chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Environment Variables Configuration
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## 🎯 Global Configuration
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Claude Context supports a global configuration file at `~/.context/.env` to simplify MCP setup across different MCP clients.
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**Benefits:**
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- Configure once, use everywhere
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- No need to specify environment variables in each MCP client
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- Cleaner MCP configurations
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## 📋 Environment Variable Priority
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1. **Process Environment Variables** (highest)
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2. **Global Configuration File** (`~/.context/.env`)
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3. **Default Values** (lowest)
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## 🔧 Required Environment Variables
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### Embedding Provider
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `EMBEDDING_PROVIDER` | Provider: `OpenAI`, `VoyageAI`, `Gemini`, `Ollama` | `OpenAI` |
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| `EMBEDDING_MODEL` | Embedding model name (works for all providers) | Provider-specific default |
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| `OPENAI_API_KEY` | OpenAI API key | Required for OpenAI |
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| `OPENAI_BASE_URL` | OpenAI API base URL (optional, for custom endpoints) | `https://api.openai.com/v1` |
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| `VOYAGEAI_API_KEY` | VoyageAI API key | Required for VoyageAI |
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| `GEMINI_API_KEY` | Gemini API key | Required for Gemini |
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| `GEMINI_BASE_URL` | Gemini API base URL (optional, for custom endpoints) | `https://generativelanguage.googleapis.com/v1beta` |
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> **💡 Note:** `EMBEDDING_MODEL` is a universal environment variable that works with all embedding providers. Simply set it to the model name you want to use (e.g., `text-embedding-3-large` for OpenAI, `voyage-code-3` for VoyageAI, etc.).
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> **Supported Model Names:**
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>
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> - OpenAI Models: See `getSupportedModels` in [`openai-embedding.ts`](https://github.com/zilliztech/claude-context/blob/master/packages/core/src/embedding/openai-embedding.ts) for the full list of supported models.
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>
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> - VoyageAI Models: See `getSupportedModels` in [`voyageai-embedding.ts`](https://github.com/zilliztech/claude-context/blob/master/packages/core/src/embedding/voyageai-embedding.ts) for the full list of supported models.
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>
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> - Gemini Models: See `getSupportedModels` in [`gemini-embedding.ts`](https://github.com/zilliztech/claude-context/blob/master/packages/core/src/embedding/gemini-embedding.ts) for the full list of supported models.
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>
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> - Ollama Models: Depends on the model you install locally.
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> **📖 For detailed provider-specific configuration examples and setup instructions, see the [MCP Configuration Guide](../../packages/mcp/README.md#embedding-provider-configuration).**
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### Vector Database
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `MILVUS_TOKEN` | Milvus authentication token. Get [Zilliz Personal API Key](https://github.com/zilliztech/claude-context/blob/master/assets/signup_and_get_apikey.png) | Recommended |
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| `MILVUS_ADDRESS` | Milvus server address. Optional when using Zilliz Personal API Key | Auto-resolved from token |
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| `MILVUS_COLLECTION_LIMIT_CHECK_TIMEOUT_MS` | Timeout for gRPC pre-check in `checkCollectionLimit()` before indexing begins | `15000` |
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### Ollama (Optional)
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `OLLAMA_HOST` | Ollama server URL | `http://127.0.0.1:11434` |
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| `OLLAMA_MODEL`(alternative to `EMBEDDING_MODEL`) | Model name | |
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### Advanced Configuration
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `HYBRID_MODE` | Enable hybrid search (BM25 + dense vector). Set to `false` for dense-only search | `true` |
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| `EMBEDDING_BATCH_SIZE` | Batch size for processing. Larger batch size means less indexing time | `100` |
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| `SPLITTER_TYPE` | Code splitter type: `ast`, `langchain` | `ast` |
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| `CUSTOM_EXTENSIONS` | Additional file extensions to include (comma-separated, e.g., `.vue,.svelte,.astro`) | None |
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| `CUSTOM_IGNORE_PATTERNS` | Additional ignore patterns (comma-separated, e.g., `temp/**,*.backup,private/**`) | None |
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| `CODE_CHUNKS_COLLECTION_NAME_OVERRIDE` | Optional custom prefix for collection names. Produces `code_chunks_<suffix>_<pathHash>` or `hybrid_code_chunks_<suffix>_<pathHash>` after sanitization. The path hash stays appended so collections remain unique per codebase even when the override is set | None |
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When `CODE_CHUNKS_COLLECTION_NAME_OVERRIDE` is set, Claude Context writes to an override-named collection instead of the default `code_chunks_<pathHash>`. The per-codebase `<pathHash>` suffix is preserved to keep multiple codebases distinct under the same override. If you later unset the variable, Claude Context returns to the plain hash-based naming for that path.
