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.
Context Discovery
Auto-Investigation
Check these project signals before asking questions:
| Signal | Where to Check | What to Look For |
|---|---|---|
| Dependencies | package.json, requirements.txt | langchain, llamaindex, qdrant-client, chromadb, weaviate-client |
| Vector DB | docker-compose.yml,.env | Qdrant, Weaviate, Chroma config/containers |
| Document dirs | /docs, /data, /content | Documents to index (PDF, MD, TXT) |
| Existing embeddings | Grep for "embed", "vector", "retriever" | Current RAG implementation |
Discovery Questions
question: "What's your RAG use case?"
header: "RAG Goal"
multiSelect: false
options:
- label: "Documentation Q&A"
description: "Answer questions from internal docs"
- label: "Knowledge base search"
description: "Semantic search over articles/guides"
- label: "Code search/retrieval"
description: "Find relevant code snippets"
- label: "Customer support"
description: "Context-aware support responses"question: "Which vector database?"
header: "Vector DB"
multiSelect: false
options:
- label: "Qdrant (recommended for production)"
description: "Fast, scalable, rich filtering"
- label: "Chroma (recommended for dev/prototyping)"
description: "Simple, local, easy setup"
- label: "Weaviate (for knowledge graphs)"
description: "Hybrid search, graph features"
- label: "pgvector (for PostgreSQL projects)"
description: "Vector extension for existing Postgres"question: "Document volume and type?"
header: "Data Characteristics"
multiSelect: false
options:
- label: "Small (<1000 docs, mostly text)"
description: "Simple chunking sufficient"
- label: "Medium (1000-10000 docs)"
description: "Consider hybrid search + reranking"
- label: "Large (>10000 docs, mixed formats)"
description: "Advanced chunking + metadata filtering"
- label: "Code repository"
description: "AST-aware chunking needed"question: "Do you need hybrid search (vector + keyword)?"
header: "Search Strategy"
multiSelect: false
options:
- label: "Yes - combine semantic + exact matching"
description: "Hybrid search for best results"
- label: "No - semantic search only"
description: "Vector similarity sufficient"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 GenerationCode 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 |
*RAG Engineer v1.0 | 22 methodologies*