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feat(ai-monitoring): Langchain integration docs #9799
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--- | ||
title: Langchain | ||
description: "Learn about using Sentry for Langchain." | ||
--- | ||
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This integration connects Sentry with [Langchain](https://github.com/langchain-ai/langchain). | ||
The integration has been confirmed to work with Langchain 0.1.11. | ||
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## Install | ||
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Install `sentry-sdk` from PyPI and the appropriate langchain packages: | ||
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```bash | ||
pip install --upgrade 'sentry-sdk' 'langchain-openai' 'langchain-core' | ||
``` | ||
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## Configure | ||
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If you have the `langchain` package in your dependencies, the Langchain integration will be enabled automatically when you initialize the Sentry SDK. | ||
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An additional dependency, `tiktoken`, is required to be installed if you want to calculate token usage for streaming chat responses. | ||
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<SignInNote /> | ||
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```python | ||
from langchain_openai import ChatOpenAI | ||
import sentry_sdk | ||
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sentry_sdk.init( | ||
dsn="___PUBLIC_DSN___", | ||
enable_tracing=True, | ||
traces_sample_rate=1.0, | ||
send_default_pii=True, # send personally-identifiable information like LLM responses to sentry | ||
) | ||
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) | ||
``` | ||
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## Verify | ||
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Verify that the integration works by inducing an error: | ||
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```python | ||
from langchain_openai import ChatOpenAI | ||
import sentry_sdk | ||
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sentry_sdk.init(...) # same as above | ||
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0, api_key="bad API key") | ||
with sentry_sdk.start_transaction(op="ai-inference", name="The result of the AI inference"): | ||
response = llm.invoke([("system", "What is the capital of paris?")]) | ||
print(response) | ||
``` | ||
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After running this script, a transaction will be created in the Performance section of [sentry.io](https://sentry.io). Additionally, an error event (about the bad API key) will be sent to [sentry.io](https://sentry.io) and will be connected to the transaction. | ||
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It may take a couple of moments for the data to appear in [sentry.io](https://sentry.io). | ||
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## Behavior | ||
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- The Langchain integration will connect Sentry with Langchain and automatically monitor all LLM, tool, and function calls. | ||
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- All exceptions in the execution of the chain are reported. | ||
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- Sentry considers LLM and tokenizer inputs/outputs as PII and, by default, does not include PII data. If you want include the data, then set `send_default_pii=True` in the `sentry_sdk.init()` call. To explicitly exclude prompts and outputs despite `send_default_pii=True`, configure the integration with `include_prompts=False` as shown in the [Options section](#options) below. | ||
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## Options | ||
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By adding `LangchainIntegration` to your `sentry_sdk.init()` call explicitly, you can set options for `LangchainIntegration` to change its behavior: | ||
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```python | ||
import sentry_sdk | ||
from sentry_sdk.integrations.langchain import LangchainIntegration | ||
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sentry_sdk.init( | ||
dsn="___PUBLIC_DSN___", | ||
enable_tracing=True, | ||
send_default_pii=True, | ||
traces_sample_rate=1.0, | ||
integrations = [ | ||
LangchainIntegration( | ||
include_prompts=False, # LLM/tokenizer inputs/outputs will be not sent to Sentry, despite send_default_pii=True | ||
), | ||
], | ||
) | ||
``` | ||
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## Supported Versions | ||
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- Langchain: 0.1.11+ | ||
- tiktoken: 0.6.0+ | ||
- Python: 3.9+ |
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