Added doc for deploying langchain application.

Author: lu-ohai

docs/source/index.rst

@@ -68,6 +68,7 @@ Oracle Accelerated Data Science (ADS)
    user_guide/logs/logs
    user_guide/pipeline/index
    user_guide/secrets/index
+   user_guide/langchain/deploy
 
 .. toctree::
    :hidden:

docs/source/user_guide/langchain/deploy.rst

@@ -0,0 +1,160 @@
+################################
+LangChain Application Deployment
+################################
+
+Oracle ADS SDK now supports the deployment of LangChain application to OCI data science model deployment and you can easily do so just by writing a couple lines of code.
+
+Installation
+************
+
+Before you start, install the ``oracle-ads >= 2.9.1`` package following the command below. 
+The Oracle ADS SDK has all of the required tools needed for the deployment.
+
+.. code-block:: shell
+
+    pip install oracle-ads —-upgrade
+
+Configuration
+*************
+
+Ensure that you have created the necessary `policies, authentication, and authorization for model deployments <https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth>`_. 
+Here we're using the ``resource_principal`` as auth type and you can configure the policy as below.
+
+.. code-block:: shell
+
+    allow dynamic-group <dynamic-group-name> to manage data-science-model-deployments in compartment <compartment-name>
+
+Set auth for ADS SDK to access the OCI data science model deployment resources.
+
+.. code-block:: python3
+
+    import ads
+    ads.set_auth(auth="resource_principal")
+
+Create LangChain Application
+****************************
+
+Create a simple LangChain application that links prompt and cohere model as below. Remember to replace the ``<cohere_api_key>`` with the actual cohere api key.
+
+.. code-block:: python3
+
+    import os
+    from langchain.llms import Cohere
+    from langchain.chains import LLMChain
+    from langchain.prompts import PromptTemplate
+    os.environ["COHERE_API_KEY"] = "<cohere_api_key>"
+    
+    cohere = Cohere()
+    prompt = PromptTemplate.from_template("Tell me a joke about {subject}")
+    llm_chain = LLMChain(prompt=prompt, llm=cohere, verbose=True)
+
+Now you have a LangChain object ``llm_chain``. Try running it with the prompt ``{"subject": "animals"}`` and it should give you the corresponding result.
+
+.. code-block:: python3
+
+    llm_chain.run({"subject": "animals"})
+
+Initialize the ChainDeployment
+******************************
+
+Initialize class ``ChainDeployment`` from ADS SDK and pass the LangChain object ``llm_chain`` from previous step as parameter.
+The ``artifact_dir`` is an optional parameter which points to the folder where the model artifacts will be put locally. 
+In this example, we're using a temporary folder generated by ``tempfile``.
+
+.. code-block:: python3
+
+    import tempfile
+    from ads.llm.chain import ChainDeployment
+    
+    artifact_dir = tempfile.mkdtemp()
+    
+    chain_deployment = ChainDeployment(
+        chain=llm_chain,
+        artifact_dir=artifact_dir
+    )
+
+Prepare the Model Artifacts
+***************************
+
+Call ``prepare`` from ``ChainDeployment`` to generate the ``score.py`` and serialize the LangChain application to ``chain.yaml`` file under ``artifact_dir`` folder. 
+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. 
+Here we're using ``pytorch21_p39_gpu_v1`` with python 3.9.
+
+.. code-block:: python3
+
+    chain_deployment.prepare(
+        inference_conda_env="pytorch21_p39_gpu_v1",
+        inference_python_version="3.9",
+    )
+
+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>`_.
+
+.. code-block:: YAML
+    
+    _type: llm_chain
+    llm:
+      _type: cohere
+      frequency_penalty: 0.0
+      k: 0
+      max_tokens: 256
+      model: null
+      p: 1
+      presence_penalty: 0.0
+      temperature: 0.75
+      truncate: null
+    llm_kwargs: {}
+    memory: null
+    metadata: null
+    output_key: text
+    output_parser:
+      _type: default
+    prompt:
+      _type: prompt
+      input_types: {}
+      input_variables:
+      - subject
+      output_parser: null
+      partial_variables: {}
+      template: Tell me a joke about {subject}
+      template_format: f-string
+      validate_template: false
+    return_final_only: true
+    tags: null
+    verbose: true
+
+Save Artifacts to OCI Model Catalog
+***********************************
+
+Call ``save`` to pack and upload the artifacts under ``artifact_dir`` to OCI data science model catalog. Once the artifacts are successfully uploaded, you should be able to see the id of the model.
+
+.. code-block:: python3
+
+    chain_deployment.save(display_name="LangChain Model")
+
+Deploy the Model
+****************
+
+Deploy the LangChain model from previous step by calling ``deploy``. Remember to replace the ``<cohere_api_key>`` with the actual cohere api key in the ``environment_variables``. 
+It usually takes a couple of minutes to deploy the model and you should see the model deployment in the output once the process completes.
+
+.. code-block:: python3
+
+    chain_deployment.deploy(
+        display_name="LangChain Model Deployment",
+        environment_variables={"COHERE_API_KEY":"<cohere_api_key>"},
+    )
+
+Invoke the Deployed Model
+*************************
+
+Now the OCI data science model deployment endpoint is ready and you can invoke it to ``tell a joke about animals``.
+
+.. code-block:: python3
+
+    chain_deployment.predict(data={"subject": "animals"})["prediction"]
+
+Alternatively, you can use OCI CLI to invoke the model deployment. Remember to replace the ``langchain_application_model_deployment_url`` with the actual model deployment url which you can find in the output from deploy step.
+
+.. code-block:: shell
+
+    oci raw-request --http-method POST --target-uri <langchain_application_model_deployment_url>/predict --request-body '{"subject": "animals"}' --auth resource_principal