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.
|Relese date||December 12, 2022|
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
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.
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.