refactor: remove clickzetta/ folder and update service endpoint
- Remove clickzetta/ development folder from PR (add to .gitignore) - Update CLICKZETTA_SERVICE from uat-api.clickzetta.com to api.clickzetta.com - Update both docker/.env.example and docker/docker-compose.yaml for consistency 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>pull/22551/head
yunqiqiliang
10 months ago
parent
0246f39564
commit
ecbe555cb0
@ -1,48 +0,0 @@
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# ClickZetta Dify Integration Environment Configuration
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# Copy this file to .env and configure your ClickZetta credentials
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# ClickZetta Database Configuration (Required)
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CLICKZETTA_USERNAME=your_username
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CLICKZETTA_PASSWORD=your_password
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CLICKZETTA_INSTANCE=your_instance
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# ClickZetta Advanced Settings (Optional)
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CLICKZETTA_SERVICE=api.clickzetta.com
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CLICKZETTA_WORKSPACE=quick_start
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CLICKZETTA_VCLUSTER=default_ap
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CLICKZETTA_SCHEMA=dify
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CLICKZETTA_BATCH_SIZE=20
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CLICKZETTA_ENABLE_INVERTED_INDEX=true
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CLICKZETTA_ANALYZER_TYPE=chinese
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CLICKZETTA_ANALYZER_MODE=smart
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CLICKZETTA_VECTOR_DISTANCE_FUNCTION=cosine_distance
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# Dify Core Settings
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SECRET_KEY=dify
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INIT_PASSWORD=
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CONSOLE_WEB_URL=
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CONSOLE_API_URL=
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SERVICE_API_URL=
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# Database Settings
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DB_USERNAME=postgres
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DB_PASSWORD=difyai123456
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DB_HOST=db
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DB_PORT=5432
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DB_DATABASE=dify
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# Redis Settings
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REDIS_HOST=redis
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REDIS_PORT=6379
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REDIS_PASSWORD=difyai123456
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REDIS_DB=0
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# Storage Settings
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STORAGE_TYPE=local
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STORAGE_LOCAL_PATH=storage
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# Nginx Settings
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EXPOSE_NGINX_PORT=80
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NGINX_SERVER_NAME=_
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NGINX_HTTPS_ENABLED=false
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NGINX_PORT=80
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@ -1,93 +0,0 @@
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## 🚀 Feature Request: Add Clickzetta Lakehouse as Vector Database Option
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### **Is your feature request related to a problem? Please describe.**
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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.
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### **Describe the solution you'd like**
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Add Clickzetta Lakehouse as a vector database option in Dify, allowing users to configure Clickzetta as their vector storage backend through standard Dify configuration.
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### **Business Justification**
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- **Customer Demand**: Real commercial customers are actively waiting for Dify + Clickzetta integration solution for trial validation
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- **Unified Data Platform**: Clickzetta Lakehouse provides a unified platform for both vector data and structured data storage
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- **Performance**: Supports HNSW vector indexing and high-performance similarity search
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- **Cost Efficiency**: Reduces the need for separate vector database infrastructure
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### **Describe alternatives you've considered**
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- **External Vector Database**: Using separate vector databases like Pinecone or Weaviate, but this adds infrastructure complexity and cost
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- **Data Duplication**: Maintaining data in both Clickzetta and external vector databases, leading to synchronization challenges
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- **Custom Integration**: Building custom connectors, but this lacks the seamless integration that native Dify support provides
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### **Proposed Implementation**
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Implement Clickzetta Lakehouse integration following Dify's existing vector database pattern:
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#### **Core Components**:
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- `ClickzettaVector` class implementing `BaseVector` interface
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- `ClickzettaVectorFactory` for instance creation
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- Configuration through Dify's standard config system
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#### **Key Features**:
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- ✅ Vector similarity search with HNSW indexing
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- ✅ Full-text search with inverted indexes
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- ✅ Concurrent write operations with queue mechanism
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- ✅ Chinese text analysis support
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- ✅ Automatic index management
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#### **Configuration Example**:
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```bash
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VECTOR_STORE=clickzetta
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CLICKZETTA_USERNAME=your_username
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CLICKZETTA_PASSWORD=your_password
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CLICKZETTA_INSTANCE=your_instance
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CLICKZETTA_SERVICE=api.clickzetta.com
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CLICKZETTA_WORKSPACE=your_workspace
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CLICKZETTA_VCLUSTER=default_ap
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CLICKZETTA_SCHEMA=dify
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```
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### **Technical Specifications**
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- **Vector Operations**: Insert, search, delete vectors with metadata
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- **Indexing**: Automatic HNSW vector index creation with configurable parameters
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- **Concurrency**: Write queue mechanism for thread safety
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- **Distance Metrics**: Support for cosine distance and L2 distance
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- **Full-text Search**: Inverted index for content search with Chinese text analysis
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- **Scalability**: Handles large-scale vector data with efficient batch operations
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### **Implementation Status**
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- ✅ Implementation is complete and ready for integration
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- ✅ Comprehensive testing completed in real Clickzetta environments
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- ✅ 100% test pass rate for core functionality
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- ✅ Performance validated with production-like data volumes
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- ✅ Backward compatibility verified with existing Dify configurations
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- ✅ Full documentation provided
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- ✅ PR submitted: #22551
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### **Testing Evidence**
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```
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🧪 Standalone Tests: 3/3 passed (100%)
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🧪 Integration Tests: 8/8 passed (100%)
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🧪 Performance Tests: Vector search ~170ms, Insert rate ~5.3 docs/sec
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🧪 Real Environment: Validated with actual Clickzetta Lakehouse instance
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```
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### **Business Impact**
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- **Customer Enablement**: Enables customers already using Clickzetta to adopt Dify seamlessly
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- **Infrastructure Simplification**: Reduces complexity by using unified data platform
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- **Enterprise Ready**: Supports enterprise-grade deployments with proven stability
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- **Cost Optimization**: Eliminates need for separate vector database infrastructure
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### **Additional Context**
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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.
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**Related Links:**
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- Implementation PR: #22551
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- User Configuration Guide: [Available in PR]
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- Testing Guide with validation results: [Available in PR]
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- Performance benchmarks: [Available in PR]
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---
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**Environment:**
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- Dify Version: Latest main branch
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- Clickzetta Version: Compatible with v1.0.0+
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- Python Version: 3.11+
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- Testing Environment: Real Clickzetta Lakehouse UAT instance
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@ -1,25 +0,0 @@
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## Related Issue
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Closes #22557
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## Summary
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This PR adds Clickzetta Lakehouse as a vector database option in Dify, enabling customers to use Clickzetta as their unified data platform for both vector and structured data storage.
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## Key Features
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- ✅ Full BaseVector interface implementation
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- ✅ HNSW vector indexing with automatic management
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- ✅ Concurrent write operations with queue mechanism
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- ✅ Chinese text analysis and full-text search
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- ✅ Comprehensive error handling and retry mechanisms
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## Testing Status
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- 🧪 **Standalone Tests**: 3/3 passed (100%)
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- 🧪 **Integration Tests**: 8/8 passed (100%)
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- 🧪 **Performance**: Vector search ~170ms, Insert rate ~5.3 docs/sec
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- 🧪 **Real Environment**: Validated with actual Clickzetta Lakehouse instance
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## Business Impact
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Real commercial customers are actively waiting for this Dify + Clickzetta integration solution for trial validation. This integration eliminates the need for separate vector database infrastructure while maintaining enterprise-grade performance and reliability.
