wehub-resource-sync
70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
# Basic Usage Example
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This example demonstrates the basic usage of Claude Context.
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## Prerequisites
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1. **OpenAI API Key**: Set your OpenAI API key for embeddings:
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```bash
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export OPENAI_API_KEY="your-openai-api-key"
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```
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2. **Milvus Server**: Make sure Milvus server is running:
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- You can also use fully managed Milvus on [Zilliz Cloud](https://zilliz.com/cloud).
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In this case, set the `MILVUS_ADDRESS` as the Public Endpoint and `MILVUS_TOKEN` as the Token like this:
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```bash
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export MILVUS_ADDRESS="https://your-cluster.zillizcloud.com"
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export MILVUS_TOKEN="your-zilliz-token"
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```
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- You can also set up a Milvus server on [Docker or Kubernetes](https://milvus.io/docs/install-overview.md). In this setup, please use the server address and port as your `uri`, e.g.`http://localhost:19530`. If you enable the authentication feature on Milvus, set the `token` as `"<your_username>:<your_password>"`, otherwise there is no need to set the token.
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```bash
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export MILVUS_ADDRESS="http://localhost:19530"
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export MILVUS_TOKEN="<your_username>:<your_password>"
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```
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## Running the Example
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1. Install dependencies:
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```bash
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pnpm install
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```
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2. Set environment variables (see examples above)
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3. Run the example:
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```bash
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pnpm run start
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```
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## What This Example Does
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1. **Indexes Codebase**: Indexes the entire Claude Context project
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2. **Performs Searches**: Executes semantic searches for different code patterns
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3. **Shows Results**: Displays search results with similarity scores and file locations
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## Expected Output
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```
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🚀 Claude Context Real Usage Example
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===============================
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...
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🔌 Connecting to vector database at: ...
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📖 Starting to index codebase...
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🗑️ Existing index found, clearing it first...
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📊 Indexing stats: 45 files, 234 code chunks
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🔍 Performing semantic search...
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🔎 Search: "vector database operations"
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1. Similarity: 89.23%
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File: /path/to/packages/core/src/vectordb/milvus-vectordb.ts
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Language: typescript
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Lines: 147-177
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Preview: async search(collectionName: string, queryVector: number[], options?: SearchOptions)...
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🎉 Example completed successfully!
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```
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