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

Cohere Neural Search top builders

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

Mohammed Arhaan Hussein
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Cohere Cohere Neural Search AI technology Hackathon projects

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

Multiagent coding assistant

BOB / FORGE is a multi-agent coding assistant designed to make software development safer, faster, and more reliable. Many developers, students, and small teams can generate code quickly, but first drafts often contain hidden bugs, incorrect assumptions, weak input validation, or untested edge cases. Reviewing and testing every generated solution manually is time-consuming, especially for beginners and teams working under tight deadlines. Our solution uses a LangGraph-powered workflow that behaves like an autonomous engineering team. The user enters a natural-language programming request through the web interface. The Builder agent creates an implementation and test contract. The Reviewer agent analyzes the code for logical, structural, and security issues. The Runner executes the tests inside a restricted sandbox, preferably using Docker with network isolation and resource limits. If problems are detected, the Fixer agent applies a targeted correction. The workflow then loops through review and testing until the solution passes or the iteration limit is reached. BOB / FORGE is intended for students, developers, educators, hackathon teams, and anyone who wants an additional layer of confidence before using generated code. Users interact with a simple engineering studio interface that displays the generated artifact, review findings, test output, agent telemetry, iteration count, and final readiness status. What makes the project unique is its “code that argues with itself” approach. Instead of relying on a single code-generation response, the system creates constructive disagreement between specialized agents. It also includes BobRiskNet, a lightweight neural bug-risk model trained on synthetic code-defect patterns. This model provides an additional local risk signal even when no external AI model is configured. By combining generation, adversarial review, sandboxed execution, automated repair, and transparent evidence, BOB / FORGE reduces the chance that users will blindly trust flawed code. It turns code generation from a one-step output into an observable, testable, and repeatable engineering process.