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---
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[保留原有的详细PR描述内容...]
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@ -1,20 +0,0 @@
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# Updated PR Description Header
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## Related Issue
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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:
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- **Direct customer demand**: Real commercial customers are actively waiting for Dify + Clickzetta integration solution for trial validation
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- **Business necessity**: Customers using Clickzetta Lakehouse need native Dify integration to avoid infrastructure duplication
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- **Technical requirement**: Unified data platform support for both vector and structured data
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## Feature Overview
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Add Clickzetta Lakehouse as a vector database option in Dify, providing:
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- Full BaseVector interface implementation
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- HNSW vector indexing support
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- Concurrent write operations with queue mechanism
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- Chinese text analysis and full-text search
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- Enterprise-grade performance and reliability
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---
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[Rest of existing PR description remains the same...]
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@ -1,296 +0,0 @@
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# Clickzetta Vector Database Integration - PR Preparation Summary
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## 🎯 Integration Completion Status
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### ✅ Completed Work
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#### 1. Core Functionality Implementation (100%)
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- **ClickzettaVector Class**: Complete implementation of BaseVector interface
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- **Configuration System**: ClickzettaConfig class with full configuration options support
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- **Connection Management**: Robust connection management with retry mechanisms and error handling
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- **Write Queue Mechanism**: Innovative design to address Clickzetta's concurrent write limitations
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- **Search Functions**: Dual support for vector search and full-text search
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#### 2. Architecture Integration (100%)
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- **Dify Framework Compatibility**: Full compliance with BaseVector interface specifications
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- **Factory Pattern Integration**: Properly registered with VectorFactory
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- **Configuration System Integration**: Environment variable configuration support
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- **Docker Environment Compatibility**: Works correctly in containerized environments
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#### 3. Code Quality (100%)
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- **Type Annotations**: Complete type hints
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- **Error Handling**: Robust exception handling and retry mechanisms
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- **Logging**: Detailed debugging and operational logs
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- **Documentation**: Clear code documentation
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#### 4. Dependency Management (100%)
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- **Version Compatibility**: Resolved urllib3 version conflicts
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- **Dependency Declaration**: Correctly added to pyproject.toml
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- **Docker Integration**: Properly installed and loaded in container environments
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### ✅ Testing Status
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#### Technical Validation (100% Complete)
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- ✅ **Module Import**: Correctly loaded in Docker environment
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- ✅ **Class Structure**: All required methods exist and are correct
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- ✅ **Configuration System**: Parameter validation and defaults working normally
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- ✅ **Connection Mechanism**: API calls and error handling correct
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- ✅ **Error Handling**: Retry and exception propagation normal
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#### Functional Validation (100% Complete)
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- ✅ **Data Operations**: Real environment testing passed (table creation, data insertion, queries)
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- ✅ **Performance Testing**: Real environment validation complete (vector search 170ms, insertion 5.3 docs/sec)
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- ✅ **Concurrent Testing**: Real database connection testing complete (3-thread concurrent writes)
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## 📋 PR Content Checklist
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### New Files
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```
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api/core/rag/datasource/vdb/clickzetta/
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├── __init__.py
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└── clickzetta_vector.py
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```
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### Modified Files
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```
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api/core/rag/datasource/vdb/vector_factory.py
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api/pyproject.toml
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docker/.env.example
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```
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### Testing and Documentation
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```
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clickzetta/
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├── test_clickzetta_integration.py
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├── standalone_clickzetta_test.py
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├── quick_test_clickzetta.py
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├── docker_test.py
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├── final_docker_test.py
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├── TESTING_GUIDE.md
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├── TEST_EVIDENCE.md
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├── REAL_TEST_EVIDENCE.md
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└── PR_SUMMARY.md
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```
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## 🔧 Technical Features
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|
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### Core Functionality
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1. **Vector Storage**: Support for 1536-dimensional vector storage and retrieval
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2. **HNSW Indexing**: Automatic creation and management of HNSW vector indexes
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||||
3. **Full-text Search**: Inverted index support for Chinese word segmentation and search
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4. **Batch Operations**: Optimized batch insertion and updates
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5. **Concurrent Safety**: Write queue mechanism to resolve concurrent conflicts
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### Innovative Design
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1. **Write Queue Serialization**: Solves Clickzetta primary key table concurrent limitations
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2. **Smart Retry**: 6-retry mechanism handles temporary network issues
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||||
3. **Configuration Flexibility**: Supports production and UAT environment switching
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||||
4. **Error Recovery**: Robust exception handling and state recovery
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||||
|
||||
### Performance Optimizations
|
||||
1. **Connection Pool Management**: Efficient database connection reuse
|
||||
2. **Batch Processing Optimization**: Configurable maximum batch size
|
||||
3. **Index Strategy**: Automatic index creation and management
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||||
4. **Query Optimization**: Configurable vector distance functions
|
||||
|
||||
## 📊 Test Evidence
|
||||
|
||||
### Real Environment Test Validation
|
||||
```
|
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🧪 Independent Connection Test: ✅ Passed (Successfully connected to Clickzetta UAT environment)
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🧪 Table Operations Test: ✅ Passed (Table creation, inserted 5 records, query validation)
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🧪 Vector Index Test: ✅ Passed (HNSW index creation successful)
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🧪 Vector Search Test: ✅ Passed (170ms search latency, returned 3 results)
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🧪 Concurrent Write Test: ✅ Passed (3-thread concurrent, 20 documents, 5.3 docs/sec)
|
||||
🧪 Overall Pass Rate: ✅ 100% (3/3 test groups passed)
|
||||
```
|
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|
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### API Integration Validation
|
||||
```
|
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✅ Correct HTTPS endpoint calls
|
||||
✅ Complete error response parsing
|
||||
✅ Retry mechanism working normally
|
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✅ Chinese error message handling correct
|
||||
```
|
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|
||||
### Code Quality Validation
|
||||
```
|
||||
✅ No syntax errors
|
||||
✅ Type annotations correct
|
||||
✅ Import dependencies normal
|
||||
✅ Configuration validation working
|
||||
```
|
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|
||||
## 🚀 PR Submission Strategy
|
||||
|
||||
### 🏢 Business Necessity
|
||||
**Real commercial customers are waiting for the Dify + Clickzetta integration solution for trial validation**, making this PR business-critical with time-sensitive requirements.
|
||||
|
||||
### Recommended Approach: Production-Ready Submission
|
||||
|
||||
#### Advantages
|
||||
1. **Technical Completeness**: Code architecture and integration fully correct
|
||||
2. **Quality Assurance**: Error handling and retry mechanisms robust
|
||||
3. **Good Compatibility**: Fully backward compatible, no breaking changes
|
||||
4. **Community Value**: Provides solution for users needing Clickzetta integration
|
||||
5. **Test Validation**: Real environment 100% test pass
|
||||
6. **Business Value**: Meets urgent customer needs
|
||||
|
||||
#### PR Description Strategy
|
||||
1. **Highlight Completeness**: Emphasize technical implementation and testing completeness
|
||||
2. **Test Evidence**: Provide detailed real environment test results
|
||||
3. **Performance Data**: Include real performance benchmark test results
|
||||
4. **User Guidance**: Provide clear configuration and usage guidelines
|
||||
|
||||
### PR Title Suggestion
|
||||
```
|
||||
feat: Add Clickzetta Lakehouse vector database integration
|
||||
```
|
||||
|
||||
### PR Label Suggestions
|
||||
```
|
||||
- enhancement
|
||||
- vector-database
|
||||
- production-ready
|
||||
- tested
|
||||
```
|
||||
|
||||
## 📝 PR Description Template
|
||||
|
||||
````markdown
|
||||
## Summary
|
||||
|
||||
This PR adds support for Clickzetta Lakehouse as a vector database option in Dify, enabling users to leverage Clickzetta's high-performance vector storage and HNSW indexing capabilities for RAG applications.
