dify/api/core/rag/datasource/vdb/clickzetta

Clickzetta Vector Database Integration

This module provides integration with Clickzetta Lakehouse as a vector database for Dify.

Features

• Vector Storage: Store and retrieve high-dimensional vectors using Clickzetta's native VECTOR type
• Vector Search: Efficient similarity search using HNSW algorithm
• Full-Text Search: Leverage Clickzetta's inverted index for powerful text search capabilities
• Hybrid Search: Combine vector similarity and full-text search for better results
• Multi-language Support: Built-in support for Chinese, English, and Unicode text processing
• Scalable: Leverage Clickzetta's distributed architecture for large-scale deployments

Configuration

Required Environment Variables

All seven configuration parameters are required:

# Authentication
CLICKZETTA_USERNAME=your_username
CLICKZETTA_PASSWORD=your_password

# Instance configuration
CLICKZETTA_INSTANCE=your_instance_id
CLICKZETTA_SERVICE=api.clickzetta.com
CLICKZETTA_WORKSPACE=your_workspace
CLICKZETTA_VCLUSTER=your_vcluster
CLICKZETTA_SCHEMA=your_schema

Optional Configuration

# Batch processing
CLICKZETTA_BATCH_SIZE=100

# Full-text search configuration
CLICKZETTA_ENABLE_INVERTED_INDEX=true
CLICKZETTA_ANALYZER_TYPE=chinese  # Options: keyword, english, chinese, unicode
CLICKZETTA_ANALYZER_MODE=smart    # Options: max_word, smart

# Vector search configuration
CLICKZETTA_VECTOR_DISTANCE_FUNCTION=cosine_distance  # Options: l2_distance, cosine_distance

Usage

1. Set Clickzetta as the Vector Store

In your Dify configuration, set:

VECTOR_STORE=clickzetta

2. Table Structure

Clickzetta will automatically create tables with the following structure:

CREATE TABLE <collection_name> (
    id STRING NOT NULL,
    content STRING NOT NULL,
    metadata JSON,
    vector VECTOR(FLOAT, <dimension>) NOT NULL,
    PRIMARY KEY (id)
);

-- Vector index for similarity search
CREATE VECTOR INDEX idx_<collection_name>_vec
ON TABLE <schema>.<collection_name>(vector) 
PROPERTIES (
    "distance.function" = "cosine_distance",
    "scalar.type" = "f32"
);

-- Inverted index for full-text search (if enabled)
CREATE INVERTED INDEX idx_<collection_name>_text
ON <schema>.<collection_name>(content)
PROPERTIES (
    "analyzer" = "chinese",
    "mode" = "smart"
);

Full-Text Search Capabilities

Clickzetta supports advanced full-text search with multiple analyzers:

Analyzer Types

1. keyword: No tokenization, treats the entire string as a single token

   • Best for: Exact matching, IDs, codes

2. english: Designed for English text

   • Features: Recognizes ASCII letters and numbers, converts to lowercase
   • Best for: English content

3. chinese: Chinese text tokenizer

   • Features: Recognizes Chinese and English characters, removes punctuation
   • Best for: Chinese or mixed Chinese-English content

4. unicode: Multi-language tokenizer based on Unicode

   • Features: Recognizes text boundaries in multiple languages
   • Best for: Multi-language content

Analyzer Modes

• max_word: Fine-grained tokenization (more tokens)
• smart: Intelligent tokenization (balanced)

Full-Text Search Functions

• MATCH_ALL(column, query): All terms must be present
• MATCH_ANY(column, query): At least one term must be present
• MATCH_PHRASE(column, query): Exact phrase matching
• MATCH_PHRASE_PREFIX(column, query): Phrase prefix matching
• MATCH_REGEXP(column, pattern): Regular expression matching

Performance Optimization

Vector Search

1. Adjust exploration factor for accuracy vs speed trade-off:

   SET cz.vector.index.search.ef=64;

1. Use appropriate distance functions:

   • cosine_distance: Best for normalized embeddings (e.g., from language models)
   • l2_distance: Best for raw feature vectors

Full-Text Search

1. Choose the right analyzer:

   • Use keyword for exact matching
   • Use language-specific analyzers for better tokenization

2. Combine with vector search:

   • Pre-filter with full-text search for better performance
   • Use hybrid search for improved relevance

Troubleshooting

Connection Issues

1. Verify all 7 required configuration parameters are set
2. Check network connectivity to Clickzetta service
3. Ensure the user has proper permissions on the schema

Search Performance

1. Verify vector index exists:

   SHOW INDEX FROM <schema>.<table_name>;

1. Check if vector index is being used:

   EXPLAIN SELECT ... WHERE l2_distance(...) < threshold;

Look for vector_index_search_type in the execution plan.

Full-Text Search Not Working

1. Verify inverted index is created
2. Check analyzer configuration matches your content language
3. Use TOKENIZE() function to test tokenization: sql SELECT TOKENIZE('your text', map('analyzer', 'chinese', 'mode', 'smart')); ## Limitations 1. Vector operations don't support ORDER BY or GROUP BY directly on vector columns 1. Full-text search relevance scores are not provided by Clickzetta 1. Inverted index creation may fail for very large existing tables (continue without error) 1. Index naming constraints: - Index names must be unique within a schema - Only one vector index can be created per column - The implementation uses timestamps to ensure unique index names 1. A column can only have one vector index at a time ## References - Clickzetta Vector Search Documentation - Clickzetta Inverted Index Documentation - Clickzetta SQL Functions