Build the retrieval infrastructure that powers AI search, RAG, and recommendation engines. From your first semantic query to a staff-level platform design.
This architecture powers
fig 1 — vector retrieval pipeline: embed → index → retrieve → rank → serve → observe
LATENCY
<100ms
p99 Query Latency
RECALL
>0.90
recall@10
COST
80%
Cost Reduction
UPTIME
99.9%
Availability SLA
This is not a database project.
This is AI infrastructure.
Vector retrieval is what makes RAG work, agents remember, and search understand meaning. Every production AI system that queries knowledge, retrieves context, or serves personalized results is built on a retrieval layer. This project teaches you to build and own that layer.
A complete, production-ready vector retrieval platform — from semantic indexing through multi-tenant observability.
pgvector HNSW and IVF indices over 1M+ documents, with Qdrant as the production-scale alternative
BM25 + semantic search fused with Reciprocal Rank Fusion and cross-encoder reranking for +23% precision
LLM function-calling integration so agents retrieve context on demand with sub-100ms round-trips
Prometheus dashboards tracking recall@10, p99 latency, cost-per-query, and embedding drift alerts
5 parts, each with a clear checkpoint. Parts 1–2 build the retrieval system. Parts 3–5 make you a platform engineer.
Production SLAs you'll design, implement, and validate across the retrieval pipeline.
Sub-100ms retrieval with HNSW indexing, Redis semantic caching, and optimised batch embedding pipelines
Hybrid BM25 + vector search with cross-encoder reranking, HyDE, and score fusion tuning
Multi-region design, GDPR-compliant deletion, RBAC access control, and structured failure runbooks
Launch the retrieval stack and run your first semantic search query in under 5 minutes.
# Clone the project & launch retrieval stack$ git clone https://github.com/aide-hub/ai-retrieval-platform.git$ cd ai-retrieval-platform# Start FastAPI + pgvector + Redis + Prometheus$ docker-compose -f docker-compose.retrieval.yml up -d# Embed documents and run hybrid search$ curl -X POST http://localhost:8000/api/search \$ -H "Content-Type: application/json" \$ -d '{"query": "transformer attention mechanisms", "top_k": 10}'
Master the underlying concepts before diving in — vector databases, approximate nearest neighbour, and hybrid search theory.
Master the skills first: Vector Databases skill trackAlready built the retrieval layer? The Enterprise RAG project adds document ingestion, streaming LLM responses, and source citations on top.
Related project: Enterprise RAG Knowledge SystemAn AI retrieval platform is a production-grade system that combines vector search, lexical matching, and reranking to deliver precise, relevant results from large document corpora. This project builds a hybrid retrieval system using pgvector for semantic search, BM25 for keyword matching, Reciprocal Rank Fusion for merging results, and cross-encoder reranking, then scales it to handle millions of documents with LLM agent integration.
Embed 1,000 tech documents with pgvector and build a FastAPI semantic search endpoint
Add BM25 lexical search, Reciprocal Rank Fusion, and cross-encoder reranking
Scale to 1M documents with batch pipelines and hash-based incremental updates
Wire retrieval as an LLM agent tool via function calling for context-on-demand
Build Prometheus dashboards for recall@10, latency, and cost-per-query with Redis caching
Retrieval systems power the backbone of RAG architectures at companies like Anthropic, Google, and Perplexity. The quality of retrieval directly determines the quality of LLM-generated answers. Pinecone, Weaviate, and pgvector have become essential infrastructure as every company builds AI-powered search and question-answering systems.
Start with Part 1: Embed, Store & Search