RAG Pipeline Blueprint.
# RAG System Architecture : Execution Trace
$ rag.execute --query "Explain production vector indexing"
[01] Gateway: Auth verified · Rate limit OK (0.8ms)
[02] Embed: text-embedding-3-small → 1536d vector (21ms)
[03] VectorDB: Pinecone ANN search top_k=10 (17ms)
[04] Rerank: Cohere cross-encoder top_n=3 (34ms)
[05] LLM: Claude 3.5 Sonnet streaming initialized (142ms TTFT)
# Status: Grounded · 0 Hallucinations · 214ms Total Pipeline
Vector Database
ANN Similarity Search
Performs approximate nearest neighbor (ANN) search using HNSW indexing across millions of pre-chunked document vectors with metadata filtering.
// Code Implementation Pattern
const matches = await pineconeIndex.query({
vector: embedding.data[0].embedding,
topK: 10,
includeMetadata: true,
});Architecture Principles.
Decoupled Pipelines
Each stage is modular and independently scalable. Embedding providers or vector DBs can be swapped without code rewrites.
Full Telemetry & Evals
Latency, token count, and RAGAS metrics (faithfulness, relevancy) are tracked across all pipeline executions.
Graceful Fallbacks
Hybrid search with BM25 keyword matching ensures relevance even if dense vector embedding similarity drops.
Guardrails & Grounding
System prompts and post-generation citation checkers ensure 0 hallucinations and strict source attribution.