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## 🚀 Quick Setup
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### 1. Create Global Config
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```bash
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mkdir -p ~/.context
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cat > ~/.context/.env << 'EOF'
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EMBEDDING_PROVIDER=OpenAI
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OPENAI_API_KEY=sk-your-openai-api-key
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EMBEDDING_MODEL=text-embedding-3-small
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MILVUS_ADDRESS=your-zilliz-cloud-public-endpoint
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MILVUS_TOKEN=your-zilliz-cloud-api-key
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EOF
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```
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See the [Example File](../../.env.example) for more details.
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### 2. Simplified MCP Configuration
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**Claude Code:**
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```bash
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claude mcp add claude-context \
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-e OPENAI_API_KEY=sk-your-openai-api-key \
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-e MILVUS_ADDRESS=your-zilliz-cloud-public-endpoint \
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-e MILVUS_TOKEN=your-zilliz-cloud-api-key \
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-- npx @zilliz/claude-context-mcp@latest
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```
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||||
**Cursor/Windsurf/Others:**
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||||
```json
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{
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||||
"mcpServers": {
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"claude-context": {
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"command": "npx",
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"args": ["-y", "@zilliz/claude-context-mcp@latest"]
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}
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}
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}
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```
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## 📚 Additional Information
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For detailed information about file processing rules and how custom patterns work, see:
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- [What files does Claude Context decide to embed?](../troubleshooting/faq.md#q-what-files-does-claude-context-decide-to-embed)
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@@ -0,0 +1,51 @@
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# Prerequisites
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Before setting up Claude Context, ensure you have the following requirements met.
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## Required Services
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### Embedding Provider (Choose One)
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#### Option 1: OpenAI (Recommended)
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- **API Key**: Get from [OpenAI Platform](https://platform.openai.com/api-keys)
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- **Billing**: Active billing account required
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- **Models**: `text-embedding-3-small` or `text-embedding-3-large`
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- **Rate Limits**: Check current limits on your OpenAI account
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||||
#### Option 2: VoyageAI
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- **API Key**: Get from [VoyageAI Console](https://dash.voyageai.com/)
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- **Models**: `voyage-code-3` (optimized for code)
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- **Billing**: Pay-per-use pricing
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||||
#### Option 3: Gemini
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- **API Key**: Get from [Google AI Studio](https://aistudio.google.com/)
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- **Models**: `gemini-embedding-001`, `gemini-embedding-2`
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- **Quota**: Check current quotas and limits
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#### Option 4: Ollama (Local)
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- **Installation**: Download from [ollama.com](https://ollama.com/)
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- **Models**: Pull embedding models like `nomic-embed-text`
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- **Hardware**: Sufficient RAM for model loading (varies by model)
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### Vector Database
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#### Zilliz Cloud (Recommended)
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- **Account**: [Sign up](https://cloud.zilliz.com/signup?utm_source=github&utm_medium=referral&utm_campaign=2507-codecontext-readme) on Zilliz Cloud to get an API key.
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- **Convenience**: Fully managed Milvus vector database service without the need to install and manage it.
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#### Local Milvus (Advanced)
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- **Docker**: Install Milvus by following [this guide](https://milvus.io/docs/install_standalone-docker-compose.md)
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- **Resources**: More complex configuration required
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## Development Tools (Optional)
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### For VSCode Extension
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- **VSCode**: Version 1.74.0 or higher
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- **Extensions**: Claude Context extension from marketplace
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### For Development Contributions
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- **Git**: For version control
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- **pnpm**: Package manager (preferred over npm)
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- **TypeScript**: Understanding of TypeScript development
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@@ -0,0 +1,463 @@
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# Quick Start Guide
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Get Claude Context running with AI assistants in under 5 minutes! This guide covers the most common setup using MCP (Model Context Protocol) with Claude Code.
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## 🚀 1-Minute Setup for Claude Code
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### Step 1: Get API Keys
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You'll need two API keys:
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1. **OpenAI API Key**: Get from [OpenAI Platform](https://platform.openai.com/api-keys)
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2. **Zilliz Cloud API Key**: 
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[Sign up](https://cloud.zilliz.com/signup?utm_source=github&utm_medium=referral&utm_campaign=2507-codecontext-readme) on Zilliz Cloud to get an API key.
