Observed Signal · Apr 4, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Layered Agentic Retrieval PoC for Retail Floor Questions
This technical write-up documents a personal proof-of-concept Python project that routes retail associate questions across three specialized TF‑IDF indexes (returns, product care, service-floor) instead of using a single concatenated corpus. The orchestrator computes a combined routing score per domain (cosine similarity + small lexical regex boosts), retrieves top hits from the primary domain, and optionally blends evidence from a secondary domain when the primary score falls below a hand-tuned threshold. The implementation is intentionally lightweight and on-device (Python 3.10+, NumPy, scikit-learn, matplotlib, Rich), with reproducibility, inspectability and explicit logging as design priorities. The public repository is provided for learning; the author frames the project as an experimental PoC with synthetic data and clear limitations.
A practical, reproducible PoC demonstrating conditional multi-domain retrieval and evidence assembly for associate-facing assistants; useful pattern for practitioners but limited scale and not industry‑shifting.
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Key Takeaways & Evidence Grounding
- Author implemented a retail-focused PoC that routes queries across three separate TF‑IDF domain indexes: returns policies, product care guidance, and service-floor procedures.
- Routing score is a hybrid of cosine similarity (TF‑IDF vectors via scikit-learn) plus small lexical regex 'boosts' for domain nudges.
- An 'agentic' confidence gate appends secondary-domain evidence when the primary domain's combined score is below a hand-tuned threshold.
- The demo runs entirely on-device (no hosted LLMs or cloud vector DB); tech stack includes Python 3.10+, NumPy, scikit-learn, matplotlib and Rich.
- Public repository available at https://github.com/aniket-work/RetailFloor-AgenticRouter-AI for reproduction and learning purposes.
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