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Integrate LangFuse for Observability

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Document AI: Summarize & Extract from Enterprise Content

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Linked challenge
Document AI: Summarize & Extract from Enterprise Content

Prompt source

Original prompt text with formatting preserved for inspection.

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Integrate LangFuse into your LlamaIndex RAG pipeline to monitor performance. Configure `LangfuseCallbackHandler` and ensure all LLM calls, embedding calls, and retrieval steps are traced. Explain how you would use LangFuse to debug common RAG issues like low recall or hallucination by inspecting traces. Provide Python code snippets showing the LangFuse integration setup, e.g., `Settings.callback_manager = CallbackManager([LangfuseCallbackHandler(...)])`.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.