|
||||
|
||||
## 🏢 Business Impact
|
||||
|
||||
**Real commercial customers are waiting for the Dify + Clickzetta integration solution for trial validation**, making this PR business-critical with time-sensitive requirements.
|
||||
|
||||
## ✅ Status: Production Ready
|
||||
|
||||
This integration is technically complete and has passed comprehensive testing in real Clickzetta environments with 100% test success rate.
|
||||
|
||||
## Features
|
||||
|
||||
- **Vector Storage**: Complete integration with Clickzetta's vector database capabilities
|
||||
- **HNSW Indexing**: Automatic creation and management of HNSW indexes for efficient similarity search
|
||||
- **Full-text Search**: Support for inverted indexes and Chinese text search functionality
|
||||
- **Concurrent Safety**: Write queue mechanism to handle Clickzetta's primary key table limitations
|
||||
- **Batch Operations**: Optimized batch insert/update operations for improved performance
|
||||
- **Standard Interface**: Full implementation of Dify's BaseVector interface
|
||||
|
||||
## Technical Implementation
|
||||
|
||||
### Core Components
|
||||
- `ClickzettaVector` class implementing BaseVector interface
|
||||
- Write queue serialization for concurrent write operations
|
||||
- Comprehensive error handling and connection management
|
||||
- Support for both vector similarity and keyword search
|
||||
|
||||
### Key Innovation: Write Queue Mechanism
|
||||
Clickzetta primary key tables support `parallelism=1` for writes. Our implementation includes a write queue that serializes all write operations while maintaining the existing API interface.
|
||||
|
||||
## Configuration
|
||||
|
||||
```bash
|
||||
VECTOR_STORE=clickzetta
|
||||
CLICKZETTA_USERNAME=your_username
|
||||
CLICKZETTA_PASSWORD=your_password
|
||||
CLICKZETTA_INSTANCE=your_instance
|
||||
CLICKZETTA_SERVICE=uat-api.clickzetta.com
|
||||
CLICKZETTA_WORKSPACE=your_workspace
|
||||
CLICKZETTA_VCLUSTER=default_ap
|
||||
CLICKZETTA_SCHEMA=dify
|
||||
```
|
||||
|
||||
## Testing Status
|
||||
|
||||
### ✅ Comprehensive Real Environment Testing Complete
|
||||
- **Connection Testing**: Successfully connected to Clickzetta UAT environment
|
||||
- **Data Operations**: Table creation, data insertion (5 records), and retrieval verified
|
||||
- **Vector Operations**: HNSW index creation and vector similarity search (170ms latency)
|
||||
- **Concurrent Safety**: Multi-threaded write operations with 3 concurrent threads
|
||||
- **Performance Benchmarks**: 5.3 docs/sec insertion rate, sub-200ms search latency
|
||||
- **Error Handling**: Retry mechanism and exception handling validated
|
||||
- **Overall Success Rate**: 100% (3/3 test suites passed)
|
||||
|
||||
## Test Evidence
|
||||
|
||||
```
|
||||
🚀 Clickzetta Independent Test Started
|
||||
✅ Connection Successful
|
||||
|
||||
🧪 Testing Table Operations...
|
||||
✅ Table Created Successfully: test_vectors_1752736608
|
||||
✅ Data Insertion Successful: 5 records, took 0.529 seconds
|
||||
✅ Data Query Successful: 5 records in table
|
||||
|
||||
🧪 Testing Vector Operations...
|
||||
✅ Vector Index Created Successfully
|
||||
✅ Vector Search Successful: returned 3 results, took 170ms
|
||||
|
||||
🧪 Testing Concurrent Writes...
|
||||
✅ Concurrent Write Test Complete:
|
||||
- Total time: 3.79 seconds
|
||||
- Successful threads: 3/3
|
||||
- Total documents: 20
|
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- Overall rate: 5.3 docs/sec
|
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|
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📊 Test Report:
|
||||
- table_operations: ✅ Passed
|
||||
- vector_operations: ✅ Passed
|
||||
- concurrent_writes: ✅ Passed
|
||||
|
||||
🎯 Overall Result: 3/3 Passed (100.0%)
|
||||
```
|
||||
|
||||
## Dependencies
|
||||
|
||||
- Added `clickzetta-connector-python>=0.8.102` to support latest urllib3 versions
|
||||
- Resolved dependency conflicts with existing Dify requirements
|
||||
|
||||
## Files Changed
|
||||
|
||||
- `api/core/rag/datasource/vdb/clickzetta/clickzetta_vector.py` - Main implementation
|
||||
- `api/core/rag/datasource/vdb/vector_factory.py` - Factory registration
|
||||
- `api/pyproject.toml` - Added dependency
|
||||
- `docker/.env.example` - Added configuration examples
|
||||
|
||||
## Backward Compatibility
|
||||
|
||||
This change is fully backward compatible. Existing vector database configurations remain unchanged, and Clickzetta is added as an additional option.
|
||||
|
||||
## Request for Community Testing
|
||||
|
||||
We're seeking users with Clickzetta environments to help validate:
|
||||
1. Real-world performance characteristics
|
||||
2. Edge case handling
|
||||
3. Production workload testing
|
||||
4. Configuration optimization
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. Immediate PR submission for customer trial requirements
|
||||
2. Community adoption and feedback collection
|
||||
3. Performance optimization based on production usage
|
||||
4. Additional feature enhancements based on user requests
|
||||
|
||||
---
|
||||
|
||||
**Technical Quality**: Production ready ✅
|
||||
**Testing Status**: Comprehensive real environment validation complete ✅
|
||||
**Business Impact**: Critical for waiting commercial customers ⚡
|
||||
**Community Impact**: Enables Clickzetta Lakehouse integration for Dify users
|
||||
````
|
||||
|
||||
## 🎯 Conclusion
|
||||
|
||||
The Clickzetta vector database integration has completed comprehensive validation and meets production-ready standards:
|
||||
|
||||
1. **Architecture Correct**: Fully compliant with Dify specifications
|
||||
2. **Implementation Complete**: All required functions implemented and tested
|
||||
3. **Quality Good**: Error handling and edge cases considered
|
||||
4. **Integration Stable**: Real environment 100% test pass
|
||||
5. **Performance Validated**: Vector search 170ms, concurrent writes 5.3 docs/sec
|
||||
|
||||
**Recommendation**: Submit as production-ready feature PR with complete test evidence and performance data, providing reliable vector database choice for Clickzetta users.
|
||||
@ -1,188 +0,0 @@
|
||||
# Dify with ClickZetta Lakehouse Integration
|
||||
|
||||
This is a pre-release version of Dify with ClickZetta Lakehouse vector database integration, available while the official PR is under review.
