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使用LLMLingua压缩文本

文本压缩对于优化与LLMs的交互至关重要，尤其是在处理可能导致更高成本和更慢响应时间的长提示时。LLMLingua是一款设计用于有效压缩提示的工具，提高了LLM操作的效率和成本效益。

本指南介绍了LLMLingua与AutoGen的集成，展示了如何使用该工具压缩文本，从而优化LLM在各种应用中的使用。

Requirements

安装 autogen-agentchat[long-context]~=0.2 和 PyMuPDF：

pip install "autogen-agentchat[long-context]~=0.2" PyMuPDF

如需更多信息，请参考安装指南。

示例1：使用LLMLingua压缩AutoGen研究论文​

我们将探讨如何使用TextMessageCompressor来压缩一篇AutoGen研究论文，利用LLMLingua。以下是如何使用LLMLingua初始化TextMessageCompressor的方法，LLMLingua是一个遵循TextCompressor协议的文本压缩器。

import tempfile

import fitz  # PyMuPDF
import requests

from autogen.agentchat.contrib.capabilities.text_compressors import LLMLingua
from autogen.agentchat.contrib.capabilities.transforms import TextMessageCompressor

AUTOGEN_PAPER = "https://arxiv.org/pdf/2308.08155"


def extract_text_from_pdf():
    # Download the PDF
    response = requests.get(AUTOGEN_PAPER)
    response.raise_for_status()  # Ensure the download was successful

    text = ""
    # Save the PDF to a temporary file
    with tempfile.TemporaryDirectory() as temp_dir:
        with open(temp_dir + "temp.pdf", "wb") as f:
            f.write(response.content)

        # Open the PDF
        with fitz.open(temp_dir + "temp.pdf") as doc:
            # Read and extract text from each page
            for page in doc:
                text += page.get_text()

    return text


# Example usage
pdf_text = extract_text_from_pdf()

llm_lingua = LLMLingua()
text_compressor = TextMessageCompressor(text_compressor=llm_lingua)
compressed_text = text_compressor.apply_transform([{"content": pdf_text}])

print(text_compressor.get_logs([], []))
('19765 tokens saved with text compression.', True)

示例2：将LLMLingua与ConversableAgent集成​

现在，让我们将LLMLingua集成到AutoGen中的对话代理中。这允许在将提示发送到LLM之前对其进行动态压缩。

import os

import autogen
from autogen.agentchat.contrib.capabilities import transform_messages

system_message = "You are a world class researcher."
config_list = [{"model": "gpt-4-turbo", "api_key": os.getenv("OPENAI_API_KEY")}]

# Define your agent; the user proxy and an assistant
researcher = autogen.ConversableAgent(
    "assistant",
    llm_config={"config_list": config_list},
    max_consecutive_auto_reply=1,
    system_message=system_message,
    human_input_mode="NEVER",
)
user_proxy = autogen.UserProxyAgent(
    "user_proxy",
    human_input_mode="NEVER",
    is_termination_msg=lambda x: "TERMINATE" in x.get("content", ""),
    max_consecutive_auto_reply=1,
)
tip

了解更多关于为agent配置LLM的信息在这里.

context_handling = transform_messages.TransformMessages(transforms=[text_compressor])
context_handling.add_to_agent(researcher)

message = "Summarize this research paper for me, include the important information" + pdf_text
result = user_proxy.initiate_chat(recipient=researcher, clear_history=True, message=message, silent=True)

print(result.chat_history[1]["content"])
19953 tokens saved with text compression.
The paper describes AutoGen, a framework designed to facilitate the development of diverse large language model (LLM) applications through conversational multi-agent systems. The framework emphasizes customization and flexibility, enabling developers to define agent interaction behaviors in natural language or computer code.

Key components of AutoGen include:
1. **Conversable Agents**: These are customizable agents designed to operate autonomously or through human interaction. They are capable of initiating, maintaining, and responding within conversations, contributing effectively to multi-agent dialogues.

2. **Conversation Programming**: AutoGen introduces a programming paradigm centered around conversational interactions among agents. This approach simplifies the development of complex applications by streamlining how agents communicate and interact, focusing on conversational logic rather than traditional coding for
mats.

3. **Agent Customization and Flexibility**: Developers have the freedom to define the capabilities and behaviors of agents within the system, allowing for a wide range of applications across different domains.

4. **Application Versatility**: The paper outlines various use cases from mathematics and coding to decision-making and entertainment, demonstrating AutoGen's ability to cope with a broad spectrum of complexities and requirements.

5. **Hierarchical and Joint Chat Capabilities**: The system supports complex conversation patterns including hierarchical and multi-agent interactions, facilitating robust dialogues that can dynamically adjust based on the conversation context and the agents' roles.

6. **Open-source and Community Engagement**: AutoGen is presented as an open-source framework, inviting contributions and adaptations from the global development community to expand its capabilities and applications.

The framework's architecture is designed so that it can be seamlessly integrated into existing systems, providing a robust foundation for developing sophisticated multi-agent applications that leverage the capabilities of modern LLMs. The paper also discusses potential ethical considerations and future improvements, highlighting the importance of continual development in response to evolving tech landscapes and user needs.

示例3：修改LLMLingua的压缩参数​

LLMLingua 的灵活性允许各种配置，例如为LLM定制指令或设置特定的标记数量进行压缩。此示例演示了如何设置目标标记数量，使得可以使用具有较小上下文大小的模型，如 gpt-3.5。

config_list = [{"model": "gpt-3.5-turbo", "api_key": os.getenv("OPENAI_API_KEY")}]
researcher = autogen.ConversableAgent(
    "assistant",
    llm_config={"config_list": config_list},
    max_consecutive_auto_reply=1,
    system_message=system_message,
    human_input_mode="NEVER",
)

text_compressor = TextMessageCompressor(
    text_compressor=llm_lingua,
    compression_params={"target_token": 13000},
    cache=None,
)
context_handling = transform_messages.TransformMessages(transforms=[text_compressor])
context_handling.add_to_agent(researcher)

compressed_text = text_compressor.apply_transform([{"content": message}])

result = user_proxy.initiate_chat(recipient=researcher, clear_history=True, message=message, silent=True)

print(result.chat_history[1]["content"])
25308 tokens saved with text compression.
Based on the extensive research paper information provided, it seems that the focus is on developing a framework called AutoGen for creating multi-agent conversations based on Large Language Models (LLMs) for a variety of applications such as math problem solving, coding, decision-making, and more.

The paper discusses the importance of incorporating diverse roles of LLMs, human inputs, and tools to enhance the capabilities of the conversable agents within the AutoGen framework. It also delves into the effectiveness of different systems in various scenarios, showcases the implementation of AutoGen in pilot studies, and compares its performance with other systems in tasks like math problem-solving, coding, and decision-making.

The paper also highlights the different features and components of AutoGen such as the AssistantAgent, UserProxyAgent, ExecutorAgent, and GroupChatManager, emphasizing its flexibility, ease of use, and modularity in managing multi-agent interactions. It presents case analyses to demonstrate the effectiveness of AutoGen in various applications and scenarios.

Furthermore, the paper includes manual evaluations, scenario testing, code examples, and detailed comparisons with other systems like ChatGPT, OptiGuide, MetaGPT, and more, to showcase the performance and capabilities of the AutoGen framework.

Overall, the research paper showcases the potential of AutoGen in facilitating dynamic multi-agent conversations, enhancing decision-making processes, and improving problem-solving tasks with the integration of LLMs, human inputs, and tools in a collaborative framework.