Merge pull request #23 from BillSchumacher/dev

Dev -> Master

Author: BillSchumacher

BULLETIN.md

@@ -1,9 +1,4 @@
 Welcome to Auto-GPT! We'll keep you informed of the latest news and features by printing messages here.
 If you don't wish to see this message, you can run Auto-GPT with the --skip-news flag
 
-# INCLUDED COMMAND 'send_tweet' IS DEPRICATED, AND WILL BE REMOVED IN THE NEXT STABLE RELEASE
-Base Twitter functionality (and more) is now covered by plugins: https://github.com/Significant-Gravitas/Auto-GPT-Plugins
-
-## Changes to Docker configuration
-The workdir has been changed from /home/appuser to /app. Be sure to update any volume mounts accordingly.
-
+# This repo does not track with upstream!! If there's functionality not included open a PR.

autogpt/agent/agent.py

@@ -3,14 +3,15 @@
 from colorama import Fore, Style
 
 from autogpt.app import execute_command, get_command
-from autogpt.chat import chat_with_ai, create_chat_message
+from autogpt.chat import chat_with_ai
 from autogpt.config import Config
 from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques
 from autogpt.json_utils.utilities import validate_json
 from autogpt.llm_utils import create_chat_completion
 from autogpt.logs import logger, print_assistant_thoughts
 from autogpt.speech import say_text
 from autogpt.spinner import Spinner
+from autogpt.types.openai import Message
 from autogpt.utils import clean_input, send_chat_message_to_user
 from autogpt.workspace import Workspace
 
@@ -337,12 +338,10 @@ def update_memory(self, assistant_reply, result, user_input):
 
     def update_history(self, result):
         if result is not None:
-            self.full_message_history.append(create_chat_message("system", result))
+            self.full_message_history.append(Message("system", result))
             logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
             return
-        self.full_message_history.append(
-            create_chat_message("system", "Unable to execute command")
-        )
+        self.full_message_history.append(Message("system", "Unable to execute command"))
         logger.typewriter_log("SYSTEM: ", Fore.YELLOW, "Unable to execute command")
 
     def _resolve_pathlike_command_args(self, command_args):
@@ -383,8 +382,8 @@ def get_self_feedback(self, thoughts: dict, llm_model: str) -> str:
         plan = thoughts.get("plan", "")
         thought = thoughts.get("thoughts", "")
         criticism = thoughts.get("criticism", "")
-        feedback_thoughts = thought + reasoning + plan + criticism
+        feedback_thoughts = f"{thought} {reasoning} {plan} {criticism}"
         return create_chat_completion(
-            [{"role": "user", "content": feedback_prompt + feedback_thoughts}],
+            [Message("user", f"{feedback_prompt} {feedback_thoughts}")],
             llm_model,
         )

autogpt/agent/agent_manager.py

@@ -1,12 +1,10 @@
 """Agent manager for managing GPT agents"""
 from __future__ import annotations
 
-from typing import List
-
 from autogpt.config.config import Config
 from autogpt.llm_utils import create_chat_completion
 from autogpt.singleton import Singleton
-from autogpt.types.openai import Message
+from autogpt.types.openai import Message, ensure_messages
 
 
 class AgentManager(metaclass=Singleton):
@@ -20,7 +18,7 @@ def __init__(self):
     # Create new GPT agent
     # TODO: Centralise use of create_chat_completion() to globally enforce token limit
 
-    def handle_preinstruction(self, messages: List[Message]) -> List[Message]:
+    def handle_preinstruction(self, messages: list[Message]) -> list[Message]:
         """Handle pre-instruction plugins
 
         Args:
@@ -34,7 +32,7 @@ def handle_preinstruction(self, messages: List[Message]) -> List[Message]:
                 continue
             if plugin_messages := plugin.pre_instruction(messages):
                 messages.extend(iter(plugin_messages))
-        return messages
+        return ensure_messages(messages)
 
     def handle_postinstruction(self, agent_reply) -> str:
         """Handle post-instruction plugins
@@ -52,7 +50,7 @@ def handle_postinstruction(self, agent_reply) -> str:
             agent_reply = plugin.post_instruction(agent_reply)
         return agent_reply
 
-    def handle_oninstruction(self, messages: List[Message]) -> List[Message]:
+    def handle_oninstruction(self, messages: list[Message]) -> list[Message]:
         """Handle on-instruction plugins
 
         Args:
@@ -70,7 +68,7 @@ def handle_oninstruction(self, messages: List[Message]) -> List[Message]:
                 plugins_reply = f"{plugins_reply}{sep}{plugin_result}"
 
         if plugins_reply and plugins_reply != "":
-            messages.append({"role": "assistant", "content": plugins_reply})
+            messages.append(Message("assistant", plugins_reply))
         return messages
 
