Accurately searching through code and documentation is more critical than ever. We're thrilled to unveil our latest embeddings in the world of coding:
jina-embeddings-v2-base-code. This new open-source programming language embedding model is designed to improve how developers interact with code and documentation. Supporting English and 30 popular programming languages, it stands out as the only open-source model of its kind that accommodates up to 8,192 input tokens. The
jina-embeddings-v2-base-code is now available on HuggingFace under an Apache 2.0 license and can be freely accessed via our Embedding API.
Why Develop an Embedding Model for Code?
Developers often find themselves navigating through vast codebases, not in search of errors, but to locate specific functionalities or understand how certain processes are implemented. This task can be time-consuming and, at times, akin to finding a needle in a haystack. Integrated Development Environments (IDEs) have significantly improved this process by providing tools and features that automate the search for information. However, the potential for further enhancement exists, and this is where our embedding model comes into play.
Use Cases of
By integrating AI-powered search capabilities, we're not just augmenting existing functionalities within IDEs; we're transforming how developers engage with codebases. This technology goes beyond simple text search, offering semantic understanding that can interpret the intent behind a query, thereby significantly reducing the time and effort required for code reviews, unit testing, and overall quality management.
Enhanced Code Navigation
- Query Format: Natural language description of the functionality or code snippet you're searching for.
- Retrieved Result Format: Relevant code files or snippets where the described functionality is implemented, along with annotations or highlights that point to the specific parts of the code.
Streamlined Code Review
- Query Format: Description of the programming concepts or patterns you want to review across the codebase.
- Retrieved Result Format: A list of code snippets or pull requests that match the described concepts, patterns, or best practices, enabling reviewers to focus on critical areas for improvement.
Automated Documentation Assistance
- Query Format: Code snippet for which you need documentation or an explanation.
- Retrieved Result Format: Suggested docstrings or documentation entries that explain the code's functionality, parameters, and return types, making it easier to maintain up-to-date and comprehensive documentation.
By addressing these specific use cases,
jina-embeddings-v2-base-code not only enhances the development experience but also promotes a more collaborative and efficient coding environment.
Benchmark the Performance
In a field where precision and accuracy are paramount,
jina-embeddings-v2-base-code has outshined its competitors, leading the pack in nine out of fifteen crucial CodeNetSearch benchmarks. What's more, our model holds highly competitive scores in the remaining benchmarks. When compared to its nearest competitors, including those from tech giants like Microsoft and Salesforce,
jina-embeddings-v2-base-code not only ranks higher but also showcases its superior design and capabilities.
- State-of-the-Art Performance: Our commitment to excellence is reflected in the performance of Jina Embedding models, which consistently top benchmark lists against other open-source offerings and even outperform models from Microsoft and Salesforce.
- Compact Yet Powerful: In the world of AI, efficiency is key. With 161 million parameters (307MB without quantization),
jina-embeddings-v2-base-codeis designed for efficiency, offering high-speed performance and cost savings without compromising on capability.
- Extended Context Capability: The ability to process up to 8192 tokens allows for the handling of large functions and numerous object files, providing a depth of understanding and context that surpasses the limitations of models supporting only a few hundred tokens.
jina-embeddings-v2-base-codemeets the diverse needs of the programming community.
- RAG Integration for Seamless Code Generation: The model's compatibility with RAG and integration with a code generation model facilitate not just code generation from general knowledge but also the ability to read relevant APIs and documentation, enabling automatic code integration that is both efficient and accurate.
Seamless API Integration
jina-embeddings-v2-base-code is designed for easy integration, supporting major vector databases like MongoDB, Qdrant, and Weaviate, and frameworks such as Haystack and LlamaIndex. This ensures that developers can effortlessly incorporate our model into their existing systems, leveraging its capabilities to enhance their code retrieval and documentation processes.
We value your feedback on
jina-embeddings-v2-base-code. Join our community channel to contribute feedback and stay informed about our advancements. Together, we're shaping a more robust and inclusive AI future.