-NLP container now splits large docs and submits them through an additional queue for embedding
-Added overall plaintext size limit that prevents larger docs from being split/indexed
-Memory and CPU limits added to NLP container to prevent starvation via the NLP container
-Average embedding vector approach eliminated due to the new streaming approach
-Additional model options pre-loaded for NLP container
-Changed default embedding model to TaylorAI/gte-tiny
-Vector and normalization added as k8s options for the NLP container
-Removed /indexing web endpoint for NLP service as text should be submitted through the Nemesis frontend
-Tokenization fixed for NLP container - now using the chosen model's tokenizer
-Removed tensorflow-serving password model as it proved not effective in production.
-Remove classic NLP summarization as it wasn't useful in production
Will