add client-side embeddings cached in IndexedDB

[Mbeaulne]

Description

Adds embedding-based semantic search as a pre-processing step before LLM reranking. When an AI provider API base is configured, the component search now fetches text embeddings for both the search query and all indexed components, ranks them by cosine similarity, and merges those results with the top lexical matches before passing candidates to the LLM reranker. This improves the quality of candidates surfaced to the reranker, particularly for queries that don't share keywords with component names or descriptions.

A new componentSearchEmbeddings service handles embedding generation, cosine similarity scoring, and persistent caching of embeddings in IndexedDB (keyed by a FNV-1a hash of the text and model name) to avoid redundant API calls across searches. A mergeUniqueMatches helper deduplicates and merges lexical and embedding results by digest before reranking.

The embedding fetch is tracked with an isEmbeddingSearchPending flag, which gates the rerank active state and disables the AI search buttons while in progress, keeping the spinner and disabled states consistent with the existing reranking UX.

Related Issue and Pull requests

Type of Change

• Bug fix
• New feature
• Improvement
• Cleanup/Refactor
• Breaking change
• Documentation update

Checklist

• I have tested this does not break current pipelines / runs functionality
• I have tested the changes on staging

Screenshots (if applicable)

Test Instructions

1. Configure an OpenAI-compatible API base (e.g. https://api.openai.com/v1) in AI provider settings.
2. Open the component search panel and enter a query that is semantically related to a component but does not share exact keywords.
3. Click the AI search (Sparkles) button and verify the spinner appears during both the embedding fetch and reranking phases.
4. Confirm that results are ranked by semantic relevance.
5. Repeat the same search and verify via network tooling that no additional embedding API calls are made (cache hit).
6. Verify that searches still work correctly when no API base is configured (embedding step is skipped gracefully).

Additional Comments

The embedding model is hardcoded to text-embedding-3-small. If no apiBase is set, the embedding step is skipped entirely and behaviour is identical to before this change.

[github-actions]

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A preview build has been created at: 06-18-add_client-side_embeddings_cached_in_indexeddb/1475699

[Mbeaulne]

Warning

This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

• Simplify component search result cards #2459
• Add context-aware component search suggestions #2458
• Add component discovery docs #2457
• Add component search analytics baseline #2456
• Limit dashboard component browse rendering #2454
• Add component search empty state suggestions #2453
• Decouple editor component search input #2452
• Polish component search explanations #2451
• Add compatible component suggestions #2450
• Add component collection search results #2449
• Add component lifecycle badges #2448
• Add component search URL state #2447
• Disable AI search when literal search finds no matches #2444
• Debounce component search input #2433
• add client-side embeddings cached in IndexedDB #2432 👈 (View in Graphite)
• Add search quality expectation tests for lexical search #2431
• Add deep AI search to rerank all components in selected sources #2430
• Add source-diverse AI rerank candidate pool #2429
• Add negative constraint parsing to lexical search #2428
• Add fuzzy/typo-tolerant matching for component name and I/O fields #2427
• Improve component search scoring relevance #2426
• Add synonym expansion to component lexical search #2425
• Normalize component search tokens for better matching #2424
• Expand component search indexing fields #2423
• master

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