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+mdb atlas vectordb [clean_final] #3000
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Hi @ranfysvalle02 , thank you. I can see the issues of notebook have not been addressed. See comments here: #2996 |
Co-authored-by: HRUSHIKESH DOKALA <96101829+Hk669@users.noreply.github.com>
The simple change of
Highlighted that I need to create a "wrapper" class around the MongoDB collection, similar to what pgvector did.
but for MongoDB. Will be working on this @Hk669 |
@thinkall - Can you please tell me how to 'fix' the notebook? Or perhaps have it as a 'suggested commit'? I'll be addressing the notebook and any final touches later today. |
What about run it successfully in your local env and remove only the sensitive info? A new user should be able to run it by fill in the missed message, which should only be the So, the connect string of mongodb should not be empty, the one I suggested in your last PR worked for me. Does it work for you? The one you previously used didn't work for me and was not connecting to the docker container. The output of the chat in the last cell is not correct. Could you please check my previous comments and the pgvector notebook example? |
It's OK, no need to wrap a |
I see what you mean @thinkall ! I found the issue with the notebook and notebook output!
vs
We are close!!! I'll push the fix/code later today |
No errors here. I've fixed this and made a commit. |
@thinkall finally tracked this down --- its all about the index! the create_collection method
does not use 'index_name' or 'similarity' -- which I had added. Working on a fix! |
@thinkall - I finally got it to run, but I have to add a strange programmatic arbitrary delay for things to work. I am working on a more elegant solution. After ~15seconds it works. Anything 5 seconds or less fails. |
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #3000 +/- ##
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- Coverage 32.90% 11.73% -21.18%
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Files 94 97 +3
Lines 10235 10807 +572
Branches 2193 2312 +119
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- Hits 3368 1268 -2100
- Misses 6580 9518 +2938
+ Partials 287 21 -266
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. |
https://github.com/microsoft/autogen/actions/runs/9629801423/job/26560601555?pr=3000 -> can you look into the tests that are failing. thanks @ranfysvalle02 |
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Hi @cozypet, @Jibola , thank you very much for the updates. I think we can merge it once the notebook example runs well. Could you please help review the notebook example (both code and documentation) and update it by running w/ a needed atlas instance? Thanks. Btw, @Jibola, the PR won't be able to be merged w/o your approval as you've requested a change. |
Sure thing! |
…e READY for querying
…autogen-mongodb-atlas
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@Jibola , thank you very much for the updates! Could you please help review and rerun the notebook example with a high tier Atlas instance? Thanks.
Yep. Just went ahead and did so. Let me know if the new notebook suffices! |
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Thank you so much, @Jibola , @ranfysvalle02 , @cozypet and all the other reviewers and contributors! What a great collaboration!
Why are these changes needed?
MongoDB has been ranked as the best vector database(https://www.mongodb.com/blog/post/atlas-vector-search-commands-highest-developer-nps-retool-state-ai-2023-survey) in the Retool AI report, so it is quite important to add MongoDB vector search as an option for Autogen RAG.
You can easily start the MongoDB vector search on a free tier M0 MongoDB Atlas cluster. Free tier cluster provides the full functionality of the MongoDB vector search. https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/
But why is MongoDB such a standout? Well, there are a few key reasons.
As such, implementing MongoDB as a Retrieval Agent can unlock new potential in your AI applications, bringing the full power of vector storage to bear.
Related issue number: 711
Closes #711
Closes #2996
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