Automated Testing for LLMOps 01：使用CircleCI进行持续集成CI

Automated Testing for LLMOps

这是学习https://www.deeplearning.ai/short-courses/automated-testing-llmops/ 这门课的笔记

Learn how LLM-based testing differs from traditional software testing and implement rules-based testing to assess your LLM application.

Build model-graded evaluations to test your LLM application using an evaluation LLM.

Automate your evals (rules-based and model-graded) using continuous integration tools from CircleCI.

文章目录

[Automated Testing for LLMOps](#Automated Testing for LLMOps)
[Lesson 1: Introduction to Continuous Integration (CI)](#Lesson 1: Introduction to Continuous Integration (CI))
[Lesson 2: Overview of Automated Evals](#Lesson 2: Overview of Automated Evals)
- [Load API tokens for our 3rd party APIs.](#Load API tokens for our 3rd party APIs.)
- [Set up our github branch](#Set up our github branch)
- [The sample application: AI-powered quiz generator](#The sample application: AI-powered quiz generator)
- - Evaluations
- [Running evaluations in a CircleCI pipeline](#Running evaluations in a CircleCI pipeline)
- - [The CircleCI config file](#The CircleCI config file)
- [Run the per-commit evals](#Run the per-commit evals)

Lesson 1: Introduction to Continuous Integration (CI)

ChatGPT对持续集成的介绍:

持续集成（Continuous Integration，简称CI）是一种软件开发实践，旨在通过频繁地将代码集成到共享存储库中，并通过自动化构建和测试过程来尽早地发现集成错误。CI的主要目标是减少集成问题的出现，以便团队能够更快地交付高质量的软件。

在持续集成的实践中，开发人员通常会将其代码提交到共享存储库（如版本控制系统），然后CI服务器会自动检测到这些变更，并触发一系列的构建和测试任务。这些任务可以包括编译代码、运行单元测试、执行静态代码分析等。如果构建或测试失败，CI系统会通知团队成员，以便他们可以及时修复问题。如果一切顺利，则可以持续集成（将新的代码更改合并到主分支）。

持续集成的优势包括：

提早发现问题：由于每次提交都会触发自动化的构建和测试，因此集成问题可以更早地被发现和解决，避免了将问题延迟到开发周期后期的情况。
提高软件质量：通过频繁地运行测试和自动化代码检查，可以确保代码质量更高，减少了潜在的缺陷。
加速交付速度：持续集成使得团队能够更快地交付新功能和修复bug，因为团队可以更加自信地修改代码，知道集成问题会被及时发现。
提高团队协作：CI促进了团队成员之间的交流和协作，因为他们需要共同努力确保代码的可集成性和质量。

综上所述，持续集成是现代软件开发中不可或缺的一环，它通过自动化和频繁的集成实践，帮助团队更快地构建高质量的软件。

持续集成的含义

持续集成的步骤

持续集成的好处

Lesson 2: Overview of Automated Evals

传统软件的测试和大语言模型LLM的测试区别

Model Evaluations（Evals）

Automating Evals

py 复制代码

import warnings
warnings.filterwarnings('ignore')

看一下具体实例

Load API tokens for our 3rd party APIs.

py 复制代码

from utils import get_circle_api_key
cci_api_key = get_circle_api_key()

from utils import get_gh_api_key
gh_api_key = get_gh_api_key()

from utils import get_openai_api_key
openai_api_key = get_openai_api_key()

utils.py文件如下

py 复制代码

import github
import os
import requests
import random
from dotenv import load_dotenv, find_dotenv
from yaml import safe_dump, safe_load
import time

adjectives = [
    "adoring",
    "affirmative",
    "appreciated",
    "available",
    "best-selling",
    "blithe",
    "brightest",
    "charismatic",
    "convincing",
    "dignified",
    "ecstatic",
    "effective",
    "engaging",
    "enterprising",
    "ethical",
    "fast-growing",
    "glad",
    "hardy",
    "idolized",
    "improving",
    "jubilant",
    "knowledgeable",
    "long-lasting",
    "lucky",
    "marvelous",
    "merciful",
    "mesmerizing",
    "problem-free",
    "resplendent",
    "restored",
    "roomier",
    "serene",
    "sharper",
    "skilled",
    "smiling",
    "smoother",
    "snappy",
    "soulful",
    "staunch",
    "striking",
    "strongest",
    "subsidized",
    "supported",
    "supporting",
    "sweeping",
    "terrific",
    "unaffected",
    "unbiased",
    "unforgettable",
    "unrivaled",
]

