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Create a MongoDB retrieval Agent #711

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cozypet
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@cozypet cozypet commented Nov 18, 2023

Why are these changes needed?

Using RetrieveChat with MongoDB for Retrieve Augmented Code Generation and Question Answering

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.

  1. MongoDB Atlas integrates smoothly with existing databases. For organizations already using MongoDB, this means a seamless expansion into the vector storage—no major system overhauls required!
  2. MongoDB Atlas is built to handle operational heavy-lifting. It excels when serving large-scale, mission-critical applications, offering robustness and reliability where it counts.
  3. MongoDB's flexibility in handling a variety of data types and structures makes it perfectly suited to the complexity of vector embeddings.

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.

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codecov-commenter commented Nov 18, 2023

Codecov Report

Attention: Patch coverage is 0% with 47 lines in your changes missing coverage. Please review.

Project coverage is 31.91%. Comparing base (140a023) to head (85cdc5a).
Report is 765 commits behind head on main.

Files Patch % Lines
...tchat/contrib/mongoDB_retrieve_user_proxy_agent.py 0.00% 47 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##             main     #711      +/-   ##
==========================================
+ Coverage   27.89%   31.91%   +4.01%     
==========================================
  Files          27       28       +1     
  Lines        3466     3513      +47     
  Branches      784      790       +6     
==========================================
+ Hits          967     1121     +154     
+ Misses       2428     2309     -119     
- Partials       71       83      +12     
Flag Coverage Δ
unittests 31.82% <0.00%> (+3.98%) ⬆️

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@cozypet
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cozypet commented Nov 18, 2023

@microsoft-github-policy-service agree [company="{}"]

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cozypet commented Nov 18, 2023

@microsoft-github-policy-service agree

@sonichi
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sonichi commented Nov 26, 2023

@thinkall could you advise how to write the test to avoid the build error?

@ranfysvalle02
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@sonichi @thinkall @cozypet -

I think the issue here is -- MongoDB Atlas search is 'cloud only' and cannot be run in a in the Docker environment like we do with Postgres.

.github>workflows>contrib-openai.yaml ankane/pgvector

Screenshot 2024-05-23 at 9 30 52 PM

Any suggested path forward here?

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thinkall commented May 24, 2024

@sonichi @thinkall @cozypet -

I think the issue here is -- MongoDB Atlas search is 'cloud only' and cannot be run in a in the Docker environment like we do with Postgres.

.github>workflows>contrib-openai.yaml ankane/pgvector

Screenshot 2024-05-23 at 9 30 52 PM Any suggested path forward here?

Hi @ranfysvalle02 , we have new abstract of vectordb https://github.com/microsoft/autogen/tree/main/autogen/agentchat/contrib/vectordb . I would suggest adding a new mongodb class. No new agent is needed. Would you like to work on it?

@ranfysvalle02
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@thinkall

Here is the Pull Request: #2942

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ghost commented Jun 14, 2024 via email

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7 participants