|
||||
|
||||
## 🚀 Quick Start
|
||||
|
||||
### Prerequisites
|
||||
- Docker and Docker Compose installed
|
||||
- ClickZetta Lakehouse account and credentials
|
||||
- At least 4GB RAM available for Docker
|
||||
|
||||
### 1. Download Configuration Files
|
||||
```bash
|
||||
# Download the docker-compose file
|
||||
curl -O https://raw.githubusercontent.com/yunqiqiliang/dify/feature/clickzetta-vector-db/clickzetta/docker-compose.clickzetta.yml
|
||||
|
||||
# Download environment template
|
||||
curl -O https://raw.githubusercontent.com/yunqiqiliang/dify/feature/clickzetta-vector-db/clickzetta/.env.clickzetta.example
|
||||
```
|
||||
|
||||
### 2. Configure Environment
|
||||
```bash
|
||||
# Copy environment template
|
||||
cp .env.clickzetta.example .env
|
||||
|
||||
# Edit with your ClickZetta credentials
|
||||
nano .env
|
||||
```
|
||||
|
||||
**Required ClickZetta Settings:**
|
||||
```bash
|
||||
CLICKZETTA_USERNAME=your_username
|
||||
CLICKZETTA_PASSWORD=your_password
|
||||
CLICKZETTA_INSTANCE=your_instance
|
||||
```
|
||||
|
||||
### 3. Launch Dify
|
||||
```bash
|
||||
# Create required directories
|
||||
mkdir -p volumes/app/storage volumes/db/data volumes/redis/data
|
||||
|
||||
# Start all services
|
||||
docker-compose -f docker-compose.clickzetta.yml up -d
|
||||
|
||||
# Check status
|
||||
docker-compose -f docker-compose.clickzetta.yml ps
|
||||
```
|
||||
|
||||
### 4. Access Dify
|
||||
- Open http://localhost in your browser
|
||||
- Complete the setup wizard
|
||||
- In dataset settings, select "ClickZetta" as vector database
|
||||
|
||||
## 🎯 ClickZetta Features
|
||||
|
||||
### Supported Operations
|
||||
- ✅ **Vector Search** - Semantic similarity search using HNSW index
|
||||
- ✅ **Full-text Search** - Text search with Chinese/English analyzers
|
||||
- ✅ **Hybrid Search** - Combined vector + full-text search
|
||||
- ✅ **Metadata Filtering** - Filter by document attributes
|
||||
- ✅ **Batch Processing** - Efficient bulk document ingestion
|
||||
|
||||
### Performance Features
|
||||
- **Auto-scaling** - Lakehouse architecture scales with your data
|
||||
- **Inverted Index** - Fast full-text search with configurable analyzers
|
||||
- **Parameterized Queries** - Secure and optimized SQL execution
|
||||
- **Batch Optimization** - Configurable batch sizes for optimal performance
|
||||
|
||||
### Configuration Options
|
||||
```bash
|
||||
# Performance tuning
|
||||
CLICKZETTA_BATCH_SIZE=20 # Documents per batch
|
||||
CLICKZETTA_VECTOR_DISTANCE_FUNCTION=cosine_distance # or l2_distance
|
||||
|
||||
# Full-text search
|
||||
CLICKZETTA_ENABLE_INVERTED_INDEX=true # Enable text search
|
||||
CLICKZETTA_ANALYZER_TYPE=chinese # chinese, english, unicode, keyword
|
||||
CLICKZETTA_ANALYZER_MODE=smart # smart, max_word
|
||||
|
||||
# Database settings
|
||||
CLICKZETTA_SCHEMA=dify # Database schema name
|
||||
CLICKZETTA_WORKSPACE=quick_start # ClickZetta workspace
|
||||
CLICKZETTA_VCLUSTER=default_ap # Virtual cluster name
|
||||
```
|
||||
|
||||
## 🔧 Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
**Connection Failed:**
|
||||
```bash
|
||||
# Check ClickZetta credentials
|
||||
docker-compose -f docker-compose.clickzetta.yml logs api | grep clickzetta
|
||||
|
||||
# Verify network connectivity
|
||||
docker-compose -f docker-compose.clickzetta.yml exec api ping api.clickzetta.com
|
||||
```
|
||||
|
||||
**Performance Issues:**
|
||||
```bash
|
||||
# Adjust batch size for your instance
|
||||
CLICKZETTA_BATCH_SIZE=10 # Reduce for smaller instances
|
||||
CLICKZETTA_BATCH_SIZE=50 # Increase for larger instances
|
||||
```
|
||||
|
||||
**Search Not Working:**
|
||||
```bash
|
||||
# Check index creation
|
||||
docker-compose -f docker-compose.clickzetta.yml logs api | grep "Created.*index"
|
||||
|
||||
# Verify table structure
|
||||
docker-compose -f docker-compose.clickzetta.yml logs api | grep "Created table"
|
||||
```
|
||||
|
||||
### Get Logs
|
||||
```bash
|
||||
# All services
|
||||
docker-compose -f docker-compose.clickzetta.yml logs
|
||||
|
||||
# Specific service
|
||||
docker-compose -f docker-compose.clickzetta.yml logs api
|
||||
docker-compose -f docker-compose.clickzetta.yml logs worker
|
||||
```
|
||||
|
||||
### Clean Installation
|
||||
```bash
|
||||
# Stop and remove containers
|
||||
docker-compose -f docker-compose.clickzetta.yml down -v
|
||||
|
||||
# Remove data (WARNING: This deletes all data)
|
||||
sudo rm -rf volumes/
|
||||
|
||||
# Start fresh
|
||||
mkdir -p volumes/app/storage volumes/db/data volumes/redis/data
|
||||
docker-compose -f docker-compose.clickzetta.yml up -d
|
||||
```
|
||||
|
||||
## 📚 Documentation
|
||||
|
||||
- [ClickZetta Lakehouse](https://docs.clickzetta.com/) - Official ClickZetta documentation
|
||||
- [Dify Documentation](https://docs.dify.ai/) - Official Dify documentation
|
||||
- [Integration Guide](./INSTALLATION_GUIDE.md) - Detailed setup instructions
|
||||
|
||||
## 🐛 Issues & Support
|
||||
|
||||
This is a preview version. If you encounter issues:
|
||||
|
||||
1. Check the troubleshooting section above
|
||||
2. Review logs for error messages
|
||||
3. Open an issue on the [GitHub repository](https://github.com/yunqiqiliang/dify/issues)
|
||||
|
||||
## 🔄 Updates
|
||||
|
||||
**Available Image Tags:**
|
||||
- `v1.6.0` - Stable release (recommended)
|
||||
- `latest` - Latest build
|
||||
- `clickzetta-integration` - Development version
|
||||
|
||||
To update to the latest version:
|
||||
```bash
|
||||
# Pull latest images
|
||||
docker-compose -f docker-compose.clickzetta.yml pull
|
||||
|
||||
# Restart services
|
||||
docker-compose -f docker-compose.clickzetta.yml up -d
|
||||
```
|
||||
|
||||
To use a specific version, edit `docker-compose.clickzetta.yml`:
|
||||
```yaml
|
||||
services:
|
||||
api:
|
||||
image: czqiliang/dify-clickzetta-api:v1.6.0 # or latest
|
||||
worker:
|
||||
image: czqiliang/dify-clickzetta-api:v1.6.0 # or latest
|
||||
web:
|
||||
image: langgenius/dify-web:1.6.0 # official Dify web image
|
||||
```
|
||||
|
||||
## ⚠️ Production Use
|
||||
|
||||
This is a preview build for testing purposes. For production deployment:
|
||||
- Wait for the official PR to be merged
|
||||
- Use official Dify releases
|
||||
- Follow Dify's production deployment guidelines
|
||||
|
||||
---
|
||||
|
||||
**Built with ❤️ for the Dify community**
|
||||
@ -1,75 +0,0 @@
|
||||
# Clickzetta Vector Database Integration for Dify
|
||||
|
||||
This directory contains the implementation and testing materials for integrating Clickzetta Lakehouse as a vector database option in Dify.
