Building an Intelligent Issue Management System with Vector Embeddings

Today, I want to dive deep into an impressive implementation that leverages vector embeddings for intelligent issue management. This system, built as a plugin for UbiquityOS, combines modern NLP techniques with robust data storage to create a sophisticated issue tracking and deduplication system.

Architecture Overview

The system is built as a plugin that processes GitHub issues and comments through a series of specialized handlers. At its core, it uses two main services:

Voyage AI for generating text embeddings

Supabase for storing and querying vector embeddings

The plugin architecture is elegantly structured to handle various GitHub events:


if (isIssueCommentEvent(context)) {
  switch (eventName) {
    case "issue_comment.created":      return await addComments(context);    case "issue_comment.deleted":      return await deleteComment(context);    case "issue_comment.edited":      return await updateComment(context);  }
} else if (isIssueEvent(context)) {
  switch (eventName) {
    case "issues.opened":      await addIssue(context);      await issueMatching(context);      return await issueChecker(context);    // ... other issue events  }
}

Vector Embeddings: The Core Technology

The most fascinating aspect of this system is its use of vector embeddings to understand and process text. The implementation uses Voyage AI’s embedding service with their large instruction model:


async createEmbedding(text: string | null, inputType: EmbedRequestInputType = "document"): Promise<number[]> {
  if (text === null) {
    throw new Error("Text is null");  } else {
    const response = await this.client.embed({
      input: text,      model: "voyage-large-2-instruct",      inputType,    });    return (response.data && response.data[0]?.embedding) || [];  }
}

This converts text into high-dimensional vectors that capture semantic meaning, allowing for sophisticated similarity comparisons between issues.

Intelligent Issue Management

The system implements several advanced features for issue management:

1. Issue Deduplication

One of the most powerful features is the ability to find similar issues using vector similarity search. The implementation uses Supabase’s vector similarity capabilities:


async findSimilarIssues({ markdown, currentId, threshold }: FindSimilarIssuesParams): Promise<IssueSimilaritySearchResult[] | null> {
  const embedding = await this.context.adapters.voyage.embedding.createEmbedding(markdown);  const { data, error } = await this.supabase.rpc("find_similar_issues", {
    query_embedding: embedding,    current_id: currentId,    threshold,    top_k: 5,  });  // ... error handling  return data;}

This allows the system to: - Detect duplicate issues automatically - Find related issues based on content similarity - Maintain a clean issue tracker by preventing redundancy

2. Privacy-Aware Storage

The system implements privacy-conscious storage of issue data:


if (isPrivate) {
  finalMarkdown = null;  finalPayload = null;  plaintext = null;}
const { data, error } = await this.supabase  .from("issues")
  .insert([{
    id: issueData.id,
    plaintext,
    embedding,
    payload: finalPayload,
    author_id: issueData.author_id,
    markdown: finalMarkdown
  }]);

This ensures that private issues are handled appropriately while still maintaining the vector embedding functionality.

3. Real-time Updates

The system maintains consistency by updating embeddings whenever issues are modified:


async updateIssue(issueData: IssueData) {
  const embedding = Array.from(await this.context.adapters.voyage.embedding.createEmbedding(issueData.markdown));  // ... privacy handling  const { error } = await this.supabase    .from("issues")
    .update({
      markdown: finalMarkdown,      plaintext,      embedding,      payload: finalPayload,      modified_at: new Date(),    })
    .eq("id", issueData.id);}

This ensures that the semantic understanding of issues stays current even as their content evolves.

Configuration and Integration

The system is designed to be easily configurable through environment variables: - SUPABASE_URL and SUPABASE_KEY for database access - VOYAGEAI_API_KEY for embedding generation

Integration with existing projects is straightforward through a YAML configuration:


- plugin: https://ubiquity-os-comment-vector-embeddings-main.ubiquity.workers.dev  with:    matchThreshold: 0.95    warningThreshold: 0.75    jobMatchingThreshold: 0.75

Technical Implementation Benefits

Scalability: The use of Supabase for vector storage and similarity search means the system can handle large numbers of issues efficiently.

Accuracy: By using Voyage AI’s large instruction model for embeddings, the system achieves high-quality semantic understanding of issue content.

Maintainability: The modular architecture with separate handlers for different events makes the code easy to maintain and extend.

Real-time Processing: The system processes issues and comments in real-time, providing immediate feedback on duplicates and similar issues.

This implementation showcases how modern NLP techniques can be practically applied to improve developer workflows. By combining vector embeddings with efficient storage and similarity search, it creates a powerful system for managing and organizing issues intelligently.