nouns = [
    "agustinia",
    "apogee",
    "bangle",
    "cake",
    "cheese",
    "clavicle",
    "client",
    "clove",
    "curler",
    "draw",
    "duke",
    "earl",
    "eustoma",
    "fireplace",
    "gem",
    "glove",
    "goal",
    "ground",
    "jasmine",
    "jodhpur",
    "laugh",
    "message",
    "mile",
    "mockingbird",
    "motor",
    "phalange",
    "pillow",
    "pizza",
    "pond",
    "potential",
    "ptarmigan",
    "puck",
    "puzzle",
    "quartz",
    "radar",
    "raver",
    "saguaro",
    "salary",
    "sale",
    "scarer",
    "skunk",
    "spatula",
    "spectacles",
    "statistic",
    "sturgeon",
    "tea",
    "teacher",
    "wallet",
    "waterfall",
    "wrinkle",
]

def inspect_config():
    with open("circle_config.yml") as f:
        print(safe_dump(safe_load(f)))

def load_env():
    _ = load_dotenv(find_dotenv())

def get_openai_api_key():
    load_env()
    openai_api_key = os.getenv("OPENAI_API_KEY")
    return openai_api_key

def get_circle_api_key():
    load_env()
    circle_token = os.getenv("CIRCLE_TOKEN")
    return circle_token

def get_gh_api_key():
    load_env()
    github_token = os.getenv("GH_TOKEN")
    return github_token

def get_repo_name():
    return "CircleCI-Learning/llmops-course"


def _create_tree_element(repo, path, content):
    blob = repo.create_git_blob(content, "utf-8")
    element = github.InputGitTreeElement(
        path=path, mode="100644", type="blob", sha=blob.sha
    )
    return element


def push_files(repo_name, branch_name, files):
    files_to_push = set(files)
    # include the config.yml file
    g = github.Github(os.environ["GH_TOKEN"])
    repo = g.get_repo(repo_name)

    elements = []
    config_element = _create_tree_element(
        repo, ".circleci/config.yml", open("circle_config.yml").read()
    )
    elements.append(config_element)

    requirements_element = _create_tree_element(
        repo, "requirements.txt", open("dev_requirements.txt").read()
    )
    elements.append(requirements_element)
    for file in files_to_push:
        print(f"uploading {file}")
        with open(file, encoding="utf-8") as f:
            content = f.read()
            element = _create_tree_element(repo, file, content)
            elements.append(element)

    head_sha = repo.get_branch("main").commit.sha
    
    print(f"pushing files to: {branch_name}")
    try:
        repo.create_git_ref(ref=f"refs/heads/{branch_name}", sha=head_sha)
        time.sleep(2)
    except Exception as _:
        print(f"{branch_name} already exists in the repository pushing updated changes")
    branch_sha = repo.get_branch(branch_name).commit.sha

    base_tree = repo.get_git_tree(sha=branch_sha)
    tree = repo.create_git_tree(elements, base_tree)
    parent = repo.get_git_commit(sha=branch_sha)
    commit = repo.create_git_commit("Trigger CI evaluation pipeline", tree, [parent])
    branch_refs = repo.get_git_ref(f"heads/{branch_name}")
    branch_refs.edit(sha=commit.sha)


def _trigger_circle_pipline(repo_name, branch, token, params=None):
    params = {} if params is None else params
    r = requests.post(
        f"{os.getenv('DLAI_CIRCLE_CI_API_BASE', 'https://circleci.com')}/api/v2/project/gh/{repo_name}/pipeline",
        headers={"Circle-Token": f"{token}", "accept": "application/json"},
        json={"branch": branch, "parameters": params},
    )
    pipeline_data = r.json()
    pipeline_number = pipeline_data["number"]
    print(
        f"Please visit https://app.circleci.com/pipelines/github/{repo_name}/{pipeline_number}"
    )


def trigger_commit_evals(repo_name, branch, token):
    _trigger_circle_pipline(repo_name, branch, token, {"eval-mode": "commit"})


def trigger_release_evals(repo_name, branch, token):
    _trigger_circle_pipline(repo_name, branch, token, {"eval-mode": "release"})

def trigger_full_evals(repo_name, branch, token):
    _trigger_circle_pipline(repo_name, branch, token, {"eval-mode": "full"})


