AI Core Infra:Semantic Code Search
The Semantic Code Search functional team within AI Core Infra, focused on GitLab’s RAG implementation for providing semantic understanding of code repositories.
Overview
The Semantic Code Search team is part of the AI Core Infra organization. The team focuses on GitLab’s RAG implementation for providing semantic understanding of code repositories.
Key Information
| Slack Channel | #f_semantic-code-search |
| Stage Label | devops::ai platform |
| Group Label | group::ai core infra |
| Category Labels | category:semantic code search |
Team Resources
| Resource | Link |
|---|---|
| Team Project | gitlab-org/ai-powered/semantic-code-search/team |
| Planning Issues | Planning Issues |
| Issue Board | Issue Board |
Runbooks
The Active Context runbook provides operational guidance for the underlying framework powering semantic code search, including the embeddings pipeline, vector store management, and codebase indexing.
Documentation
- Semantic Code Search — user documentation
- Semantic Search development — development documentation covering the Active Context framework internals
Design Documents
- Codebase as Chat Context — feature design (semantic search, embeddings, chunking, ad-hoc indexing)
- AI Context Abstraction Layer — ActiveContext framework design (tasks, embedding models)
Last modified July 9, 2026: Semantic Code Search: add team resources, runbooks, and documentation links (
316941ee)
