rag-architect

RAG Architect

Core Workflow

1. Requirements Analysis — Identify retrieval needs, latency constraints, accuracy requirements, and scale
2. Vector Store Design — Select database, schema design, indexing strategy, sharding approach
3. Chunking Strategy — Document splitting, overlap, semantic boundaries, metadata enrichment
4. Retrieval Pipeline — Embedding selection, query transformation, hybrid search, reranking
5. 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)