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### Step 2: Configure Claude Code
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Run this single command to add Claude Context to Claude Code:
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```bash
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claude mcp add claude-context \
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-e OPENAI_API_KEY=sk-your-openai-api-key \
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-e MILVUS_ADDRESS=your-zilliz-cloud-public-endpoint \
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-e MILVUS_TOKEN=your-zilliz-cloud-api-key \
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-- npx @zilliz/claude-context-mcp@latest
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```
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Replace the API keys with your actual keys.
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### Step 3: Start Using Claude Context
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1. **Open Claude Code** in your project directory
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2. **Index your codebase**:
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```
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Index this codebase
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```
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3. **Check indexing status**:
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||||
```
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Check the indexing status
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```
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4. **Start searching**:
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```
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Find functions that handle user authentication
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```
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🎉 **That's it!** You now have semantic code search in Claude Code.
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## Alternative Quick Setups
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||||
<details>
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<summary><strong>OpenAI Codex CLI</strong></summary>
|
||||
|
||||
Codex CLI uses TOML configuration files:
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||||
|
||||
1. Create or edit the `~/.codex/config.toml` file.
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||||
|
||||
2. Add the following configuration:
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||||
|
||||
```toml
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||||
# IMPORTANT: the top-level key is `mcp_servers` rather than `mcpServers`.
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||||
[mcp_servers.claude-context]
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||||
command = "npx"
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||||
args = ["@zilliz/claude-context-mcp@latest"]
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||||
env = { "OPENAI_API_KEY" = "your-openai-api-key", "MILVUS_TOKEN" = "your-zilliz-cloud-api-key" }
|
||||
# Optional: override the default 10s startup timeout
|
||||
startup_timeout_ms = 20000
|
||||
```
|
||||
|
||||
3. Save the file and restart Codex CLI to apply the changes.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Gemini CLI</strong></summary>
|
||||
|
||||
Gemini CLI requires manual configuration through a JSON file:
|
||||
|
||||
1. Create or edit the `~/.gemini/settings.json` file.
|
||||
|
||||
2. Add the following configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
3. Save the file and restart Gemini CLI to apply the changes.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Qwen Code</strong></summary>
|
||||
|
||||
Create or edit the `~/.qwen/settings.json` file and add the following configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Cursor</strong></summary>
|
||||
|
||||
Go to: `Settings` -> `Cursor Settings` -> `MCP` -> `Add new global MCP server`
|
||||
|
||||
Pasting the following configuration into your Cursor `~/.cursor/mcp.json` file is the recommended approach. You may also install in a specific project by creating `.cursor/mcp.json` in your project folder. See [Cursor MCP docs](https://cursor.com/docs/context/mcp) for more info.
|
||||
|
||||
**OpenAI Configuration (Default):**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"EMBEDDING_PROVIDER": "OpenAI",
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**VoyageAI Configuration:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"EMBEDDING_PROVIDER": "VoyageAI",
|
||||
"VOYAGEAI_API_KEY": "your-voyageai-api-key",
|
||||
"EMBEDDING_MODEL": "voyage-code-3",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Gemini Configuration:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"EMBEDDING_PROVIDER": "Gemini",
|
||||
"GEMINI_API_KEY": "your-gemini-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Ollama Configuration:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"EMBEDDING_PROVIDER": "Ollama",
|
||||
"EMBEDDING_MODEL": "nomic-embed-text",
|
||||
"OLLAMA_HOST": "http://127.0.0.1:11434",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary><strong>Void</strong></summary>
|
||||
|
||||
Go to: `Settings` -> `MCP` -> `Add MCP Server`
|
||||
|
||||
Add the following configuration to your Void MCP settings:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"code-context": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Claude Desktop</strong></summary>
|
||||
|
||||
Add to your Claude Desktop configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Windsurf</strong></summary>
|
||||
|
||||
Windsurf supports MCP configuration through a JSON file. Add the following configuration to your Windsurf MCP settings:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>VS Code</strong></summary>
|
||||
|
||||
The Claude Context MCP server can be used with VS Code through MCP-compatible extensions. Add the following configuration to your VS Code MCP settings:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Cherry Studio</strong></summary>
|
||||
|
||||
Cherry Studio allows for visual MCP server configuration through its settings interface. While it doesn't directly support manual JSON configuration, you can add a new server via the GUI:
