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agentaiassistantchatchatgptemacsimage-generationllama3llamacppllmobsidianobsidian-mdoffline-llmproductivityragresearchself-hostedsemantic-searchsttwhatsapp-ai
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- Improves organization of config files required for application - Declutters the application root directory from configs |
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.github/workflows | ||
config | ||
src | ||
tests | ||
views | ||
.gitignore | ||
docker-compose.yml | ||
LICENSE | ||
README.org |
Semantic Search
Allow natural language search on user content like notes, images, transactions using transformer based models
All search is done locally. User can interface with semantic-search app via Emacs, API or Commandline
Setup
Setup using Docker
1. Clone Repository
git clone https://github.com/debanjum/semantic-search && cd semantic-search
2. Configure
- Add Content Directories for Semantic Search to Docker-Compose
- Update docker-compose.yml to mount your images, org-mode notes, ledger/beancount directories
- If required, edit config settings in docker_sample_config.yml.
3. Run
docker-compose up -d
Troubleshooting
- The first run will take time. Let it run, it's mostly not hung
-
Symptom: Errors out with "Killed" in error message
- Fix: Increase RAM available to Docker Containers in Docker Settings
- Refer: StackOverflow Solution, Configure Resources on Docker for Mac
-
Symptom: Errors out complaining about Tensors mismatch, null etc
- Mitigation: Delete content-type > image section from docker_sample_config.yml
Setup on Local Machine
1. Install Dependencies
- Install Python3 [Required[
- Install Conda [Required]
-
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 environment.yml
conda activate semantic-search
3. Configure
- Configure files/directories to search in
content-type
section ofsample_config.yml
-
To run application on test data, update file paths containing
/data/
totests/data/
insample_config.yml
- Example replace
/data/notes/*.org
withtests/data/notes/*.org
- Example replace
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=sample_config.yml -vv
Use
-
Semantic Search via Emacs
- Install semantic-search.el
- Run
M-x semantic-search <user-query>
-
Semantic Search via API
- Query:
GET
http://localhost:8000/search?q="What is the meaning of life"&t=notes - Regenerate Embeddings:
GET
http://localhost:8000/regenerate - Semantic Search API Docs
- Query:
-
UI to Edit Config
Upgrade
On Docker
docker-compose build
On Local Machine
cd semantic-search
git pull origin master
conda deactivate semantic-search
conda env update -f environment.yml
conda activate semantic-search
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
Acknowledgments
- MiniLM Model for Asymmetric Text Search. See SBert Documentation
- OpenAI CLIP Model for Image Search. See SBert Documentation
- Charles Cave for OrgNode Parser
- Sven Marnach for PyExifTool