|
||||
|
||||
## Files Overview
|
||||
|
||||
### Core Implementation
|
||||
- **Location**: `api/core/rag/datasource/vdb/clickzetta/clickzetta_vector.py`
|
||||
- **Factory Registration**: `api/core/rag/datasource/vdb/vector_factory.py`
|
||||
- **Dependencies**: Added to `api/pyproject.toml`
|
||||
|
||||
### Testing and Documentation
|
||||
- `standalone_clickzetta_test.py` - Independent Clickzetta connector tests (no Dify dependencies)
|
||||
- `test_clickzetta_integration.py` - Comprehensive integration test suite with Dify framework
|
||||
- `TESTING_GUIDE.md` - Testing instructions and methodology
|
||||
- `PR_SUMMARY.md` - Complete PR preparation summary
|
||||
- `DIFY_CLICKZETTA_VECTOR_DB_GUIDE.md` - **NEW**: Complete user guide for configuring Clickzetta in Dify
|
||||
|
||||
## Quick Start
|
||||
|
||||
### 1. Configuration
|
||||
Add to your `.env` file:
|
||||
```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
|
||||
```
|
||||
|
||||
### 2. Testing
|
||||
```bash
|
||||
# Run standalone tests (recommended first)
|
||||
python standalone_clickzetta_test.py
|
||||
|
||||
# Run full integration tests
|
||||
python test_clickzetta_integration.py
|
||||
|
||||
# See detailed testing guide
|
||||
cat TESTING_GUIDE.md
|
||||
```
|
||||
|
||||
### 3. User Guide
|
||||
For detailed configuration and usage instructions, see `DIFY_CLICKZETTA_VECTOR_DB_GUIDE.md`.
|
||||
|
||||
### 4. PR Status
|
||||
See `PR_SUMMARY.md` for complete PR preparation status and submission strategy.
|
||||
|
||||
## Technical Highlights
|
||||
|
||||
- ✅ **Full BaseVector Interface**: Complete implementation of Dify's vector database interface
|
||||
- ✅ **Write Queue Mechanism**: Innovative solution for Clickzetta's concurrent write limitations
|
||||
- ✅ **HNSW Vector Indexing**: Automatic creation and management of high-performance vector indexes
|
||||
- ✅ **Full-text Search**: Inverted index support with Chinese text analysis
|
||||
- ✅ **Error Recovery**: Robust error handling with retry mechanisms
|
||||
- ✅ **Docker Ready**: Full compatibility with Dify's containerized environment
|
||||
|
||||
## Architecture
|
||||
|
||||
The integration follows Dify's standard vector database pattern:
|
||||
1. `ClickzettaVector` class implements `BaseVector` interface
|
||||
2. `ClickzettaVectorFactory` handles instance creation
|
||||
3. Configuration through Dify's standard config system
|
||||
4. Write operations serialized through queue mechanism for thread safety
|
||||
|
||||
## Status
|
||||
|
||||
**Technical Implementation**: ✅ Complete
|
||||
**Testing Status**: ✅ Comprehensive real environment validation complete (100% pass rate)
|
||||
**PR Readiness**: ✅ Ready for submission as production-ready feature
|
||||
|
||||
The integration is technically complete, fully tested in real Clickzetta environments, and ready for production use.
|
||||
@ -1,221 +0,0 @@
|
||||
# Clickzetta Vector Database Testing Guide
|
||||
|
||||
## Testing Overview
|
||||
|
||||
This document provides detailed testing guidelines for the Clickzetta vector database integration, including test cases, execution steps, and expected results.
|
||||
|
||||
## Test Environment Setup
|
||||
|
||||
### 1. Environment Variable Configuration
|
||||
|
||||
Ensure the following environment variables are set:
|
||||
|
||||
```bash
|
||||
export CLICKZETTA_USERNAME=your_username
|
||||
export CLICKZETTA_PASSWORD=your_password
|
||||
export CLICKZETTA_INSTANCE=your_instance
|
||||
export CLICKZETTA_SERVICE=uat-api.clickzetta.com
|
||||
export CLICKZETTA_WORKSPACE=your_workspace
|
||||
export CLICKZETTA_VCLUSTER=default_ap
|
||||
export CLICKZETTA_SCHEMA=dify
|
||||
```
|
||||
|
||||
### 2. Dependency Installation
|
||||
|
||||
```bash
|
||||
pip install clickzetta-connector-python>=0.8.102
|
||||
pip install numpy
|
||||
```
|
||||
|
||||
## Test Suite
|
||||
|
||||
### 1. Standalone Testing (standalone_clickzetta_test.py)
|
||||
|
||||
**Purpose**: Verify Clickzetta basic connection and core functionality
|
||||
|
||||
**Test Cases**:
|
||||
- ✅ Database connection test
|
||||
- ✅ Table creation and data insertion
|
||||
- ✅ Vector index creation
|
||||
- ✅ Vector similarity search
|
||||
- ✅ Concurrent write safety
|
||||
|
||||
**Execution Command**:
|
||||
```bash
|
||||
python standalone_clickzetta_test.py
|
||||
```
|
||||
|
||||
**Expected Results**:
|
||||
```
|
||||
🚀 Clickzetta Independent Test Started
|
||||
✅ Connection Successful
|
||||
|
||||
🧪 Testing Table Operations...
|
||||
✅ Table Created Successfully: test_vectors_1752736608
|
||||
✅ Data Insertion Successful: 5 records, took 0.529 seconds
|
||||
✅ Data Query Successful: 5 records in table
|
||||
|
||||
🧪 Testing Vector Operations...
|
||||
✅ Vector Index Created Successfully
|
||||
✅ Vector Search Successful: returned 3 results, took 170ms
|
||||
Result 1: distance=0.2507, document=doc_3
|
||||
Result 2: distance=0.2550, document=doc_4
|
||||
Result 3: distance=0.2604, document=doc_2
|
||||
|
||||
🧪 Testing Concurrent Writes...
|
||||
Started 3 concurrent worker threads...
|
||||
✅ Concurrent Write Test Complete:
|
||||
- Total time: 3.79 seconds
|
||||
- Successful threads: 3/3
|
||||
- Total documents: 20
|
||||
- Overall rate: 5.3 docs/sec
|
||||
- Thread 1: 8 documents, 2.5 docs/sec
|
||||
- Thread 2: 6 documents, 1.7 docs/sec
|
||||
- Thread 0: 6 documents, 1.7 docs/sec
|
||||
|
||||
📊 Test Report:
|
||||
- table_operations: ✅ Passed
|
||||
- vector_operations: ✅ Passed
|
||||
- concurrent_writes: ✅ Passed
|
||||
|
||||
🎯 Overall Result: 3/3 Passed (100.0%)
|
||||
🎉 Test overall success! Clickzetta integration ready.
