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Auto Hashing ID for VectorDB Classes #4746

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Why are these changes needed?

These changes allow for users to use the MongoDBAtlas class with more modifiability. This includes enabling users to pass in embeddings, without need to create in the class (note this functionality can be extended to other VectorDB), and make use of MongoDB auto ID assignment, removing the need to insert ID at upload.

Related issue number

Closes #4728

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codecov-commenter commented Dec 18, 2024

Codecov Report

Attention: Patch coverage is 0% with 39 lines in your changes missing coverage. Please review.

Project coverage is 28.86%. Comparing base (8a8fcd8) to head (3ca0a7a).
Report is 10 commits behind head on 0.2.

Files with missing lines Patch % Lines
autogen/agentchat/contrib/vectordb/mongodb.py 0.00% 39 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##              0.2    #4746      +/-   ##
==========================================
- Coverage   29.30%   28.86%   -0.44%     
==========================================
  Files         117      117              
  Lines       13013    13049      +36     
  Branches     2469     2474       +5     
==========================================
- Hits         3813     3767      -46     
- Misses       8854     8935      +81     
- Partials      346      347       +1     
Flag Coverage Δ
unittests 28.86% <0.00%> (-0.44%) ⬇️

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@ekzhu ekzhu requested a review from Copilot December 18, 2024 01:57

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Copilot reviewed 1 out of 1 changed files in this pull request and generated no comments.

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Thank you very much for the PR, @mattbeardey ! I've left some comments. However, my major concern is that with this PR, MongoDBAtlasVectorDB is no more compatible with other vectordbs in autogen if a user created db w/o giving document ids.

I'd prefer set default id for Document with something like

chunk_ids = (
[hashlib.blake2b(chunk.encode("utf-8")).hexdigest()[:HASH_LENGTH] for chunk in chunks]
if not self._vector_db.type == "qdrant"
else [str(uuid.UUID(hex=hashlib.md5(chunk.encode("utf-8")).hexdigest())) for chunk in chunks]
)

if users just don't want to set document ids manually. Is this your case, or you need to use mongodb auto generated ids for some reason?

WDYT? Thanks.

autogen/agentchat/contrib/vectordb/mongodb.py Outdated Show resolved Hide resolved
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@thinkall thinkall added the 0.2 Issues which are related to the pre 0.4 codebase label Dec 18, 2024
@mattbeardey
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Hi @thinkall , thoughts on fbfc164, where the ID Hashing is implemented in the base.py class, and inherited throughout, with qdrant.py implementing its own class, as per your provided code. Thanks

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Hi @thinkall , thoughts on fbfc164, where the ID Hashing is implemented in the base.py class, and inherited throughout, with qdrant.py implementing its own class, as per your provided code. Thanks

This commit looks good to me. Thank you, @mattbeardey .

@mattbeardey mattbeardey changed the title MongoDB ID None allow and pre made embeddings allow Auto Hashing ID and PreMade Embedding for VectorDB Classes Dec 21, 2024
@mattbeardey mattbeardey changed the title Auto Hashing ID and PreMade Embedding for VectorDB Classes Auto Hashing ID for VectorDB Classes Dec 21, 2024
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Hi @thinkall , @ekzhu

Scaled back the PR to focus on ID only, will look to embeddings as a separate topic, as I believe there may be a need for definition on what a "Embedding Function" is, and what it returns (i.e. errors with the "to_list" method not guaranteed to be present. Will handle separately.

Please find latest pull. Thanks. Please review when possible.

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Thank you @mattbeardey , focusing on ID is a good idea!
I've just one suggestion, others look good to me!

@@ -123,7 +123,11 @@ def _wait_for_document(self, collection: Collection, index_name: str, doc: Docum
if query_result and query_result[0][0]["_id"] == doc["id"]:
return
sleep(_DELAY)

if query_result and float(query_result[0][1]) == 1.0:
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We may want to have documents with exactly the same content but different metadata. Like comments from different users. I'd suggest log a warning message instead of raising an error. And it's not a TimeoutError if it's an error. WDYT?

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Adjusted now with a logger.warning, and check for metadata.

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LGTM, thank you @mattbeardey !

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@ekzhu , do we still support openai tests for 0.2 in the CI pipeline?

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ekzhu commented Dec 22, 2024

It's still supported but the branch must be on the Microsoft repo. We need to create a branch, create a new PR from there, and have the author submit PR to that branch. Then we can run it.

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MongoDB Vector Database File Upload requires "Id" despite MongoDB Atlas automatically assigning an ID.
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