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* Add Langfuse-based LLM observability * Document Langfuse configuration * Don't hardcode model in use
38 lines
1.2 KiB
Markdown
38 lines
1.2 KiB
Markdown
# Langfuse
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This app can send traces of all LLM interactions to [Langfuse](https://langfuse.com) for debugging and usage analytics. Find them here [on
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GitHub](https://github.com/langfuse/langfuse) and look at their [Open
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Source statement](https://langfuse.com/open-source).
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## Prerequisites
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1. Create a Langfuse project (self‑hosted or using their cloud offering).
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2. Copy the **public key** and **secret key** from the project's settings.
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## Configuration
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Set the following environment variables for the Rails app:
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```bash
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LANGFUSE_PUBLIC_KEY=your_public_key
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LANGFUSE_SECRET_KEY=your_secret_key
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# Optional if self‑hosting or using a non‑default domain
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LANGFUSE_HOST=https://your-langfuse-domain.com
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```
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In Docker setups, add the variables to `compose.yml` and the accompanying `.env` file.
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The initializer reads these values on boot and automatically enables tracing. If the keys are absent, the app runs normally without Langfuse.
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## What Gets Tracked
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* `chat_response`
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* `auto_categorize`
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* `auto_detect_merchants`
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Each call records the prompt, model, response, and token usage when available.
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## Viewing Traces
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After starting the app with the variables set, visit your Langfuse dashboard to see traces and generations grouped under the `openai.*` traces.
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