Add Python usage docs

site/src/docs/python/basic-usage.md

@@ -0,0 +1,246 @@
+---
+layout: doc-page
+title: Basic Python Usage
+---
+
+TypeChat is currently a small library, so we can get a solid understanding just by going through the following example:
+
+```py
+import asyncio
+
+import sys
+import schema as sentiment
+from typechat import Failure, TypeChatJsonTranslator, TypeChatValidator, create_language_model, process_requests
+
+async def main():
+env_vals = dotenv_values()
+
+# Create a model.
+model = create_language_model(env_vals)
+
+# Create a validator
+validator = TypeChatValidator(sentiment.Sentiment)
+
+# Create a translator.
+translator = TypeChatJsonTranslator(model, validator, sentiment.Sentiment)
+
+async def request_handler(message: str):
+    result = await translator.translate(message)
+    if isinstance(result, Failure):
+        print(result.message)
+    else:
+        result = result.value
+        print(f"The sentiment is {result.sentiment}")
+
+# Process requests interactively or from the input file specified on the command line.
+file_path = sys.argv[1] if len(sys.argv) == 2 else None
+await process_requests("😀> ", file_path, request_handler)
+```
+
+Let's break it down step-by-step.
+
+## Providing a Model
+
+TypeChat can be used with any language model.
+As long as you have a class with the following shape...
+
+```py
+class TypeChatLanguageModel(Protocol):
+
+    async def complete(self, prompt: str | list[PromptSection]) -> Result[str]:
+        """
+        Represents a AI language model that can complete prompts.
+
+        TypeChat uses an implementation of this protocol to communicate
+        with an AI service that can translate natural language requests to JSON
+        instances according to a provided schema.
+        The `create_language_model` function can create an instance.
+        """
+        ...
+```
+
+then you should be able to try TypeChat out with such a model.
+
+The key thing here is providing a `complete` method.
+`complete` is just a function that takes a `string` and eventually returns a `string` if all goes well.
+
+For convenience, TypeChat provides two functions out of the box to connect to the OpenAI API and Azure's OpenAI Services.
+You can call these directly.
+
+```py
+def create_openai_language_model(
+    api_key: str,
+    model: str,
+    endpoint: str = "https://api.openai.com/v1/chat/completions",
+    org: str = ""
+):
+    ...
+
+def create_azure_openai_language_model(api_key: str, endpoint: str):
+```
+
+For even more convenience, TypeChat also provides a function to infer whether you're using OpenAI or Azure OpenAI.
+
+```ts
+def create_language_model(vals: dict[str, str | None]) -> TypeChatLanguageModel:
+```
+
+With `create_language_model`, you can populate your environment variables and pass them in.
+Based on whether `OPENAI_API_KEY` or `AZURE_OPENAI_API_KEY` is set, you'll get a model of the appropriate type.
+
+The `TypeChatLanguageModel` returned by these functions has a few attributes you might find useful:
+
+- `max_retry_attempts`
+- `retry_pause_seconds`
+- `timeout_seconds`
+
+Though note that these are unstable.
+
+Regardless, of how you decide to construct your model, it is important to avoid committing credentials directly in source.
+One way to make this work between production and development environments is to use a `.env` file in development, and specify that `.env` in your `.gitignore`.
+You can use a library like [`python-dotenv`](https://pypi.org/project/python-dotenv/) to help load these up.
+
+```py
+from dotenv import load_dotenv
+load_dotenv()
+
+// ...
+
+import typechat
+model = typechat.create_language_model(os.environ)
+```
+
+## Defining and Loading the Schema
+
+TypeChat describes types to language models to help guide their responses.
+To do so, all we have to do is define either a [`@dataclass`](https://docs.python.org/3/library/dataclasses.html) or a [`TypedDict`](https://typing.readthedocs.io/en/latest/spec/typeddict.html) class to describe the response we're expecting.
+Here's what our schema file `schema.py` look like:
+
+```py
+from dataclasses import dataclass
+from typing import Literal
+
+@dataclass
+class Sentiment:
+    """
+    The following is a schema definition for determining the sentiment of a some user input.
+    """
+
+    sentiment: Literal["negative", "neutral", "positive"]
+```
+
+Here, we're saying that the `sentiment` attribute has to be one of three possible strings: `negative`, `neutral`, or `positive`.
+We did this with [the `typing.Literal` hint](https://docs.python.org/3/library/typing.html#typing.Literal).
+
+We defined `Sentiment` as a `@dataclass` so we could have all of the conveniences of standard Python objects - for example, to access the `sentiment` attribute, we can just write `value.sentiment`.
+If we declared `Sentiment` as a `TypedDict`, TypeChat would provide us with a `dict`.
+That would mean that to access the value of `sentiment`, we would have to write `value["sentiment"]`.
+
+Note that while we used [the built-in `typing` module](https://docs.python.org/3/library/typing.html), [`typing_extensions`](https://pypi.org/project/typing-extensions/) is supported as well.
+TypeChat also understands constructs like `Annotated` and `Doc` to add comments to individual attributes.
+
+## Creating a Validator
+
+A validator really has two jobs generating a textual schema for language models, and making sure any data fits a given shape.
+The built-in validator looks roughly like this:
+
+```py
+class TypeChatValidator(Generic[T]):
+    """
+    Validates an object against a given Python type.
+    """
+
+    def __init__(self, py_type: type[T]):
+        """
+        Args:
+
+            py_type: The schema type to validate against.
+        """
+        ...
+
+    def validate_object(self, obj: object) -> Result[T]:
+        """
+        Validates the given Python object according to the associated schema type.
+
+        Returns a `Success[T]` object containing the object if validation was successful.
+        Otherwise, returns a `Failure` object with a `message` property describing the error.
+        """
+        ...
+```
+
+To construct a validator, we just have to pass in the type we defined:
+
+```py
+import schema as sentiment
+validator = TypeChatValidator(sentiment.Sentiment)
+```
+
+## Creating a JSON Translator
+
+A `TypeChatJsonTranslator` brings all these concepts together.
+A translator takes a language model, a validator, and our expected type, and provides a way to translate some user input into objects following our schema.
+To do so, it crafts a prompt based on the schema, reaches out to the model, parses out JSON data, and attempts validation.
+Optionally, it will craft repair prompts and retry if validation fails.
+
+```py
+translator = TypeChatJsonTranslator(model, validator, sentiment.Sentiment)
+```
+
+When we are ready to translate a user request, we can call the `translate` method.
+
+```ts
+translator.translate("Hello world! 🙂");
+```
+
+We'll come back to this.
+
+## Creating the Prompt
+
+TypeChat exports a `process_requests` function that makes it easy to experiment with TypeChat.
+Depending on its second argument, it either creates an interactive command line prompt (if given `None`), or reads lines in from the given a file path.
+
+```ts
+async def request_handler(message: str):
+    ...
+
+file_path = sys.argv[1] if len(sys.argv) == 2 else None
+await process_requests("😀> ", file_path, request_handler)
+```
+
+`process_requests` takes 3 things.
+First, there's the prompt prefix - this is what a user will see before their own text in interactive scenarios.
+You can make this playful.
+We like to use emoji here. 😄
+
+Next, we take a text file name.
+Input strings will be read from this file on a per-line basis.
+If the file name was `None`, `process_requests` will work on standard input and provide an interactive prompt.
+By checking `sys.argv`, our script makes our program interactive unless the person running the program provided an input file as a command line argument (e.g. `python ./example.py inputFile.txt`).
+
+Finally, there's the request handler.
+We'll fill that in next.
+
+## Translating Requests
+
+Our handler receives some user input (the `message` string) each time it's called.
+It's time to pass that string into over to our `translator` object.
+
+```ts
+async def request_handler(message: str):
+    result = await translator.translate(message)
+    if isinstance(result, Failure):
+        print(result.message)
+    else:
+        result = result.value
+        print(f"The sentiment is {result.sentiment}")
+```
+
+We're calling the `translate` method on each string and getting a response.
+If something goes wrong, TypeChat will retry requests up to a maximum specified by `retry_max_attempts` on our `model`.
+However, if the initial request as well as all retries fail, `result` will be a `typechat.Failure` and we'll be able to grab a `message` explaining what went wrong.
+
+In the ideal case, `result` will be a `typechat.Success` and we'll be able to access our well-typed `value` property!
+This will correspond to the type that we passed in when we created our translator object (i.e. `Sentiment`).
+
+That's it!
+You should now have a basic idea of TypeChat's APIs and how to get started with a new project. 🎉