## magic to write and run
from IPython.core.magic import register_cell_magic


@register_cell_magic
def write_and_run(line, cell):
    argz = line.split()
    file = argz[-1]
    mode = "w"
    if len(argz) == 2 and argz[0] == "-a":
        mode = "a"
    with open(file, mode) as f:
        f.write(cell)
    get_ipython().run_cell(cell)


def get_branch() -> str:
    """Generate a random branch name."""
    prefix = "dl-cci"
    adjective = random.choice(adjectives)
    noun = random.choice(nouns)
    number = random.randint(1, 100)

    return f"dl-cci-{adjective}-{noun}-{number}"

Set up our github branch

py 复制代码

from utils import get_repo_name
course_repo = get_repo_name()
course_repo

Output

py 复制代码

'CircleCI-Learning/llmops-course'

py 复制代码

from utils import get_branch
course_branch = get_branch()
course_branch

Output：这是我的分支（不同的人参加这门课会有不同的分支）

py 复制代码

'dl-cci-brightest-pond-67'

可以前往github的仓库查看一下：

https://github.com/CircleCI-Learning/llmops-course/branches

The sample application: AI-powered quiz generator

We are going to build a AI powered quiz generator.

Create the dataset for the quiz.

py 复制代码

human_template  = "{question}"

quiz_bank = """1. Subject: Leonardo DaVinci
   Categories: Art, Science
   Facts:
    - Painted the Mona Lisa
    - Studied zoology, anatomy, geology, optics
    - Designed a flying machine
  
2. Subject: Paris
   Categories: Art, Geography
   Facts:
    - Location of the Louvre, the museum where the Mona Lisa is displayed
    - Capital of France
    - Most populous city in France
    - Where Radium and Polonium were discovered by scientists Marie and Pierre Curie

3. Subject: Telescopes
   Category: Science
   Facts:
    - Device to observe different objects
    - The first refracting telescopes were invented in the Netherlands in the 17th Century
    - The James Webb space telescope is the largest telescope in space. It uses a gold-berillyum mirror

4. Subject: Starry Night
   Category: Art
   Facts:
    - Painted by Vincent van Gogh in 1889
    - Captures the east-facing view of van Gogh's room in Saint-Rémy-de-Provence

5. Subject: Physics
   Category: Science
   Facts:
    - The sun doesn't change color during sunset.
    - Water slows the speed of light
    - The Eiffel Tower in Paris is taller in the summer than the winter due to expansion of the metal."""

Build the prompt template.

python 复制代码

delimiter = "####"

prompt_template = f"""
Follow these steps to generate a customized quiz for the user.
The question will be delimited with four hashtags i.e {delimiter}

The user will provide a category that they want to create a quiz for. Any questions included in the quiz
should only refer to the category.

Step 1:{delimiter} First identify the category user is asking about from the following list:
* Geography
* Science
* Art

Step 2:{delimiter} Determine the subjects to generate questions about. The list of topics are below:

{quiz_bank}

Pick up to two subjects that fit the user's category. 

Step 3:{delimiter} Generate a quiz for the user. Based on the selected subjects generate 3 questions for the user using the facts about the subject.