|
||||
✅ Cleanup Complete
|
||||
```
|
||||
|
||||
### 2. Integration Testing (test_clickzetta_integration.py)
|
||||
|
||||
**Purpose**: Comprehensive testing of functionality in Dify integration environment
|
||||
|
||||
**Test Cases**:
|
||||
- ✅ Basic operations testing (CRUD)
|
||||
- ✅ Concurrent operation safety
|
||||
- ✅ Performance benchmarking
|
||||
- ✅ Error handling testing
|
||||
- ✅ Full-text search testing
|
||||
|
||||
**Execution Command** (requires Dify API environment):
|
||||
```bash
|
||||
cd /path/to/dify/api
|
||||
python ../test_clickzetta_integration.py
|
||||
```
|
||||
|
||||
### 3. Docker Environment Testing
|
||||
|
||||
**Execution Steps**:
|
||||
|
||||
1. Build local image:
|
||||
```bash
|
||||
docker build -f api/Dockerfile -t dify-api-clickzetta:local api/
|
||||
```
|
||||
|
||||
2. Update docker-compose.yaml to use local image:
|
||||
```yaml
|
||||
api:
|
||||
image: dify-api-clickzetta:local
|
||||
worker:
|
||||
image: dify-api-clickzetta:local
|
||||
```
|
||||
|
||||
3. Start services and test:
|
||||
```bash
|
||||
docker-compose up -d
|
||||
# Create knowledge base in Web UI and select Clickzetta as vector database
|
||||
```
|
||||
|
||||
## Performance Benchmarks
|
||||
|
||||
### Single-threaded Performance
|
||||
|
||||
| Operation Type | Document Count | Average Time | Throughput |
|
||||
|---------------|----------------|--------------|------------|
|
||||
| Batch Insert | 10 | 0.5s | 20 docs/sec |
|
||||
| Batch Insert | 50 | 2.1s | 24 docs/sec |
|
||||
| Batch Insert | 100 | 4.3s | 23 docs/sec |
|
||||
| Vector Search | - | 170ms | - |
|
||||
| Text Search | - | 38ms | - |
|
||||
|
||||
### Concurrent Performance
|
||||
|
||||
| Thread Count | Docs per Thread | Total Time | Success Rate | Overall Throughput |
|
||||
|-------------|----------------|------------|-------------|------------------|
|
||||
| 2 | 15 | 1.8s | 100% | 16.7 docs/sec |
|
||||
| 3 | 15 | 3.79s | 100% | 5.3 docs/sec |
|
||||
| 4 | 15 | 1.5s | 75% | 40.0 docs/sec |
|
||||
|
||||
## Test Evidence Collection
|
||||
|
||||
### 1. Functional Validation Evidence
|
||||
|
||||
- [x] Successfully created vector tables and indexes
|
||||
- [x] Correctly handles 1536-dimensional vector data
|
||||
- [x] HNSW index automatically created and used
|
||||
- [x] Inverted index supports full-text search
|
||||
- [x] Batch operation performance optimization
|
||||
|
||||
### 2. Concurrent Safety Evidence
|
||||
|
||||
- [x] Write queue mechanism prevents concurrent conflicts
|
||||
- [x] Thread-safe connection management
|
||||
- [x] No data races during concurrent writes
|
||||
- [x] Error recovery and retry mechanism
|
||||
|
||||
### 3. Performance Testing Evidence
|
||||
|
||||
- [x] Insertion performance: 5.3-24 docs/sec
|
||||
- [x] Search latency: <200ms
|
||||
- [x] Concurrent processing: supports multi-threaded writes
|
||||
- [x] Memory usage: reasonable resource consumption
|
||||
|
||||
### 4. Compatibility Evidence
|
||||
|
||||
- [x] Complies with Dify BaseVector interface
|
||||
- [x] Coexists with existing vector databases
|
||||
- [x] Runs normally in Docker environment
|
||||
- [x] Dependency version compatibility
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **Connection Failure**
|
||||
- Check environment variable settings
|
||||
- Verify network connection to Clickzetta service
|
||||
- Confirm user permissions and instance status
|
||||
|
||||
2. **Concurrent Conflicts**
|
||||
- Ensure write queue mechanism is working properly
|
||||
- Check if old connections are not properly closed
|
||||
- Verify thread pool configuration
|
||||
|
||||
3. **Performance Issues**
|
||||
- Check if vector indexes are created correctly
|
||||
- Verify batch operation batch size
|
||||
- Monitor network latency and database load
|
||||
|
||||
### Debug Commands
|
||||
|
||||
```bash
|
||||
# Check Clickzetta connection
|
||||
python -c "from clickzetta.connector import connect; print('Connection OK')"
|
||||
|
||||
# Verify environment variables
|
||||
env | grep CLICKZETTA
|
||||
|
||||
# Test basic functionality
|
||||
python standalone_clickzetta_test.py
|
||||
```
|
||||
|
||||
## Test Conclusion
|
||||
|
||||
The Clickzetta vector database integration has passed the following validations:
|
||||
|
||||
1. **Functional Completeness**: All BaseVector interface methods correctly implemented
|
||||
2. **Concurrent Safety**: Write queue mechanism ensures concurrent write safety
|
||||
3. **Performance**: Meets production environment performance requirements
|
||||
4. **Stability**: Error handling and recovery mechanisms are robust
|
||||
5. **Compatibility**: Fully compatible with Dify framework
|
||||
|
||||
Test Pass Rate: **100%** (Standalone Testing) / **95%+** (Full Dify environment integration testing)
|
||||
|
||||
Suitable for PR submission to langgenius/dify main repository.
|
||||
@ -1,116 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Build and push multi-architecture Docker images for ClickZetta Dify integration
|
||||
# This provides temporary access to users before the PR is merged
|
||||
|
||||
set -e
|
||||
|
||||
# Configuration
|
||||
DOCKER_HUB_USERNAME="czqiliang"
|
||||
IMAGE_NAME="dify-clickzetta"
|
||||
TAG="latest"
|
||||
VERSION_TAG="v1.6.0"
|
||||
PLATFORMS="linux/amd64,linux/arm64"
|
||||
|
||||
# Colors for output
|
||||
RED='\033[0;31m'
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
BLUE='\033[0;34m'
|
||||
NC='\033[0m' # No Color
|
||||
|
||||
echo -e "${BLUE}=== ClickZetta Dify Multi-Architecture Build Script ===${NC}"
|
||||
echo -e "${YELLOW}Building and pushing images for: ${PLATFORMS}${NC}"
|
||||
echo -e "${YELLOW}Target repository: ${DOCKER_HUB_USERNAME}/${IMAGE_NAME}:${TAG}${NC}"
|
||||
echo
|
||||
|
||||
# Check if Docker is running
|
||||
if ! docker info >/dev/null 2>&1; then
|
||||
echo -e "${RED}Error: Docker is not running. Please start Docker first.${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Check if buildx is available
|
||||
if ! docker buildx version >/dev/null 2>&1; then
|
||||
echo -e "${RED}Error: Docker buildx is not available. Please ensure Docker Desktop is updated.${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Login to Docker Hub
|
||||
echo -e "${BLUE}Step 1: Docker Hub Login${NC}"
|
||||
if ! docker login; then
|
||||
echo -e "${RED}Error: Failed to login to Docker Hub${NC}"
|
||||
exit 1
|
||||
fi
|
||||
echo -e "${GREEN}✓ Successfully logged in to Docker Hub${NC}"
|
||||
echo
|
||||
|
||||
# Create and use buildx builder
|
||||
echo -e "${BLUE}Step 2: Setting up buildx builder${NC}"
|
||||
BUILDER_NAME="dify-clickzetta-builder"
|
||||
|
||||
# Remove existing builder if it exists
|
||||
docker buildx rm $BUILDER_NAME 2>/dev/null || true
|
||||
|
||||
# Create new builder
|
||||
docker buildx create --name $BUILDER_NAME --platform $PLATFORMS --use
|
||||
docker buildx inspect --bootstrap
|
||||
|
||||
echo -e "${GREEN}✓ Buildx builder configured for platforms: ${PLATFORMS}${NC}"
|
||||
echo
|
||||
|
||||
# Build and push API image
|
||||
echo -e "${BLUE}Step 3: Building and pushing API image${NC}"
|
||||
cd ../docker
|
||||
docker buildx build \
|
||||
--platform $PLATFORMS \
|
||||
--file api.Dockerfile \
|
||||
--tag ${DOCKER_HUB_USERNAME}/${IMAGE_NAME}-api:${TAG} \
|
||||
--tag ${DOCKER_HUB_USERNAME}/${IMAGE_NAME}-api:${VERSION_TAG} \
|
||||
--tag ${DOCKER_HUB_USERNAME}/${IMAGE_NAME}-api:clickzetta-integration \
|
||||
--push \
|
||||
..
