Enable azure openai engines (#20)

Love the library!

I had to make modifications to get it to work with the Azure instances
of OpenAI models that I use.

Primarily, I made it possible to pass an `engine` parameter to the
LLMClassifier as well/instead of the `model` parameter [which is what
the Azure instance
requires](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions).

I tried to do it in a way that minimized the # of edits on the existing
codebase - if this is something you'd be interested in including, happy
to make any changes to fit with your design principles.

```
import openai
openai.api_type = "azure"
openai.api_version = "2023-07-01-preview"
openai.api_base = "https://azure-openai-xxxxx.openai.azure.com/"  # Your Azure OpenAI resource's endpoint value.
openai.api_key = "<AZURE-OPENAI_API-KEY>"
evaluator = Factuality(engine="<Azure-Deployment-Name>", model=None)
```

---------

Co-authored-by: Ankur Goyal <ankrgyl@gmail.com>

py/autoevals/llm.py

@@ -78,18 +78,11 @@ def __init__(
         render_args=None,
         max_tokens=None,
         temperature=None,
+        engine=None,
     ):
-        found = False
-        for m in SUPPORTED_MODELS:
-            # Prefixes are ok, because they are just time snapshots
-            if model.startswith(m):
-                found = True
-                break
-        if not found:
-            raise ValueError(f"Unsupported model: {model}. Currently only supports OpenAI chat models.")
-
         self.name = name
         self.model = model
+        self.engine = engine
         self.messages = messages
         self.choice_scores = choice_scores
         self.classification_functions = classification_functions
@@ -136,6 +129,7 @@ def _request_args(self, output, expected, **kwargs):
         return dict(
             Completion=openai.ChatCompletion,
             model=self.model,
+            engine=self.engine,
             messages=self._render_messages(output=output, expected=expected, **kwargs),
             functions=self.classification_functions,
             function_call={"name": "select_choice"},
@@ -176,6 +170,7 @@ class ModelGradedSpec:
     prompt: str
     choice_scores: dict[str, float]
     model: Optional[str] = None
+    engine: Optional[str] = None
     use_cot: Optional[bool] = None
     temperature: Optional[float] = None
 
@@ -195,6 +190,7 @@ def __init__(
         use_cot=True,
         max_tokens=512,
         temperature=0,
+        engine=None,
     ):
         choice_strings = list(choice_scores.keys())
 
@@ -214,6 +210,7 @@ def __init__(
             classification_functions=build_classification_functions(use_cot),
             max_tokens=max_tokens,
             temperature=temperature,
+            engine=engine,
             render_args={"__choices": choice_strings},
         )
 
@@ -229,10 +226,12 @@ def from_spec_file(cls, name: str, path: str, **kwargs):
 
 
 class SpecFileClassifier(LLMClassifier):
-    def __new__(cls, model=None, use_cot=None, max_tokens=None, temperature=None):
+    def __new__(cls, model=None, engine=None, use_cot=None, max_tokens=None, temperature=None):
         kwargs = {}
         if model is not None:
             kwargs["model"] = model
+        if engine is not None:
+            kwargs["engine"] = engine
         if use_cot is not None:
             kwargs["use_cot"] = use_cot
         if max_tokens is not None: