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Debanjum Singh Solanky 4a90972e38 Use a better model for asymmetric semantic search 
- The multi-qa-MiniLM-L6-cos-v1 is more extensively benchmarked[1]
- It has the right mix of model query speed, size and performance on benchmarks
- On hugging face it has way more downloads and likes than the msmarco model[2]
- On very preliminary evaluation of the model
  - It doubles the encoding speed of all entries (down from ~8min to 4mins)
  - It gave more entries that stay relevant to the query (3/5 vs 1/5 earlier)

[1]: https://www.sbert.net/docs/pretrained_models.html
[2]: https://huggingface.co/sentence-transformers
2022-07-18 20:27:26 +04:00
.github/workflows Run build on PR 2022-07-04 18:09:47 -04:00
config Use a better model for asymmetric semantic search 2022-07-18 20:27:26 +04:00
src Use a better model for asymmetric semantic search 2022-07-18 20:27:26 +04:00
tests Use a better model for asymmetric semantic search 2022-07-18 20:27:26 +04:00
views Fix input text behavior for null/empty value fields 2021-12-04 10:45:48 -05:00
.dockerignore Make Docker ignore unnecessary files 2022-06-29 22:29:34 +04:00
.gitignore Create Basic Landing Page to Query Semantic Search and Render Results 2022-07-16 03:36:19 +04:00
demo.mp4 Add demo of semantic search to repository 2022-05-14 04:29:25 -04:00
docker-compose.yml Correct syntax of memory limit in docker-compose.yml 2022-07-06 20:07:11 -04:00
Dockerfile Add specific version for Python packages and downgrade miniconda Docker image to potentially fix build issues 2022-07-04 18:01:55 -04:00
LICENSE Add Readme, License. Update .gitignore 2021-08-15 22:52:37 -07:00
README.org Use a better model for asymmetric semantic search 2022-07-18 20:27:26 +04:00

README.org

https://github.com/debanjum/semantic-search/actions/workflows/test.yml/badge.svg https://github.com/debanjum/semantic-search/actions/workflows/build.yml/badge.svg

Semantic Search

Allow natural language search on user content like notes, images, transactions using transformer ML models

User can interface with semantic-search via the API or Emacs. All search is done locally*

Demo

Setup

1. Clone 

  git clone https://github.com/debanjum/semantic-search && cd semantic-search

2. Configure

3. Run 

docker-compose up -d

Note: The first run will take time. Let it run, it's mostly not hung, just generating embeddings

Use

Run Unit tests 

pytest

Upgrade 

  docker-compose build --pull

Troubleshooting

  • Symptom: Errors out with "Killed" in error message

  • Symptom: Errors out complaining about Tensors mismatch, null etc

    • Mitigation: Delete content-type > image section from docker_sample_config.yml

Miscellaneous

  • The experimental chat API endpoint uses the OpenAI API

    • It is disabled by default
    • To use it add your openai-api-key to config.yml

Development Setup

Setup on Local Machine

1. Install Dependencies
  1. Install Python3 [Required]
  2. Install Conda [Required]

  3. Install Exiftool [Optional]

    sudo apt-get -y install libimage-exiftool-perl
2. Install Semantic Search 
git clone https://github.com/debanjum/semantic-search && cd semantic-search
conda env create -f config/environment.yml
conda activate semantic-search
3. Configure
  • Configure files/directories to search in content-type section of sample_config.yml

  • To run application on test data, update file paths containing /data/ to tests/data/ in sample_config.yml

    • Example replace /data/notes/*.org with tests/data/notes/*.org
4. Run

Load ML model, generate embeddings and expose API to query notes, images, transactions etc specified in config YAML

python3 -m src.main -c=config/sample_config.yml -vv

Upgrade On Local Machine 

  cd semantic-search
  git pull origin master
  conda deactivate semantic-search
  conda env update -f config/environment.yml
  conda activate semantic-search

Acknowledgments