wehub-resource-sync
90 lines
5.5 KiB
Markdown
90 lines
5.5 KiB
Markdown
# Frequently Asked Questions (FAQ)
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## Q: What files does Claude Context decide to embed?
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**A:** Claude Context uses a comprehensive rule system to determine which files to include in indexing:
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**Simple Rule:**
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```
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Final Files = (All Supported Extensions) - (All Ignore Patterns)
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```
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- **Extensions are additive**: Default extensions + MCP custom + Environment variables
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- **Ignore patterns are additive**: Default patterns + MCP custom + Environment variables + .gitignore + .xxxignore files + global .contextignore
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**For detailed explanation see:** [File Inclusion Rules](../dive-deep/file-inclusion-rules.md)
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## Q: Can I use a fully local deployment setup?
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**A:** Yes, you can deploy Claude Context entirely on your local infrastructure. While we recommend using the fully managed [Zilliz Cloud](https://cloud.zilliz.com/signup?utm_source=github&utm_medium=referral&utm_campaign=2507-codecontext-readme) service for ease of use, you can also set up your own private local deployment.
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**For local deployment:**
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1. **Vector Database (Milvus)**: Deploy Milvus locally using Docker Compose by following the [official Milvus installation guide](https://milvus.io/docs/install_standalone-docker-compose.md). Configure the following environment variables:
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- `MILVUS_ADDRESS=127.0.0.1:19530` (or your Milvus server address)
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- `MILVUS_TOKEN=your-optional-token` (if authentication is enabled)
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2. **Embedding Service (Ollama)**: Install and run [Ollama](https://ollama.com/) locally for embedding generation. Configure:
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- `EMBEDDING_PROVIDER=Ollama`
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- `OLLAMA_HOST=http://127.0.0.1:11434` (or your Ollama server URL)
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- `OLLAMA_MODEL=nomic-embed-text` (or your preferred embedding model)
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This setup gives you complete control over your data while maintaining full functionality. See our [environment variables guide](../getting-started/environment-variables.md) for detailed configuration options.
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## Q: Does it support multiple projects / codebases?
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**A:** Yes, Claude 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.
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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, Claude Context automatically discovers the change and adapts accordingly - no need to manually input specific codebase paths.
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**Key features for multi-project support:**
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- **Automatic Path Detection**: Leverages MCP client's workspace awareness to identify current working directory
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- **Seamless Project Switching**: Automatically detects when you switch between different codebases
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- **Background Code Synchronization**: Continuously monitors for changes and automatically re-indexes modified parts
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- **Context-Aware Operations**: All indexing and search operations are scoped to the current project context
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**Important path detail:** Claude Context keys each indexed codebase by its absolute path. If you index the same repository through different paths (for example, a symlinked path, a second clone, or a mounted path), those are treated as separate indexed codebases.
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This makes it effortless to work across multiple projects while maintaining isolated, up-to-date indexes for each codebase.
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## Q: Why does `get_indexing_status` jump quickly to 10% or feel coarse?
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**A:** The percentage is a **phase-based progress indicator**, not a live fraction of indexed files.
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In practice, Claude Context moves through broad stages:
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- collection preparation
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- file scanning
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- file processing, chunking, embedding, and insertion
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The status output can therefore jump quickly to around `10%` once setup is complete, even for very large repositories. That is expected behavior.
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For the full background workflow, see [Asynchronous Indexing Workflow](../dive-deep/asynchronous-indexing-workflow.md).
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## Q: Why does `get_indexing_status` show `0 files, 0 chunks` for a completed codebase?
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**A:** `get_indexing_status` reads the MCP snapshot metadata, not a live aggregate directly from the vector database.
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If a completed entry shows `0 files, 0 chunks`, the most common explanation is that the local snapshot metadata is stale or was created before final statistics were refreshed.
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What to do:
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1. Make sure you are checking the **same absolute path** that you originally indexed.
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2. If the entry still shows zero counts, run `clear_index` for that path.
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3. Re-run `index_codebase` for that exact absolute path.
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This refreshes the stored file/chunk totals used by `get_indexing_status`.
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## Q: How does Claude Context compare to other coding tools like Serena, Context7, or DeepWiki?
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**A:** Claude Context is specifically focused on **codebase indexing and semantic search**. Here's how we compare:
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- **[Serena](https://github.com/oraios/serena)**: A comprehensive coding agent toolkit with language server integration and symbolic code understanding. Provides broader AI coding capabilities.
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- **[Context7](https://github.com/upstash/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](https://docs.devin.ai/work-with-devin/deepwiki-mcp)**: Generates interactive documentation from GitHub repositories. Creates documentation from code.
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**Our focus**: Making your entire codebase searchable and contextually available to AI assistants through efficient vector-based indexing and hybrid search.
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