forked from Archives/langchain
Update Graphsignal ecosystem page (#2662)
Added/updated information due to new automatic data recording feature.
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# Graphsignal
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# Graphsignal
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This page covers how to use the Graphsignal ecosystem to trace and monitor LangChain.
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This page covers how to use [Graphsignal](https://app.graphsignal.com) to trace and monitor LangChain. Graphsignal enables full visibility into your application. It provides latency breakdowns by chains and tools, exceptions with full context, data monitoring, compute/GPU utilization, OpenAI cost analytics, and more.
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## Installation and Setup
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## Installation and Setup
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## Tracing and Monitoring
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## Tracing and Monitoring
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Graphsignal automatically instruments and starts tracing and monitoring chains. Traces, metrics and errors are then available in your [Graphsignal dashboard](https://app.graphsignal.com/). No prompts or other sensitive data are sent to Graphsignal cloud, only statistics and metadata.
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Graphsignal automatically instruments and starts tracing and monitoring chains. Traces and metrics are then available in your [Graphsignal dashboards](https://app.graphsignal.com).
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Initialize the tracer by providing a deployment name:
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Initialize the tracer by providing a deployment name:
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@ -20,7 +20,13 @@ import graphsignal
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graphsignal.configure(deployment='my-langchain-app-prod')
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graphsignal.configure(deployment='my-langchain-app-prod')
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```
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```
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In order to trace full runs and see a breakdown by chains and tools, you can wrap the calling routine or use a decorator:
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To additionally trace any function or code, you can use a decorator or a context manager:
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```python
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@graphsignal.trace_function
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def handle_request():
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chain.run("some initial text")
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```
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```python
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```python
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with graphsignal.start_trace('my-chain'):
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with graphsignal.start_trace('my-chain'):
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