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feature(embeddings): add embedding workflow + cpu/gpu workers - #123
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winsomeglint
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Oct 21, 2026
winsomeglint
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- adds an HFEmbedder working with HuggingFace models exposing a jina-embeddings-like encode method (task + prompt name)
- adds publish jobs for datashare-extract-cpu-worker and datashare-extract-gpu-worker
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ClemDoum
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Oct 21, 2026
| output_path: Path, | ||
| progress: AsyncProgressRateHandler | None = None, | ||
| ) -> EmbeddingResponse: | ||
| docs = list(read_jsonl_as(batch, PROCESSED_FILE_TA)) |
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To avoid heartbeat timeout we can do something like using the async API:
[d async for d in async_read_jsonl_as()]
| if await es_client.indices.exists(index=index): | ||
| return | ||
| logger.info("creating embeddings index %s...", index) | ||
| await es_client.indices.create( |
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I don't think we'll have permission to do so, maybe we can just raise
for tests we can create the index in the fixtures
| # run chunking and inference in a separate thread to avoid | ||
| # blocking the event loop | ||
| chunks = await asyncio.to_thread( | ||
| _embed_doc, |
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if heartbeat timeouts are recurrent we can also make _embed_doc an async corouting, use async_open when reading files and isolate calls to text embedding with asyncio.to_thread
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maybe it would be better to put heartbeating in a background task?
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