|
||||
|
||||
1. Navigate to **Settings → MCP Servers → Add Server**.
|
||||
2. Fill in the server details:
|
||||
- **Name**: `claude-context`
|
||||
- **Type**: `STDIO`
|
||||
- **Command**: `npx`
|
||||
- **Arguments**: `["-y", "@zilliz/claude-context-mcp@latest"]`
|
||||
- **Environment Variables**:
|
||||
- `OPENAI_API_KEY`: `your-openai-api-key`
|
||||
- `MILVUS_TOKEN`: `your-zilliz-cloud-api-key`
|
||||
3. Save the configuration to activate the server.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Cline</strong></summary>
|
||||
|
||||
Cline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:
|
||||
|
||||
1. Open Cline and click on the **MCP Servers** icon in the top navigation bar.
|
||||
|
||||
2. Select the **Installed** tab, then click **Advanced MCP Settings**.
|
||||
|
||||
3. In the `cline_mcp_settings.json` file, add the following configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
4. Save the file.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Augment</strong></summary>
|
||||
|
||||
To configure Claude Context MCP in Augment Code, you can use either the graphical interface or manual configuration.
|
||||
|
||||
#### **A. Using the Augment Code UI**
|
||||
|
||||
1. Click the hamburger menu.
|
||||
|
||||
2. Select **Settings**.
|
||||
|
||||
3. Navigate to the **Tools** section.
|
||||
|
||||
4. Click the **+ Add MCP** button.
|
||||
|
||||
5. Enter the following command:
|
||||
|
||||
```
|
||||
npx @zilliz/claude-context-mcp@latest
|
||||
```
|
||||
|
||||
6. Name the MCP: **Claude Context**.
|
||||
|
||||
7. Click the **Add** button.
|
||||
|
||||
------
|
||||
|
||||
#### **B. Manual Configuration**
|
||||
|
||||
1. Press Cmd/Ctrl Shift P or go to the hamburger menu in the Augment panel
|
||||
2. Select Edit Settings
|
||||
3. Under Advanced, click Edit in settings.json
|
||||
4. Add the server configuration to the `mcpServers` array in the `augment.advanced` object
|
||||
|
||||
```json
|
||||
"augment.advanced": {
|
||||
"mcpServers": [
|
||||
{
|
||||
"name": "claude-context",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@zilliz/claude-context-mcp@latest"]
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Roo Code</strong></summary>
|
||||
|
||||
Roo Code utilizes a JSON configuration file for MCP servers:
|
||||
|
||||
1. Open Roo Code and navigate to **Settings → MCP Servers → Edit Global Config**.
|
||||
|
||||
2. In the `mcp_settings.json` file, add the following configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"claude-context": {
|
||||
"command": "npx",
|
||||
"args": ["@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
3. Save the file to activate the server.
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary><strong>Zencoder</strong></summary>
|
||||
|
||||
Zencoder offers support for MCP tools and servers in both its JetBrains and VS Code plugin versions.
|
||||
|
||||
1. Go to the Zencoder menu (...)
|
||||
2. From the dropdown menu, select `Tools`
|
||||
3. Click on the `Add Custom MCP`
|
||||
4. Add the name (i.e. `Claude Context` and server configuration from below, and make sure to hit the `Install` button
|
||||
|
||||
```json
|
||||
{
|
||||
"command": "npx",
|
||||
"args": ["@zilliz/claude-context-mcp@latest"],
|
||||
"env": {
|
||||
"OPENAI_API_KEY": "your-openai-api-key",
|
||||
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
|
||||
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
|
||||
}
|
||||
}
|
||||
|
||||
```
|
||||
|
||||
5. Save the server by hitting the `Install` button.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>LangChain/LangGraph</strong></summary>
|
||||
|
||||
For LangChain/LangGraph integration examples, see [this example](https://github.com/zilliztech/claude-context/blob/643796a0d30e706a2a0dff3d55621c9b5d831807/evaluation/retrieval/custom.py#L88).
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Other MCP Clients</strong></summary>
|
||||
|
||||
The server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:
|
||||
|
||||
```bash
|
||||
npx @zilliz/claude-context-mcp@latest
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
> 💡 **Tip**: For easier configuration management, you can use [global environment variables](environment-variables.md) instead of specifying them in each MCP client configuration.
|
||||
Reference in New Issue
Block a user