Cohere Cohere Neural Search AI technology Top Builders

Explore the top contributors showcasing the highest number of Cohere Cohere Neural Search AI technology app submissions within our community.

Cohere Neural Search

Language models give computers the ability to search by meaning and go beyond searching by matching keywords. This capability is called semantic search.

Popular use case of semantic search is building a next generation web search engine. Impressive, but the applications of semantic search go beyond that! They can empower a private search engine for internal documents or records. They can be used to power features like StackOverflow's "similar questions" feature. And you can build many more things with it.

Semantic search is the most successful with text sources where the answer to a query is likely to be in a single, concrete paragraph, such as technical documentation or wikis which are organized as a list of instructions or facts.

General
Relese date	December 12, 2022
Author	Cohere
Repository	https://github.com/cohere-ai/sandbox-multilingual
Type	Language model

Start building with Cohere Neural Search

Cohere Neural Search has a rich ecosystem of libraries and resources to help you get started. We have collected the best Cohere Neural Search libraries and resources to help you get started to build with Cohere Neural Search today. To see what others are building with Cohere Neural Search, check out the community built Cohere Neural Search Use Cases and Applications.

Cohere Semantic Search Sandbox

We encourage you to explore semantic search with Basic Semantic Search notebook, Cohere’s docs and Toy Semantic Search sandbox. Sandbox is a collection of experimental, open-source GitHub repositories by Cohere that make building applications for developers fast and easy, regardless of ML experience.

Basic Semantic Search example notebook by Cohere
Docs on how to build a Try out semantic search
Build a simple Semantic Search engine with Cohere
Toy Semantic Search Sandbox

Cohere Multilingual Semantic Search

Text embeddings are a central component in machine language understanding. They are numeric representations of text (be it a document, an email, or even a sentence). An embedding model translates text into a list of numbers that capture its meaning. A multilingual embedding model is able to do that well for many languages.

This video demonstrates Cohere's multilingual embedding model, and its ability to represent many languages.
Multilingual model Github repo
Multilingual movies search and recommendation demo

Edit on Github

Cohere Cohere Neural Search AI technology Hackathon projects

Discover innovative solutions crafted with Cohere Cohere Neural Search AI technology, developed by our community members during our engaging hackathons.

ScholarIntel-Intel Decision Making for Scholar

ScholarIntel | Intelligence Decisions Making for Scholars. ScholarIntel is the cutting-edge technologies with solutions of Intelligence Decisions Making Platform for Researchers and Scholars to Empowering Advance and Automate Research Capabilities. ScholarIntel is an empowering research and scholars application, providing intelligence informed decision making for research and academic writing. Leveraging advanced technologies, ScholarIntel enables data-driven decisions, impactful campaigns, and a competitive edge in the ever-evolving researcher and scholars landscape. • Efficient Discovery & Analysis: Quickly find and analyze groundbreaking research papers from PubMed and ArXiv. • Engaging Tools: Interact with research through innovative chat interfaces and audiobooks. • Writing Support: Enhance writing with paraphrasing, citations, and plagiarism detection. • Visualize Research: Simplify complex information with mind maps and paper embedding explorers. • Collaborative Platform: Connect with mentors, peers, and AI tools for validation and refinement.

PyLibrarian

It's no secret that when working with a new library, SDK, or API, software developers often waste hours and hours hopelessly poring over a sea of scattered documentation pages to find the one syntax example or function parameter datatype they needed. With the arrival of AI tools such as ChatGPT, sometimes developers can get lucky and get the exact code they need simply by asking the LLM. However, traditional LLM’s knowledge pools are limited to their training data, so when they are asked about perhaps newer tech, they may be rendered useless, or even worse, hallucinate and spew nonsense, wasting even more of a developer’s time. Pylibrarian is a special chatbot that solves all of these headaches by granting LLM access to complete documentation for Python’s most popular libraries using RAG architecture. Pylibrarian was built by processing, embedding (using cohere.embed), and storing documentation pages into Weaviate’s vector database. Upon a user query, we can semantically search for the most relevant pages of documentation to that query. Using Cohere’s chat endpoint’s document mode, the chatbot synthesizes a response citing the documents, leading to far more consistent, grounded responses.