Discover similar choices.
Use embeddings to retrieve items with related meaning or characteristics, beyond an exact category match.
Recommendations
Find a good match.
Make it a useful choice.
Combine similar items, related records and current business facts. Give your application the candidates it needs to recommend something relevant and available.
Relevant suggestions
Find related choices and check which ones are available to recommend.
Illustrative applicationWhat you can build
Build a more considered shortlist.
Include what belongs together.
Follow declared relationships to retrieve compatible, complementary or otherwise connected items.
Check what can be offered.
Load current records and apply availability, market and budget rules before showing a recommendation.
The application workflow
Discover. Qualify. Order.
Each signal contributes candidates. Your application turns those candidates into a shortlist with a reason for every choice.
Suggested record structure
An item with business context
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item_id -
description -
availability -
category -
related_item_id
Your schema, built around your application.
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Find candidate IDs
Query vector similarity for related items and graph relations for connected choices. Keep the source of each candidate.
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Check the current facts
Fetch the candidate rows, deduplicate IDs and apply your eligibility rules. Remove unavailable items before ranking.
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Choose the ordering
Combine relevance, relationships and merchandising priorities in your application. Recheck availability at the point of action.
How it fits together
The data in one place.
The workflow in your hands.
Explore the architecture 
OriginChainDBStore and retrieve.
Holds item records, embeddings and declared relationships. Retrieves matching IDs and the current fields needed to check candidates.
Coordinate and decide.
Coordinates queries, applies business rules and ranks eligible items. Owns the recommendation recipe and the final availability check.
These are coordinated requests, not a single cross-model query or a shared-snapshot guarantee. Vector metadata-filter support varies by index; check the selected index contract.
Build for real use
Start with a workflow.
Check the whole result.
Are enough eligible results returned?
Measure the shortlist after availability and business rules have been applied. Keep a curated fallback when too few candidates remain.
What changes when an item is updated?
Refresh the affected embedding and any application-managed search entries when source content changes. Track partial progress and retry failed stages.
How will you compare recommendation recipes?
Use a representative set of starting items. Review result relevance, coverage and latency across the entire application flow.
Build with OriginChainDB