Use the following format for the quiz:
Question 1:{delimiter} <question 1>

Question 2:{delimiter} <question 2>

Question 3:{delimiter} <question 3>

"""

Use langchain to build the prompt template.

py 复制代码

from langchain.prompts import ChatPromptTemplate
chat_prompt = ChatPromptTemplate.from_messages([("human", prompt_template)])

# print to observe the content or generated object
chat_prompt

Output

python 复制代码

ChatPromptTemplate(input_variables=[], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template="\nFollow these steps to generate a customized quiz for the user.\nThe question will be delimited with four hashtags i.e ####\n\nThe user will provide a category that they want to create a quiz for. Any questions included in the quiz\nshould only refer to the category.\n\nStep 1:#### First identify the category user is asking about from the following list:\n* Geography\n* Science\n* Art\n\nStep 2:#### Determine the subjects to generate questions about. The list of topics are below:\n\n1. Subject: Leonardo DaVinci\n   Categories: Art, Science\n   Facts:\n    - Painted the Mona Lisa\n    - Studied zoology, anatomy, geology, optics\n    - Designed a flying machine\n  \n2. Subject: Paris\n   Categories: Art, Geography\n   Facts:\n    - Location of the Louvre, the museum where the Mona Lisa is displayed\n    - Capital of France\n    - Most populous city in France\n    - Where Radium and Polonium were discovered by scientists Marie and Pierre Curie\n\n3. Subject: Telescopes\n   Category: Science\n   Facts:\n    - Device to observe different objects\n    - The first refracting telescopes were invented in the Netherlands in the 17th Century\n    - The James Webb space telescope is the largest telescope in space. It uses a gold-berillyum mirror\n\n4. Subject: Starry Night\n   Category: Art\n   Facts:\n    - Painted by Vincent van Gogh in 1889\n    - Captures the east-facing view of van Gogh's room in Saint-Rémy-de-Provence\n\n5. Subject: Physics\n   Category: Science\n   Facts:\n    - The sun doesn't change color during sunset.\n    - Water slows the speed of light\n    - The Eiffel Tower in Paris is taller in the summer than the winter due to expansion of the metal.\n\nPick up to two subjects that fit the user's category. \n\nStep 3:#### Generate a quiz for the user. Based on the selected subjects generate 3 questions for the user using the facts about the subject.\n\nUse the following format for the quiz:\nQuestion 1:#### <question 1>\n\nQuestion 2:#### <question 2>\n\nQuestion 3:#### <question 3>\n\n"))])

Choose the LLM.

python 复制代码

from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
llm

Output

python 复制代码

ChatOpenAI(client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, temperature=0.0, openai_api_key='eyJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJhcHAiLCJzdWIiOiIxNzQ2MDYyIiwiYXVkIjoiV0VCIiwiaWF0IjoxNzA4NjAyMDk3LCJleHAiOjE3MTExOTQwOTd9.dnCBPsdZ7nf9TjS3lSwddk6JINpKRuKPB7cjfq0mWts', openai_api_base='http://jupyter-api-proxy.internal.dlai/rev-proxy', openai_organization='', openai_proxy='')

Set up an output parser in LangChain that converts the llm response into a string.

python 复制代码

chain = chat_prompt | llm | output_parser
chain

Output

python 复制代码

ChatPromptTemplate(input_variables=[], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template="\nFollow these steps to generate a customized quiz for the user.\nThe question will be delimited with four hashtags i.e ####\n\nThe user will provide a category that they want to create a quiz for. Any questions included in the quiz\nshould only refer to the category.\n\nStep 1:#### First identify the category user is asking about from the following list:\n* Geography\n* Science\n* Art\n\nStep 2:#### Determine the subjects to generate questions about. The list of topics are below:\n\n1. Subject: Leonardo DaVinci\n   Categories: Art, Science\n   Facts:\n    - Painted the Mona Lisa\n    - Studied zoology, anatomy, geology, optics\n    - Designed a flying machine\n  \n2. Subject: Paris\n   Categories: Art, Geography\n   Facts:\n    - Location of the Louvre, the museum where the Mona Lisa is displayed\n    - Capital of France\n    - Most populous city in France\n    - Where Radium and Polonium were discovered by scientists Marie and Pierre Curie\n\n3. Subject: Telescopes\n   Category: Science\n   Facts:\n    - Device to observe different objects\n    - The first refracting telescopes were invented in the Netherlands in the 17th Century\n    - The James Webb space telescope is the largest telescope in space. It uses a gold-berillyum mirror\n\n4. Subject: Starry Night\n   Category: Art\n   Facts:\n    - Painted by Vincent van Gogh in 1889\n    - Captures the east-facing view of van Gogh's room in Saint-Rémy-de-Provence\n\n5. Subject: Physics\n   Category: Science\n   Facts:\n    - The sun doesn't change color during sunset.\n    - Water slows the speed of light\n    - The Eiffel Tower in Paris is taller in the summer than the winter due to expansion of the metal.\n\nPick up to two subjects that fit the user's category. \n\nStep 3:#### Generate a quiz for the user. Based on the selected subjects generate 3 questions for the user using the facts about the subject.\n\nUse the following format for the quiz:\nQuestion 1:#### <question 1>\n\nQuestion 2:#### <question 2>\n\nQuestion 3:#### <question 3>\n\n"))])
| ChatOpenAI(client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, temperature=0.0, openai_api_key='eyJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJhcHAiLCJzdWIiOiIxNzQ2MDYyIiwiYXVkIjoiV0VCIiwiaWF0IjoxNzA4NjAyMDk3LCJleHAiOjE3MTExOTQwOTd9.dnCBPsdZ7nf9TjS3lSwddk6JINpKRuKPB7cjfq0mWts', openai_api_base='http://jupyter-api-proxy.internal.dlai/rev-proxy', openai_organization='', openai_proxy='')
| StrOutputParser()

