Entry point: /faion-net — invoke this skill for automatic routing to the appropriate domain.

RAG Engineer Skill

Communication: User's language. Code: English.

Purpose

Specializes in RAG (Retrieval Augmented Generation) systems. Covers document processing, embeddings, vector search, and retrieval optimization.

Scope

Area	Coverage
Chunking	Text splitting, semantic chunking, overlap strategies
Embeddings	Text vectorization, similarity search, models
Vector DBs	Qdrant, Weaviate, Chroma, pgvector
Retrieval	Hybrid search, reranking, metadata filtering
RAG Systems	Architecture, evaluation, agentic RAG

Quick Start

Task	Files
Basic RAG	chunking-basics.md → embedding-basics.md → rag-architecture.md
Vector DB setup	db-comparison.md → db-qdrant.md (recommended)
Advanced retrieval	hybrid-search-basics.md → reranking-basics.md
RAG evaluation	rag-eval-metrics.md → rag-eval-methods.md
Agentic RAG	agentic-rag.md

Methodologies (22)

Chunking (2):

• chunking-basics: Size, overlap, delimiters
• chunking-advanced: Semantic, recursive, custom

Embeddings (4):

• embedding-basics: Fundamentals, similarity
• embedding-generation: API usage, batching
• embedding-models: Comparison, selection
• embedding-applications: Use cases, patterns

Vector Databases (4):

• db-comparison: Feature comparison, selection
• db-qdrant: Setup, indexing, search (recommended)
• db-weaviate: Knowledge graphs, hybrid search
• db-chroma: Local dev, prototyping
• vector-database-setup: General setup patterns

Retrieval (4):

• hybrid-search-basics: Vector + keyword search
• hybrid-search-implementation: Production patterns
• reranking-basics: Cross-encoder fundamentals
• reranking-models: Cohere, MixedBread, custom

RAG Systems (7):

• rag: RAG overview, fundamentals
• rag-architecture: System design, components
• rag-implementation: Production patterns
• rag-eval-metrics: Relevance, faithfulness, correctness
• rag-eval-methods: Evaluation frameworks
• agentic-rag: Agent-driven retrieval
• graph-rag-advanced-retrieval: Knowledge graphs

Architecture

Document Ingestion
    ↓
Chunking (semantic/fixed)
    ↓
Embedding Generation
    ↓
Vector Database Storage
    ↓
Query Processing
    ↓
Retrieval (vector + hybrid)
    ↓
Reranking
    ↓
Context Assembly
    ↓
LLM Generation

Code Examples

Basic RAG Pipeline

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma

# Chunk documents
splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = splitter.split_documents(docs)

# Generate embeddings and store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

# Retrieve
retriever = vectorstore.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 5}
)
results = retriever.invoke("query")

Hybrid Search with Qdrant

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, Filter

client = QdrantClient("localhost", port=6333)

# Create collection
client.create_collection(
    collection_name="docs",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
)

# Hybrid search
results = client.search(
    collection_name="docs",
    query_vector=query_embedding,
    query_filter=Filter(...),
    limit=10
)

Reranking

from cohere import Client

co = Client(api_key="...")

# Rerank retrieved docs
reranked = co.rerank(
    query="query text",
    documents=[doc.text for doc in results],
    top_n=3,
    model="rerank-english-v3.0"
)

Evaluation Metrics

Metric	Measures
Retrieval Precision	Relevant docs in results
Retrieval Recall	Coverage of relevant docs
MRR	Mean reciprocal rank
NDCG	Ranking quality
Faithfulness	Grounding in context
Answer Relevance	Response matches query

Related Skills

Skill	Relationship
faion-llm-integration	Uses embedding APIs
faion-ai-agents	Agentic RAG patterns
faion-ml-ops	RAG evaluation

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RAG Engineer v1.0 | 22 methodologies