RAG Infrastructure Agent Skill

Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.

2k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

511

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill rag-infrastructure

The instruction itself

13 sections, as written by the author

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 → Answer

Embedding 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 [{"text": p.payload["text"], "score": p.score} for p in results.points]

Reranking

import cohere

co = cohere.Client("your-api-key")

def rerank(query: str, candidates: list[str], top_n: int = 5) -> list[str]:
    """Rerank retrieved chunks for relevance (improves RAG quality ~20-30%)."""
    response = co.rerank(
        model="rerank-english-v3.0",
        query=query,
        documents=candidates,
        top_n=top_n,
    )
    return [candidates[r.index] for r in response.results]

# Alternative: local reranker (no API cost)
from sentence_transformers import CrossEncoder
reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")

def local_rerank(query: str, candidates: list[str], top_n: int = 5) -> list[str]:
    pairs = [[query, c] for c in candidates]
    scores = reranker.predict(pairs)
    ranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
    return [text for text, _ in ranked[:top_n]]

RAG Query Pipeline

from openai import OpenAI

llm = OpenAI(base_url="http://localhost:8000/v1", api_key="your-key")

def rag_query(user_question: str) -> str:
    # 1. Retrieve
    candidates = hybrid_search(user_question, top_k=20)
    texts = [c["text"] for c in candidates]

    # 2. Rerank
    top_chunks = local_rerank(user_question, texts, top_n=5)

    # 3. Generate
    context = "\n\n---\n\n".join(top_chunks)
    response = llm.chat.completions.create(
        model="meta-llama/Llama-3.1-8B-Instruct",
        messages=[
            {"role": "system", "content": (
                "Answer the question using only the provided context. "
                "If the answer isn't in the context, say so.\n\nContext:\n" + context
            )},
            {"role": "user", "content": user_question},
        ],
        temperature=0.1,
        max_tokens=1024,
    )
    return response.choices[0].message.content

Docker Compose: Full RAG Stack

services:
  qdrant:
    image: qdrant/qdrant:latest
    volumes:
      - qdrant-data:/qdrant/storage
    ports:
      - "6333:6333"
    restart: unless-stopped

  redis:
    image: redis:7-alpine
    volumes:
      - redis-data:/data
    restart: unless-stopped

  ingestion-worker:
    build: ./ingestion
    environment:
      - QDRANT_URL=http://qdrant:6333
      - REDIS_URL=redis://redis:6379
    depends_on: [qdrant, redis]
    restart: unless-stopped

  rag-api:
    build: ./api
    ports:
      - "8080:8080"
    environment:
      - QDRANT_URL=http://qdrant:6333
      - LLM_BASE_URL=http://vllm:8000/v1
    depends_on: [qdrant]
    restart: unless-stopped

volumes:
  qdrant-data:
  redis-data:

Common Issues

| Issue | Cause | Fix |

|-------|-------|-----|

| Poor retrieval quality | Chunk size too large | Try 256–512 tokens; overlap 10–15% |

| LLM ignores retrieved context | Context too long | Rerank and keep top 3–5 chunks |

| Slow ingestion | Sequential embedding | Use batch_size=64 and async upserts |

| Stale documents | No re-ingestion pipeline | Track doc_hash; re-embed on change |

| High embedding costs | All chunks re-embedded | Cache embeddings with hash-based dedup |

Best Practices

Use BAAI/bge-large-en-v1.5 or nomic-embed-text for strong free embeddings.
Always rerank before passing to LLM — 5 precise chunks beat 20 noisy ones.
Store source metadata (URL, page, section) in vector payloads for citations.
Use namespace/tenant isolation in the vector store for multi-tenant RAG.
Evaluate with RAGAS metrics: faithfulness, answer relevancy, context precision.

vector-database-ops - Qdrant/Weaviate management
vllm-server - Self-hosted LLM endpoint
ollama-stack - Local LLM for development
ai-pipeline-orchestration - Ingestion pipelines

Other skills for the same job

different authors, same section of the catalogue

LLM App Patterns

by ComeOnOliver

×2

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.

35k tokens scripts

Pinecone

by Orchestra-Research

×1

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffold, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: azd ai agent, azd provision/deploy, deploy agent, hosted agent, create agent, add tool to agent, invoke agent, evaluate agent, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, agent optimizer, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, AI Services, create Foundry resource, provision, knowledge index, customize deployment, onboard, availability, fine-tune, SFT, DPO, RFT, training-data, grader, distillation, fine-tuned model, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

285k tokens scripts