SOTA Code Retrieval with Efficient Code Embedding Models

Today, we’re excited to announce Qodo-Embed-1, a new code embedding model family that achieves state-of-the-art performance while maintaining a significantly smaller footprint than existing models. On the CoIR benchmark—which measures the model’s proficiency in retrieving context—our 1.5B model scored 68.53 surpassing larger 7B models. Qodo-Embed-1-7B, Qodo’s larger model, also outperforms models of the same size, scoring 71.5. In this blog, we’ll share our approach to training code embedding models using synthetic data generation. The challenge with code embedding models The main challenge with existing code embedding models is their difficulty in accurately retrieving relevant code snippets based on natural language queries. Many general-purpose embedding models like OpenAI’s text-embedding-3-large focus on language patterns rather than code-specific elements like syntax, variable dependencies, control flow and API usage. This gap leads to irrelevant or imprecise search results and ... Read full article.