How I Managed to Optimize the Retriever Phase in RAG

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A Deep Dive into Cutting Latency from 3 Seconds to Sub-500ms

When I first built a Retrieval-Augmented Generation (RAG) system, the generation quality was decent but the retrieval time was a major bottleneck. Clocking in at around 3 seconds, the retriever phase was slowing everything down. It was unacceptable for real-time use cases like chat interfaces or responsive dashboards.

In this article, I’ll walk you through:

• My original architecture

• What I measured and optimized

• Concrete changes that helped

• Benchmarks before and after

• Lessons learned for anyone building scalable RAG pipelines.

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My Original Setup

Like many GenAI builders, my first production-grade RAG system used:

• Embedding model: AWS Bedrock Embedding API

• Vector store: Pinecone (starter pod, HNSW index)

• Frontend latency: 3.1 seconds on average (measured via Postman and OpenTelemetry)

The Bottleneck Breakdown

I measured latency across the following phases:

• Embedding Generation : 500–800 ms

• Vector Store Search ": 1800–2200 ms

• Network Overhead : 200–300 ms

• Total : ~3000 ms

The biggest culprits? Vector search and embedding generation latency.

Optimization Strategy

Here’s exactly what I did to bring down the latency.

Switched to Local Embedding Models

Instead of relying on AWS Bedrock's remote embedding API (which took 500–800ms per request), I:

• Deployed a local instance of bge-small-en-v1.5 using sentence-transformers

• Quantized the model using ONNX Runtime for fast inference

• Got inference time down to ~40ms

✅ Impact: Embedding generation latency reduced by >90%

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Upgraded Pinecone Configuration

I learned that Pinecone performance varies heavily based on pod type and index settings. I made these changes:

• Switched from starter to p1 performance-optimized pods

• Tuned efSearch and efConstruction parameters

• Reduced top_k from 10 → 5 (no loss in quality)

• Applied metadata filtering to restrict search to relevant chunks only

✅ Impact: Retrieval latency dropped from ~2.2s → ~300ms

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Hybrid Search Optimization

I integrated hybrid search (dense + sparse). Pinecone supports hybrid scoring using a combination of:

• Dense vector embeddings (semantic relevance)

• Sparse keyword-based scoring (BM25)

I indexed sparse vectors using TfidfVectorizer and merged them with the dense vector queries.

✅ Impact: Higher relevance + reduced need for large top_k

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Caching for Smart Retrieval

For frequently asked queries, especially in FAQ-based chatbots, I cached:

• Query → Top-k document IDs

• Query → Embedding vectors

Used Redis to store and fetch these for repeated patterns.

✅ Impact: Retrieval phase dropped to <100ms for cached queries

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Deployed Close to Pinecone Region

Earlier, my compute was in us-west, while Pinecone was set up in us-east. This caused ~300ms in extra latency.

Solution: Moved all compute (embedding + API logic) to same region (us-east-1) using AWS Lambda + ECS.

✅ Impact: Reduced ~250ms in network overhead

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Final Results :

• Embedding Generation : From 500–800 ms to 40–60 ms

• Vector Retrieval : From 1800–2200 ms to 250–350 ms

• Network Overhead : From 200–300 ms to <100 ms

• Total Latency : From ~3 sec to ~400 – 500 ms

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Key Points to Remember :

• Don’t ignore infra-level latency
  Even the best model is useless if it's hosted 3 regions away.

• Start with good defaults, then tweak aggressively
  Pod type, efSearch, and hybrid tuning make a world of difference.

• Cache the obvious
  Real-time doesn’t always mean re-compute. Cache what you can.

• Local > Remote when possible
  If your scale allows, run things locally (or on a cheap GPU instance). It’s cheaper, faster, and more controllable.

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Tools I Used :

• Pinecone Hybrid Search

• sentence-transformers, ONNX

• Redis

• OpenTelemetry for tracing

• AWS Lambda + ECS

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Optimizing the retriever phase in RAG is a stack of performance tuning decisions. From models to infrastructure to indexes and caches, every millisecond counts.

If you're working on a RAG system, start by measuring everything. Then optimize one layer at a time