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  • 🚀 Get Started
    • Installation
    • Generate a Synthetic Test Set
    • Evaluating Using Your Test Set
    • Monitor Your RAG in Production
  • 📚 Core Concepts
    • Metrics-Driven Development
    • Metrics
      • Faithfulness
      • Answer Relevance
      • Context Precision
      • Context utilization
      • Context Recall
      • Context entities recall
      • Noise Sensitivity
      • Answer semantic similarity
      • Answer Correctness
      • Aspect Critique
      • Domain Specific Evaluation
      • Summarization Score
    • Prompt Objects
    • Automatic prompt Adaptation
    • Synthetic Test Data generation
    • Utilizing User Feedback
  • 🛠️ How-to Guides
    • Customizations
      • Bring Your Own LLMs and Embeddings
      • Using Ragas Critic Model instead of GPT-4
      • Max Workers, Timeouts, Retries and more with RunConfig
      • Using Azure OpenAI
      • Using Amazon Bedrock
      • Vertex AI
    • Applications
      • Building HF Dataset with your own Data
      • Understand Cost and Usage of Operations
      • Compare Embeddings for retriever
      • Compare LLMs using Ragas Evaluations
      • Write custom prompts with ragas
      • Automatic language adaptation
      • Explainability through Logging and tracing
      • Adding to your CI pipeline with Pytest
    • Integrations
      • LlamaIndex
      • Langchain
      • Langsmith
      • Phoenix (Arize)
      • Langfuse
      • Athina AI
      • Zeno
      • Tonic Validate
      • Haystack
      • OpenLayer
      • Helicone
  • 📖 References
    • Evaluation
    • Metrics
    • RunConfig
    • Integrations
  • ❤️ Community
Ragas
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🛠️ How-to Guides
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Applications

Applications¶

Ragas in action. Examples of how to use Ragas in various applications and usecases to solve problems you might encounter when your building.

Applications

  • Building HF Dataset with your own Data
    • Example dataset
  • Understand Cost and Usage of Operations
    • Understanding TokenUsageParser
  • Compare Embeddings for retriever
    • Create synthetic test data
    • Build your RAG
    • Import metrics from ragas
    • Evaluate OpenAI embeddings
    • Evaluate Bge embeddings
    • Compare Scores
  • Compare LLMs using Ragas Evaluations
    • Create synthetic test data
    • Build your RAG
    • Import metrics from ragas
    • Evaluate Zephyr 7B Alpha LLM
    • Evaluate Falcon-7B-Instruct LLM
    • Compare Scores
      • Score distribution analysis
  • Write custom prompts with ragas
  • Automatic language adaptation
    • Language Adaptation for Metrics
      • Dataset
      • Adapt metrics to target language
      • Evaluate
    • Language Adaptation for Testset Generation
      • Documents
      • Import and adapt evolutions
      • Generate dataset
  • Explainability through Logging and tracing
  • Adding to your CI pipeline with Pytest
    • Using Pytest Markers for Ragas E2E tests
Vertex AI
Building HF Dataset with your own Data

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