big-ann-benchmarks VS awesome-semantic-search

Compare big-ann-benchmarks vs awesome-semantic-search and see what are their differences.

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big-ann-benchmarks awesome-semantic-search
1 3
296 322
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9.3 5.7
8 days ago 5 months ago
Jupyter Notebook
MIT License Creative Commons Zero v1.0 Universal
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

big-ann-benchmarks

Posts with mentions or reviews of big-ann-benchmarks. We have used some of these posts to build our list of alternatives and similar projects.

awesome-semantic-search

Posts with mentions or reviews of awesome-semantic-search. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

What are some alternatives?

When comparing big-ann-benchmarks and awesome-semantic-search you can also consider the following projects:

Milvus - A cloud-native vector database, storage for next generation AI applications

similarity - TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

contract-discovery - Data and additional information regarding the paper: Contract Discovery. Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (to appear in Findings of EMNLP).

elasticsearch-learning-to-rank - Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch

ColBERT - ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)

haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.