Como otimizar aplicativos LLM com compactação de prompts usando LLMLingua e LangChain

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from llmlingua import PromptCompressor
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llm_lingua = PromptCompressor(
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    model_name="microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank",
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    model_config={"revision": "main"},
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    use_llmlingua2=True,
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    device_map="cpu"
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)

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import pprint
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def handle_user_query_with_compression(query, collection):
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  get_knowledge = vector_search(query, collection)
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  # Concatenate the search results to reflect the employee profile
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  search_result = ''
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  for result in get_knowledge:      
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      employee_profile = f"""
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      Employee ID: {result.get('employee_id', 'N/A')}
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      Name: {result.get('first_name', 'N/A')} {result.get('last_name', 'N/A')}
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      Gender: {result.get('gender', 'N/A')}
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      Date of Birth: {result.get('date_of_birth', 'N/A')}
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      """
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      search_result += employee_profile + "\n"
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  # Prepare information for compression
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  query_info = {
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    'demonstration_str': search_result,  # Results from information retrieval process
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    'instruction': "Write a high-quality answer for the given question using only the provided search results.",
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    'question': query
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  }
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  # Compress the query prompt
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  compressed_prompt = compress_query_prompt(query_info)
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  prompt =  f"Answer this user query: {query} with the following context:\n{compressed_prompt}"
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  print("Compressed Prompt:\n")
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  pprint.pprint(prompt)
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  completion = openai.chat.completions.create(
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      model=OPEN_AI_MODEL,
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      messages=[
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          {"role": "system", "content": "You are a Human Resource System within a corporate company."},
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          {"role": "user", "content": prompt}
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      ]
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  )
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  return (completion.choices[0].message.content), search_result

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from langchain.retrievers import ContextualCompressionRetriever
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from langchain_community.document_compressors import LLMLinguaCompressor
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compressor = LLMLinguaCompressor(model_name="openai-community/gpt2", device_map="cpu")
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compression_retriever = ContextualCompressionRetriever(
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    base_compressor=compressor, base_retriever=retriever
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)
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compressed_docs = compression_retriever.invoke(
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    "Who is the CEO?"
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)
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print(compressed_docs)

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from langchain.chains import RetrievalQA
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chain = RetrievalQA.from_chain_type(llm=llm, retriever=compression_retriever)
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chain.invoke({"query": "Who is the CEO?"})

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from langchain.retrievers import ContextualCompressionRetriever
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from langchain_community.document_compressors import LLMLinguaCompressor
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from langchain.agents import AgentExecutor, create_tool_calling_agent
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compressor = LLMLinguaCompressor(model_name="openai-community/gpt2", device_map="cpu")
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@tool
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def compress_prompt_using_llmlingua(prompt: str, compression_rate: float = 0.5) -> str:
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    """
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    Compresses a long data or prompt using the LLMLinguaCompressor.
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    Args:
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        data (str): The data or prompt to be compressed.
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        compression_rate (float): The rate at which to compress the data (default is 0.5).
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    Returns:
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        str: The compressed data or prompt.
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    """
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    compressed_data = compressor.compress_prompt(
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        prompt,
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        rate=compression_rate,
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        force_tokens=["!", ".", "?", "\n"],
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        drop_consecutive=True
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    )
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    return compressed_data
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tools = [compress_prompt_using_llmlingua]
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agent = create_tool_calling_agent(llm, tools, prompt)
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agent_executor = AgentExecutor(
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    agent=agent,
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    tools=tools,
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    verbose=True,
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    handle_parsing_errors=True,
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    memory=memory,
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)
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agent_executor.invoke({"input": "Get me a list of research papers on the topic Prompt Compression"})

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from langchain.retrievers import ContextualCompressionRetriever
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from langchain_community.document_compressors import LLMLinguaCompressor
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from langchain.tools.retriever import create_retriever_tool
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compressor = LLMLinguaCompressor(model_name="openai-community/gpt2", device_map="cpu")
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compression_retriever = ContextualCompressionRetriever(
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    base_compressor=compressor, base_retriever=retriever
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)
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retriever_tool = create_retriever_tool(
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    retriever=retriever,
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    name="knowledge_base",
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    description="This serves as the base knowledge source of the agent and contains some records of research papers from Arxiv. This tool is used as the first step for exploration and research efforts."
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)