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  • Overview
    • Overview
    • Evaluation of Rag with Ragas
    • Evaluation with Langchain
    • Evaluation with Uptrain
    • Migrating Data from One Litefuse Project to Another
    • Example Decorator Openai Langchain
    • Evaluating Multi-Turn Conversations
    • Example - Tracing and Evaluation for the OpenAI-Agents SDK
    • External Evaluation Pipelines
    • Guide - Building an intent classification pipeline
    • Example - Trace and Evaluate LangGraph Agents
    • Example Llm Security Monitoring
    • Example Multi Modal Traces
    • Agent Evaluation - How to Evaluate LLM Agents
    • Query Data in Litefuse via the SDK
    • Evaluating Multi-Turn Conversations (Simulation)
    • Synthetic Datasets
    • Amazon Bedrock
    • Anthropic (Python)
    • Integration Azure Openai Langchain
    • Databricks
    • Integration Langchain
    • Open Source Observability for LangGraph
    • Langserve
    • Cookbook - LiteLLM (Proxy) + Litefuse OpenAI Integration + Python Decorator
    • Integration Llama Index Callback
    • Integration Llama Index Instrumentation
    • Integration Llama Index Milvus Lite
    • LlamaIndex
    • Monitoring LlamaIndex applications with PostHog and Litefuse
    • LlamaIndex Workflows
    • OpenAI Assistants API
    • Cookbook - OpenAI Integration (Python)
    • Observe OpenAI Structured Outputs with Litefuse
    • Anthropic (JS/TS)
    • Langchain Integration (JS/TS)
    • LiteLLM (Proxy) + Litefuse OpenAI Integration (JS/TS)
    • OpenAI Integration (JS/TS)
    • JS/TS SDK Example
    • Prompt Management with Langchain (JS)
    • Langfuse SDK Performance Test
    • Tracing using the OpenInference SDK
    • MLflow Integration via OpenTelemetry
    • OpenLIT Integration via OpenTelemetry
    • Otel Integration Openllmetry
    • Example - Litefuse Prompt Management with Langchain (Python)
    • Prompt Management Openai Functions
    • Prompt Management Performance Benchmark
    • Overview
    • Beginner's Guide to RAG Evaluation with Litefuse and Ragas
    • External Evaluation Pipelines
    • Introducing Datasets v2
    • Introducing Litefuse 2.0
    • Introducing the observe() decorator for Python
    • LLM-as-a-Judge Evaluators for Dataset Experiments
    • LLM Playground
    • Posthog Integration
    • Run Litefuse Locally in 3 Minutes
    • Webinar: Traceability and Observability in Multi-Step LLM Systems
GuidesCookbooksOverview

Litefuse Cookbook

You can find the ipynb files in the cookbook directory of the repository.

OverviewEvaluation of Rag with Ragas
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