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You can use more than just vector databases to feed additional context to LLMs. However, the naive approach of generating database queries is too brittle at the moment. Therefore, it is advisable to implement an agent and give it tools to interact with a graph database. The tools use predefined flows and allow agent to solve specific problems. I have a version with OpenAI here: https://towardsdatascience.com/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49 Would probably need to use Mixtral 8x7b as an agent.
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That's a great idea, and I agree with using an open source model instead, Mixtral 8x7b is a good choice. Feel free to add the notebook (don't forget to also add it to the _toctree.yml), and ping me for a review.
You can use more than just vector databases to feed additional context to LLMs. However, the naive approach of generating database queries is too brittle at the moment. Therefore, it is advisable to implement an agent and give it tools to interact with a graph database. The tools use predefined flows and allow agent to solve specific problems. I have a version with OpenAI here: https://towardsdatascience.com/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49 Would probably need to use Mixtral 8x7b as an agent.
The text was updated successfully, but these errors were encountered: