1 files changed, 0 insertions, 195 deletions
diff --git a/autogpt/llm/base.py b/autogpt/llm/base.py
deleted file mode 100644
index 14a146b3c..000000000
--- a/autogpt/llm/base.py
+++ /dev/null
@@ -1,195 +0,0 @@
-from __future__ import annotations
-
-from copy import deepcopy
-from dataclasses import dataclass, field
-from math import ceil, floor
-from typing import TYPE_CHECKING, Literal, Optional, Type, TypedDict, TypeVar, overload
-
-if TYPE_CHECKING:
-    from autogpt.llm.providers.openai import OpenAIFunctionCall
-
-MessageRole = Literal["system", "user", "assistant", "function"]
-MessageType = Literal["ai_response", "action_result"]
-
-TText = list[int]
-"""Token array representing tokenized text"""
-
-
-class MessageDict(TypedDict):
-    role: MessageRole
-    content: str
-
-
-class ResponseMessageDict(TypedDict):
-    role: Literal["assistant"]
-    content: Optional[str]
-    function_call: Optional[FunctionCallDict]
-
-
-class FunctionCallDict(TypedDict):
-    name: str
-    arguments: str
-
-
-@dataclass
-class Message:
-    """OpenAI Message object containing a role and the message content"""
-
-    role: MessageRole
-    content: str
-    type: MessageType | None = None
-
-    def raw(self) -> MessageDict:
-        return {"role": self.role, "content": self.content}
-
-
-@dataclass
-class ModelInfo:
-    """Struct for model information.
-
-    Would be lovely to eventually get this directly from APIs, but needs to be scraped from
-    websites for now.
-    """
-
-    name: str
-    max_tokens: int
-    prompt_token_cost: float
-
-
-@dataclass
-class CompletionModelInfo(ModelInfo):
-    """Struct for generic completion model information."""
-
-    completion_token_cost: float
-
-
-@dataclass
-class ChatModelInfo(CompletionModelInfo):
-    """Struct for chat model information."""
-
-
-@dataclass
-class TextModelInfo(CompletionModelInfo):
-    """Struct for text completion model information."""
-
-
-@dataclass
-class EmbeddingModelInfo(ModelInfo):
-    """Struct for embedding model information."""
-
-    embedding_dimensions: int
-
-
-# Can be replaced by Self in Python 3.11
-TChatSequence = TypeVar("TChatSequence", bound="ChatSequence")
-
-
-@dataclass
-class ChatSequence:
-    """Utility container for a chat sequence"""
-
-    model: ChatModelInfo
-    messages: list[Message] = field(default_factory=list[Message])
-
-    @overload
-    def __getitem__(self, key: int) -> Message:
-        ...
-
-    @overload
-    def __getitem__(self: TChatSequence, key: slice) -> TChatSequence:
-        ...
-
-    def __getitem__(self: TChatSequence, key: int | slice) -> Message | TChatSequence:
-        if isinstance(key, slice):
-            copy = deepcopy(self)
-            copy.messages = self.messages[key]
-            return copy
-        return self.messages[key]
-
-    def __iter__(self):
-        return iter(self.messages)
-
-    def __len__(self):
-        return len(self.messages)
-
-    def add(
-        self,
-        message_role: MessageRole,
-        content: str,
-        type: MessageType | None = None,
-    ) -> None:
-        self.append(Message(message_role, content, type))
-
-    def append(self, message: Message):
-        return self.messages.append(message)
-
-    def extend(self, messages: list[Message] | ChatSequence):
-        return self.messages.extend(messages)
-
-    def insert(self, index: int, *messages: Message):
-        for message in reversed(messages):
-            self.messages.insert(index, message)
-
-    @classmethod
-    def for_model(
-        cls: Type[TChatSequence],
-        model_name: str,
-        messages: list[Message] | ChatSequence = [],
-        **kwargs,
-    ) -> TChatSequence:
-        from autogpt.llm.providers.openai import OPEN_AI_CHAT_MODELS
-
-        if not model_name in OPEN_AI_CHAT_MODELS:
-            raise ValueError(f"Unknown chat model '{model_name}'")
-
-        return cls(
-            model=OPEN_AI_CHAT_MODELS[model_name], messages=list(messages), **kwargs
-        )
-
-    @property
-    def token_length(self) -> int:
-        from autogpt.llm.utils import count_message_tokens
-
-        return count_message_tokens(self.messages, self.model.name)
-
-    def raw(self) -> list[MessageDict]:
-        return [m.raw() for m in self.messages]
-
-    def dump(self) -> str:
-        SEPARATOR_LENGTH = 42
-
-        def separator(text: str):
-            half_sep_len = (SEPARATOR_LENGTH - 2 - len(text)) / 2
-            return f"{floor(half_sep_len)*'-'} {text.upper()} {ceil(half_sep_len)*'-'}"
-
-        formatted_messages = "\n".join(
-            [f"{separator(m.role)}\n{m.content}" for m in self.messages]
-        )
-        return f"""
-============== {__class__.__name__} ==============
-Length: {self.token_length} tokens; {len(self.messages)} messages
-{formatted_messages}
-==========================================
-"""
-
-
-@dataclass
-class LLMResponse:
-    """Standard response struct for a response from an LLM model."""
-
-    model_info: ModelInfo
-
-
-@dataclass
-class EmbeddingModelResponse(LLMResponse):
-    """Standard response struct for a response from an embedding model."""
-
-    embedding: list[float] = field(default_factory=list)
-
-
-@dataclass
-class ChatModelResponse(LLMResponse):
-    """Standard response struct for a response from a chat LLM."""
-
-    content: Optional[str]
-    function_call: Optional[OpenAIFunctionCall]