A: Code Context uses a comprehensive rule system to determine which files to include in indexing:
Simple Rule:
Final Files = (All Supported Extensions) - (All Ignore Patterns)
- Extensions are additive: Default extensions + MCP custom + Environment variables
- Ignore patterns are additive: Default patterns + MCP custom + Environment variables + .gitignore + .xxxignore files + global .contextignore
For detailed explanation see: File Inclusion Rules
A: Yes, you can deploy Code Context entirely on your local infrastructure using Hologres as the vector database backend.
For local deployment:
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Vector Database (Hologres): Deploy Hologres and configure the following environment variables:
HOLOGRES_HOST=your-hologres-host(your Hologres instance address)HOLOGRES_PORT=80(default port)HOLOGRES_DATABASE=your-databaseHOLOGRES_USER=your-access-idHOLOGRES_PASSWORD=your-access-secret
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Embedding Service (Ollama): Install and run Ollama locally for embedding generation. Configure:
EMBEDDING_PROVIDER=OllamaOLLAMA_HOST=http://127.0.0.1:11434(or your Ollama server URL)OLLAMA_MODEL=nomic-embed-text(or your preferred embedding model)
This setup gives you complete control over your data while maintaining full functionality. See our environment variables guide for detailed configuration options.
A: Yes, Code Context fully supports multiple projects and codebases. In MCP mode, it automatically leverages the MCP client's AI Agent to detect and obtain the current codebase path where you're working.
You can seamlessly use queries like index this codebase or search the main function without specifying explicit paths. When you switch between different codebase working directories, Code Context automatically discovers the change and adapts accordingly - no need to manually input specific codebase paths.
Key features for multi-project support:
- Automatic Path Detection: Leverages MCP client's workspace awareness to identify current working directory
- Seamless Project Switching: Automatically detects when you switch between different codebases
- Background Code Synchronization: Continuously monitors for changes and automatically re-indexes modified parts
- Context-Aware Operations: All indexing and search operations are scoped to the current project context
This makes it effortless to work across multiple projects while maintaining isolated, up-to-date indexes for each codebase.
A: Code Context is specifically focused on codebase indexing and semantic search. Here's how we compare:
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Serena: A comprehensive coding agent toolkit with language server integration and symbolic code understanding. Provides broader AI coding capabilities.
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Context7: Focuses on providing up-to-date documentation and code examples to prevent "code hallucination" in LLMs. Targets documentation accuracy.
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DeepWiki: Generates interactive documentation from GitHub repositories. Creates documentation from code.
Our focus: Making your entire codebase searchable and contextually available to AI assistants through efficient vector-based indexing and hybrid search.