     def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
@@ -84,19 +82,27 @@ def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
         Returns:
             The key of the new agent
         """
-        messages: List[Message] = [
-            {"role": "system", "content": "You are an autonomous AI agent. You can ask me to do things. I will try my best to do them."},
-            {"role": "user", "content": prompt},
+        messages: list[Message] = [
+            Message(
+                "system",
+                "You are an autonomous AI agent. You can ask me to do things."
+                " I will try my best to do them.",
+            ),
+            Message("user", prompt),
         ]
         messages = self.handle_preinstruction(messages)
-        token_limit = self.cfg.fast_token_limit if not self.cfg.use_fastchat else self.cfg.fastchat_token_limit
+        token_limit = (
+            self.cfg.fast_token_limit
+            if not self.cfg.use_fastchat
+            else self.cfg.fastchat_token_limit
+        )
         agent_reply = create_chat_completion(
             model=model,
             messages=messages,
             max_tokens=token_limit,
             use_fastchat=self.cfg.use_fastchat,
         )
-        messages.append({"role": "assistant", "content": agent_reply})
+        messages.append(Message("assistant", agent_reply))
         messages = self.handle_oninstruction(messages)
         key = self.next_key
         self.next_key += 1
@@ -114,16 +120,21 @@ def message_agent(self, key: str | int, message: str) -> str:
             The agent's response
         """
         task, messages, model = self.agents[int(key)]
-        messages.append({"role": "user", "content": message})
+        messages.append(Message("user", message))
         messages = self.handle_preinstruction(messages)
-        token_limit = self.cfg.fast_token_limit if not self.cfg.use_fastchat else self.cfg.fastchat_token_limit
+
+        token_limit = (
+            self.cfg.fast_token_limit
+            if not self.cfg.use_fastchat
+            else self.cfg.fastchat_token_limit
+        )
         agent_reply = create_chat_completion(
             model=model,
             messages=messages,
             max_tokens=token_limit,
             use_fastchat=self.cfg.use_fastchat,
         )
-        messages.append({"role": "assistant", "content": agent_reply})
+        messages.append(Message("assistant", agent_reply))
         messages = self.handle_oninstruction(messages)
         return self.handle_postinstruction(agent_reply)
 

autogpt/api_manager.py

@@ -1,6 +1,12 @@
 from __future__ import annotations
 
 import openai
+from tenacity import (
+    retry,
+    retry_if_not_exception_type,
+    stop_after_attempt,
+    wait_random_exponential,
+)
 
 from autogpt.config import Config
 from autogpt.logs import logger
@@ -21,6 +27,11 @@ def reset(self):
         self.total_cost = 0
         self.total_budget = 0.0
 
+    @retry(
+        wait=wait_random_exponential(min=1, max=20),
+        stop=stop_after_attempt(10),
+        retry=retry_if_not_exception_type(openai.InvalidRequestError),
+    )
     def create_chat_completion(
         self,
         messages: list,  # type: ignore
@@ -31,13 +42,14 @@ def create_chat_completion(
     ) -> str:
         """
         Create a chat completion and update the cost.
+
         Args:
-        messages (list): The list of messages to send to the API.
-        model (str): The model to use for the API call.
-        temperature (float): The temperature to use for the API call.
-        max_tokens (int): The maximum number of tokens for the API call.
+            messages (list): The list of messages to send to the API.
+            model (str): The model to use for the API call.
+            temperature (float): The temperature to use for the API call.
+            max_tokens (int): The maximum number of tokens for the API call.
         Returns:
-        str: The AI's response.
+            str: The AI's response.
         """
         cfg = Config()
         if temperature is None:
@@ -70,9 +82,9 @@ def update_cost(self, prompt_tokens, completion_tokens, model):
         Update the total cost, prompt tokens, and completion tokens.
 
         Args:
-        prompt_tokens (int): The number of tokens used in the prompt.
-        completion_tokens (int): The number of tokens used in the completion.
-        model (str): The model used for the API call.
+            prompt_tokens (int): The number of tokens used in the prompt.
+            completion_tokens (int): The number of tokens used in the completion.
+            model (str): The model used for the API call.
         """
         self.total_prompt_tokens += prompt_tokens
         self.total_completion_tokens += completion_tokens
@@ -87,7 +99,7 @@ def set_total_budget(self, total_budget):
         Sets the total user-defined budget for API calls.
 
         Args:
-        total_budget (float): The total budget for API calls.
+            total_budget (float): The total budget for API calls.
         """
         self.total_budget = total_budget
 
@@ -96,7 +108,7 @@ def get_total_prompt_tokens(self):
         Get the total number of prompt tokens.
 
         Returns:
-        int: The total number of prompt tokens.
+            int: The total number of prompt tokens.
         """
         return self.total_prompt_tokens
 
@@ -105,7 +117,7 @@ def get_total_completion_tokens(self):
         Get the total number of completion tokens.
 
         Returns:
-        int: The total number of completion tokens.
+            int: The total number of completion tokens.
         """
         return self.total_completion_tokens
 
@@ -114,7 +126,7 @@ def get_total_cost(self):
         Get the total cost of API calls.
 
         Returns:
-        float: The total cost of API calls.
+            float: The total cost of API calls.
         """
         return self.total_cost
 
@@ -123,6 +135,6 @@ def get_total_budget(self):
         Get the total user-defined budget for API calls.
 
         Returns:
-        float: The total budget for API calls.
+            float: The total budget for API calls.
         """
         return self.total_budget

autogpt/app.py

@@ -213,9 +213,17 @@ def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) ->
     return f"Agent {name} created with key {key}. First response: {agent_response}"
 
 
-@command("message_agent", "Message GPT Agent", '"key": "<key>", "message": "<message>"')
+@command("message_agent", "Message GPT Agent", '"key": "<int>", "message": "<message>"')
 def message_agent(key: str, message: str) -> str:
-    """Message an agent with a given key and message"""
+    """Message an agent with a given key and message
+
+    Args:
+        key (str): The key of the agent, should be a number
+        message (str): The message to send to the agent
+
+    Returns:
+        str: The response of the agent
+    """
     # Check if the key is a valid integer
     if is_valid_int(key):
         agent_response = AGENT_MANAGER.message_agent(int(key), message)
@@ -233,7 +241,7 @@ def list_agents() -> str:
     """List all agents
 
     Returns:
-        str: A list of all agents
+        str: A list of all agents, ids (numbers)
     """
     return "List of agents:\n" + "\n".join(
         [str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]