L1 Simple_ReAct_Agent

参考自https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph，以下为代码的实现。

Basic ReAct Agent(manual action)

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import openai
import re
import httpx
import os
from dotenv import load_dotenv, find_dotenv

OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
from openai import OpenAI

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client = OpenAI(
    api_key=OPENAI_API_KEY,
    base_url="https://api.chatanywhere.tech/v1"
)

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chat_completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello world"}]
)

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chat_completion.choices[0].message.content

'Hello! How can I assist you today?'

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prompt = """
You run in a loop of Thought, Action, PAUSE, Observation.
At the end of the loop you output an Answer
Use Thought to describe your thoughts about the question you have been asked.
Use Action to run one of the actions available to you - then return PAUSE.
Observation will be the result of running those actions.

Your available actions are:

calculate:
e.g. calculate: 4 * 7 / 3
Runs a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary

average_dog_weight:
e.g. average_dog_weight: Collie
returns average weight of a dog when given the breed

Example session:

Question: How much does a Bulldog weigh?
Thought: I should look the dogs weight using average_dog_weight
Action: average_dog_weight: Bulldog
PAUSE

You will be called again with this:

Observation: A Bulldog weights 51 lbs

You then output:

Answer: A bulldog weights 51 lbs
""".strip()

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class Agent:
    def __init__(self, system=""):
        self.system = system
        self.messages = []
        if self.system:
            self.messages.append({"role": "system", "content": system})

    def __call__(self, message):
        self.messages.append({"role": "user", "content": message})
        result = self.execute()
        self.messages.append({"role": "assistant", "content": result})
        return result

    def execute(self):
        completion = client.chat.completions.create(
            model="gpt-3.5-turbo",
            temperature=0,
            messages=self.messages
        )
        return completion.choices[0].message.content

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def calculate(what):
    return eval(what)

def average_dog_weight(name):
    if name in "Scottish Terrier":
        return("Scottish Terriers average 20 lbs")
    elif name in "Border Collie":
        return("a Border Collies weight is 37 lbs")
    elif name in "Toy Poodle":
        return("a toy poodles average weight is 7 lbs")
    else:
        return("An average dog weights 50 lbs")

known_actions = {
    "calculate": calculate,
    "average_dog_weight": average_dog_weight
}

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abot = Agent(prompt)
result = abot("How much does a toy poodle weigh?")
print(result)

Thought: I should look up the average weight of a Toy Poodle using the average_dog_weight action.
Action: average_dog_weight: Toy Poodle
PAUSE

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result = average_dog_weight("Toy Poodle")

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result

'a toy poodles average weight is 7 lbs'

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next_prompt = "Observation: {}".format(result)

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abot(next_prompt)

'Answer: A Toy Poodle weighs 7 lbs'

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abot.messages

[{'role': 'system',
  'content': 'You run in a loop of Thought, Action, PAUSE, Observation.\nAt the end of the loop you output an Answer\nUse Thought to describe your thoughts about the question you have been asked.\nUse Action to run one of the actions available to you - then return PAUSE.\nObservation will be the result of running those actions.\n\nYour available actions are:\n\ncalculate:\ne.g. calculate: 4 * 7 / 3\nRuns a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary\n\naverage_dog_weight:\ne.g. average_dog_weight: Collie\nreturns average weight of a dog when given the breed\n\nExample session:\n\nQuestion: How much does a Bulldog weigh?\nThought: I should look the dogs weight using average_dog_weight\nAction: average_dog_weight: Bulldog\nPAUSE\n\nYou will be called again with this:\n\nObservation: A Bulldog weights 51 lbs\n\nYou then output:\n\nAnswer: A bulldog weights 51 lbs'},
 {'role': 'user', 'content': 'How much does a toy poodle weigh?'},
 {'role': 'assistant',
  'content': 'Thought: I should look up the average weight of a Toy Poodle using the average_dog_weight action.\nAction: average_dog_weight: Toy Poodle\nPAUSE'},
 {'role': 'user',
  'content': 'Observation: a toy poodles average weight is 7 lbs'},
 {'role': 'assistant', 'content': 'Answer: A Toy Poodle weighs 7 lbs'}]

A little more complex question

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abot = Agent(prompt)

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question = """I have 2 dogs, a border collie and a scottish terrier. \
What is their combined weight"""
abot(question)

'Thought: I can find the average weight of a Border Collie and a Scottish Terrier using the average_dog_weight action, then calculate their combined weight.\n\nAction: average_dog_weight: Border Collie\nPAUSE'

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print(abot.messages[-1]['content'])

Thought: I can find the average weight of a Border Collie and a Scottish Terrier using the average_dog_weight action, then calculate their combined weight.

Action: average_dog_weight: Border Collie
PAUSE

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next_prompt = "Observation: {}".format(average_dog_weight("Border Collie"))
print(next_prompt)

Observation: a Border Collies weight is 37 lbs

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abot(next_prompt)

'Action: average_dog_weight: Scottish Terrier\nPAUSE'

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next_prompt = "Observation: {}".format(average_dog_weight("Scottish Terrier"))
print(next_prompt)

Observation: Scottish Terriers average 20 lbs

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abot(next_prompt)

'Action: calculate: 37 + 20\nPAUSE'

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next_prompt = "Observation: {}".format(eval("37 + 20"))
print(next_prompt)

Observation: 57

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abot(next_prompt)

'Answer: The combined weight of a Border Collie and a Scottish Terrier is 57 lbs'

Add loop

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action_re = re.compile(r'^Action: (\w+): (.*)$')

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def query(question, max_turns=5):
    i = 0
    bot = Agent(prompt)
    next_prompt = question
    while i < max_turns:
        i += 1
        result = bot(next_prompt)
        print(result)
        actions = [
            action_re.match(a) 
            for a in result.split('\n') 
            if action_re.match(a)
        ] 
        if actions:
            # There is an action to run
            action, action_input = actions[0].groups()
            if action not in known_actions:
                raise Exception("Unknown action: {}: {}".format(action, action_input))
            print(" -- running {} {}".format(action, action_input))
            observation = known_actions[action](action_input)
            print("Observation:", observation)
            next_prompt = "Observation: {}".format(observation)
        else:
            return

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question = """I have 2 dogs, a border collie and a scottish terrier. \
What is their combined weight"""
query(question)

Thought: I can find the average weight of a Border Collie and a Scottish Terrier using the average_dog_weight action, then calculate their combined weight.

Action: average_dog_weight: Border Collie
PAUSE
 -- running average_dog_weight Border Collie
Observation: a Border Collies weight is 37 lbs
Action: average_dog_weight: Scottish Terrier
PAUSE
 -- running average_dog_weight Scottish Terrier
Observation: Scottish Terriers average 20 lbs
Action: calculate: 37 + 20
PAUSE
 -- running calculate 37 + 20
Observation: 57
Answer: The combined weight of a Border Collie and a Scottish Terrier is 57 lbs