autogen/website/docs/topics/handling_long_contexts/compressing_text_w_llmligua.md
Example 3: Modifying LLMLingua's Compression Parameters  LLMLingua's flexibility allows for various configurations, such as customizing instructions for the LLM or setting specific token counts for compression. This example demonstrates how to set a target token count, enabling the use of models with smaller context sizes like gpt-3.5.  ```python 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"]) ```  ```console 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. ``` 