Update Agent Code to Use Reduced Embeddings from `token` Table

[p3nGu1nZz]

Is your feature request related to a problem? Please describe. Currently, our agent code uses high-dimensional vectors from the vocab table for calculating reward vectors. We need to update the code to use the reduced embeddings from the token table.

Describe the solution you'd like

• Identify and modify the classes and methods that use high-dimensional vectors.
• Update the data loading process to fetch embeddings from the token table.
• Adjust the reward calculation logic to use the reduced embeddings.
• Update configuration and initialization routines to reflect the changes.
• Thoroughly test and validate the updated agent code.

Describe alternatives you've considered

• Continuing to use high-dimensional vectors, but this is inefficient and not scalable.
• Using a hybrid approach, but it adds unnecessary complexity.

Additional context This change is necessary to leverage the reduced embeddings efficiently and improve the scalability of our agent code. The token table will contain the reduced embeddings generated by the PCA reduction script (reduce.py).

[p3nGu1nZz]

everything seems to be working as far as optimizing the embeddings and loading into the correct database table. this task is ready to be worked on

[p3nGu1nZz]

implemented needs testing

[p3nGu1nZz]

building new embeddings now.