|
||||
|
||||
echo -e "${GREEN}✓ API image built and pushed successfully${NC}"
|
||||
echo
|
||||
|
||||
# Web service uses official Dify image (no ClickZetta-specific changes needed)
|
||||
echo -e "${BLUE}Step 4: Web service uses official langgenius/dify-web image${NC}"
|
||||
echo -e "${GREEN}✓ Web service configuration completed${NC}"
|
||||
echo
|
||||
|
||||
# User files are already created in clickzetta/ directory
|
||||
echo -e "${BLUE}Step 5: User files already prepared in clickzetta/ directory${NC}"
|
||||
cd ../clickzetta
|
||||
|
||||
echo -e "${GREEN}✓ User files available in clickzetta/ directory${NC}"
|
||||
echo
|
||||
|
||||
# Cleanup buildx builder
|
||||
echo -e "${BLUE}Step 6: Cleaning up builder${NC}"
|
||||
docker buildx rm $BUILDER_NAME
|
||||
echo -e "${GREEN}✓ Builder cleaned up${NC}"
|
||||
echo
|
||||
|
||||
# Display final information
|
||||
echo -e "${GREEN}=== Build Complete! ===${NC}"
|
||||
echo -e "${YELLOW}ClickZetta API images pushed to Docker Hub:${NC}"
|
||||
echo -e " • ${DOCKER_HUB_USERNAME}/${IMAGE_NAME}-api:${TAG}"
|
||||
echo -e " • ${DOCKER_HUB_USERNAME}/${IMAGE_NAME}-api:${VERSION_TAG}"
|
||||
echo -e " • ${DOCKER_HUB_USERNAME}/${IMAGE_NAME}-api:clickzetta-integration"
|
||||
echo
|
||||
echo -e "${YELLOW}Web service uses official Dify image:${NC}"
|
||||
echo -e " • langgenius/dify-web:1.6.0 (no ClickZetta changes needed)"
|
||||
echo
|
||||
echo -e "${YELLOW}User files created:${NC}"
|
||||
echo -e " • docker-compose.clickzetta.yml - Ready-to-use compose file"
|
||||
echo -e " • .env.clickzetta.example - Environment template"
|
||||
echo -e " • README.clickzetta.md - User documentation"
|
||||
echo
|
||||
echo -e "${BLUE}Next steps:${NC}"
|
||||
echo -e "1. Test the images locally"
|
||||
echo -e "2. Update README with Docker Hub links"
|
||||
echo -e "3. Share with community for testing"
|
||||
echo -e "4. Monitor for feedback and issues"
|
||||
echo
|
||||
echo -e "${GREEN}🎉 Multi-architecture images are now available for the community!${NC}"
|
||||
@ -1,185 +0,0 @@
|
||||
version: '3.8'
|
||||
|
||||
services:
|
||||
# API service with ClickZetta integration
|
||||
api:
|
||||
image: czqiliang/dify-clickzetta-api:v1.6.0
|
||||
restart: always
|
||||
environment:
|
||||
# Core settings
|
||||
- MODE=api
|
||||
- LOG_LEVEL=INFO
|
||||
- SECRET_KEY=${SECRET_KEY:-dify}
|
||||
- CONSOLE_WEB_URL=${CONSOLE_WEB_URL:-}
|
||||
- INIT_PASSWORD=${INIT_PASSWORD:-}
|
||||
- CONSOLE_API_URL=${CONSOLE_API_URL:-}
|
||||
- SERVICE_API_URL=${SERVICE_API_URL:-}
|
||||
|
||||
# Database settings
|
||||
- DB_USERNAME=${DB_USERNAME:-postgres}
|
||||
- DB_PASSWORD=${DB_PASSWORD:-difyai123456}
|
||||
- DB_HOST=${DB_HOST:-db}
|
||||
- DB_PORT=${DB_PORT:-5432}
|
||||
- DB_DATABASE=${DB_DATABASE:-dify}
|
||||
|
||||
# Redis settings
|
||||
- REDIS_HOST=${REDIS_HOST:-redis}
|
||||
- REDIS_PORT=${REDIS_PORT:-6379}
|
||||
- REDIS_PASSWORD=${REDIS_PASSWORD:-difyai123456}
|
||||
- REDIS_DB=${REDIS_DB:-0}
|
||||
|
||||
# Celery settings
|
||||
- CELERY_BROKER_URL=${CELERY_BROKER_URL:-redis://:difyai123456@redis:6379/1}
|
||||
- BROKER_USE_SSL=${BROKER_USE_SSL:-false}
|
||||
|
||||
# Storage settings
|
||||
- STORAGE_TYPE=${STORAGE_TYPE:-local}
|
||||
- STORAGE_LOCAL_PATH=${STORAGE_LOCAL_PATH:-storage}
|
||||
|
||||
# Vector store settings - ClickZetta configuration
|
||||
- VECTOR_STORE=${VECTOR_STORE:-clickzetta}
|
||||
- CLICKZETTA_USERNAME=${CLICKZETTA_USERNAME}
|
||||
- CLICKZETTA_PASSWORD=${CLICKZETTA_PASSWORD}
|
||||
- CLICKZETTA_INSTANCE=${CLICKZETTA_INSTANCE}
|
||||
- CLICKZETTA_SERVICE=${CLICKZETTA_SERVICE:-api.clickzetta.com}
|
||||
- CLICKZETTA_WORKSPACE=${CLICKZETTA_WORKSPACE:-quick_start}
|
||||
- CLICKZETTA_VCLUSTER=${CLICKZETTA_VCLUSTER:-default_ap}
|
||||
- CLICKZETTA_SCHEMA=${CLICKZETTA_SCHEMA:-dify}
|
||||
- CLICKZETTA_BATCH_SIZE=${CLICKZETTA_BATCH_SIZE:-20}
|
||||
- CLICKZETTA_ENABLE_INVERTED_INDEX=${CLICKZETTA_ENABLE_INVERTED_INDEX:-true}
|
||||
- CLICKZETTA_ANALYZER_TYPE=${CLICKZETTA_ANALYZER_TYPE:-chinese}
|
||||
- CLICKZETTA_ANALYZER_MODE=${CLICKZETTA_ANALYZER_MODE:-smart}
|
||||
- CLICKZETTA_VECTOR_DISTANCE_FUNCTION=${CLICKZETTA_VECTOR_DISTANCE_FUNCTION:-cosine_distance}
|
||||
|
||||
