John Mitchell

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What I Deliver

• Agentic RAG orchestration (tool-calling ReAct loop over 606M+ vectors)
• RAG-ready vector indices (FAISS IndexFlatIP, FP16)
• Audit reports with explicit quality gates
• Protocol-governed pipeline rebuilds
• Chunk manifests with metadata and provenance
• Verification artifacts (checksums, summaries)

Agentic Retrieval

The reasoning model is the first stage, not the last. An orchestrator (Qwen3-Coder-30B-A3B) decides what to retrieve from 447 datasets across 12 domains using tool-calling over FAISS indexes, rerankers, and classifiers. Deep reasoning (DeepSeek R1) runs once over curated evidence.

Full architecture and performance data

Evidence

• Deliverable Structure Example PDF
• RAG Readiness Audit PDF
• Sample Audit Report PDF
• RAG-Ready Indexing Example PDF
• Large-Scale Indexing Example PDF
• Multi-Domain Pipeline Verification PDF

Operating Principles

See It Work

Output from the mini-index demo (20 documents, runs in under 60 seconds):

Query: "machine learning and neural networks"
  Top result: Neural Networks and Deep Learning (score: 0.878)

Query: "semantic search and vector retrieval"
  Top result: Semantic Search and Dense Retrieval (score: 0.879)

Query: "how to build a FAISS index"
  Top result: FAISS: Fast Similarity Search at Scale (score: 0.844)

Scores > 0.83 = strong semantic match (cosine similarity on L2-normalized embeddings).

git clone https://github.com/whmatrix/semantic-indexing-batch-02
cd semantic-indexing-batch-02/mini-index
pip install sentence-transformers faiss-cpu
python demo_query.py

What You Get

What this is not: No human-judged relevance labels. No MRR/MAP/NDCG claims. Scores are cosine similarity (vector alignment), not precision or recall. Domain suitability requires independent evaluation.

Reproduce it: git clone & cd mini-index & python demo_query.py — see mini-index

Find Your Path