Langfuse Seeder System

System for generating test data in ClickHouse and PostgreSQL for Langfuse development and testing.

Architecture Overview

seeder/
├── types.ts                 # Core interfaces and types
├── data-generators.ts       # Data generation logic
├── clickhouse-builder.ts    # ClickHouse query building
├── seeder-orchestrator.ts   # Main orchestration logic
├── postgres-seed-constants.ts  # PostgreSQL data constants
├── clickhouse-seed-constants.ts  # ClickHouse data constants
└── seed-helpers.ts          # Utility functions

Quick Start

import { SeederOrchestrator } from "./seeder/seeder-orchestrator";

const orchestrator = new SeederOrchestrator();

// Full seed (datasets + evaluation + synthetic data)
await orchestrator.executeFullSeed(projectIds, {
  numberOfDays: 30,
  totalObservations: 10000,
  numberOfRuns: 3,
});

// Individual data types
await orchestrator.createDatasetExperimentData(projectIds, config);
await orchestrator.createEvaluationData(projectIds);
await orchestrator.createSyntheticData(projectIds, config);

Generated Data

1. Dataset Experiment Data

• Purpose: Realistic experiment traces based on actual datasets
• Environment: langfuse-prompt-experiment
• Structure: Each dataset item links to a trace with a single generation observation
• ID Pattern: trace-dataset-{datasetName}-{itemIndex}-{projectId}-{runNumber}

2. Evaluation Data

• Purpose: Evaluation metrics and scoring data - to be linked to evaluation logs
• Environment: langfuse-evaluation
• Structure: Traces with multiple observations and comprehensive scoring
• ID Pattern: trace-eval-{index}-{projectId}

3. Synthetic Data

• Purpose: Large-scale realistic tracing data
• Environment: default
• Structure: Hierarchical traces with multiple observations and scores
• ID Pattern: trace-synthetic-{index}-{projectId}

Abstraction Architecture

DataGenerator

Generates realistic data for all three types. If you need to change any clickhouse data, you should modify this class. Key methods:

• generateDatasetTrace() - Creates traces from dataset items
• generateSyntheticTraces() - Creates realistic synthetic traces
• generateEvaluationTraces() - Creates evaluation-focused traces

ClickHouseQueryBuilder

Builds optimized ClickHouse insert queries. No need to edit this file. Handles proper escaping and type handling.

SeederOrchestrator

Main coordination class that:

• Loads file content for realistic inputs/outputs
• Coordinates data generation and insertion
• Handles batching and error recovery
• Provides logging and statistics

Making Changes

Configuration Options

interface SeederConfig {
  numberOfDays: number; // How far back to generate timestamps
  numberOfRuns?: number; // How many experiment runs per dataset
  totalObservations?: number; // Total observations for synthetic data
}

Extending the System

Adding New Data Types

1. Add interface to types.ts
2. Add generator method to DataGenerator
3. Add query builder method to ClickHouseQueryBuilder
4. Add orchestration method to SeederOrchestrator
5. Update interdependency documentation

Adding New File Sources

1. Add file path to SeederOrchestrator.loadFileContent()
2. Add processing logic to DataGenerator
3. Update FileContent interface if needed

Changing Data Distribution

1. Modify generator methods in DataGenerator
2. Update constants in clickhouse-seed-constants.ts
3. Test with small datasets first

Changing ID Generation

1. Check: All places that query ClickHouse by ID
2. Check: PostgreSQL foreign key references
3. Check: Dataset run item and evaluation trace creation logic
4. Action: Update seed-helpers.ts functions consistently

Changing Environment Names

1. Check: All ClickHouse queries that filter by environment
2. Check: PostgreSQL dataset and prompt environment fields
3. Check: UI environment filtering logic
4. Action: Update constants in both systems

Changing Data Structure

1. Check: ClickHouse table schema compatibility
2. Check: PostgreSQL table relationships
3. Check: API response serialization
4. Action: Update both schemas before changing data generation

Adding New Data Types

1. Check: Whether PostgreSQL needs corresponding tables
2. Check: Whether new foreign key relationships are needed
3. Check: Whether UI needs to handle new data types
4. Action: Plan database migrations carefully

File Dependencies

Required Files

packages/shared/clickhouse/
├── nested_json.json      # Large JSON for realistic inputs
├── markdown.txt          # Markdown content for document analysis
└── chat_ml_json.json     # Chat ML format examples

Constants Files

• postgres-seed-constants.ts - Datasets, prompts, and PostgreSQL data
• clickhouse-seed-constants.ts - ClickHouse-specific constants (models, names)