Update Users section of the docs

docs/source/users/index.md

@@ -155,110 +155,38 @@ in the SageMaker documentation.
 To use SageMaker's models, you will need to authenticate via
 [boto3](https://github.com/boto/boto3).
 
-For example, to use OpenAI models, install the necessary package, and set an environment
-variable when you start JupyterLab from a terminal:
+For example, to use OpenAI models, use the chat interface settings panel to choose the OpenAI language model:
 
-```bash
-pip install openai
-OPENAI_API_KEY=your-api-key-here jupyter lab
-```
-
-:::{attention}
-:name: open-ai-cost
-Model providers may charge users for API usage. Jupyter AI users are
-responsible for all charges they incur when they make API requests. Review your model
-provider's pricing information before submitting requests via Jupyter AI.
-:::
-
-### Custom model providers
-
-You can define new providers using the LangChain framework API. Custom providers
-inherit from both `jupyter-ai`'s ``BaseProvider`` and `langchain`'s [``LLM``][LLM].
-You can either import a pre-defined model from [LangChain LLM list][langchain_llms],
-or define a [custom LLM][custom_llm].
-In the example below, we define a provider with two models using
-a dummy ``FakeListLLM`` model, which returns responses from the ``responses``
-keyword argument.
-
-```python
-# my_package/my_provider.py
-from jupyter_ai_magics import BaseProvider
-from langchain.llms import FakeListLLM
-
-
-class MyProvider(BaseProvider, FakeListLLM):
-    id = "my_provider"
-    name = "My Provider"
-    model_id_key = "model"
-    models = [
-        "model_a",
-        "model_b"
-    ]
-    def __init__(self, **kwargs):
-        model = kwargs.get("model_id")
-        kwargs["responses"] = (
-            ["This is a response from model 'a'"]
-            if model == "model_a" else
-            ["This is a response from model 'b'"]
-        )
-        super().__init__(**kwargs)
-```
-
-
-If the new provider inherits from [``BaseChatModel``][BaseChatModel], it will be available
-both in the chat UI and with magic commands. Otherwise, users can only use the new provider
-with magic commands.
-
-To make the new provider available, you need to declare it as an [entry point](https://setuptools.pypa.io/en/latest/userguide/entry_point.html):
+<img src="../_static/chat-settings-choose-language-model.png"
+    alt="Screen shot of the chat settings interface with language model dropdown open"
+    class="screenshot" />
 
-```toml
-# my_package/pyproject.toml
-[project]
-name = "my_package"
-version = "0.0.1"
+Then, enter your API key in the 'API Keys' section:
 
-[project.entry-points."jupyter_ai.model_providers"]
-my-provider = "my_provider:MyProvider"
-```
+<img src="../_static/chat-settings-enter-api-key.png"
+    alt="Screen shot of the API key input in the chat settings interface"
+    class="screenshot" />
 
-To test that the above minimal provider package works, install it with:
+Alternatively, to set the API key through a config file, first determine your data directory path by running the following command in your terminal:
 
-```sh
-# from `my_package` directory
-pip install -e .
+```bash
+echo "$(jupyter --data-dir)/jupyter_ai/config.json"
 ```
 
-Then, restart JupyterLab. You should now see an info message in the log that mentions
-your new provider's `id`:
+Then, add your API key to `config.json`:
 
-```
-[I 2023-10-29 13:56:16.915 AiExtension] Registered model provider `my_provider`.
+```json
+"api_keys": {
+    "OPENAI_API_KEY": "your-api-key-here"
+}
 ```
 
-[langchain_llms]: https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.llms
-[custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm
-[LLM]: https://api.python.langchain.com/en/latest/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM
-[BaseChatModel]: https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.base.BaseChatModel.html
-
-
-### Customizing prompt templates
-
-To modify the prompt template for a given format, override the ``get_prompt_template`` method:
-
-```python
-from langchain.prompts import PromptTemplate
-
-
-class MyProvider(BaseProvider, FakeListLLM):
-    # (... properties as above ...)
-    def get_prompt_template(self, format) -> PromptTemplate:
-        if format === "code":
-            return PromptTemplate.from_template(
-                "{prompt}\n\nProduce output as source code only, "
-                "with no text or explanation before or after it."
-            )
-        return super().get_prompt_template(format)
-```
+:::{attention}
+:name: open-ai-cost
+Model providers may charge users for API usage. Jupyter AI users are
+responsible for all charges they incur when they make API requests. Review your model
+provider's pricing information before submitting requests via Jupyter AI.
+:::
 
 ## The chat interface
 
@@ -1028,3 +956,93 @@ data:
 runtime:
     /Users/3coins/Library/Jupyter/runtime
 ```
+
+## Custom model providers
+
+You can define new providers using the LangChain framework API. Custom providers
+inherit from both `jupyter-ai`'s ``BaseProvider`` and `langchain`'s [``LLM``][LLM].
+You can either import a pre-defined model from [LangChain LLM list][langchain_llms],
+or define a [custom LLM][custom_llm].
+In the example below, we define a provider with two models using
+a dummy ``FakeListLLM`` model, which returns responses from the ``responses``
+keyword argument.
+
+```python
+# my_package/my_provider.py
+from jupyter_ai_magics import BaseProvider
+from langchain.llms import FakeListLLM
+
+
+class MyProvider(BaseProvider, FakeListLLM):
+    id = "my_provider"
+    name = "My Provider"
+    model_id_key = "model"
+    models = [
+        "model_a",
+        "model_b"
+    ]
+    def __init__(self, **kwargs):
+        model = kwargs.get("model_id")
+        kwargs["responses"] = (
+            ["This is a response from model 'a'"]
+            if model == "model_a" else
+            ["This is a response from model 'b'"]
+        )
+        super().__init__(**kwargs)
+```
+
+
+If the new provider inherits from [``BaseChatModel``][BaseChatModel], it will be available
+both in the chat UI and with magic commands. Otherwise, users can only use the new provider
+with magic commands.
+
+To make the new provider available, you need to declare it as an [entry point](https://setuptools.pypa.io/en/latest/userguide/entry_point.html):
+
+```toml
+# my_package/pyproject.toml
+[project]
+name = "my_package"
+version = "0.0.1"
+
+[project.entry-points."jupyter_ai.model_providers"]
+my-provider = "my_provider:MyProvider"
+```
+
+To test that the above minimal provider package works, install it with:
+
+```sh
+# from `my_package` directory
+pip install -e .
+```
+
+Then, restart JupyterLab. You should now see an info message in the log that mentions
+your new provider's `id`:
+
+```
+[I 2023-10-29 13:56:16.915 AiExtension] Registered model provider `my_provider`.
+```
+
+[langchain_llms]: https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.llms
+[custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm
+[LLM]: https://api.python.langchain.com/en/latest/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM
+[BaseChatModel]: https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.base.BaseChatModel.html
+
+
+### Customizing prompt templates
+
+To modify the prompt template for a given format, override the ``get_prompt_template`` method:
+
+```python
+from langchain.prompts import PromptTemplate
+
+
+class MyProvider(BaseProvider, FakeListLLM):
+    # (... properties as above ...)
+    def get_prompt_template(self, format) -> PromptTemplate:
+        if format === "code":
+            return PromptTemplate.from_template(
+                "{prompt}\n\nProduce output as source code only, "
+                "with no text or explanation before or after it."
+            )
+        return super().get_prompt_template(format)
+```