Build the function 'assistance_chain' to put together all steps above.

python 复制代码

# taking all components and making reusable as one piece
def assistant_chain(
    system_message,
    human_template="{question}",
    llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
    output_parser=StrOutputParser()):
  
  chat_prompt = ChatPromptTemplate.from_messages([
      ("system", system_message),
      ("human", human_template),
  ])
  return chat_prompt | llm | output_parser

Evaluations

Create the function 'eval_expected_words' for the first example.

py 复制代码

def eval_expected_words(
    system_message,
    question,
    expected_words,
    human_template="{question}",
    llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
    output_parser=StrOutputParser()):
    
  assistant = assistant_chain(
      system_message,
      human_template,
      llm,
      output_parser)
    
  
  answer = assistant.invoke({"question": question})
    
  print(answer)
    
  assert any(word in answer.lower() \
             for word in expected_words), \
    f"Expected the assistant questions to include \
    '{expected_words}', but it did not"

Test: Generate a quiz about science.

py 复制代码

question  = "Generate a quiz about science."
expected_words = ["davinci", "telescope", "physics", "curie"]

Create the eval.

py 复制代码

eval_expected_words(
    prompt_template,
    question,
    expected_words
)

Output

py 复制代码

Step 1:#### First identify the category user is asking about from the following list:
* Geography
* Science
* Art

Step 2:#### Determine the subjects to generate questions about. The list of topics are below:

1. Subject: Telescopes
   Category: Science
   Facts:
    - Device to observe different objects
    - The first refracting telescopes were invented in the Netherlands in the 17th Century
    - The James Webb space telescope is the largest telescope in space. It uses a gold-berillyum mirror

2. Subject: Physics
   Category: Science
   Facts:
    - The sun doesn't change color during sunset.
    - Water slows the speed of light
    - The Eiffel Tower in Paris is taller in the summer than the winter due to expansion of the metal.

Based on the selected subjects, I will generate 3 questions for your science quiz.

Question 1:#### What is the purpose of a telescope?
Question 2:#### In which country were the first refracting telescopes invented in the 17th Century?
Question 3:#### Why is the Eiffel Tower in Paris taller in the summer than the winter?

Create the function 'evaluate_refusal' to define a failing test case where the app should decline to answer.

py 复制代码

def evaluate_refusal(
    system_message,
    question,
    decline_response,
    human_template="{question}", 
    llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
    output_parser=StrOutputParser()):
    
  assistant = assistant_chain(human_template, 
                              system_message,
                              llm,
                              output_parser)
  
  answer = assistant.invoke({"question": question})
  print(answer)
  
  assert decline_response.lower() in answer.lower(), \
    f"Expected the bot to decline with \
    '{decline_response}' got {answer}"

Define a new question (which should be a bad request)

py 复制代码

question  = "Generate a quiz about Rome."
decline_response = "I'm sorry"

Create the refusal eval.

