From 2eccf04a308dfaec76f64b8135f87320cd17871a Mon Sep 17 00:00:00 2001
From: Kye <kye@apacmediasolutions.com>
Date: Sun, 30 Jul 2023 15:15:52 -0400
Subject: [PATCH] clean up
Former-commit-id: c2019717e26d6a58d92b409bff8de51dd2e4ad9b
---
swarms/agents/base.py | 17 ++--
swarms/agents/prompts/agent_prompt_auto.py | 95 ++++++++++++++++++++++
2 files changed, 102 insertions(+), 10 deletions(-)
create mode 100644 swarms/agents/prompts/agent_prompt_auto.py
diff --git a/swarms/agents/base.py b/swarms/agents/base.py
index f8244703..b8a692ee 100644
--- a/swarms/agents/base.py
+++ b/swarms/agents/base.py
@@ -9,6 +9,7 @@ from swarms.agents.utils.human_input import HumanInputRun
from swarms.agents.prompts.prompt_generator import FINISH_NAME
from swarms.agents.models.base import AbstractModel
from swarms.agents.prompts.agent_output_parser import AgentOutputParser
+from swarms.agents.prompts.agent_prompt_auto import PromptConstructor, MessageFormatter
@@ -19,7 +20,6 @@ from langchain.schema.messages import AIMessage, HumanMessage, SystemMessage
from langchain.tools.base import BaseTool
from langchain.vectorstores.base import VectorStoreRetriever
-
class Agent:
"""Base Agent class"""
@@ -54,15 +54,12 @@ class Agent:
output_parser: Optional[AgentOutputParser] = None,
chat_history_memory: Optional[BaseChatMessageHistory] = None,
) -> Agent:
- prompt = AgentPrompt(
- ai_name=ai_name,
- ai_role=ai_role,
- tools=tools,
- input_variables=["memory", "messages", "goals", "user_input"],
- token_counter=llm.get_num_tokens,
- )
+ prompt_constructor = PromptConstructor(ai_name=ai_name,
+ ai_role=ai_role,
+ tools=tools)
+ message_formatter = MessageFormatter()
human_feedback_tool = HumanInputRun() if human_in_the_loop else None
- chain = LLMChain(llm=llm, prompt=prompt)
+ chain = LLMChain(llm=llm, prompt_constructor=prompt_constructor, message_formatter=message_formatter)
return cls(
ai_name,
memory,
@@ -135,4 +132,4 @@ class Agent:
memory_to_add += feedback
self.memory.add_documents([Document(page_content=memory_to_add)])
- self.chat_history_memory.add_message(SystemMessage(content=result))
\ No newline at end of file
+ self.chat_history_memory.add_message(SystemMessage(content=result))
diff --git a/swarms/agents/prompts/agent_prompt_auto.py b/swarms/agents/prompts/agent_prompt_auto.py
new file mode 100644
index 00000000..cdf8fd33
--- /dev/null
+++ b/swarms/agents/prompts/agent_prompt_auto.py
@@ -0,0 +1,95 @@
+import time
+from typing import Any, Callable, List
+from pydantic import BaseModel
+
+class TokenUtils:
+ @staticmethod
+ def count_tokens(text: str) -> int:
+ return len(text.split())
+
+
+class PromptConstructor:
+ def __init__(self, ai_name: str, ai_role: str, tools: List[BaseTool]):
+ self.ai_name = ai_name
+ self.ai_role = ai_role
+ self.tools = tools
+
+ def construct_full_prompt(self, goals: List[str]) -> str:
+ prompt_start = (
+ "Your decisions must always be made independently "
+ "without seeking user assistance.\n"
+ "Play to your strengths as an LLM and pursue simple "
+ "strategies with no legal complications.\n"
+ "If you have completed all your tasks, make sure to "
+ 'use the "finish" command.'
+ )
+ # Construct full prompt
+ full_prompt = (
+ f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
+ )
+ for i, goal in enumerate(goals):
+ full_prompt += f"{i+1}. {goal}\n"
+ full_prompt += f"\n\n{get_prompt(self.tools)}"
+ return full_prompt
+
+
+class Message(BaseModel):
+ content: str
+
+ def count_tokens(self) -> int:
+ return TokenUtils.count_tokens(self.content)
+
+ def format_content(self) -> str:
+ return self.content
+
+
+class SystemMessage(Message):
+ pass
+
+
+class HumanMessage(Message):
+ pass
+
+
+class MessageFormatter:
+ send_token_limit: int = 4196
+
+ def format_messages(self, **kwargs: Any) -> List[Message]:
+ prompt_constructor = PromptConstructor(ai_name=kwargs["ai_name"],
+ ai_role=kwargs["ai_role"],
+ tools=kwargs["tools"])
+ base_prompt = SystemMessage(content=prompt_constructor.construct_full_prompt(kwargs["goals"]))
+ time_prompt = SystemMessage(
+ content=f"The current time and date is {time.strftime('%c')}"
+ )
+ used_tokens = base_prompt.count_tokens() + time_prompt.count_tokens()
+ memory: VectorStoreRetriever = kwargs["memory"]
+ previous_messages = kwargs["messages"]
+ relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:]))
+ relevant_memory = [d.page_content for d in relevant_docs]
+ relevant_memory_tokens = sum(
+ [TokenUtils.count_tokens(doc) for doc in relevant_memory]
+ )
+ while used_tokens + relevant_memory_tokens > 2500:
+ relevant_memory = relevant_memory[:-1]
+ relevant_memory_tokens = sum(
+ [TokenUtils.count_tokens(doc) for doc in relevant_memory]
+ )
+ content_format = (
+ f"This reminds you of these events "
+ f"from your past:\n{relevant_memory}\n\n"
+ )
+ memory_message = SystemMessage(content=content_format)
+ used_tokens += memory_message.count_tokens()
+ historical_messages: List[Message] = []
+ for message in previous_messages[-10:][::-1]:
+ message_tokens = message.count_tokens()
+ if used_tokens + message_tokens > self.send_token_limit - 1000:
+ break
+ historical_messages = [message] + historical_messages
+ used_tokens += message_tokens
+ input_message = HumanMessage(content=kwargs["user_input"])
+ messages: List[Message] = [base_prompt, time_prompt, memory_message]
+ messages += historical_messages
+ messages.append(input_message)
+ return messages