Transforming Code Search with Voyage AI: Why Your Continue Assistant Needs Better Embedding Models and Rerankers

Chad Metcalf

• 4 min read
Transforming Code Search with Voyage AI: Why Your Continue Assistant Needs Better Embedding Models and Rerankers

As your codebase grows, finding the right information at the right time becomes increasingly difficult. Everyone who's spent hours hunting for a function they wrote months ago or searching for documentation on how to use a library understands this pain acutely.

While Continue's default search capabilities are already powerful, specialized tools from Voyage AI can dramatically improve how you search and retrieve code-related information. Let's explore why these tools matter for practical, everyday development.

What Are Embedding Models and Rerankers, Anyway?

Before diving into the benefits, let's break down what these tools actually do using some analogies that might help clarify things.

Embeddings: Your Code's Digital Fingerprint

Think of embeddings as creating a digital fingerprint for each piece of code or documentation in your repository. Instead of just matching text (which typically comes in the form of phrases, sentences, or paragraphs) or keywords like a traditional search engine, code embeddings must capture the semantic essence of the underlying algorithms within code snippets:

# These functions would be considered similar by embeddings, 
# despite having different variable names and syntax
def calculate_average(numbers):
    return sum(numbers) / len(numbers)

function computeMean(array) {
    return array.reduce((a, b) => a + b, 0) / array.length;
}

In this example, a good code-specific embedding model, like voyage-code-3, recognizes these functions as semantically similar because they both calculate an average, even though they use different languages, variable names, and approaches.

Rerankers: Your Personal Code Librarian

If embeddings create the fingerprints, rerankers are like experienced librarians who look at a stack of potentially relevant books and reorder them based on a deeper understanding of what you're actually looking for.

When you search for "how to implement pagination for a REST API," a reranker evaluates each candidate result more thoroughly and might prioritize a code example that includes both pagination logic and HTTP response formatting, even if it didn't have the highest initial relevance score.

For a deeper dive into what makes rerankers different from embedding models, check out Voyage's blog post on rerank-2.

Standard search tools often fall short for programmers because:

  1. Different languages, same concepts: You might be writing Python but could benefit from seeing a JavaScript implementation. Generic search tools miss these cross-language connections.

  2. Intention vs. syntax: When you search for "efficiently sort large arrays," you're looking for algorithms and optimizations, not just the word "sort."

  3. Context matters: A function that works with user authentication in a web app context is different from one in a CLI tool, even if both handle credentials.

  4. Documentation-to-code mapping: Finding the implementation that matches API documentation can be frustratingly difficult without semantic understanding.

Voyage AI has explored these challenges extensively in its analysis of code retrieval evaluation methods, identifying common subtasks and limitations of existing approaches.

How Voyage AI Makes a Difference

Voyage's specialized models address these challenges in several practical ways:

Truly Understanding Code Structure

Voyage AI's voyage-code-3 embeddings model was built specifically for code, and evaluated using a curated set of training data across 300+ programming languages designed to overcome the common shortcomings of existing benchmarks. This means voyage-code-3 understands:

  • The relationship between function signatures and implementations
  • How similar patterns appear across different languages
  • The connection between natural language descriptions and code
  • Looking beyond pure variable names and understanding the algorithms implemented by code snippets

Here's what this looks like in practice:

Query: "implement binary search tree insertion"

Traditional search might return:
- Files with the words "binary", "search", "tree" and "insertion"
- Maybe documentation about BSTs

voyage-code-3 returns:
- Actual BST implementations across multiple languages
- Relevant insertion functions even if they use terms like "add" instead of "insert"
- Related helper methods that support BST operations

The voyage-code-3 blog post details how they've optimized their model specifically for code retrieval tasks, with impressive gains over general-purpose embedding models.

Smart Refinement with Reranking

Voyage's rerank-2 takes your initial search results and intelligently reorders them by:

  1. Looking at the relationship between your query and each result more deeply
  2. Evaluating code structure and functionality rather than just text similarity
  3. Considering context from surrounding code when determining relevance
  4. Prioritizing examples that are most directly applicable to your need

This means you spend less time sifting through search results and more time writing code.

embedding-reranking.png

Real-World Benefits

Adding Voyage AI's search capabilities to your Continue assistant translates to concrete improvements in your daily workflow:

Finding Examples When You Need Them

Need to implement a specific algorithm or pattern? Voyage AI helps you find the most relevant examples in your codebase instantly and more accurately than alternatives, even if they use different terminology or implementation strategies.

Better Context for Your Assistant

Your Continue assistant works best when it has relevant context. Better embeddings and reranking means it can pull in more appropriate references before generating suggestions.

Cross-Language Learning

Working in an unfamiliar language? Voyage can help you find similar patterns from languages you know better, bridging the knowledge gap.

Documentation Discovery

Connect API documentation to actual implementations with semantic search that understands both code and natural language descriptions.

Setting Up Voyage AI with Continue

Adding these capabilities to your Continue assistant is straightforward:

  1. Visit https://hub.continue.dev/signup to create your account
  2. Sign up for the free trial or model addon. The Voyage AI blocks are included.
  3. The default assistant we create for you already includes the Voyage AI blocks.
  4. If you've created your own assistant, add the blocks:
  5. Enjoy more powerful, relevant code search in your development workflow

Why This Matters for Developers

The difference between good and great tooling often comes down to reducing friction. When you need to find a specific implementation or pattern, every minute spent searching is a minute not spent solving problems.

By enhancing your Continue assistant with specialized search models, you're removing one of the most persistent sources of developer friction - the hunt for relevant code and documentation.

Try It Yourself

The true test of any development tool is how it performs in your actual workflow. Voyage AI offers a generous free tier that lets you experience the difference firsthand. The first 200 million tokens for both voyage-code-3 and rerank-2 are free for every account.

By combining Continue's flexible AI assistant framework with Voyage's specialized search capabilities, you're building a development environment that understands your code the way you do - semantically, structurally, and contextually.

Start your journey toward more efficient code discovery today by adding Voyage's blocks to your Continue assistant.