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feat(embeddings): add OpenRouter support (#6396)

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* feat(knowledge): add OpenRouter embedding fallback

* fix(knowledge): preserve successful embedding batches

* feat(embeddings): add OpenRouter provider

* fix(knowledge): bill only platform embedding tokens

* test(embeddings): include OpenRouter provider

* feat(embeddings): load OpenRouter model catalog

* fix(embeddings): preserve legacy provider default

* fix(embeddings): batch OpenRouter requests

* fix(embeddings): reset stale OpenRouter model
This commit is contained in:
Theodore Li
2026-08-10 15:15:18 -04:00
committed by GitHub
parent bc8826af56
commit 56910c002e
42 changed files with 1999 additions and 247 deletions
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apps/docs/content/docs/en/integrations/embeddings.mdx
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@@ -25,7 +25,7 @@ Sim's knowledge bases embed separately, at a fixed vector width and from a small
## Usage Instructions
Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, Google Gemini, Cohere, and Mistral embedding models.
Turn text into embedding vectors for semantic search, clustering, and similarity. Supports OpenAI, OpenRouter, Google Gemini, Cohere, and Mistral embedding models.
@@ -55,6 +55,30 @@ Generate embeddings from text using OpenAI's embedding models
| `dimensions` | number | Dimensionality of each vector |
| `usage` | json | Token usage |
### OpenRouter Embeddings
Generate embeddings through OpenRouter
#### Input
| Parameter | Type | Required | Description |
| --------- | ---- | -------- | ----------- |
| `input` | string | Yes | Text to embed, or an array of texts to embed in one call |
| `model` | string | No | Embedding model to use |
| `taskType` | string | No | What the embedding is for, when the model supports task conditioning: document, query, similarity, classification, or clustering |
| `dimensions` | number | No | Output dimensions, when the model supports truncation. Defaults to native. |
| `apiKey` | string | Yes | API key for the selected embedding provider |
#### Output
| Parameter | Type | Description |
| --------- | ---- | ----------- |
| `embeddings` | json | Generated embeddings |
| `model` | string | Model used |
| `provider` | string | Provider used |
| `dimensions` | number | Dimensionality of each vector |
| `usage` | json | Token usage |
### Gemini Embeddings
Generate embeddings from text using Google's Gemini embedding models