and rationale
- Retrieval pipeline design (query → results flow)
- Evaluation plan with metrics, benchmarks, and pass/fail thresholds
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions
FAQ
What does the rag-architect skill do?
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
How do I install rag-architect?
Run npx skills add https://github.com/jeffallan/claude-skills --skill rag-architect in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-architect/ (personal) or .claude/skills/rag-architect/ (project).
Which AI agents can use rag-architect?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is rag-architect safe to install?
Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket flagged items to review; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains rag-architect?
It is published by jeffallan in jeffallan/claude-skills. It has 4,705 recorded installs.
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