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Fixes on representation model, visualisations, and embeddings in CPU mode. Package updates and optimisation for compatibility
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Corrected reference to sentence transformers dependency. Updated Dockerfile packages
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Added and replaced relevant files to download in download_model.py to allow for app use on AWS
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Updated Dockerfile with latest packages
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Corrected minor Dockerfile package version issue
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App now retains original index following cleaning to allow for referring back to original data
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Now installed dependencies into correct folder in Dockerfile
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Finally managed to enforce cpu torch install in Dockerfile
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Further optimised Dockerfile and requirements (smaller torch installation now hopefully)
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Transferring across installed packages from build stage in Dockerfile
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Changed Dockerfile to multi-stage build to further reduce size
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Trying to make container image smaller through Dockerfile
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Minor changes to reduce Dockerfile size
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Removed some requirements from Dockerfile for AWS deployment to reduce container size
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Added NUMBA_CACHE_DIR to Docker environmental variables
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Allowed for app running on AWS to use smaller embedding model and not to load representation LLM (due to size restrictions).
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Dockerfile now installs models directly into user folder instead of moving from base folder
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Updated Gradio version for spaces. Updated Dockerfile to enable Llama.cpp build with Cmake
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Updated packages. Improve hierarchy vis. Better models - mixedbread and phi3. Now option to split texts into sentences before modelling.
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first commit
