Comments (4)
@GauravWaghmare, take a look at the CIP PR for this - #1636. There is a bit more "nuance" to things.
I'd love your input on the CIP.
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@tazarov If I understand correctly this is happening because we're only inserting metadata when updating records and not touching the existing metadata. Does deleting the older metadata for the record and then inserting the new metadata sound like a good approach? If yes, can raise a PR.
from chroma.
@tazarov Why should the behaviour for metadata update be any different from document update?
from chroma.
@GauravWaghmare, to accommodate different use cases of how people update metadata. Users will update a document in a single overwriting operation. The intent is clear, and they don't necessarily need to know what is in the document to make the update. Metadata differs significantly, depending on the use case. The PR addresses as many use cases as I could think of:
- Clear the metadata
- Partial update
- Full overwrite of metadata
Please elaborate on your thoughts in the PR if you feel the above use cases can somehow be merged into one or if you have a different idea about the implementation.
from chroma.
Related Issues (20)
- [Feature Request]: Chroma on GPU HOT 2
- [Bug]: Batch Size Variation in Collection.add Leads to Inconsistent Query Results HOT 4
- [Install issue]: sqlite3.IntegrityError: NOT NULL constraint failed: collections.database_id HOT 4
- [Bug]: `chroma:document` as metadata key
- [Feature Request]: Universal Sentence Encoder(USE) Embedding Function HOT 1
- [Bug]: Warning raised when query to Persistent Client HOT 4
- [Feature Request]: Use AWS S3 or Azure Blob Storage for persisting chroma db
- [Bug]: Chromadb will fail to return the embeddings with the closest results unless I set n_results to a sufficiently large number. HOT 3
- [Bug]: Error creating and inserting to collections using Persistent Client HOT 3
- [Feature Request]: Query max distance in addition to n_results HOT 1
- [Bug]: getCollection missing DefaultEmbeddingFunction (JavaScript client) HOT 1
- [Feature Request]: Faster default EF on apple silicon
- [Feature Request]: Passing pre-computed embeddings directly to VectorStore HOT 2
- [Bug]: Cosine Similarity: Unusual Negative Distance in Same Sentence Search HOT 2
- [Bug]: The Write-ahead Log (embeddings_queue) doesn't get cleaned up HOT 4
- [Feature Request]: Ability to close local clients
- [Bug]: When using AzureOpenAI for embedding `azure_deployment` needs to be provided as well HOT 1
- Extending Search Filter to Include Multiple Metadata Fields HOT 3
- what happens if I call chroma.from_documents twice? HOT 4
- [Bug]: ONNXRuntime error on multiple document upserts HOT 2
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from chroma.