remove is_chat argument

evoprompt/models.py

-import functools
+import hashlib
 import inspect
+import json
 import logging
+import warnings
 from abc import ABC, abstractmethod
 from argparse import ArgumentParser, Namespace
 from pathlib import Path
 from typing import Any, Callable, ClassVar
-import warnings
 
 import llama_cpp
 import openai
@@ -22,7 +23,6 @@ warnings.simplefilter("once")
 
 class LLMModel(ABC):
     models: ClassVar[dict[str, type["LLMModel"]]] = {}
-    chat: bool
 
     def __init_subclass__(cls) -> None:
         if inspect.isabstract(cls):
@@ -36,25 +36,33 @@ class LLMModel(ABC):
             raise ValueError("Model %s does not exist", name)
         return cls.models[name](options=options, **kwargs)
 
-    @functools.lru_cache
-    def _compute_cache_key(self, name, **kwargs):
-        # we use a tuple of the model name, the options, and the kwargs as the cache key
-        return (name,) + tuple((key, value) for key, value in kwargs.items())
-
     def __init__(self, options: Namespace, **kwargs):
         self.usage = ModelUsage()
-        self.chat = options.chat
 
         # store kwargs for caching
         self.options = options
         self.kwargs = kwargs
 
         # set up caching for model calls
+        self._call_model_cached = None
         if not options.disable_cache:
             cache = Cache(Path(".cache_dir", self.model_cache_key))
-            self._call_model_cached = cache.memoize(typed=True, ignore=[0, "func"])(
-                self._call_model_cached
-            )
+
+            @cache.memoize(typed=True, ignore=["func"])
+            def _call_function(func, *args, **kwargs):
+                return func(*args, **kwargs)
+
+            self._call_model_cached = _call_function
+
+    @abstractmethod
+    def build_model_input(
+        self,
+        prompt: str,
+        system_message: str | None,
+        messages: list[dict[str, str]],
+        history: list[dict[str, str]] | None = None,
+    ):
+        pass
 
     def create_completion(
         self,
@@ -65,42 +73,18 @@ class LLMModel(ABC):
         prompt_appendix: str = "",
         prompt_prefix: str = "",
         prompt_suffix: str = "",
-        chat: bool | None = None,
         stop: str = None,
-        history: dict = None,
+        history: list[dict[str, str]] | None = None,
         **kwargs: Any,
     ) -> tuple[str, ModelUsage]:
-        if chat is None:
-            chat = self.chat
-        max_tokens = kwargs.pop("max_tokens", self.options.max_tokens)
-
         # create prompt
         prompt = prompt_prefix + prompt + prompt_suffix + prompt_appendix
-
-        if not chat and system_message:
-            prompt = system_message + prompt
-
         messages = [self._get_user_message(prompt)]
-
-        if chat:
-            # a history is prepended to the messages, and we assume that it also includes a system message, i.e., we never add a system message in this case
-            # TODO is it better to check for a system message in the history?
-            if history is not None:
-                messages = history + messages
-            elif system_message:
-                messages.insert(
-                    0,
-                    self._get_system_message(system_message),
-                )
-            model_input = {"messages": messages}
-        else:
-            model_input = {"prompt": prompt}
+        model_input = self.build_model_input(prompt, system_message, messages, history)
 
         reponse, usage = self._create_completion(
-            chat=chat,
             **model_input,
             stop=stop,
-            max_tokens=max_tokens,
             use_cache=use_cache,
             **kwargs,
         )
@@ -134,18 +118,26 @@ class LLMModel(ABC):
             warnings.warn("Caching is disabled when a grammar is provided.")
             use_cache = False
 
-        if use_cache:
-            # use cached function call
-            cache_key = self._compute_cache_key(
-                model_completion_fn.__name__, **self.options.__dict__, **self.kwargs
-            )
-            return self._call_model_cached(model_completion_fn, cache_key, **kwargs)
+        if use_cache and self._call_model_cached is not None:
+            return self._call_model_cached(model_completion_fn, **kwargs)
         else:
             return model_completion_fn(**kwargs)
 
-    def _call_model_cached(self, func, cache_key, *args, **kwargs):
-        # `cache_key` is added to the cache key (e.g., to distinguish between different models), but it is not used in the function
-        return func(*args, **kwargs)
+    @property
+    def model_cache_key(self):
+        unique_options_key = json.dumps(
+            vars(self.options),
+            sort_keys=True,
+        ) + json.dumps(
+            self.kwargs,
+            sort_keys=True,
+        )
+        cache_key = (
+            str(self.model_name).replace("/", "_")
+            + "/"
+            + hashlib.sha1(unique_options_key.encode()).hexdigest()
+        )
+        return cache_key
 
