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embedding providers — OpenAI, Cohere, and Voyage adapters over AI SDK v6.","maintainers":[{"name":"octalpixel","email":"mithushancj@gmail.com"}],"readme":"# @arivie/embeddings\n\nOne model-router factory (Mastra `ModelRouterEmbeddingModel`, 40+ providers via a `provider/model` id) that returns an `EmbeddingProvider` record (`model`, `modelName`, `dimensions`, `costPerMillionTokens`). Downstream code calls `embedMany({ model: provider.model, values })` using the re-exported `embed` / `embedMany` from this package — no Arivie-owned embed wrapper.\n\n```ts\nimport { embedMany, modelRouterEmbeddings } from \"@arivie/embeddings\";\n\nconst provider = modelRouterEmbeddings(\"openai/text-embedding-3-small\", { dimensions: 1536 });\nconst { embeddings, usage } = await embedMany({\n  model: provider.model,\n  values: [\"chunk one\", \"chunk two\"],\n});\n// cost rollup (e.g. buildIndex): usage.tokens * provider.costPerMillionTokens / 1_000_000\n```\n\nAll three providers use `createProvider({ apiKey }).embedding(modelId)` (AI SDK v6 idiom). Cohere and Voyage accept arbitrary `model` strings at runtime; metadata `dimensions` defaults are caller overrides when the model variant differs from the package default.\n\n## buildIndex\n\nChunk a semantic layer with `ParagraphChunker`, embed via `embedMany`, and upsert into a Mastra vector store (typically `PgVector`):\n\n```ts\nimport { PgVector } from \"@mastra/pg\";\nimport { buildIndex, modelRouterEmbeddings } from \"@arivie/embeddings\";\n\nconst provider = modelRouterEmbeddings(\"openai/text-embedding-3-small\", { dimensions: 1536 });\nconst vector = new PgVector({\n  id: \"arivie-rag\",\n  connectionString: process.env.DATABASE_URL!,\n});\n\nconst { chunkCount, totalEmbeddingCost } = await buildIndex({\n  layer,\n  provider,\n  vector,\n  indexName: \"sem\",\n});\n```\n\nReturns `{ chunkCount, totalEmbeddingCost }` where cost is `usage.tokens × provider.costPerMillionTokens / 1_000_000` summed across embedding batches.\n\n## retrieve\n\nEmbed a single query string, run similarity search against a populated index, and return ranked `Chunk[]` (same shape as `ParagraphChunker`):\n\n```ts\nimport { PgVector } from \"@mastra/pg\";\nimport { buildIndex, modelRouterEmbeddings, retrieve } from \"@arivie/embeddings\";\n\nconst provider = modelRouterEmbeddings(\"openai/text-embedding-3-small\", { dimensions: 1536 });\nconst vector = new PgVector({\n  id: \"arivie-rag\",\n  connectionString: process.env.DATABASE_URL!,\n});\n\nawait buildIndex({ layer, provider, vector, indexName: \"sem\" });\n\nconst chunks = await retrieve({\n  query: \"monthly revenue by region\",\n  vector,\n  indexName: \"sem\",\n  provider,\n  topK: 5,\n  entityHint: \"orders\", // optional metadata filter: { entity: \"orders\" }\n});\n```\n\nUses AI SDK `embed` (single value) for the query vector, then `vector.query` with optional `entityHint` filter. Results are sorted by descending similarity score.\n\nContract and RAG pipeline: [RFC-002 §4.4](../../../.research/07-rfc/RFC-002-concrete-tech-implementation/02-requirements-interfaces.md#44-anaclipembeddings--rag-mode-mastra-vector-extension) (amended in Sprint 2 — `EmbeddingProvider` holds `EmbeddingModel` + cost metadata, not a custom `embed()` method).\n","readmeFilename":"README.md"}