I will build a large scale semantic index for your rag pipeline

J
john_whmatrix
J
john_whmatrix
John M.
build a large scale semantic index for your rag pipeline
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build a large scale semantic index for your rag pipeline
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build a large scale semantic index for your rag pipelinebuild a large scale semantic index for your rag pipelinebuild a large scale semantic index for your rag pipelinebuild a large scale semantic index for your rag pipeline

About this gig

Choose this if you need enterprise-scale / high-stakes semantic indexing with verified, reproducible, audit-ready outputs (correctness over speed).


I build deterministic FAISS-based indexing pipelines with controlled batching + checkpointing + integrity checks + post-build validation to prevent partial indexes, misalignment, and drift.


Deliverables

  • Cleaned + normalized text
  • Chunked dataset
  • Embeddings
  • FAISS index (sharded if needed)
  • Validation artifacts + documentation


Validation Pack (Included)

  • 1:1:1 alignment (chunks metadata vectors)
  • Zero null/corrupt vectors
  • Index integrity test (loads + searches)
  • Build manifest (model, dims, normalization, policy, counts, hashes)
  • Processing log (audit trail / reproducibility)


Definition of Done:

Index loads + searches successfully. 1:1:1 alignment verified (chunks = metadata = vectors). Zero null/corrupt vectors. Build manifest delivered (model, dims, counts, hashes). Processing log included for reproducibility. Sharded indexes load independently if applicable.


If you only need a fast RAG-ready index without audit-grade validation, use my Production-Ready FAISS Index service instead. See Portfolio for full example outputs.

  • Model expertise

    • Custom model development
    • Generative AI

  • Industry

    • Biotech
    • Cyber security
    • Data analytics
    • Financial services
    • Legal
    • Other

  • Programming language

    • Python
    • PyTorch
    • Tensorflow
    • Other

  • Language

    • English

  • Technical expertise

    • Machine learning (Supervised, Unsupervised, Reinforcement)
    • Natural language processing (NLP)
    • Algorithm development and optimization
    • Feature engineering and data processing

Get to know John M.

John M.

Semantic Indexing Engineer RAG Pipelines FAISS and E5 Large V2

  • FromUnited States
  • Member sinceDec 2025

  • Languages

    English
I design and deliver production-ready semantic indexing systems for RAG, semantic search, and document retrieval. I transform raw text into structured vector datasets using semantic chunking, dense embeddings, FAISS indexing, and metadata alignment — with validation so retrieval stays reliable over time. Clients use my indexes to power document Q&A, compliance search, knowledge base retrieval, and research discovery. Applied across multiple research organizations and 100+ datasets. Compatible with LangChain, LlamaIndex, Haystack, pgvector, and Pinecone.

My Portfolio

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