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305d74c67a
**Description:** Currently, `CacheBackedEmbeddings` computes vectors for *all* uncached documents before updating the store. This pull request updates the embedding computation loop to compute embeddings in batches, updating the store after each batch. I noticed this when I tried `CacheBackedEmbeddings` on our 30k document set and the cache directory hadn't appeared on disk after 30 minutes. The motivation is to minimize compute/data loss when problems occur: * If there is a transient embedding failure (e.g. a network outage at the embedding endpoint triggers an exception), at least the completed vectors are written to the store instead of being discarded. * If there is an issue with the store (e.g. no write permissions), the condition is detected early without computing (and discarding!) all the vectors. **Issue:** Implements enhancement #18026. **Testing:** I was unable to run unit tests; details in [this post](https://github.com/langchain-ai/langchain/discussions/15019#discussioncomment-8576684). --------- Signed-off-by: chrispy <chrispy@synopsys.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> |
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| .. | ||
| __init__.py | ||
| _merge.py | ||
| aiter.py | ||
| env.py | ||
| formatting.py | ||
| function_calling.py | ||
| html.py | ||
| image.py | ||
| input.py | ||
| interactive_env.py | ||
| iter.py | ||
| json_schema.py | ||
| loading.py | ||
| pydantic.py | ||
| strings.py | ||
| utils.py | ||