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camel/agents/chat_agent.py

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@@ -1029,7 +1029,7 @@ def _update_memory_with_summary(
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on whether it's a progressive or full compression.
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"""
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summary_content: str = summary.get("summary_content", "")
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summary_content: str = summary.get("summary", "")
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existing_summaries = []
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if not include_summaries:
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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import os
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from camel.agents import ChatAgent
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from camel.models import ModelFactory
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from camel.toolkits import TerminalToolkit
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from camel.types import ModelPlatformType, ModelType
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# Get current script directory
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base_dir = os.path.dirname(os.path.abspath(__file__))
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# Define workspace directory for the toolkit
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workspace_dir = os.path.join(
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os.path.dirname(os.path.dirname(base_dir)), "workspace"
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)
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# Define system message
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sys_msg = (
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"You are a System Administrator helping with log management tasks. "
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"You have access to terminal tools that can help you execute "
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"shell commands and search files. "
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)
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# Set model config
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tools = TerminalToolkit(working_directory=workspace_dir).get_tools()
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model = ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI,
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model_type=ModelType.GPT_3_5_TURBO,
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)
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# Set agent
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camel_agent = ChatAgent(
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system_message=sys_msg,
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model=model,
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tools=tools,
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summarize_threshold=1,
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summary_window_ratio=0.02
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)
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camel_agent.reset()
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# Define a user message for creating logs directory
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usr_msg = (
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f"Create a 'logs' directory in '{workspace_dir}' and list its contents"
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)
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# simulate a long conversation
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for i in range(20):
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response = camel_agent.step(usr_msg)
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print(camel_agent._summary_token_count)
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#handle a large file
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usr_msg = (
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f"../../uv.lock,read this file"
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)
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response = camel_agent.step(usr_msg)

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