Feature/llm data structs (#3486)

* Organize all the llm stuff into a subpackage

* Add structs for interacting with llms

autogpt/agent/agent_manager.py

@@ -4,9 +4,8 @@
 from typing import List
 
 from autogpt.config.config import Config
-from autogpt.llm import create_chat_completion
+from autogpt.llm import Message, create_chat_completion
 from autogpt.singleton import Singleton
-from autogpt.types.openai import Message
 
 
 class AgentManager(metaclass=Singleton):

autogpt/llm/__init__.py

@@ -1,4 +1,13 @@
 from autogpt.llm.api_manager import ApiManager
+from autogpt.llm.base import (
+    ChatModelInfo,
+    ChatModelResponse,
+    EmbeddingModelInfo,
+    EmbeddingModelResponse,
+    LLMResponse,
+    Message,
+    ModelInfo,
+)
 from autogpt.llm.chat import chat_with_ai, create_chat_message, generate_context
 from autogpt.llm.llm_utils import (
     call_ai_function,
@@ -10,6 +19,13 @@
 
 __all__ = [
     "ApiManager",
+    "Message",
+    "ModelInfo",
+    "ChatModelInfo",
+    "EmbeddingModelInfo",
+    "LLMResponse",
+    "ChatModelResponse",
+    "EmbeddingModelResponse",
     "create_chat_message",
     "generate_context",
     "chat_with_ai",

autogpt/llm/base.py

@@ -0,0 +1,65 @@
+from dataclasses import dataclass, field
+from typing import List, TypedDict
+
+
+class Message(TypedDict):
+    """OpenAI Message object containing a role and the message content"""
+
+    role: str
+    content: str
+
+
+@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
+    prompt_token_cost: float
+    completion_token_cost: float
+    max_tokens: int
+
+
+@dataclass
+class ChatModelInfo(ModelInfo):
+    """Struct for chat model information."""
+
+    pass
+
+
+@dataclass
+class EmbeddingModelInfo(ModelInfo):
+    """Struct for embedding model information."""
+
+    embedding_dimensions: int
+
+
+@dataclass
+class LLMResponse:
+    """Standard response struct for a response from an LLM model."""
+
+    model_info: ModelInfo
+    prompt_tokens_used: int = 0
+    completion_tokens_used: int = 0
+
+
+@dataclass
+class EmbeddingModelResponse(LLMResponse):
+    """Standard response struct for a response from an embedding model."""
+
+    embedding: List[float] = field(default_factory=list)
+
+    def __post_init__(self):
+        if self.completion_tokens_used:
+            raise ValueError("Embeddings should not have completion tokens used.")
+
+
+@dataclass
+class ChatModelResponse(LLMResponse):
+    """Standard response struct for a response from an LLM model."""
+
+    content: str = None

autogpt/llm/chat.py

@@ -5,13 +5,13 @@
 
 from autogpt.config import Config
 from autogpt.llm.api_manager import ApiManager
+from autogpt.llm.base import Message
 from autogpt.llm.llm_utils import create_chat_completion
 from autogpt.llm.token_counter import count_message_tokens
 from autogpt.logs import logger
 from autogpt.memory_management.store_memory import (
     save_memory_trimmed_from_context_window,
 )
-from autogpt.types.openai import Message
 
 cfg = Config()
 

autogpt/llm/llm_utils.py

@@ -10,8 +10,8 @@
 
 from autogpt.config import Config
 from autogpt.llm.api_manager import ApiManager
+from autogpt.llm.base import Message
 from autogpt.logs import logger
-from autogpt.types.openai import Message
 
 
 def retry_openai_api(

autogpt/llm/providers/openai.py

@@ -0,0 +1,37 @@
+from autogpt.llm.base import ChatModelInfo, EmbeddingModelInfo
+
+OPEN_AI_CHAT_MODELS = {
+    "gpt-3.5-turbo": ChatModelInfo(
+        name="gpt-3.5-turbo",
+        prompt_token_cost=0.002,
+        completion_token_cost=0.002,
+        max_tokens=4096,
+    ),
+    "gpt-4": ChatModelInfo(
+        name="gpt-4",
+        prompt_token_cost=0.03,
+        completion_token_cost=0.06,
+        max_tokens=8192,
+    ),
+    "gpt-4-32k": ChatModelInfo(
+        name="gpt-4-32k",
+        prompt_token_cost=0.06,
+        completion_token_cost=0.12,
+        max_tokens=32768,
+    ),
+}
+
+OPEN_AI_EMBEDDING_MODELS = {
+    "text-embedding-ada-002": EmbeddingModelInfo(
+        name="text-embedding-ada-002",
+        prompt_token_cost=0.0004,
+        completion_token_cost=0.0,
+        max_tokens=8191,
+        embedding_dimensions=1536,
+    ),
+}
+
+OPEN_AI_MODELS = {
+    **OPEN_AI_CHAT_MODELS,
+    **OPEN_AI_EMBEDDING_MODELS,
+}

autogpt/llm/token_counter.py

@@ -5,8 +5,8 @@
 
 import tiktoken
 
+from autogpt.llm.base import Message
 from autogpt.logs import logger
-from autogpt.types.openai import Message
 
 
 def count_message_tokens(