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README.md

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# LangSmith Cookbook
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This repository stores code tutorials showing different ways to get more out of [LangSmith](https://smith.langchain.com/). LangSmith is a platform that helps you debug, test, evaluate, and monitor your LLM applications.
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This repository is your practical guide to maximizing [LangSmith](https://smith.langchain.com/). As a tool, LangSmith empowers you to debug, evaluate, test, and improve your LLM applications continuously. These recipes dive deeper than the [standard documentation](https://docs.smith.langchain.com/), presenting real-world scenarios for you to adapt and implement.
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These cookbook recipes are meant to complement the [LangSmith Documentation](https://docs.smith.langchain.com/) by showing common use cases and tactics within "end-to-end" examples, which you can take and adapt to your needs.
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If you have any specific requests or common patterns you'd like to see highlighted, create a GitHub issue or let one of the core LangChain devs know. We also welcome contributions!
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**Your Input Matters**
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We're always evolving. If there's a specific use case or pattern you want to see or if you'd like to contribute, raise a GitHub issue or reach out to the LangChain development team. Your feedback helps everyone!
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## Tracing your code
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exploratory-data-analysis/README.md

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# Exploratory Data Analysis
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Once you've logged your trace data, there is a wealth of insight you can glean from analyzing the logs. Use them for fine-tuning, to build better eval suites, to drive product insights, and more. The following are some example notebooks showing how you can export the data to other tools for analysis.
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- The [Lilac](./exploratory-data-analysis/lilac/lilac.ipynb) notebook demonstrates how you can enrich a dataset by defining custom patterns, check for PII, and performing near-duplicate detection using the open-source unstructured data analytics tool, [Lilac](https://github.com/lilacai/lilac).
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- The [Lilac](./lilac/lilac.ipynb) notebook demonstrates how you can enrich a dataset by defining custom patterns, check for PII, and performing near-duplicate detection using the open-source unstructured data analytics tool, [Lilac](https://github.com/lilacai/lilac).

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