[New Example] Long Term Memory for Gemini

[lucifertrj]

• Save the preference in the Mem0.AI for the memory layer
• Based on the given user query, search and retrieve similar preferences from the memory layer.
• This example is use case driven that is useful for the recommendation systems and travel or food place planner.
• It uses Gemini as LLM, Qdrant as Vector Store and FastEmbed as Embeddings (open source)

[gemini-code-assist]

Note

Gemini is unable to generate a summary for this pull request due to the file types involved not being currently supported.

[review-notebook-app]

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[Giom-V]

/gemini review

[gemini-code-assist]

Note

Gemini is unable to generate a review for this pull request due to the file types involved not being currently supported.

[lucifertrj]

do you need it to be in ipynb or should I replace it with py for Gemini code assistant to perform the review.

[chunduriv]

Hi @lucifertrj, thanks for the contribution!

Please keep the example as a Interactive Python Notebook (.ipynb). Notebooks are the standard format for examples in this cookbook.

It looks like the notebook file isn't rendering correctly on GitHub due to a metadata issue.

Could you please try clearing all the cell outputs in your notebook and then pushing the updated file to this PR? That should resolve the rendering issue.

If you're still having trouble, feel free to share a Colab Gist so I can review your work in the meantime.

[lucifertrj]

sure will modify

[lucifertrj]

Hey @chunduriv I have removed the cell outputs.

you can view the notebook here: https://github.com/lucifertrj/cookbook/blob/longtermmemory/examples/Long_Term_Memory.ipynb

[lucifertrj]

For the GitHub gist, you can refer to this: https://colab.research.google.com/gist/lucifertrj/91c7fc265a6d40733c7a99bcf9aaa31d/long_term_memory.ipynb