Note: The following function call will throw an exception.

py 复制代码

evaluate_refusal(
    prompt_template,
    question,
    decline_response
)

Output

py 复制代码

#### Step 1:
I would like to create a quiz about Rome.

#### Step 2:
I will choose the subjects "Paris" and "Starry Night" as they both fall under the category of Art and Geography.

#### Step 3:
Question 1:####
In which city is the Louvre located, the museum where the Mona Lisa is displayed?
a) Rome
b) Paris
c) London
d) Madrid

Question 2:####
Who painted the famous artwork "Starry Night" in 1889?
a) Leonardo DaVinci
b) Vincent van Gogh
c) Michelangelo
d) Pablo Picasso

Question 3:####
What does "Starry Night" by Vincent van Gogh capture?
a) A view of the Eiffel Tower
b) A view of van Gogh's room in Saint-Rémy-de-Provence
c) A scene from the Louvre museum
d) A landscape of Rome
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
Cell In[21], line 1
----> 1 evaluate_refusal(
      2     prompt_template,
      3     question,
      4     decline_response
      5 )

Cell In[19], line 17, in evaluate_refusal(system_message, question, decline_response, human_template, llm, output_parser)
     14 answer = assistant.invoke({"question": question})
     15 print(answer)
---> 17 assert decline_response.lower() in answer.lower(), \
     18   f"Expected the bot to decline with \
     19   '{decline_response}' got {answer}"

AssertionError: Expected the bot to decline with     'I'm sorry' got #### Step 1:
I would like to create a quiz about Rome.

#### Step 2:
I will choose the subjects "Paris" and "Starry Night" as they both fall under the category of Art and Geography.

#### Step 3:
Question 1:####
In which city is the Louvre located, the museum where the Mona Lisa is displayed?
a) Rome
b) Paris
c) London
d) Madrid

Question 2:####
Who painted the famous artwork "Starry Night" in 1889?
a) Leonardo DaVinci
b) Vincent van Gogh
c) Michelangelo
d) Pablo Picasso

Question 3:####
What does "Starry Night" by Vincent van Gogh capture?
a) A view of the Eiffel Tower
b) A view of van Gogh's room in Saint-Rémy-de-Provence
c) A scene from the Louvre museum
d) A landscape of Rome

Running evaluations in a CircleCI pipeline

Put all these steps together into files to reuse later.

Note: fixing the system_message by adding additional rules:

Only use explicit matches for the category, if the category is not an exact match to categories in the quiz bank, answer that you do not have information.
If the user asks a question about a subject you do not have information about in the quiz bank, answer "I'm sorry I do not have information about that".

py 复制代码

%%writefile app.py
from langchain.prompts                import ChatPromptTemplate
from langchain.chat_models            import ChatOpenAI
from langchain.schema.output_parser   import StrOutputParser

delimiter = "####"

quiz_bank = """1. Subject: Leonardo DaVinci
   Categories: Art, Science
   Facts:
    - Painted the Mona Lisa
    - Studied zoology, anatomy, geology, optics
    - Designed a flying machine
  
2. Subject: Paris
   Categories: Art, Geography
   Facts:
    - Location of the Louvre, the museum where the Mona Lisa is displayed
    - Capital of France
    - Most populous city in France
    - Where Radium and Polonium were discovered by scientists Marie and Pierre Curie

3. Subject: Telescopes
   Category: Science
   Facts:
    - Device to observe different objects
    - The first refracting telescopes were invented in the Netherlands in the 17th Century
    - The James Webb space telescope is the largest telescope in space. It uses a gold-berillyum mirror

4. Subject: Starry Night
   Category: Art
   Facts:
    - Painted by Vincent van Gogh in 1889
    - Captures the east-facing view of van Gogh's room in Saint-Rémy-de-Provence

5. Subject: Physics
   Category: Science
   Facts:
    - The sun doesn't change color during sunset.
    - Water slows the speed of light
    - The Eiffel Tower in Paris is taller in the summer than the winter due to expansion of the metal.
"""

system_message = f"""
Follow these steps to generate a customized quiz for the user.
The question will be delimited with four hashtags i.e {delimiter}

The user will provide a category that they want to create a quiz for. Any questions included in the quiz
should only refer to the category.

