Codebase Indexing

Codebase Indexing enables semantic code search across your entire project using AI embeddings. Instead of searching for exact text matches, it understands the meaning of your queries, helping Kilo Code find relevant code even when you don't know specific function names or file locations.

{% callout type="info" title="Opt-in indexing" %} Codebase Indexing is disabled by default. It starts only after you enable indexing globally or for an individual project. Configuring an embedding provider without enabling one of those toggles does not start indexing. {% /callout %}

What It Does

When enabled, the indexing system:

1. Parses your code using Tree-sitter to identify semantic blocks (functions, classes, methods)
2. Creates embeddings of each code block using AI models
3. Stores vectors in a vector database for fast similarity search
4. Provides the semantic_search tool to Kilo Code for intelligent code discovery

This enables natural language queries like "user authentication logic" or "database connection handling" to find relevant code across your entire project.

Key Benefits

• Semantic Search: Find code by meaning, not just keywords
• Enhanced AI Understanding: Kilo Code can better comprehend and work with your codebase
• Cross-Project Discovery: Search across all files, not just what's open
• Pattern Recognition: Locate similar implementations and code patterns

Setup

{% tabs %} {% tab label="VSCode" %}

Configure indexing

1. Open Kilo Code Settings → Indexing, or click the indexing indicator at the bottom of the prompt input panel.
2. Turn on Global Enable to index every workspace, or turn on Enable for This Project to index only the current workspace. Both toggles are off until explicitly enabled.
3. Pick an Embedding Provider and fill in its required fields.
4. Pick a Vector Store (Qdrant or LanceDB) and configure it.
5. Optionally adjust Tuning Parameters (search score, batch size, retries, max results).
6. Save to start the initial scan.

You can also edit the indexing section in kilo.jsonc directly:

{
  "indexing": {
    "enabled": true,
    "provider": "openai",
    "model": "text-embedding-3-small",
    "vectorStore": "lancedb",
    "openai": { "apiKey": "sk-..." },
    "lancedb": {}
  }
}

Embedding providers

Vector stores

• Qdrant (default) — external server. Recommended for team deployments and larger codebases. See Setting Up Qdrant.
• LanceDB — embedded, file-based. No server to run. Stores data under your Kilo data directory by default.

{% callout type="tip" %} For a fully local, zero-cost setup, combine Ollama (embeddings) with LanceDB (vector store — no separate server needed). {% /callout %}

Status indicator

The prompt input panel shows a compact indexing status indicator that reflects the current state (Standby / In Progress / Complete / Error) along with progress when scanning or embedding.

{% /tab %} {% tab label="CLI" %}

Configure indexing

The /indexing command (and aliases /index, /embedding) is available when the indexing plugin is installed. Indexing remains disabled until it is enabled globally or for the current project.

Open a Kilo TUI session and run:

/indexing

(aliases: /index, /embedding)

This opens an interactive configuration dialog where you can:

• Toggle indexing on/off
• Choose an Embedding Provider and fill in provider settings (API key, base URL, AWS region, etc.)
• Set the Embedding Model (blank = provider default)
• Set the Vector Dimension (blank = auto-detect from the model)
• Choose a Vector Store (Qdrant or LanceDB) and configure its connection
• Adjust Tuning Parameters (search threshold, batch size, retries, max results)

All changes are written to your kilo.jsonc config and take effect immediately.

You can also edit the indexing section directly. This is the full shape of the section:

{
  "indexing": {
    "enabled": true,
    "provider": "voyage",
    "model": "voyage-code-3",
    "dimension": 1024,
    "vectorStore": "qdrant",
    "voyage": {
      "apiKey": "pa-..."
    },
    "qdrant": {
      "url": "http://localhost:6333",
      "apiKey": ""
    },
    "searchMinScore": 0.4,
    "searchMaxResults": 50,
    "embeddingBatchSize": 60,
    "scannerMaxBatchRetries": 3
  }
}

Embedding providers

Vector stores

• qdrant — { url?, apiKey? } (default). See Setting Up Qdrant.
• lancedb — { directory? } — embedded, file-based. No server to run. Uses a default Kilo data directory when omitted.

{% callout type="tip" %} For a fully local, zero-cost setup, combine Ollama (embeddings) with LanceDB (vector store — no separate server needed). {% /callout %}

Status indicator

When indexing is enabled, the CLI shows an indexing status badge at the bottom of the TUI in the form IDX <state> (for example IDX In Progress 40% 120/300, IDX Complete, IDX Standby, or IDX Error <message>).

{% /tab %} {% tab label="VSCode (Legacy)" %}

The legacy extension uses its own Codebase Indexing settings panel.

Open Codebase Indexing Settings

1. In the chat header, click the database icon (indexing status).
2. The Codebase Indexing settings panel opens.
3. If you don't see the icon, open Kilo Code settings ({% codicon name="gear" /%}) and search for Codebase Indexing.

{% image src="/docs/img/codebase-indexing/codebase-indexing.png" alt="Codebase Indexing Settings" width="800" caption="Codebase Indexing Settings (legacy)" /%}

Configure Settings

1. Enable "Enable Codebase Indexing" using the toggle switch.
2. Configure your embedding provider:
   • OpenAI: Enter API key and select model
   • Gemini: Enter Google AI API key and select embedding model
   • Ollama: Enter base URL and select model
3. Set Qdrant URL and optional API key.
4. Configure Max Search Results (default: 20, range: 1-100).
5. Click Save to start initial indexing.