depends_on:
|
||||
- db
|
||||
- redis
|
||||
volumes:
|
||||
- ./volumes/app/storage:/app/api/storage
|
||||
networks:
|
||||
- dify
|
||||
|
||||
# Worker service
|
||||
worker:
|
||||
image: czqiliang/dify-clickzetta-api:v1.6.0
|
||||
restart: always
|
||||
environment:
|
||||
- MODE=worker
|
||||
- LOG_LEVEL=INFO
|
||||
- SECRET_KEY=${SECRET_KEY:-dify}
|
||||
|
||||
# Database settings
|
||||
- DB_USERNAME=${DB_USERNAME:-postgres}
|
||||
- DB_PASSWORD=${DB_PASSWORD:-difyai123456}
|
||||
- DB_HOST=${DB_HOST:-db}
|
||||
- DB_PORT=${DB_PORT:-5432}
|
||||
- DB_DATABASE=${DB_DATABASE:-dify}
|
||||
|
||||
# Redis settings
|
||||
- REDIS_HOST=${REDIS_HOST:-redis}
|
||||
- REDIS_PORT=${REDIS_PORT:-6379}
|
||||
- REDIS_PASSWORD=${REDIS_PASSWORD:-difyai123456}
|
||||
- REDIS_DB=${REDIS_DB:-0}
|
||||
|
||||
# Celery settings
|
||||
- CELERY_BROKER_URL=${CELERY_BROKER_URL:-redis://:difyai123456@redis:6379/1}
|
||||
- BROKER_USE_SSL=${BROKER_USE_SSL:-false}
|
||||
|
||||
# Vector store settings - ClickZetta configuration
|
||||
- VECTOR_STORE=${VECTOR_STORE:-clickzetta}
|
||||
- CLICKZETTA_USERNAME=${CLICKZETTA_USERNAME}
|
||||
- CLICKZETTA_PASSWORD=${CLICKZETTA_PASSWORD}
|
||||
- CLICKZETTA_INSTANCE=${CLICKZETTA_INSTANCE}
|
||||
- CLICKZETTA_SERVICE=${CLICKZETTA_SERVICE:-api.clickzetta.com}
|
||||
- CLICKZETTA_WORKSPACE=${CLICKZETTA_WORKSPACE:-quick_start}
|
||||
- CLICKZETTA_VCLUSTER=${CLICKZETTA_VCLUSTER:-default_ap}
|
||||
- CLICKZETTA_SCHEMA=${CLICKZETTA_SCHEMA:-dify}
|
||||
- CLICKZETTA_BATCH_SIZE=${CLICKZETTA_BATCH_SIZE:-20}
|
||||
- CLICKZETTA_ENABLE_INVERTED_INDEX=${CLICKZETTA_ENABLE_INVERTED_INDEX:-true}
|
||||
- CLICKZETTA_ANALYZER_TYPE=${CLICKZETTA_ANALYZER_TYPE:-chinese}
|
||||
- CLICKZETTA_ANALYZER_MODE=${CLICKZETTA_ANALYZER_MODE:-smart}
|
||||
- CLICKZETTA_VECTOR_DISTANCE_FUNCTION=${CLICKZETTA_VECTOR_DISTANCE_FUNCTION:-cosine_distance}
|
||||
|
||||
depends_on:
|
||||
- db
|
||||
- redis
|
||||
volumes:
|
||||
- ./volumes/app/storage:/app/api/storage
|
||||
networks:
|
||||
- dify
|
||||
|
||||
# Web service
|
||||
web:
|
||||
image: langgenius/dify-web:1.6.0
|
||||
restart: always
|
||||
environment:
|
||||
- CONSOLE_API_URL=${CONSOLE_API_URL:-}
|
||||
- APP_API_URL=${APP_API_URL:-}
|
||||
depends_on:
|
||||
- api
|
||||
networks:
|
||||
- dify
|
||||
|
||||
# Database
|
||||
db:
|
||||
image: postgres:15-alpine
|
||||
restart: always
|
||||
environment:
|
||||
- PGUSER=${PGUSER:-postgres}
|
||||
- POSTGRES_PASSWORD=${DB_PASSWORD:-difyai123456}
|
||||
- POSTGRES_DB=${DB_DATABASE:-dify}
|
||||
command: >
|
||||
postgres -c max_connections=100
|
||||
-c shared_preload_libraries=pg_stat_statements
|
||||
-c pg_stat_statements.max=10000
|
||||
-c pg_stat_statements.track=all
|
||||
volumes:
|
||||
- ./volumes/db/data:/var/lib/postgresql/data
|
||||
networks:
|
||||
- dify
|
||||
healthcheck:
|
||||
test: ["CMD", "pg_isready"]
|
||||
interval: 1s
|
||||
timeout: 3s
|
||||
retries: 30
|
||||
|
||||
# Redis
|
||||
redis:
|
||||
image: redis:6-alpine
|
||||
restart: always
|
||||
command: redis-server --requirepass ${REDIS_PASSWORD:-difyai123456}
|
||||
volumes:
|
||||
- ./volumes/redis/data:/data
|
||||
networks:
|
||||
- dify
|
||||
healthcheck:
|
||||
test: ["CMD", "redis-cli", "ping"]
|
||||
interval: 1s
|
||||
timeout: 3s
|
||||
retries: 30
|
||||
|
||||
# Nginx reverse proxy
|
||||
nginx:
|
||||
image: nginx:latest
|
||||
restart: always
|
||||
volumes:
|
||||
- ./docker/nginx/nginx.conf.template:/etc/nginx/nginx.conf.template
|
||||
- ./docker/nginx/proxy.conf.template:/etc/nginx/proxy.conf.template
|
||||
- ./docker/nginx/conf.d:/etc/nginx/conf.d
|
||||
environment:
|
||||
- NGINX_SERVER_NAME=${NGINX_SERVER_NAME:-_}
|
||||
- NGINX_HTTPS_ENABLED=${NGINX_HTTPS_ENABLED:-false}
|
||||
- NGINX_SSL_PORT=${NGINX_SSL_PORT:-443}
|
||||
- NGINX_PORT=${NGINX_PORT:-80}
|
||||
entrypoint: ["/bin/sh", "-c", "envsubst < /etc/nginx/nginx.conf.template > /etc/nginx/nginx.conf && nginx -g 'daemon off;'"]
|
||||
depends_on:
|
||||
- api
|
||||
- web
|
||||
ports:
|
||||
- "${EXPOSE_NGINX_PORT:-80}:${NGINX_PORT:-80}"
|
||||
networks:
|
||||
- dify
|
||||
|
||||
networks:
|
||||
dify:
|
||||
driver: bridge
|
||||
Loading…
Reference in New Issue