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RUNLOCALAI · v38
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  5. /How to use Continue.dev codebase index for fast semantic search across your project
HOW-TO · DEV

How to use Continue.dev codebase index for fast semantic search across your project

intermediate·20 min·By Fredoline Eruo
Target environment
Ubuntu 24.04 · Continue.dev 0.9.x
PREREQUISITES

Continue.dev installed, project indexed

What this does

Continue.dev builds a semantic index of the local codebase using embeddings, enabling natural-language queries that return relevant code locations even when the search terms do not appear verbatim in the source. This transforms a large project into a navigable knowledge base where developers can ask questions such as "Where is authentication handled?" and receive precise file references with context snippets.

Steps

  1. Open the project workspace in VS Code.
  2. Locate the Continue.dev sidebar panel on the left side of the editor.
  3. Click the Index icon (a database symbol) in the Continue.dev panel header.
  4. Confirm the index status: it should show "Indexing in progress" and then "Index complete" once all files have been processed.
  5. Return to the chat input and type a semantic query, for example: "Where is the HTTP request handler defined?"
  6. Review the returned results: each entry shows a file path, a relevance score, and a snippet of surrounding code.
  7. Click any result to open the file at the indicated location.
  8. Re-run the index after adding or renaming many files by clicking Refresh Index to keep results accurate.

Verification

ls -la ~/.continue/indexes/$(basename "$(pwd)")/ 2>/dev/null && echo "Index directory exists and contains files"

Expected output: confirmation that the index directory for the current project was created and contains at least one index artifact file.

Common failures

  • Index stuck at zero files: Large node_modules or binary directories may be excluded; add the project root to Continue.dev's allow list in config.json if it is outside the default scope.
  • Slow embeddings: Using a local Ollama embeddings model (such as nomic-embed-text) significantly accelerates indexing compared to remote API calls.
  • Stale results after refactoring: Click Refresh Index after bulk file changes; otherwise the semantic search returns references to old code that no longer exists.

Related guides

  • How to use Continue.dev to connect a local codebase to a custom LLM backend
  • How to configure Continue.dev with multiple AI providers and switch between them
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