Step 1:{delimiter} First identify the category user is asking about from the following list:
* Geography
* Science
* Art

Step 2:{delimiter} Determine the subjects to generate questions about. The list of topics are below:

{quiz_bank}

Pick up to two subjects that fit the user's category. 

Step 3:{delimiter} Generate a quiz for the user. Based on the selected subjects generate 3 questions for the user using the facts about the subject.

Use the following format for the quiz:
Question 1:{delimiter} <question 1>

Question 2:{delimiter} <question 2>

Question 3:{delimiter} <question 3>

Additional rules:

- Only use explicit matches for the category, if the category is not an exact match to categories in the quiz bank, answer that you do not have information.
- If the user asks a question about a subject you do not have information about in the quiz bank, answer "I'm sorry I do not have information about that".
"""

"""
  Helper functions for writing the test cases
"""

def assistant_chain(
    system_message=system_message,
    human_template="{question}",
    llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
    output_parser=StrOutputParser()):

  chat_prompt = ChatPromptTemplate.from_messages([
      ("system", system_message),
      ("human", human_template),
  ])
  return chat_prompt | llm | output_parser

Output

py 复制代码

Overwriting app.py

Create new file to include the evals.

py 复制代码

%%writefile test_assistant.py
from app import assistant_chain
from app import system_message
from langchain.prompts                import ChatPromptTemplate
from langchain.chat_models            import ChatOpenAI
from langchain.schema.output_parser   import StrOutputParser

import os

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

def eval_expected_words(
    system_message,
    question,
    expected_words,
    human_template="{question}",
    llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
    output_parser=StrOutputParser()):

  assistant = assistant_chain(system_message)
  answer = assistant.invoke({"question": question})
  print(answer)
    
  assert any(word in answer.lower() \
             for word in expected_words), \
    f"Expected the assistant questions to include \
    '{expected_words}', but it did not"

def evaluate_refusal(
    system_message,
    question,
    decline_response,
    human_template="{question}", 
    llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
    output_parser=StrOutputParser()):
    
  assistant = assistant_chain(human_template, 
                              system_message,
                              llm,
                              output_parser)
  
  answer = assistant.invoke({"question": question})
  print(answer)
  
  assert decline_response.lower() in answer.lower(), \
    f"Expected the bot to decline with \
    '{decline_response}' got {answer}"

"""
  Test cases
"""

def test_science_quiz():
  
  question  = "Generate a quiz about science."
  expected_subjects = ["davinci", "telescope", "physics", "curie"]
  eval_expected_words(
      system_message,
      question,
      expected_subjects)

def test_geography_quiz():
  question  = "Generate a quiz about geography."
  expected_subjects = ["paris", "france", "louvre"]
  eval_expected_words(
      system_message,
      question,
      expected_subjects)

def test_refusal_rome():
  question  = "Help me create a quiz about Rome"
  decline_response = "I'm sorry"
  evaluate_refusal(
      system_message,
      question,
      decline_response)

Output

py 复制代码

Overwriting test_assistant.py

The CircleCI config file

Now let's set up our tests to run automatically in CircleCI.

For this course, we've created a working CircleCI config file. Let's take a look at the configuration.

py 复制代码

!cat circle_config.yml

Output

yaml 复制代码

version: 2.1
orbs:
  # The python orb contains a set of prepackaged circleci configuration you can use repeatedly in your configurations files
  # Orb commands and jobs help you with common scripting around a language/tool
  # so you dont have to copy and paste it everywhere.
  # See the orb documentation here: https://circleci.com/developer/orbs/orb/circleci/python
  python: circleci/python@2.1.1

parameters:
  eval-mode:
    type: string
    default: "commit"

