LogoLogo
Slack CommunityCatalyst Login
  • Welcome
  • RagaAI Catalyst
    • User Quickstart
    • Concepts
      • Configure Your API Keys
      • Supported LLMs
        • OpenAI
        • Gemini
        • Azure
        • AWS Bedrock
        • ANTHROPIC
      • Catalyst Access/Secret Keys
      • Enable Custom Gateway
      • Uploading Data
        • Create new project
        • RAG Datset
        • Chat Dataset
          • Prompt Format
        • Logging traces (LlamaIndex, Langchain)
        • Trace Masking Functions
        • Trace Level Metadata
        • Correlating Traces with External IDs
        • Add Dataset
      • Running RagaAI Evals
        • Executing Evaluations
        • Compare Datasets
      • Analysis
      • Embeddings
    • RagaAI Metric Library
      • RAG Metrics
        • Hallucination
        • Faithfulness
        • Response Correctness
        • Response Completeness
        • False Refusal
        • Context Relevancy
        • Context Precision
        • Context Recall
        • PII Detection
        • Toxicity
      • Chat Metrics
        • Agent Quality
        • Instruction Adherence
        • User Chat Quality
      • Text-to-SQL
        • SQL Response Correctness
        • SQL Prompt Ambiguity
        • SQL Context Ambiguity
        • SQL Context Sufficiency
        • SQL Prompt Injection
      • Text Summarization
        • Summary Consistency
        • Summary Relevance
        • Summary Fluency
        • Summary Coherence
        • SummaC
        • QAG Score
        • ROUGE
        • BLEU
        • METEOR
        • BERTScore
      • Information Extraction
        • MINEA
        • Subjective Question Correction
        • Precision@K
        • Chunk Relevance
        • Entity Co-occurrence
        • Fact Entropy
      • Code Generation
        • Functional Correctness
        • ChrF
        • Ruby
        • CodeBLEU
        • Robust Pass@k
        • Robust Drop@k
        • Pass-Ratio@n
      • Marketing Content Evaluation
        • Engagement Score
        • Misattribution
        • Readability
        • Topic Coverage
        • Fabrication
      • Learning Management System
        • Topic Coverage
        • Topic Redundancy
        • Question Redundancy
        • Answer Correctness
        • Source Citability
        • Difficulty Level
      • Additional Metrics
        • Guardrails
          • Anonymize
          • Deanonymize
          • Ban Competitors
          • Ban Substrings
          • Ban Topics
          • Code
          • Invisible Text
          • Language
          • Secret
          • Sentiment
          • Factual Consistency
          • Language Same
          • No Refusal
          • Reading Time
          • Sensitive
          • URL Reachability
          • JSON Verify
        • Vulnerability Scanner
          • Bullying
          • Deadnaming
          • SexualContent
          • Sexualisation
          • SlurUsage
          • Profanity
          • QuackMedicine
          • DAN 11
          • DAN 10
          • DAN 9
          • DAN 8
          • DAN 7
          • DAN 6_2
          • DAN 6_0
          • DUDE
          • STAN
          • DAN_JailBreak
          • AntiDAN
          • ChatGPT_Developer_Mode_v2
          • ChatGPT_Developer_Mode_RANTI
          • ChatGPT_Image_Markdown
          • Ablation_Dan_11_0
          • Anthropomorphisation
      • Guardrails
        • Competitor Check
        • Gibberish Check
        • PII
        • Regex Check
        • Response Evaluator
        • Toxicity
        • Unusual Prompt
        • Ban List
        • Detect Drug
        • Detect Redundancy
        • Detect Secrets
        • Financial Tone Check
        • Has Url
        • HTML Sanitisation
        • Live URL
        • Logic Check
        • Politeness Check
        • Profanity Check
        • Quote Price
        • Restrict Topics
        • SQL Predicates Guard
        • Valid CSV
        • Valid JSON
        • Valid Python
        • Valid Range
        • Valid SQL
        • Valid URL
        • Cosine Similarity
        • Honesty Detection
        • Toxicity Hate Speech
    • Prompt Playground
      • Concepts
      • Single-Prompt Playground
      • Multiple Prompt Playground
      • Run Evaluations
      • Using Prompt Slugs with Python SDK
      • Create with AI using Prompt Wizard
      • Prompt Diff View
    • Synthetic Data Generation
    • Gateway
      • Quickstart
    • Guardrails
      • Quickstart
      • Python SDK
    • RagaAI Whitepapers
      • RagaAI RLEF (RAG LLM Evaluation Framework)
    • Agentic Testing
      • Quickstart
      • Concepts
        • Tracing
          • Langgraph (Agentic Tracing)
          • RagaAI Catalyst Tracing Guide for Azure OpenAI Users
        • Dynamic Tracing
        • Application Workflow
      • Create New Dataset
      • Metrics
        • Hallucination
        • Toxicity
        • Honesty
        • Cosine Similarity
      • Compare Traces
      • Compare Experiments
      • Add metrics locally
    • Custom Metric
    • Auto Prompt Optimization
    • Human Feedback & Annotations
      • Thumbs Up/Down
      • Add Metric Corrections
      • Corrections as Few-Shot Examples
      • Tagging
    • On-Premise Deployment
      • Enterprise Deployment Guide for AWS
      • Enterprise Deployment Guide for Azure
      • Evaluation Deployment Guide
        • Evaluation Maintenance Guide
    • Fine Tuning (OpenAI)
    • Integration
    • SDK Release Notes
      • ragaai-catalyst 2.1.7
  • RagaAI Prism
    • Quickstart
    • Sandbox Guide
      • Object Detection
      • LLM Summarization
      • Semantic Segmentation
      • Tabular Data
      • Super Resolution
      • OCR
      • Image Classification
      • Event Detection
    • Test Inventory
      • Object Detection
        • Failure Mode Analysis
        • Model Comparison Test
        • Drift Detection
        • Outlier Detection
        • Data Leakage Test
        • Labelling Quality Test
        • Scenario Imbalance
        • Class Imbalance
        • Active Learning
        • Image Property Drift Detection
      • Large Language Model (LLM)
        • Failure Mode Analysis
      • Semantic Segmentation
        • Failure Mode Analysis
        • Labelling Quality Test
        • Active Learning
        • Drift Detection
        • Class Imbalance
        • Scenario Imbalance
        • Data Leakage Test
        • Outlier Detection
        • Label Drift
        • Semantic Similarity
        • Near Duplicates Detection
        • Cluster Imbalance Test
        • Image Property Drift Detection
        • Spatio-Temporal Drift Detection
        • Spatio-Temporal Failure Mode Analysis
      • Tabular Data
        • Failure Mode Analysis
      • Instance Segmentation
        • Failure Mode Analysis
        • Labelling Quality Test
        • Drift Detection
        • Class Imbalance
        • Scenario Imbalance
        • Label Drift
        • Data Leakage Test
        • Outlier Detection
        • Active Learning
        • Near Duplicates Detection
      • Super Resolution
        • Semantic Similarity
        • Active Learning
        • Near Duplicates Detection
        • Outlier Detection
      • OCR
        • Missing Value Test
        • Outlier Detection
      • Image Classification
        • Failure Mode Analysis
        • Labelling Quality Test
        • Class Imbalance
        • Drift Detection
        • Near Duplicates Test
        • Data Leakage Test
        • Outlier Detection
        • Active Learning
        • Image Property Drift Detection
      • Event Detection
        • Failure Mode Analysis
        • A/B Test
    • Metric Glossary
    • Upload custom model
    • Event Detection
      • Upload Model
      • Generate Inference
      • Run tests
    • On-Premise Deployment
      • Enterprise Deployment Guide for AWS
      • Enterprise Deployment Guide for Azure
  • Support
Powered by GitBook
On this page
  • Steps to generate Synthetic Dataset:
  • Steps to generate data using SDK:

Was this helpful?

  1. RagaAI Catalyst

Synthetic Data Generation

Use LLMs to generate numerous synthetic prompts; currently supported via SDK only.

PreviousPrompt Diff ViewNextGateway

Last updated 5 months ago

Was this helpful?

Exclusive to enterprise customers. to activate this feature.

RagaAI offers a powerful Synthetic Data Generation feature, designed to streamline and enhance the process of building and evaluating large language models (LLMs). This feature enables users to generate use-case-specific golden datasets tailored to their applications by leveraging advanced techniques and a given context document.

The system can generate synthetic data for various applications, such as chatbot development, customer service automation, document summarisation, or code generation.

Models Supported:

  • Groq

  • Gemini

  • OpenAI

Documents Supported:

  • PDF

  • Text

  • Markdown

  • CSV

Question Types Supported:

  • Simple

  • MCQ

  • Complex

Steps to generate Synthetic Dataset:

  1. Inside a Project, select "generate synthetic data" option

  1. Use a unique dataset name, upload relevant context documents, configure question types, select the LLM model (ensuring the context stays within the model's token limit), specify the desired number of rows, and generate the dataset.

  1. The generated dataset will appear under the "Dataset" tab with the assigned name.

Steps to generate data using SDK:

from ragaai_catalyst import SyntheticDataGeneration
synthetic_data_generation = SyntheticDataGeneration()

# Provide your context file
text_file = "your-context-file-path"
text = synthetic_data_generation.process_document(input_data=text_file)

# For simple questions
result1 = synthetic_data_generation.generate_qna(text, question_type ='simple',model_config={"provider":"gemini","model":"gemini-1.5-flash","api_base":"your-api-base"},n=20)
# For complex questions
result2 = synthetic_data_generation.generate_qna(text, question_type ='complex',model_config={"provider":"gemini","model":"gemini-1.5-flash","api_base":"your-api-base"},n=20)
# For MCQ questions
result3 = synthetic_data_generation.generate_qna(text, question_type ='mcq',model_config={"provider":"gemini","model":"gemini-1.5-flash","api_base":"your-api-base"},n=20)

print(result1.head())

This feature provides a critical advantage by reducing the manual effort required to create and test datasets, speeding up the development and evaluation cycle for LLMs, and ensuring that the datasets are specifically aligned with the user’s goals.

Contact us