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### What problem does this PR solve? update for v0.19.0 ### Type of change - [x] Documentation Update
73 lines
2.7 KiB
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73 lines
2.7 KiB
Plaintext
---
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sidebar_position: 9
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slug: /tracing
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---
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# Tracing
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Observability & Tracing with Langfuse.
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---
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:::info KUDOS
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This document is contributed by our community contributor [jannikmaierhoefer](https://github.com/jannikmaierhoefer). 👏
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:::
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RAGFlow ships with a built-in [Langfuse](https://langfuse.com) integration so that you can **inspect and debug every retrieval and generation step** of your RAG pipelines in near real-time.
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Langfuse stores traces, spans and prompt payloads in a purpose-built observability backend and offers filtering and visualisations on top.
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:::info NOTE
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• RAGFlow **≥ 0.19.0** (contains the Langfuse connector)
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• A Langfuse workspace (cloud or self-hosted) with a _Project Public Key_ and _Secret Key_
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:::
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---
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## 1. Collect your Langfuse credentials
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1. Sign in to your Langfuse dashboard.
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2. Open **Settings ▸ Projects** and either create a new project or select an existing one.
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3. Copy the **Public Key** and **Secret Key**.
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4. Note the Langfuse **host** (e.g. `https://cloud.langfuse.com`). Use the base URL of your own installation if you self-host.
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> The keys are _project-scoped_: one pair of keys is enough for all environments that should write into the same project.
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---
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## 2. Add the keys to RAGFlow
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RAGFlow stores the credentials _per tenant_. You can configure them either via the web UI or the HTTP API.
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1. Log in to RAGFlow and click your avatar in the top-right corner.
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2. Select **API ▸ Scroll down to the bottom ▸ Langfuse Configuration**.
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3. Fill in you Langfuse **Host**, **Public Key** and **Secret Key**.
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4. Click **Save**.
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Once saved, RAGFlow starts emitting traces automatically – no code change required.
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---
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## 3. Run a pipeline and watch the traces
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1. Execute any chat or retrieval pipeline in RAGFlow (e.g. the Quickstart demo).
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2. Open your Langfuse project ▸ **Traces**.
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3. Filter by **name ~ `ragflow-*`** (RAGFlow prefixes each trace with `ragflow-`).
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For every user request you will see:
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• a **trace** representing the overall request
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• **spans** for retrieval, ranking and generation steps
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• the complete **prompts**, **retrieved documents** and **LLM responses** as metadata
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([Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/0bde9629-4251-4386-b583-26101b8e7561?timestamp=2025-05-09T19%3A15%3A37.797Z&display=details&observation=823997d8-ac40-40f3-8e7b-8aa6753b499e))
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:::tip NOTE
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Use Langfuse's diff view to compare prompt versions or drill down into long-running retrievals to identify bottlenecks.
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:::
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