Embedding providers

The legacy extension supports a smaller set of providers:

Vector store

The legacy extension only supports Qdrant. See Setting Up Qdrant.

{% /tab %} {% /tabs %}

Setting Up Qdrant

If you choose Qdrant as your vector store, you need a running Qdrant server.

Quick Local Setup

Using Docker:

docker run -p 6333:6333 qdrant/qdrant

Using Docker Compose:

version: "3.8"
services:
  qdrant:
    image: qdrant/qdrant
    ports:
      - "6333:6333"
    volumes:
      - qdrant_storage:/qdrant/storage
volumes:
  qdrant_storage:

Production Deployment

For team or production use:

• Qdrant Cloud — managed service
• Self-hosted on AWS, GCP, or Azure
• Local server with network access for team sharing

Understanding Index Status

The interface shows real-time status:

• Standby: Not running, awaiting configuration or paused
• In Progress: Currently processing files (with a progress percentage and processed/total count)
• Complete: Up-to-date and ready for searches
• Error: Failed state, with an error message
• Disabled: Indexing is turned off or not yet configured

How Files Are Processed

Smart Code Parsing

• Tree-sitter Integration: Uses AST parsing to identify semantic code blocks
• Language Support: Broad language coverage via Tree-sitter — C, C#, C++, CSS, Elisp, Elixir, Go, HTML, Java, JavaScript, Kotlin, Lua, OCaml, PHP, Python, Ruby, Rust, Scala, Solidity, Swift, SystemRDL, TLA+, TOML, TSX, TypeScript, Vue, Zig, and more
• Markdown Support: Dedicated parser for markdown and documentation
• Fallback: Line-based chunking for unsupported file types
• Block Sizing:
  • Minimum: 100 characters
  • Maximum: 1,000 characters
  • Splits large functions intelligently

Automatic File Filtering

The indexer automatically excludes:

• Binary files and images
• Large files (>1MB)
• Git repositories (.git folders)
• Dependencies (node_modules, vendor, etc.)
• Files matching .gitignore and .kilocodeignore patterns

Incremental Updates

• File Watching: Monitors the workspace for changes and re-indexes in the background
• Smart Updates: Only reprocesses modified files
• Hash-based Caching: Avoids reprocessing unchanged content
• Branch Switching: Automatically handles Git branch changes

Tuning Parameters

These advanced settings live under the indexing key and are exposed in the CLI's /indexing → Tuning Parameters menu and the VS Code extension's Indexing settings:

Best Practices

Model Selection

OpenAI:

• text-embedding-3-small: Best balance of performance and cost
• text-embedding-3-large: Higher accuracy, 5x more expensive
• text-embedding-ada-002: Legacy model, lower cost

Ollama:

• mxbai-embed-large: The largest and highest-quality embedding model
• nomic-embed-text: Best balance of performance and embedding quality
• all-minilm: Compact model with lower quality but faster performance

Voyage:

• voyage-code-3: Code-tuned embeddings; strong default for source-heavy repos

Security Considerations

• API Keys: Stored in your kilo.jsonc config. Treat that file as a secret in shared environments.
• Code Privacy: Only small code snippets are sent for embedding — never whole files.
• Local Processing: All parsing (Tree-sitter) happens locally.
• Fully Local Option: Pair Ollama (embeddings) with LanceDB (local vector store) for a setup that never leaves your machine.
• Qdrant Security: Use authentication for production deployments.

Current Limitations

• File Size: 1MB maximum per file
• Single Workspace: One workspace at a time
• Dependencies: Requires an embedding provider, and — for Qdrant — a running Qdrant instance
• Language Coverage: Optimal parsing is limited to Tree-sitter supported languages

Troubleshooting

Embeddings fail or indexing stalls (llama.cpp / Ollama)

If your local embedding server is based on llama.cpp (including Ollama), indexing can fail with errors about n_ubatch or GGML_ASSERT. Ensure both batch size (-b) and micro-batch size (-ub) are set to the same value for embedding models, then restart the server. For Ollama, configure num_batch in your Modelfile or request options to match the same effective value.

Indexing status stays on "Disabled"

• Check that indexing.enabled is true in your kilo.jsonc
• Verify that the selected provider has all required credentials set
• If using Qdrant, make sure the Qdrant server is reachable at the configured URL

Rate-limit or batch errors with a hosted provider

Lower embeddingBatchSize under indexing (default 60). Smaller batches send fewer segments per request and are less likely to hit per-request or per-minute rate limits.

Using the Search Feature

Once indexed, Kilo Code can use the semantic_search tool to find relevant code:

Example Queries:

• "How is user authentication handled?"
• "Database connection setup"
• "Error handling patterns"
• "API endpoint definitions"

The tool provides Kilo Code with:

• Relevant code snippets (up to your configured searchMaxResults)
• File paths and line numbers
• Similarity scores
• Contextual information

Search Results Configuration

Tune result volume and quality via:

• searchMaxResults — default 50. Lower for faster, more focused responses; higher for more context.
• searchMinScore — default 0.4. Raise to require closer matches; lower to include more tangentially related code.

Privacy & Security

• Code stays local: Only small code snippets are sent for embedding
• Embeddings are numeric: Not human-readable representations
• Secure storage: API keys are stored in your local kilo.jsonc configuration
• Fully local option: Use Ollama + LanceDB for completely local processing
• Access control: Respects existing file permissions and .kilocodeignore