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* fix: nightly now properly gets 1.9.0 branch (#12215) before it was attempting to pull release-notes as letters are alphanumerically after numbers when we sort -V then grab tail now we only look at branch names that follow the pattern '^release-[0-9]+\.[0-9]+\.[0-9]+$' * docs: add search icon (#12216) add-back-svg * initial-content * cut-1.8-release-and-include-next-version * stage-1.8.0-and-next --------- Co-authored-by: Adam-Aghili <149833988+Adam-Aghili@users.noreply.github.com>
108 lines
4.1 KiB
Plaintext
108 lines
4.1 KiB
Plaintext
---
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title: Arize
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slug: /integrations-arize
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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Arize is a tool built on [OpenTelemetry](https://opentelemetry.io/) and [OpenInference](https://docs.arize.com/phoenix/reference/open-inference) for monitoring and optimizing LLM applications.
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To enable Arize tracing, set the required Arize environment variables in your Langflow deployment.
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Arize begins monitoring and collecting telemetry data from your LLM applications automatically.
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:::tip
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Instructions for integrating Langflow and Arize are also available in the Arize documentation:
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* [Langflow tracing with Arize Platform](https://arize.com/docs/ax/integrations/frameworks-and-platforms/langflow/langflow-tracing)
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* [Langflow tracing with Arize Phoenix](https://arize.com/docs/phoenix/integrations/langflow/langflow-tracing)
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:::
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## Prerequisites
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* If you are using the [standard Arize platform](https://docs.arize.com/arize), you need an **Arize Space ID** and **Arize API Key**.
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* If you are using the open-source [Arize Phoenix platform](https://docs.arize.com/phoenix), you need an **Arize Phoenix API key**.
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## Connect Arize to Langflow
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<Tabs>
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<TabItem value="platform" label="Arize Platform" default>
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1. In your [Arize dashboard](https://app.arize.com/), copy your **Space ID** and [**API Key (Ingestion Service Account Key)**](https://arize.com/docs/ax/security-and-settings/api-keys).
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2. In the root of your Langflow application, edit your existing Langflow `.env` file or create a new one.
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3. Add `ARIZE_SPACE_ID` and `ARIZE_API_KEY` environment variables:
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```bash
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ARIZE_SPACE_ID=SPACE_ID
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ARIZE_API_KEY=API_KEY
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```
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Replace `SPACE_ID` and `API_KEY` with the values you copied from the Arize platform.
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You don't need to specify the Arize project name if you're using the standard Arize platform.
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4. Start your Langflow application with your `.env` file:
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```bash
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uv run langflow run --env-file .env
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```
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</TabItem>
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<TabItem value="phoenix" label="Arize Phoenix">
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1. In your [Arize Phoenix dashboard](https://app.phoenix.arize.com/), copy your **API Key**.
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2. In the root of your Langflow application, edit your existing Langflow `.env` file or create a new one.
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3. Add a `PHOENIX_API_KEY` environment variable:
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```bash
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PHOENIX_API_KEY=API_KEY
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```
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Replace `API_KEY` with the Arize Phoenix API key that you copied from the Arize Phoenix platform.
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4. Start your Langflow application with your `.env` file:
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```bash
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uv run langflow run --env-file .env
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```
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</TabItem>
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</Tabs>
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## Run a flow and view metrics in Arize
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1. In Langflow, run a flow that has an LLM-driven component, such as an **Agent** component or any language model component.
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You must chat with the flow or trigger the LLM to produce traffic for Arize to trace.
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For example, you can create a flow with the **Simple Agent** template, add your OpenAI API key to the **Agent** component, and then click **Playground** to chat with the flow and generate traffic.
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2. In Arize, open your project dashboard, and then wait for Arize to process the data.
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This can take a few minutes.
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3. To view metrics for your flows, go to the **LLM Tracing** tab.
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Each Langflow execution generates two traces in Arize:
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* The `AgentExecutor` trace is the Arize trace of LangChain's `AgentExecutor`.
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* The `UUID` trace is the trace of the Langflow components.
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4. To view traces, go to the **Traces** tab.
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A _trace_ is the complete journey of a request, made of multiple _spans_.
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5. To view spans, go to the **Spans** tab.
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A _span_ is a single operation within a trace.
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For example, a _span_ could be a single API call to OpenAI or a single function call to a custom tool.
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For information about tracing metrics in Arize, see the [Arize LLM tracing documentation](https://docs.arize.com/arize/llm-tracing/tracing).
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6. To add a span to a [dataset](https://docs.arize.com/arize/llm-datasets-and-experiments/datasets-and-experiments), click **Add to Dataset**.
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All metrics on the **LLM Tracing** tab can be added to datasets.
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7. To view a dataset, click the **Datasets** tab, and then select your dataset. |