     @classmethod
     @abstractmethod
@@ -205,34 +197,32 @@ class Llama(LLMModel):
         # needs to be called after model is initialized
         super().__init__(options=options, n_ctx=n_ctx, **kwargs)
 
+    def build_model_input(
+        self,
+        prompt: str,
+        system_message: str | None,
+        messages: list[dict[str, str]],
+        history: list[dict[str, str]] | None = None,
+    ):
+        if system_message is not None:
+            prompt = system_message + prompt
+        return {"prompt": prompt}
+
     def _create_completion(
         self,
-        chat: bool,
         use_cache: bool = False,
         **kwargs,
     ):
-        if chat:
-            response = self._call_model(
-                self.model.create_chat_completion,
-                use_cache=use_cache,
-                **kwargs,
-            )
-            response_text = response["choices"][0]["message"]["content"]
-        else:
-            response = self._call_model(
-                self.model.create_completion,
-                use_cache=use_cache,
-                **kwargs,
-            )
-            response_text = response["choices"][0]["text"]
+        response = self._call_model(
+            self.model.create_completion,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        response_text = response["choices"][0]["text"]
 
         usage = ModelUsage(**response["usage"])
         return response_text, usage
 
-    @property
-    def model_cache_key(self):
-        return self.model_name
-
     @classmethod
     def register_arguments(cls, parser: ArgumentParser):
         group = parser.add_argument_group(f"{cls.__name__} model arguments")
@@ -272,6 +262,43 @@ class Llama(LLMModel):
         )
 
 
+class LlamaChat(Llama):
+
+    def _create_completion(
+        self,
+        use_cache: bool = False,
+        **kwargs,
+    ):
+        response = self._call_model(
+            self.model.create_chat_completion,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        response_text = response["choices"][0]["message"]["content"]
+
+        usage = ModelUsage(**response["usage"])
+        return response_text, usage
+
+    def build_model_input(
+        self,
+        prompt: str,
+        system_message: str | None,
+        messages: list[dict[str, str]],
+        history: list[dict[str, str]] | None = None,
+    ):
+
+        # a history is prepended to the messages, and we assume that it also includes a system message, i.e., we never add a system message in this case
+        # TODO is it better to check for a system message in the history?
+        if history is not None:
+            [messages.insert(index, entry) for index, entry in enumerate(history)]
+        elif system_message:
+            messages.insert(
+                0,
+                self._get_system_message(system_message),
+            )
+        return {"messages": messages}
+
+
 class OpenAI(LLMModel):
     """Queries an OpenAI model using its API."""
 
@@ -288,33 +315,33 @@ class OpenAI(LLMModel):
 
     def _create_completion(
         self,
-        chat: bool,
         use_cache: bool = False,
         **kwargs,
     ):
-        if chat:
-            response = self._call_model(
-                self.openai_client.chat.completions.create,
-                model=self.model_name,
-                use_cache=use_cache,
-                **kwargs,
-            )
-            response_text = response.choices[0].message.content
-        else:
-            response = self._call_model(
-                self.openai_client.completions.create,
-                model=self.model,
-                use_cache=use_cache,
-                **kwargs,
-            )
-            response_text = response.choices[0].text
+        response = self._call_model(
+            self.openai_client.completions.create,
+            model=self.model,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        response_text = response.choices[0].text
 
         usage = ModelUsage(**response.usage.__dict__)
         return response_text, usage
 
-    @property
-    def model_cache_key(self):
-        return self.model_name
+    def build_model_input(
+        self,
+        prompt: str,
+        system_message: str | None,
+        messages: list[dict[str, str]],
+        history: list[dict[str, str]] | None = None,
+    ):
+        return {
+            "prompt": prompt,
+            "system_message": system_message,
+            "messages": messages,
+            "history": history,
+        }
 
     @classmethod
     def register_arguments(cls, parser: ArgumentParser):
@@ -322,6 +349,24 @@ class OpenAI(LLMModel):
         group.add_argument("--openai-model", "-m", type=str, default="gpt-3.5-turbo")
 
 
+class OpenAiChat(OpenAI):
+
+    def _create_completion(
+        self,
+        use_cache: bool = False,
+        **kwargs,
+    ):
+        response = self._call_model(
+            self.openai_client.chat.completions.create,
+            model=self.model_name,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        response_text = response.choices[0].message.content
+        usage = ModelUsage(**response.usage.__dict__)
+        return response_text, usage
+
+
 argument_group = argument_parser.add_argument_group("Model arguments")
 argument_group.add_argument(
     "--evolution-engine",
@@ -339,4 +384,3 @@ argument_group.add_argument(
     type=int,
     help="Maximum number of tokens being generated from LLM. ",
 )
-argument_group.add_argument("--chat", "-c", action="store_true")