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60 lines
1.7 KiB
Markdown
60 lines
1.7 KiB
Markdown
Semantic Search
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===
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> Provide natural language search on user personal content like notes, images using ML models
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All data is processed locally. User can interface with semantic-search app via [Emacs](https://github.com/debanjum/emacs-semantic-search), API or Commandline
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Dependencies
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----
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- Python3
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- [Miniconda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links)
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Install
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---
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```sh
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git clone https://github.com/debanjum/semantic-search && cd semantic-search
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conda env create -f environment.yml
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conda activate semantic-search
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```
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Setup
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---
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Generate compressed JSONL from specified org-mode files
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```sh
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python3 processor/org-mode/org-to-jsonl.py \
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--org-files "Schedule.org" "Incoming.org" \
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--org-directory "~/Notes" \
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--jsonl-file ".data/notes.jsonl" \
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--compress \
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--verbose
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```
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Run
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---
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Load ML model, generate embeddings and expose API interface to run user queries on above org-mode files
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```sh
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python3 main.py -j .data/notes.jsonl.gz -e .data/notes_embeddings.pt
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```
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Use
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---
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- *Calls Semantic Search via Emacs*
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- `M-x semantic-search "<users-query"`
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- `C-c C-s`
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- *Call Semantic Search via API*
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- `GET` [http://localhost:8000/search?q="What is the meaning of life"](http://localhost:8000/search?q=%22what%20is%20the%20meaning%20of%20life%22)
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- *Call Semantic Search via Python Script Directly*
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```sh
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python3 search_types/asymmetric.py \
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-j .data/notes.jsonl.gz \
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-e .data/notes_embeddings.pt \
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-n 5 \
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--verbose \
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--interactive
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```
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Acknowledgments
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--
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- Charles Cave for [OrgNode Parser](http://members.optusnet.com.au/~charles57/GTD/orgnode.html)
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