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Update langchain doc (#480)
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docs/source/user_guide/large_language_model/deploy_langchain_application.rst

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@@ -63,13 +63,16 @@ Prepare the Model Artifacts
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Call ``prepare`` from ``ChainDeployment`` to generate the ``score.py`` and serialize the LangChain application to ``chain.yaml`` file under ``artifact_dir`` folder.
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Parameters ``inference_conda_env`` and ``inference_python_version`` are passed to define the conda environment where your LangChain application will be running on OCI cloud.
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Here we're using ``pytorch21_p39_gpu_v1`` with python 3.9.
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Here, replace ``custom_conda_environment_uri`` with your conda environment uri that has the latest ADS 2.9.1 and replace ``python_version`` with your conda environment python version.
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.. note::
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For how to customize and publish conda environment, take reference to `Publishing a Conda Environment to an Object Storage Bucket <https://docs.oracle.com/en-us/iaas/data-science/using/conda_publishs_object.htm>`_
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.. code-block:: python3
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chain_deployment.prepare(
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inference_conda_env="pytorch21_p39_gpu_v1",
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inference_python_version="3.9",
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inference_conda_env="<custom_conda_environment_uri>",
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inference_python_version="<python_version>",
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)
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Below is the ``chain.yaml`` file that was saved from ``llm_chain`` object. For more information regarding LLMs model serialization, see `here <https://python.langchain.com/docs/modules/model_io/llms/llm_serialization>`_.

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