• Natural Language Search: Query any video or image using Natural Language. (powered by CLIP )
• Simple, Clean Interface: Index any media just by providing a path to a local directory/folder and start querying.
• Full Index Search: Enable Natural Language Search across ALL Indexed Videos or Images.
• Fast: Start Getting Results in milliseconds. All Indices are stored on user's system.
• Minimal Requirements: Any consumer Grade CPU with AVX2 instructions enabled and minimal software dependencies. (No dependence on deep-learning frameworks like pytorch/tensorflow.)
• Private: Fully self hosted on users' system with no dependence on outside Network in any manner.

Intel-64/AMD CPU with AVX2 instructions enabled.

• Python3 with pip installed (tested with >= 3.6.x)

• Caddy: Open Source Web server. Download from here

• Windows (Tested on 10/11).
• Linux (GlibC >= 2.27) [ Run command ldd --version to check glibc version.]

1. Install Caddy (Open source webserver, For serving static files and out of the box HTTPS configuration, if needed.)

2. Python 3 (any version should work. Tested with versions >= 3.6.x)

3. Download source.zip from latest release or git clone https://github.com/ramanlabs-in/hachi.git

4. cd into the cloned/downloaded repository. ( i.e change path to the root of cloned repository)

5. Collect Model Weights by downloading data.zip from releases, extract/unzip it at the root of the directory, such that there is now a data directory with 2 .bin files in it.

6. Run command pip install -r requirements.txt ( This would install opencv-python, numpy, flask, regex, ftfy python packages, if not found .)

   Extra steps (for Linux distributions Only.)

   7. Run command conda install -c conda-forge onednn-cpu-omp=2.6.0 ( conda is the most sane way i could find to install onednn shared library, without getting frustrated due to GlibC mismatching.)

      • update the LD_LIBRARY_PATH to make dynamic linker search for shared objects in the Conda path (if not already done!)

   8. Install openblas if not already included/installed with your OS.

      • sudo dnf install openblas-devel (Fedora)
      • sudo apt-get install openblas-dev (Ubuntu/Debian)

1. cd into the downloaded directory.
2. Run command: caddy run
3. Run command: python semantic_search.py OR python3 semantic_search.py
4. Visit http://localhost:5000

Front-end code of the app lies in ./static/ directory and is generated automatically based on the svelte components in ./hachi_frontend directory. Front-end development requires Node(tested with v18.13.0) to be installed on user's machine.

Checkout readme.md in ./hachi_frontend for more details.

• Machine learning model powering this webapp is based on CLIP architecture.

For Windows, shared libraries dnnl.dll and dnnl_v3.dll are included in this repository and are based on the ONEDNN Project. Specifically dnnl.dll corresponds to a version >= 2.6.x but less than version 3.x.x and dnnl_v3.dll corresponds to the version >= 3.x.x but less than 4.x.x. Developers can choose to build their own corresponding DLLs based on the instructions on the project page, provided they name it as dnnl.dll and dnnl_v3.dll after building.