Skip to main content

Core interfaces for hybrid search implementations (CUDA version)

Project description

just-semantic-search

PyPI version Python Version License Downloads

LLM-agnostic semantic-search library with hybrid search support and multiple backends.

Features

  • 🔍 Hybrid search combining semantic and keyword search
  • 🚀 Multiple backend support (Meilisearch, more coming soon)
  • 📄 Smart document splitting with semantic awareness
  • 🔌 LLM-agnostic - works with any embedding model
  • 🎯 Optimized for scientific and technical content
  • 🛠 Easy to use API and CLI tools

Installation

Make sure you have at least Python 3.11 installed.

Using pip

pip install just-semantic-search        # Core package
pip install just-semantic-search-meili  # Meilisearch backend

Using Poetry

poetry add just-semantic-search        # Core package
poetry add just-semantic-search-meili  # Meilisearch backend

From Source

# Install Poetry if you haven't already
curl -sSL https://install.python-poetry.org | python3 -

# Clone the repository
git clone https://github.com/your-username/just-semantic-search.git
cd just-semantic-search

# Install dependencies and create virtual environment
poetry install

# Activate the virtual environment
poetry shell

Docker Setup for Meilisearch

The project includes a Docker Compose configuration for running Meilisearch. Simply run:

./bin/meili.sh

This will start a Meilisearch instance with vector search enabled and persistent data storage.

Quick Start

Document Splitting

from just_semantic_search.article_semantic_splitter import ArticleSemanticSplitter
from sentence_transformers import SentenceTransformer

# Initialize model and splitter
model = SentenceTransformer('thenlper/gte-base')
splitter = ArticleSemanticSplitter(model)

# Split document with metadata
documents = splitter.split_file(
    "path/to/document.txt",
    embed=True,
    title="Document Title",
    source="https://source.url"
)

Hybrid Search with Meilisearch

from just_semantic_search.meili.rag import MeiliConfig, MeiliRAG

# Configure Meilisearch
config = MeiliConfig(
    host="127.0.0.1",
    port=7700,
    api_key="your_api_key"
)

# Initialize RAG
rag = MeiliRAG(
    "test_index",
    "thenlper/gte-base",
    config,
    create_index_if_not_exists=True
)

# Add documents and search
rag.add_documents_sync(documents)
results = rag.search(
    text_query="What are CAD-genes?",
    vector=model.encode("What are CAD-genes?")
)

Project Structure

The project consists of multiple components:

  • core: Core interfaces for hybrid search implementations
  • meili: Meilisearch backend implementation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Citation

If you use this software in your research, please cite:

@software{just_semantic_search,
  title = {just-semantic-search: LLM-agnostic semantic search library},
  author = {Karmazin, Alex and Kulaga, Anton},
  year = {2024},
  url = {https://github.com/your-username/just-semantic-search}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

just_semantic_search_cuda-0.4.5.tar.gz (23.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

just_semantic_search_cuda-0.4.5-py3-none-any.whl (31.5 kB view details)

Uploaded Python 3

File details

Details for the file just_semantic_search_cuda-0.4.5.tar.gz.

File metadata

  • Download URL: just_semantic_search_cuda-0.4.5.tar.gz
  • Upload date:
  • Size: 23.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.4 CPython/3.12.3 Linux/6.8.0-79-lowlatency

File hashes

Hashes for just_semantic_search_cuda-0.4.5.tar.gz
Algorithm Hash digest
SHA256 15b9ce8d9cd25a389f0b4700a8221b7272c25f57c298c14de0b6ff39f069d737
MD5 d8fcaecd1cbeeaa907e3e5dcc3eef89e
BLAKE2b-256 e8c40e4fa0dbd6381109cc6553f47637d5c05fdc04fd5ea316374bc7f8e0d8a3

See more details on using hashes here.

File details

Details for the file just_semantic_search_cuda-0.4.5-py3-none-any.whl.

File metadata

File hashes

Hashes for just_semantic_search_cuda-0.4.5-py3-none-any.whl
Algorithm Hash digest
SHA256 0c8dc9eb683eb8010f929efb3f6554fdd98dcade94b412e10e1b22664acc63c0
MD5 a96df3aa5d76dcf5ecb890a8240a6f87
BLAKE2b-256 e40025a4ef9074b8265291783c76237e089721a3396d0b0bbf38eee5cb01a9fa

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page