Langfuse LogoLangfuse Logo
DocsGuidesFAQPricingChangelogBlog
Discord
GitHub
Sign Up
  • Docs
    Guides
    FAQ
  • Overview
    • Python Cookbook
    • Evaluation of Rag with Ragas
    • Evaluation with Langchain
    • Evaluation with Uptrain
    • Example Decorator Openai Langchain
    • External Evaluation Pipelines
    • Example Intent Classification
    • Example Python
    • Example Multi Modal Traces
    • Observability and Metrics for Amazon Bedrock
    • Integration Azure Openai Langchain
    • Dspy
    • Integration Haystack
    • Observability & Tracing for Instructor
    • Integration Langchain
    • Open Source Observability for LangGraph
    • Integration Langserve
    • Integration Litellm Proxy
    • Integration Llama Index
    • Integration Llama Index Instrumentation
    • Integration Llama Index Milvus Lite
    • Monitoring LlamaIndex applications with PostHog and Langfuse
    • Integration Mirascope
    • Mistral Sdk
    • Ollama Observability and Tracing for local LLMs using Langfuse
    • OSS Observability for OpenAI Assistants API
    • Integration Openai Sdk
    • Observe OpenAI Structured Outputs with Langfuse
    • JS Integration Langchain
    • JS Integration Litellm Proxy
    • JS Integration Openai
    • Example Langchain JS
    • JS Tracing Example Vercel Ai Sdk
    • Langfuse Sdk Performance Test
    • Example Langchain
    • Example Openai Functions
    • Python Decorators
    • Python SDK (Low-level)
    • Beginner's Guide to RAG Evaluation with Langfuse and Ragas
    • External Evaluation Pipelines
    • Haystack Integration
    • Introducing Datasets v2
    • Introducing Langfuse 2.0
    • Introducing the observe() decorator for Python
    • LlamaIndex Integration
    • LLM-as-a-Judge Evaluators for Dataset Experiments
    • LLM Playground
    • Posthog Integration
    • Webinar: Traceability and Observability in Multi-Step LLM Systems
Question? Give us feedback →Edit this page on GitHub
GuidesVideosLlamaIndex Integration

Tutorial: LlamaIndex Integration

LlamaIndex Integration

Based on the LlamaIndex Cookbook.

Introducing the observe() decorator for PythonLLM-as-a-Judge Evaluators for Dataset Experiments

Was this page useful?

Questions? We're here to help

GitHub Q&AGitHubEmailTalk to sales

Subscribe to updates

Platform

  • LLM Tracing
  • Prompt Management
  • Evaluation
  • Manual Annotation
  • Datasets
  • Metrics
  • Playground

Integrations

  • Python SDK
  • JS/TS SDK
  • OpenAI SDK
  • Langchain
  • Llama-Index
  • Litellm
  • Dify
  • Flowise
  • Langflow
  • Vercel AI SDK
  • Instructor
  • Mirascope
  • API

Resources

  • Documentation
  • Interactive Demo
  • Video demo (3 min)
  • Changelog
  • Roadmap
  • Pricing
  • Enterprise
  • Self-hosting
  • Open Source
  • Why Langfuse?
  • Status
  • 🇯🇵 Japanese
  • 🇰🇷 Korean
  • 🇨🇳 Chinese

About

  • Blog
  • Careers2
  • About us
  • Support
  • Schedule Demo
  • OSS Friends
  • Twitter
  • LinkedIn

Legal

  • Security
  • Imprint
  • Terms
  • Privacy
© 2022-2024 Langfuse GmbH / Finto Technologies Inc.