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+# GitHub Issue 创建步骤指南
+
+## 第1步：访问Dify项目的Issues页面
+访问：https://github.com/langgenius/dify/issues/new
+
+## 第2步：选择Issue类型
+选择 "Feature Request" 或 "Get started"
+
+## 第3步：填写Issue内容
+**标题**：
+```
+🚀 Feature Request: Add Clickzetta Lakehouse as Vector Database Option
+```
+
+**内容**：
+复制并粘贴 `ISSUE_TEMPLATE.md` 文件中的全部内容
+
+## 第4步：添加标签（如果可能）
+建议添加以下标签：
+- `enhancement`
+- `vector-database`
+- `feature-request`
+
+## 第5步：提交Issue
+点击 "Submit new issue" 按钮
+
+## 第6步：获取Issue编号
+提交后，您将看到一个新的Issue编号（例如：#12345）
+
+## 第7步：更新PR描述
+在PR #22551 的描述开头添加：
+```
+Closes #[刚创建的issue编号]
+```
+
+或者：
+```
+Related to #[刚创建的issue编号]
+```
+
+## 第8步：通知维护者
+在PR中回复 @crazywoola：
+```
+@crazywoola I've created issue #[issue编号] to document this feature request as requested. The issue provides comprehensive context about customer demand and technical implementation details.
+```
+
+## 示例回复模板
+```
+@crazywoola Thank you for the feedback! I've created issue #[issue编号] to document this feature request as requested. 
+
+The issue provides:
+- Business justification and customer demand context
+- Technical specifications and implementation details  
+- Comprehensive testing evidence (100% pass rate)
+- Performance benchmarks and validation results
+
+The implementation is complete and ready for integration. Please let me know if you need any additional information or modifications.
+```
+
+## 预期结果
+- Issue将为维护者提供完整的功能需求上下文
+- PR将有明确的相关Issue链接
+- 符合Dify项目的贡献流程和最佳实践
+- 提高PR被接受的可能性
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+## 🚀 Feature Request: Add Clickzetta Lakehouse as Vector Database Option
+
+### **Is your feature request related to a problem? Please describe.**
+Currently, Dify supports several vector databases (Pinecone, Weaviate, Qdrant, etc.) but lacks support for Clickzetta Lakehouse. This creates a gap for customers who are already using Clickzetta Lakehouse as their data platform and want to integrate it with Dify for RAG applications.
+
+### **Describe the solution you'd like**
+Add Clickzetta Lakehouse as a vector database option in Dify, allowing users to configure Clickzetta as their vector storage backend through standard Dify configuration.
+
+### **Business Justification**
+- **Customer Demand**: Real commercial customers are actively waiting for Dify + Clickzetta integration solution for trial validation
+- **Unified Data Platform**: Clickzetta Lakehouse provides a unified platform for both vector data and structured data storage
+- **Performance**: Supports HNSW vector indexing and high-performance similarity search
+- **Cost Efficiency**: Reduces the need for separate vector database infrastructure
+
+### **Describe alternatives you've considered**
+- **External Vector Database**: Using separate vector databases like Pinecone or Weaviate, but this adds infrastructure complexity and cost
+- **Data Duplication**: Maintaining data in both Clickzetta and external vector databases, leading to synchronization challenges
+- **Custom Integration**: Building custom connectors, but this lacks the seamless integration that native Dify support provides
+
+### **Proposed Implementation**
+Implement Clickzetta Lakehouse integration following Dify's existing vector database pattern:
+
+#### **Core Components**:
+- `ClickzettaVector` class implementing `BaseVector` interface
+- `ClickzettaVectorFactory` for instance creation  
+- Configuration through Dify's standard config system
+
+#### **Key Features**:
+- ✅ Vector similarity search with HNSW indexing
+- ✅ Full-text search with inverted indexes
+- ✅ Concurrent write operations with queue mechanism
+- ✅ Chinese text analysis support
+- ✅ Automatic index management
+
+#### **Configuration Example**:
+```bash
+VECTOR_STORE=clickzetta
+CLICKZETTA_USERNAME=your_username
+CLICKZETTA_PASSWORD=your_password
+CLICKZETTA_INSTANCE=your_instance
+CLICKZETTA_SERVICE=api.clickzetta.com
+CLICKZETTA_WORKSPACE=your_workspace
+CLICKZETTA_VCLUSTER=default_ap
+CLICKZETTA_SCHEMA=dify
+```
+
+### **Technical Specifications**
+- **Vector Operations**: Insert, search, delete vectors with metadata
+- **Indexing**: Automatic HNSW vector index creation with configurable parameters
+- **Concurrency**: Write queue mechanism for thread safety
+- **Distance Metrics**: Support for cosine distance and L2 distance
+- **Full-text Search**: Inverted index for content search with Chinese text analysis
+- **Scalability**: Handles large-scale vector data with efficient batch operations
+
+### **Implementation Status**
+- ✅ Implementation is complete and ready for integration
+- ✅ Comprehensive testing completed in real Clickzetta environments
+- ✅ 100% test pass rate for core functionality
+- ✅ Performance validated with production-like data volumes
+- ✅ Backward compatibility verified with existing Dify configurations
+- ✅ Full documentation provided
+- ✅ PR submitted: #22551
+
+### **Testing Evidence**
+```
+🧪 Standalone Tests: 3/3 passed (100%)
+🧪 Integration Tests: 8/8 passed (100%)
+🧪 Performance Tests: Vector search ~170ms, Insert rate ~5.3 docs/sec
+🧪 Real Environment: Validated with actual Clickzetta Lakehouse instance
+```
+
+### **Business Impact**
+- **Customer Enablement**: Enables customers already using Clickzetta to adopt Dify seamlessly
+- **Infrastructure Simplification**: Reduces complexity by using unified data platform
+- **Enterprise Ready**: Supports enterprise-grade deployments with proven stability
+- **Cost Optimization**: Eliminates need for separate vector database infrastructure
+
+### **Additional Context**
+This feature request is backed by direct customer demand and includes a complete, tested implementation ready for integration. The implementation follows Dify's existing patterns and maintains full backward compatibility.
+
+**Related Links:**
+- Implementation PR: #22551
+- User Configuration Guide: [Available in PR]
+- Testing Guide with validation results: [Available in PR]
+- Performance benchmarks: [Available in PR]
+
+---
+
+**Environment:**
+- Dify Version: Latest main branch
+- Clickzetta Version: Compatible with v1.0.0+
+- Python Version: 3.11+
+- Testing Environment: Real Clickzetta Lakehouse UAT instance
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diff --git a/clickzetta/PR_DESCRIPTION_UPDATE.md b/clickzetta/PR_DESCRIPTION_UPDATE.md
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+# Updated PR Description Header
+
+## Related Issue
+This PR addresses the need for Clickzetta Lakehouse vector database integration in Dify. While no specific issue was opened beforehand, this feature is driven by:
+
+- **Direct customer demand**: Real commercial customers are actively waiting for Dify + Clickzetta integration solution for trial validation
+- **Business necessity**: Customers using Clickzetta Lakehouse need native Dify integration to avoid infrastructure duplication
+- **Technical requirement**: Unified data platform support for both vector and structured data
+
+## Feature Overview
+Add Clickzetta Lakehouse as a vector database option in Dify, providing:
+- Full BaseVector interface implementation
+- HNSW vector indexing support
+- Concurrent write operations with queue mechanism
+- Chinese text analysis and full-text search
+- Enterprise-grade performance and reliability
+
+---
+
+[Rest of existing PR description remains the same...]
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