Catalog›Plugins›AI, search & memory›gemini-code-intelGCgemini-code-intelSemantic code search and indexing for Gemini CLI Open site Open in the app JSON README (API)READMELoading documentation…# Gemini Code Intel Gemini Code Intel is a Model Context Protocol (MCP) plugin that enables semantic code search for AI assistants. It provides deep context from an entire codebase by indexing it into a local vector database, allowing efficient and cost-effective retrieval of relevant code snippets. ## Modules ### Core (@gemini/gemini-code-intel-core) The core indexing engine responsible for semantic search and codebase analysis. - **Key Features**: - Multi-language support (TypeScript, JavaScript, Python, Java, C++, and more). - Hybrid search combining BM25 and vector search for improved accuracy. - AST-based intelligent code chunking to preserve syntax and context. - Incremental synchronization using Merkle trees for efficient re-indexing. - **Configuration**: - ContextConfig: Configures embedding provider, vector database instance, splitting strategy, and file patterns. - **API Reference**: - indexCodebase: Indexes an entire directory. - reindexByChange: Incrementally updates the index based on file changes. - semanticSearch: Performs semantic queries against the indexed code. - clearIndex: Removes existing index data. ### MCP (@gemini/gemini-code-intel-mcp) Integrates the core engine with the Model Context Protocol for use with AI agents and clients. - **Prerequisites**: Node.js (20+), Gemini API key. - **Environment Variables**: - GEMINI_API_KEY: Required API key for embeddings. - EMBEDDING_MODEL: Optional model specification (default: gemini-embedding-001). - DB_URI: Optional local storage path (default: ~/.gemini-code-intel/db). - EMBEDDING_BATCH_SIZE: Optional batch size for performance tuning. - **Available Tools**: - index_codebase: Index a codebase directory. - search_code: Search using natural language queries. - clear_index: Reset the search index. - get_indexing_status: Retrieve current indexing progress. ## Installation ### Recommended: Install as Gemini Extension This is the recommended way to use Gemini Code Intel as it automatically configures the MCP server and provides an integrated **Agent Skill**. #### Direct Installation (Pre-built) You can install the extension directly from the repository. The build artifacts (`dist` folders) are included, so you don't need to manually build the TypeScript code. However, **you must still install dependencies** because this extension relies on native system bindings (like `tree-sitter` and `vectordb`). 1. Install the extension using the CLI: ```bash gemini extensions install https://github.com/akh64bit/semantic-code-intelligence ``` 2. Navigate to the installed extension directory (usually `~/.gemini/extensions/semantic-code-intelligence` or similar). 3. Install the dependencies to fetch the required native binaries for your OS: ```bash pnpm install ``` #### Manual Installation (From Source) If you are working from the source code, follow these steps: 1. **Build the Project**: ```bash pnpm install pnpm build ``` 2. **Link the Extension**: ```bash gemini extensions link . ``` ### Troubleshooting "Disconnected" Status If the extension shows as **Disconnected** 🔴 after installation, it's usually due to missing configuration or native dependencies. #### 1. Configure your API Key The MCP server requires a Gemini API key. You can set it using the extension configuration: ```bash # This will prompt you to enter the key gemini extensions config gemini-code-intel GEMINI_API_KEY ``` #### 2. Install Native Dependencies This extension uses native binaries for high-performance code parsing and vector search. If they weren't installed correctly: 1. Navigate to the extension directory: `~/.gemini/extensions/gemini-code-intel` 2. Run `pnpm install` 3. If using `pnpm`, you may need to allow the build scripts for native modules: ```bash pnpm approve-builds pnpm install ``` #### 3. Check logs If it's still disconnected, check the extension logs for detailed error messages. ## Manual MCP Setup (Optional) If you prefer to configure the MCP server manually in your `settings.json`: 1. Build the project as shown above. 2. Add the following to your `mcpServers` section: ```json "gemini-code-intel": { "command": "node", "args": ["/path/to/gemini-code-intel/packages/mcp/dist/index.js"], "env": { "GEMINI_API_KEY": "your-api-key", "DB_URI": "~/.gemini-code-intel/db" } } ``` ## Technical Overview - **Storage**: Uses high-performance, serverless embedded vector storage. - **Embeddings**: Optimized for Google's Gemini embedding models. - **Languages**: Supports TypeScript, JavaScript, Python, Java, C++, C#, Go, Rust, PHP, Ruby, Swift, Kotlin, Scala, and Markdown. DetailsTypePluginsTopicAI, search & memoryPublisherakh64bitSourcegeminiCategoryToolsVersion0.1.3Proposed changes2Last updateApr 1, 20267 months agoProject statusActiveLanguageTypeScriptAddedAug 30, 20263 months agoUpdatedAug 30, 20263 months agoSource idakh64bit/semantic-code-intelligenceMoreMore plugins on AI, search & memoryAll from akh64bitA–Z index
