Best Neural Search Software for GitHub
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Compare the Top Neural Search Software that integrates with GitHub as of September 2025
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This a list of Neural Search software that integrates with GitHub. Use the filters on the left to add additional filters for products that have integrations with GitHub. View the products that work with GitHub in the table below.
What is Neural Search Software for GitHub?
Neural search software is a type of artificial intelligence technology that uses deep learning algorithms to help users find relevant information. It works by understanding the user's query and analysis language, context, and relationships between data points. Neural search is becoming more popular due its ability to provide fast and accurate results. The technology has numerous potential applications across a variety of industries. Compare and read user reviews of the best Neural Search software for GitHub currently available using the table below. This list is updated regularly.
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1
Cohere
Cohere AI
Cohere is an enterprise AI platform that enables developers and businesses to build powerful language-based applications. Specializing in large language models (LLMs), Cohere provides solutions for text generation, summarization, and semantic search. Their model offerings include the Command family for high-performance language tasks and Aya Expanse for multilingual applications across 23 languages. Focused on security and customization, Cohere allows flexible deployment across major cloud providers, private cloud environments, or on-premises setups to meet diverse enterprise needs. The company collaborates with industry leaders like Oracle and Salesforce to integrate generative AI into business applications, improving automation and customer engagement. Additionally, Cohere For AI, their research lab, advances machine learning through open-source projects and a global research community.Starting Price: Free -
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Embedditor
Embedditor
Improve your embedding metadata and embedding tokens with a user-friendly UI. Seamlessly apply advanced NLP cleansing techniques like TF-IDF, normalize, and enrich your embedding tokens, improving efficiency and accuracy in your LLM-related applications. Optimize the relevance of the content you get back from a vector database, intelligently splitting or merging the content based on its structure and adding void or hidden tokens, making chunks even more semantically coherent. Get full control over your data, effortlessly deploying Embedditor locally on your PC or in your dedicated enterprise cloud or on-premises environment. Applying Embedditor advanced cleansing techniques to filter out embedding irrelevant tokens like stop-words, punctuations, and low-relevant frequent words, you can save up to 40% on the cost of embedding and vector storage while getting better search results.
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