site/src/docs/typescript/basic-usage.md

@@ -3,7 +3,7 @@ layout: doc-page
 title: Basic TypeScript Usage
 ---
 
-TypeChat is currently a small library, so let's take a look at some basic usage to understand it.
+TypeChat is currently a small library, so we can get a solid understanding just by understanding the following example:
 
 ```ts
 import fs from "fs";
@@ -34,6 +34,8 @@ processRequests("😀> ", process.argv[2], async (request) => {
 });
 ```
 
+Let's break it down step-by-step.
+
 ## Providing a Model
 
 TypeChat can be used with any language model.
@@ -77,19 +79,25 @@ For even more convenience, TypeChat also provides a function to infer whether yo
 export function createLanguageModel(env: Record<string, string | undefined>): TypeChatLanguageModel
 ```
 
-You can populate your environment variables, and based on whether `OPENAI_API_KEY` or `AZURE_OPENAI_API_KEY` is set, you'll get a model of the appropriate type.
+With `createLanguageModel`, you can populate your environment variables and pass them in.
+Based on whether `OPENAI_API_KEY` or `AZURE_OPENAI_API_KEY` is set, you'll get a model of the appropriate type.
+
+Regardless, of how you decide to construct your model, it is important to avoid committing credentials directly in source.
+One way to make this work between production and development environments is to use a `.env` file in development, and specify that `.env` in your `.gitignore`.
+You can use a library like [`dotenv`](https://www.npmjs.com/package/dotenv) to help load these up.
 
 ```ts
 import dotenv from "dotenv";
 dotenv.config(/*...*/);
+
+// ...
+
 import * as typechat from "typechat";
 const model = typechat.createLanguageModel(process.env);
 ```
 
-Regardless, of how you decide to construct your model, we recommend keeping your secret tokens/API keys in a `.env` file, and specifying `.env` in a `.gitignore`.
-You can use a library like [`dotenv`](https://www.npmjs.com/package/dotenv) to help load these up.
 
-## Loading the Schema
+## Defining and Loading the Schema
 
 TypeChat describes types to language models to help guide their responses.
 In this case, we are using a `TypeScriptJsonValidator` which uses the TypeScript compiler to validate data against a set of types.
@@ -243,9 +251,9 @@ A `TypeChatJsonTranslator` brings these together.
 import { createJsonTranslator } from "typechat";
 ```
 
-A translator takes both a model and a validator, and provides a way to translate some user input into objects within our schema.
+A translator takes both a model and a validator, and provides a way to translate some user input into objects following our schema.
 To do so, it crafts a prompt based on the schema, reaches out to the model, parses out JSON data, and attempts validation.
-Optionally, it will craft repair prompts and retry if validation failed..
+Optionally, it will craft repair prompts and retry if validation fails.
 
 ```ts
 const translator = createJsonTranslator(model, validator);