Add LangChain integration page (#813)

docs/integrations/langchain.md

@@ -0,0 +1,69 @@
+---
+title: LangChain
+---
+
+:::tip
+If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+instructions.
+:::
+
+[LangChain](https://github.com/langchain-ai/langchain) is a popular framework for developing applications powered by 
+language models. You can integrate ClearML into your LangChain code using the built-in `ClearMLCallbackHandler`. This 
+class is used to create a ClearML Task to log LangChain assets and metrics. 
+
+Integrate ClearML with the following steps:
+1. Set up the `ClearMLCallbackHandler`. The following code creates a [ClearML Task](../fundamentals/task.md) called 
+   `llm` in the `langchain_callback_demo` project, which captures your script's information, including Git details, 
+   uncommitted code, and python environment: 
+
+   ```python
+   from langchain.callbacks import ClearMLCallbackHandler
+   from langchain_openai import OpenAI
+
+   # Set up and use the ClearML Callback
+   clearml_callback = ClearMLCallbackHandler(
+      task_type="inference",
+      project_name="langchain_callback_demo",
+      task_name="llm",
+      tags=["test"],
+      # Change the following parameters based on the amount of detail you want tracked
+      visualize=True,
+      complexity_metrics=True,
+      stream_logs=True,
+   )
+
+   llm = OpenAI(temperature=0, callbacks=[clearml_callback])
+   ```
+  
+  You can also pass the following parameters to the `ClearMLCallbackHandler` object:
+  * `task_type` – The type of ClearML task to create (see [task types](../fundamentals/task.md#task-types))
+  * `tags` – A list of tags to add to the task
+  * `visualize` - Set to `True` for ClearML to capture the run's Dependencies and Entities plots to the ClearML task
+  * `complexity_metrics` - Set to `True` to log complexity metrics
+  * `stream_logs` - Set to `True` to stream callback actions to ClearML Parameters.
+1. Use `ClearMLCallbackHandler.flush_tracker()` after each model request to make sure all outputs, including metrics and  
+   prompts, are logged to ClearML:
+
+   ```python
+   llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)
+
+   clearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential")
+   ```   
+
+   Specify the following parameters: 
+   * `name` - A string identifying a context for the logged information (Tip: Use different names to log different model 
+      conversations).
+   * `langchain_asset` - The LangChain asset to save. This can also be a LangChain agent, and ClearML will track its results.
+   * `finish` - Set to `True` to close the ClearML task after logging. If set to `True`, the Callback cannot be used anymore.
+
+## Additional Logging Options
+To augment its automatic logging, ClearML also provides an explicit logging interface.
+
+See more information about explicitly logging information to a ClearML Task:
+* [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
+* [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Scalars](../clearml_sdk/task_sdk.md#scalars) 
+* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
+
+See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).

sidebars.js

@@ -91,6 +91,7 @@ module.exports = {
                 {"HuggingFace": ['integrations/transformers', 'integrations/accelerate']},
                 'integrations/hydra', 'integrations/jsonargparse',
                 'integrations/keras', 'integrations/keras_tuner',
+                'integrations/langchain',
                 'integrations/lightgbm', 'integrations/matplotlib',
                 'integrations/megengine', 'integrations/monai', 'integrations/mmcv', 'integrations/tao',
                 'integrations/optuna',