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(docs) langfuse callback
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import Image from '@theme/IdealImage';
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# LangFuse Tutorial
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# Langfuse - Logging LLM Input/Output
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LangFuse is open Source Observability & Analytics for LLM Apps
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Detailed production traces and a granular view on quality, cost and latency
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liteLLM provides `callbacks`, making it easy for you to log data depending on the status of your responses.
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## Pre-Requisites
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Ensure you have run `pip install langfuse` for this integration
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```shell
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pip install litellm langfuse
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pip install langfuse litellm
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```
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## Using Callbacks
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## Quick Start
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```python
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# pip install langfuse
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import litellm
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import os
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# from https://cloud.langfuse.com/
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os.environ["LANGFUSE_PUBLIC_KEY"] = ""
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os.environ["LANGFUSE_SECRET_KEY"] = ""
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os.environ['OPENAI_API_KEY']=""
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# set langfuse as a callback, litellm will send the data to langfuse
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litellm.success_callback = ["langfuse"]
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# openai call
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "user", "content": "Hi 👋 - i'm openai"}
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]
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)
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```
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<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/logging_observability/LiteLLM_Langfuse.ipynb">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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