llmfoo下載llmfoo源代碼下載

llmfoo

其他源碼

LLM

下載

llm foo

概述

LLM FOO是一個尖端的項目，將功夫的藝術與大語言模型的科學融合在一起……或者實際上是關於自動製作OpenAI工具JSON模式，解析呼叫並為聊天模型構建結果。然後有第二個實用程序is_statement_true ，它使用Genius Logit_bias Trick僅使用一個輸出令牌。

但是，嘿，我希望這將成為一組小型有用的LLM輔助功能，這將使構建內容變得更加容易，因為當前出血的邊緣API有點混亂，我認為我們可以做得更好。

安裝

pip install llmfoo

用法

您需要在Env中擁有OpenAI_API_KEY，並可以致電gpt-4-1106-preview模型
is_statement_true應該易於理解。做一些自然語言陳述，並根據標准或一般真實性進行檢查。你回來布爾。

將PDF文檔轉換為Markdown

新介紹的pdf2md功能允許將PDF文檔轉換為降級格式，從而使它們更易於處理並與基於LLM的系統集成。此功能對於從PDF中提取文本和表並將其轉換為更易於管理的格式特別有用。

先決條件

必須在系統上安裝pdftocairo ，以將PDF頁面轉換為圖像。

示例用法

 from llmfoo . pdf2md import process_pdf
from pathlib import Path

pdf_path = Path ( "path/to/your/document.pdf" )
output_dir = Path ( "path/to/output/directory" )

# Process the PDF and generate Markdown
markdown_file = process_pdf ( pdf_path , output_dir )

此功能將處理PDF的每個頁面，試圖提取文本，圖形和表格，並將其轉換為指定輸出目錄中的Markdown文件。

對於LLM FOO工具：

添加@tool註釋。
llmfoo將與gpt-4-turbo一起生成json架構。
註釋的功能有幫助者：
- openai_schema返回架構（如果您對機器所做的不滿意，則可以從JSON進行編輯）
- openai_tool_call進行工具調用並返回聊天API消息格式中的結果
- openai_tool_output進行工具調用並返回助手API工具輸出格式中的結果

 from time import sleep

from openai import OpenAI

from llmfoo . functions import tool
from llmfoo import is_statement_true


def test_is_statement_true_with_default_criteria ():
    assert is_statement_true ( "Earth is a planet." )
    assert not is_statement_true ( "1 + 2 = 5" )


def test_is_statement_true_with_own_criteria ():
    assert not is_statement_true ( "Temperature outside is -2 degrees celsius" ,
                                 criteria = "Temperature above 0 degrees celsius" )
    assert is_statement_true ( "1984 was written by George Orwell" ,
                             criteria = "George Orwell is the author of 1984" )


def test_is_statement_true_criteria_can_change_truth_value ():
    assert is_statement_true ( "Earth is 3rd planet from the Sun" )
    assert not is_statement_true ( "Earth is 3rd planet from the Sun" ,
                                 criteria = "Earth is stated to be 5th planet from the Sun" )


@ tool
def adder ( x : int , y : int ) -> int :
    return x + y


@ tool
def multiplier ( x : int , y : int ) -> int :
    return x * y


client = OpenAI ()


def test_chat_completion_with_adder ():
    number1 = 3267182746
    number2 = 798472847
    messages = [
        {
            "role" : "user" ,
            "content" : f"What is { number1 } + { number2 } ?"
        }
    ]
    response = client . chat . completions . create (
        model = "gpt-4-1106-preview" ,
        messages = messages ,
        tools = [ adder . openai_schema ]
    )
    messages . append ( response . choices [ 0 ]. message )
    messages . append ( adder . openai_tool_call ( response . choices [ 0 ]. message . tool_calls [ 0 ]))
    response2 = client . chat . completions . create (
        model = "gpt-4-1106-preview" ,
        messages = messages ,
        tools = [ adder . openai_schema ]
    )
    assert str ( adder ( number1 , number2 )) in response2 . choices [ 0 ]. message . content . replace ( "," , "" )


def test_assistant_with_multiplier ():
    number1 = 1238763428176
    number2 = 172388743612
    assistant = client . beta . assistants . create (
        name = "The Calc Machina" ,
        instructions = "You are a calculator with a funny pirate accent." ,
        tools = [ multiplier . openai_schema ],
        model = "gpt-4-1106-preview"
    )
    thread = client . beta . threads . create ( messages = [
        {
            "role" : "user" ,
            "content" : f"What is { number1 } * { number2 } ?"
        }
    ])
    run = client . beta . threads . runs . create (
        thread_id = thread . id ,
        assistant_id = assistant . id
    )
    while True :
        run_state = client . beta . threads . runs . retrieve (
            run_id = run . id ,
            thread_id = thread . id ,
        )
        if run_state . status not in [ 'in_progress' , 'requires_action' ]:
            break
        if run_state . status == 'requires_action' :
            tool_call = run_state . required_action . submit_tool_outputs . tool_calls [ 0 ]
            run = client . beta . threads . runs . submit_tool_outputs (
                thread_id = thread . id ,
                run_id = run . id ,
                tool_outputs = [
                    multiplier . openai_tool_output ( tool_call )
                ]
            )
            sleep ( 1 )
        sleep ( 0.1 )
    messages = client . beta . threads . messages . list ( thread_id = thread . id )
    assert str ( multiplier ( number1 , number2 )) in messages . data [ 0 ]. content [ 0 ]. text . value . replace ( "," , "" )