workflows:
  evaluate-commit:
    when:
      equal: [ commit, << pipeline.parameters.eval-mode >> ]
    jobs:
      - run-commit-evals:
          context:
            - dl-ai-courses
  evaluate-release:
    when:
      equal: [ release, << pipeline.parameters.eval-mode >> ]
    jobs:
      - run-pre-release-evals:
          context:
            - dl-ai-courses
  evaluate-all:
    when:
      equal: [ full, << pipeline.parameters.eval-mode >> ]
    jobs:
      - run-manual-evals:
          context:
            - dl-ai-courses
  report-evals:
    when:
      equal: [ report, << pipeline.parameters.eval-mode >> ]
    jobs:
      - store-eval-artifacts:
          context:
            - dl-ai-courses

jobs:
  run-commit-evals:  # This is the name of the job, feel free to change it to better match what you're trying to do!
    # These next lines defines a docker executors: https://circleci.com/docs/2.0/executor-types/
    # You can specify an image from dockerhub or use one of the convenience images from CircleCI's Developer Hub
    # A list of available CircleCI docker convenience images are available here: https://circleci.com/developer/images/image/cimg/python
    # The executor is the environment in which the steps below will be executed - below will use a python 3.9 container
    # Change the version below to your required version of python
    docker:
      - image: cimg/python:3.10.5
    # Checkout the code as the first step. This is a dedicated CircleCI step.
    # The python orb's install-packages step will install the dependencies from a Pipfile via Pipenv by default.
    # Here we're making sure we use just use the system-wide pip. By default it uses the project root's requirements.txt.
    # Then run your tests!
    # CircleCI will report the results back to your VCS provider.
    steps:
      - checkout
      - python/install-packages:
          pkg-manager: pip
          # app-dir: ~/project/package-directory/  # If your requirements.txt isn't in the root directory.
          # pip-dependency-file: test-requirements.txt  # if you have a different name for your requirements file, maybe one that combines your runtime and test requirements.
      - run:
          name: Run assistant evals.
          command: python -m pytest --junitxml results.xml test_assistant.py
      - store_test_results:
          path: results.xml
  run-pre-release-evals:
    docker:
      - image: cimg/python:3.10.5
    steps:
      - checkout
      - python/install-packages:
          pkg-manager: pip
          # app-dir: ~/project/package-directory/  # If your requirements.txt isn't in the root directory.
          # pip-dependency-file: test-requirements.txt  # if you have a different name for your requirements file, maybe one that combines your runtime and test requirements.
      - run:
          name: Run release evals.
          command: python -m pytest --junitxml results.xml test_release_evals.py
      - store_test_results:
          path: results.xml
  run-manual-evals: 
    docker:
      - image: cimg/python:3.10.5
    steps:
      - checkout
      - python/install-packages:
          pkg-manager: pip
          # app-dir: ~/project/package-directory/  # If your requirements.txt isn't in the root directory.
          # pip-dependency-file: test-requirements.txt  # if you have a different name for your requirements file, maybe one that combines your runtime and test requirements.
      - run:
          name: Run end to end evals.
          command: python -m pytest --junitxml results.xml test_assistant.py test_release_evals.py
      - store_test_results:
          path: results.xml
  store-eval-artifacts:
    docker:
      - image: cimg/python:3.10.5
    steps:
      - checkout
      - python/install-packages:
          pkg-manager: pip
          # app-dir: ~/project/package-directory/  # If your requirements.txt isn't in the root directory.
          # pip-dependency-file: test-requirements.txt  # if you have a different name for your requirements file, maybe one that combines your runtime and test requirements.
      - run:
          name: Save eval to html file
          command: python save_eval_artifacts.py
      - store_artifacts:
          path: /tmp/eval_results.html
          destination: eval_results.html

Run the per-commit evals

Push files into the github repo.

py 复制代码

from utils import push_files
push_files(course_repo, course_branch, ["app.py", "test_assistant.py"])

Output

py 复制代码

uploading app.py
uploading test_assistant.py
pushing files to: dl-cci-brightest-pond-67

Trigger the pipeline in CircleCI pipeline.

py 复制代码

from utils import trigger_commit_evals
trigger_commit_evals(course_repo, course_branch, cci_api_key)

Output：点击链接前往circleci界面查看集成后运行的结果：是否通过测试

py 复制代码

Please visit https://app.circleci.com/pipelines/github/CircleCI-Learning/llmops-course/3011

但是我在circleci里面的集成测试是失败的。