Denser Retriever is an enterprise-grade AI retrieval system designed to enhance AI application accuracy by unifying multiple search technologies. It targets developers and researchers seeking to integrate sophisticated retrieval capabilities, offering improved relevance through a hybrid approach.

How It Works

Denser Retriever combines keyword search, vector databases, and ML rerankers using an XGBoost model. This ensemble approach leverages the precision of keyword matching, the broad relevance of vector search, and the fine-tuned accuracy of reranking models. Experiments on MTEB datasets demonstrate significant improvements over pure vector search baselines.

Quick Start & Requirements

• Installation: pip install git+https://github.com/denser-org/denser-retriever.git#main or poetry add git+https://github.com/denser-org/denser-retriever.git#main.
• Prerequisites: Python (Anaconda recommended due to reported NumPy installation issues).
• Documentation: retriever.denser.ai

Highlighted Details

• Combines keyword, vector, and ML reranking for enhanced retrieval accuracy.
• Utilizes XGBoost for effectively merging heterogeneous retrieval signals.
• Claims state-of-the-art accuracy on MTEB Retrieval benchmarks.
• Provides examples for powering end-to-end applications like chatbots and semantic search.

Maintenance & Community

• Active development indicated by recent dependency updates and pre-commit hooks.
• No specific community links (Discord/Slack) or roadmap mentioned in the README.

Licensing & Compatibility

• Licensed under the MIT license, permitting commercial use and integration into closed-source projects.

Limitations & Caveats

• The README notes potential NumPy installation issues when not using Anaconda, suggesting a potential setup hurdle for some users.