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測試，評估和監視您的AI應用程序

測試，評估和監視您的AI應用程序

？ Twitter/X • ？不和諧• parea ai • ？文件

Parea AI提供了一個SDK來評估和監視您的AI應用程序。您可以在下面看到快速入門：

評估和測試您的LLM應用程序
LLM應用程序的儀器記錄和可觀察性
部署提示以實現工程師與主題專家之間的合作

我們的完整文檔在這裡。

安裝

pip install -U parea-ai

或與Poetry安裝

poetry add parea-ai

評估您的LLM應用程序

測試您的AI應用意味著通過數據集執行並使用評估功能進行評分。這是通過定義和運行實驗在Parea中完成的。在下面，您可以看到如何使用Levenshtein距離度量標準測試如何測試問候機器人的示例。

 from parea import Parea , trace
from parea . evals . general import levenshtein

p = Parea ( api_key = "<<PAREA_API_KEY>>" )  # replace with Parea AI API key

# use the trace decorator to score the output with the Levenshtein distance  
@ trace ( eval_funcs = [ levenshtein ])
def greeting ( name : str ) -> str :
    return f"Hello { name } "

data = [
    { "name" : "Foo" , "target" : "Hi Foo" },
    { "name" : "Bar" , "target" : "Hello Bar" },
]

p . experiment (
    name = "Greeting" ,
    data = data ,
    func = greeting ,
). run ()

在上面的片段中，我們使用trace裝飾器捕獲功能的所有輸入和輸出。該裝飾器還可以通過在後台執行levenshtein評估來評分輸出。然後，我們通過p.experiment定義了一個實驗，以評估數據集（此處詞典列表）上的功能（ greeting ）。最後，調用run將執行實驗，並為數據集的任何示例創建輸出，分數和痕蹟的報告。您可以在此處找到指向執行實驗的鏈接。（Todo：填充實驗）

記錄和可觀察性

通過包裝各自的客戶，您可以自動將所有LLM調用訪問到OpenAI＆Anthropic。此外，使用trace Decorator，您可以創建LLM應用程序的層次結構軌跡，以與RAG管道的檢索步驟相關聯的LLM調用。您可以在此處看到完整的可觀察性文檔，以及我們在這裡的Langchain，DSPY，Litellm等人的集成。

自動記錄所有OpenAi電話

要自動記錄任何OpenAI調用，您可以使用wrap_openai_client方法與parea客戶端包裝OpenAI客戶端。

 from openai import OpenAI
from parea import Parea

client = OpenAI ( api_key = "OPENAI_API_KEY" )

# All you need to do is add these two lines
p = Parea ( api_key = "PAREA_API_KEY" )  # replace with your API key
p . wrap_openai_client ( client )

response = client . chat . completions . create (
    model = "gpt-4o" ,
    messages = [
        {
            "role" : "user" ,
            "content" : "Write a Hello World program in Python using FastAPI." ,
        }
    ],
)
print ( response . choices [ 0 ]. message . content )

自動記錄所有人類通話

要自動記錄任何人類呼叫，您可以使用wrap_anthropic_client方法與Parea客戶端一起包裹人類客戶端。

 import anthropic
from parea import Parea

p = Parea ( api_key = "PAREA_API_KEY" )  # replace with your API key

client = anthropic . Anthropic ()
p . wrap_anthropic_client ( client )

message = client . messages . create (
    model = "claude-3-opus-20240229" ,
    max_tokens = 1024 ,
    messages = [
        {
            "role" : "user" ,
            "content" : "Write a Hello World program in Python using FastAPI." ,
        }
    ],
)
print ( message . content [ 0 ]. text )

嵌套跡線

通過使用trace Decorator，您可以創建LLM應用程序的層次結構痕跡。

 from openai import OpenAI
from parea import Parea , trace

client = OpenAI ( api_key = "OPENAI_API_KEY" )  # replace with your API key

p = Parea ( api_key = "PAREA_API_KEY" )  # replace with your API key
p . wrap_openai_client ( client )


# We generally recommend creating a helper function to make LLM API calls.
def llm ( messages : list [ dict [ str , str ]]) -> str :
    response = client . chat . completions . create ( model = "gpt-4o" , messages = messages )
    return response . choices [ 0 ]. message . content


# This will give the Span the name of the function.
# Without the decorator the default name for all LLM call logs is `llm-openai`
@ trace
def hello_world ( lang : str , framework : str ):
    return llm ([{ "role" : "user" , "content" : f"Write a Hello World program in { lang } using { framework } ." }])

@ trace
def critique_code ( code : str ):
    return llm ([{ "role" : "user" , "content" : f"How can we improve this code: n { code } " }])

# Our top level function is called chain. By adding the trace decorator here,
# all sub-functions will automatically be logged and associated with this trace.
# Notice, you can also add metadata to the trace, we'll revisit this functionality later.
@ trace ( metadata = { "purpose" : "example" }, end_user_identifier = "John Doe" )
def chain ( lang : str , framework : str ) -> str :
    return critique_code ( hello_world ( lang , framework ))


print ( chain ( "Python" , "FastAPI" ))

部署提示

部署的提示可以與產品經理和主題專家等非工程師合作。用戶可以在Parea的操場上迭代，完善和測試提示。修補後，您可以部署該提示，這意味著它是通過API端點暴露的，以將其集成到您的應用程序中。在這裡查看我們的完整文檔。

 from parea import Parea
from parea . schemas . models import Completion , UseDeployedPrompt , CompletionResponse , UseDeployedPromptResponse


p = Parea ( api_key = "<PAREA_API_KEY>" )

# You will find this deployment_id in the Parea dashboard
deployment_id = '<DEPLOYMENT_ID>'

# Assuming your deployed prompt's message is:
# {"role": "user", "content": "Write a hello world program using {{x}} and the {{y}} framework."}
inputs = { "x" : "Golang" , "y" : "Fiber" }

# You can easily unpack a dictionary into an attrs class
test_completion = Completion (
  ** {
    "deployment_id" : deployment_id ,
    "llm_inputs" : inputs ,
    "metadata" : { "purpose" : "testing" }
  }
)

# By passing in my inputs, in addition to the raw message with unfilled variables {{x}} and {{y}}, 
# you we will also get the filled-in prompt:
# {"role": "user", "content": "Write a hello world program using Golang and the Fiber framework."}
test_get_prompt = UseDeployedPrompt ( deployment_id = deployment_id , llm_inputs = inputs )


def main ():
  completion_response : CompletionResponse = p . completion ( data = test_completion )
  print ( completion_response )
  deployed_prompt : UseDeployedPromptResponse = p . get_prompt ( data = test_get_prompt )
  print ( " n n " )
  print ( deployed_prompt )

？執照

該項目是根據Apache Software License 2.0許可證的條款許可的。有關更多詳細信息，請參見許可證。

？引用

 @misc { parea-sdk ,
  author = { joel-parea-ai,joschkabraun } ,
  title = { Parea python sdk } ,
  year = { 2023 } ,
  publisher = { GitHub } ,
  journal = { GitHub repository } ,
  howpublished = { url{https://github.com/parea-ai/parea-sdk} }
}