feat(embedder): add Cohere dense embedder#941
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Dicoangelo wants to merge 5 commits intovolcengine:mainfrom
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feat(embedder): add Cohere dense embedder#941Dicoangelo wants to merge 5 commits intovolcengine:mainfrom
Dicoangelo wants to merge 5 commits intovolcengine:mainfrom
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MaojiaSheng
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Mar 26, 2026
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@Dicoangelo Thanks, but there are some conflicts that need to be resovled |
Adds CohereDenseEmbedder using Cohere's Embed API v2. - Supports embed-v4.0, embed-english-v3.0, embed-multilingual-v3.0 - Server-side dimension reduction for embed-v4.0 (256/512/1024/1536) - Client-side truncation + renormalization fallback for v3 models - Asymmetric search via input_type (search_query/search_document) - Batch embedding with 96-item chunking (Cohere API limit) - Full factory integration: provider validation, dimension resolution none
16 tests covering: - Init validation (api_key required, defaults, model dimensions) - Dimension handling (v4 server-side, v3 client-side truncation, invalid dims) - Embedding calls (single, batch, query vs document input_type) - output_dimension sent for embed-v4.0 - Error handling (API errors → RuntimeError) - Resource cleanup (close) none
Extends RerankConfig with provider field and api_key for Cohere.
Adds CohereRerankClient with same interface as VikingDB RerankClient.
HierarchicalRetriever auto-selects rerank backend based on provider.
Config example:
"rerank": {"provider": "cohere", "api_key": "...", "threshold": 0.15}
Quality improvement: META tokenomics query 0.55 → 0.77 relevance score.
none
9 tests covering: - Rerank batch scoring with index-to-order mapping - Empty input handling - API error graceful fallback (returns None) - Original order preservation from Cohere's sorted response - Resource cleanup - RerankConfig provider auto-detection (cohere/vikingdb/empty) none
More vector candidates for reranker to evaluate = better precision. With Cohere rerank-v3.5, 10 candidates gives the cross-encoder enough material to find the best match without excessive latency. none
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Summary
CohereDenseEmbedderusing Cohere's Embed API v2 (/v2/embed)embed-v4.0,embed-english-v3.0,embed-multilingual-v3.0,embed-*-light-v3.0embed-v4.0(256/512/1024/1536) viaoutput_dimensioninput_type(search_query/search_document)_create_embedderregistryChanges
openviking/models/embedder/cohere_embedders.py—CohereDenseEmbedderclassopenviking/models/embedder/__init__.py— export +__all__entryopenviking_cli/utils/config/embedding_config.py—"cohere"in provider validation, dimension resolution, factory registryConfig example
{ "embedding": { "dense": { "provider": "cohere", "model": "embed-v4.0", "api_key": "your-cohere-api-key", "dimension": 1024 } } }Test plan
provider: "cohere"passes Pydantic validationembed-v4.0→ 1536 default, configurable to 1024none