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70 lines
1.6 KiB
Markdown
70 lines
1.6 KiB
Markdown
# Vector-sync HTTP embedding provider
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This provider supports two endpoints:
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- `POST {baseUrl}/vector-sync` for single-text requests
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- `POST {baseUrl}/vectorize-batch` for batch document requests
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## Single request
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Request body:
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```json
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{
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"model": "intfloat/multilingual-e5-large",
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"text": "This is a sample text to vectorize"
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}
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```
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## Batch request
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Request body:
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```json
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{
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"model": "intfloat/multilingual-e5-large",
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"truncate_text": false,
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"truncate_length": 512,
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"chunk_size": 20,
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"items": [
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{
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"id": "2f48fd5c-9d39-4d80-9225-ea0c59c77c9a",
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"text": "This is a sample text to vectorize"
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}
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]
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}
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```
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## Provider configuration
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```yaml
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batch-request:
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truncate-text: false
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truncate-length: 512
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chunk-size: 20
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```
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These values are used for `/vectorize-batch` calls and can also be overridden per request via `EmbeddingRequest.providerOptions()`.
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## Orchestrator batch processing
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To let `RepresentationEmbeddingOrchestrator` send multiple representations in one provider call, enable batch processing for jobs and for the model:
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```yaml
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dip:
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embedding:
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jobs:
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enabled: true
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parallel-batch-count: 1
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process-in-batches: true
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execution-batch-size: 20
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models:
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e5-default:
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supports-batch: true
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```
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Notes:
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- jobs are grouped by `modelKey`
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- non-batch-capable models still fall back to single-item execution
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- `parallel-batch-count` controls how many claimed job batches may be started in parallel
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- `execution-batch-size` controls how many texts are sent in one `/vectorize-batch` request inside each claimed job batch
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