ContextForge
Production AI Retrieval & Evaluation Platform
Role
Lead AI Engineer: LangGraph retrieval pipeline, Redis semantic cache, RAGAS benchmark engine
Primary Stack
Python · LangGraph · FastAPI · Redis · RAGAS · PostgreSQL · OpenAI API
“Production RAG systems frequently suffer from silent hallucinations and high token costs because they lack query intent routing, semantic caching, and systematized RAGAS metric evaluation.”
100K+ enterprise document corpus with multi-tenant search isolation
Redis semantic caching to slash repeat LLM embedding and token costs
Continuous automated RAGAS evaluation pipeline (faithfulness, answer relevance)
Why Redis semantic cache over raw database lookups?▼
40% of user queries in technical documentation are semantically identical rephrasings. By calculating cosine similarity against historical query embeddings in Redis with a 0.92 threshold, we return cached answers in 14ms while cutting OpenAI API costs by 40%.
Why automated RAGAS evaluation on every deployment?▼
Small tweaks to chunk size or prompt templates often cause subtle regressions. ContextForge runs automated RAGAS test suites (measuring Context Recall, Context Precision, and Faithfulness) as a mandatory CI/CD gate before deploying new retrieval pipelines.
Distributed System Architecture
Explore the multi-tier topology below. Switch between the interactive blueprint canvas, standard Mermaid.js flowcharts, and the step-by-step request simulator.
Two-stage hybrid retrieval architecture with query classification routing, Redis semantic caching, LangGraph reranking, and RAGAS evaluation gates.
Engineering Post-Mortem & Next Iteration
I would implement contextual compression embeddings at the chunking stage to reduce prompt token sizes by another 30%.
Next Case Study
08 // NEXT
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