让LLM精确计算两数之和

让LLM 100%精确计算两数之和

Just show code

LLM chain

from langchain.prompts import PromptTemplate
from langchain.chat_models.azure_openai import AzureChatOpenAI
from langchain.chains import LLMChain
from langchain.utilities import PythonREPL

llm = AzureChatOpenAI(deployment_name="保密省略", model_name="gpt-35-turbo",
                      openai_api_version="2023-03-15-preview")

multiply_by_python_promot = PromptTemplate(template="写一段python代码,用于计算{question},只输出代码",
                                           input_variables=["question"])
math_by_python_chain = LLMChain(llm=llm, prompt=multiply_by_python_promot, output_key="answer")

answer = math_by_python_chain.run("342*932")

python_repl = PythonREPL()
print(f"GPT Answer: {answer}")
print(f"GPT Answer with Python exec: {python_repl.run(answer)}")
print(f"Calc Answer: {342*932}")


# ======Output======
"""
GPT Answer: print(342*932)
GPT Answer with Python exec: 318744

Calc Answer: 318744
"""

LLM agent

from langchain.agents import initialize_agent, Tool, AgentType
from langchain.chat_models import AzureChatOpenAI

"""
prompt位置: https://github.com/hwchase17/langchain/blob/master/langchain/agents/mrkl/prompt.py
思路参考paper: https://arxiv.org/pdf/2205.00445.pdf
"""
llm = AzureChatOpenAI(deployment_name="保密省略", model_name="gpt-35-turbo",
                      openai_api_version="2023-03-15-preview", temperature=0)

# 只对加法进行了调整
def add(s: str) -> int:
    a, b = s.split(",")
    a = int(a.strip())
    b = int(b.strip())
    return a + b

def subtract(a: int, b: int) -> int:
    return a - b


def multiply(a: int, b: int) -> int:
    return a * b


tools = [
    Tool(
        name="Add", func=add,
        description="Used to add two numerical values."
    ),
    Tool(name="Subtract", func=subtract,
         description="Used to subtract two numerical values"
         ),
    Tool(name="Multiply", func=multiply,
         description="Used to multiply two numerical values"
         )
]

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Calculate the sum of 222231 and 33312")


# Test Case

"""
> Entering new AgentExecutor chain...
I need to add two numerical values
Action: Add
Action Input: 222231, 33312
Observation: 255543
Thought:I now know the final answer
Final Answer: 255543

> Finished chain.
"""

Prompt:

PREFIX = """Answer the following questions as best you can. You have access to the following tools:"""
FORMAT_INSTRUCTIONS = """Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question"""
SUFFIX = """Begin!

Question: {input}
Thought:{agent_scratchpad}"""


"""介绍
Action，就是根据用户的输入，选择应该选取哪一个 Tool，然后行动。
Action Input，就是根据需要使用的 Tool，从用户的输入里提取出相关的内容，可以输入到 Tool 里面。
Oberservation，就是观察通过使用 Tool 得到的一个输出结果。
Thought，就是再看一眼用户的输入，判断一下该怎么做。
Final Answer，就是 Thought 在看到 Obersavation 之后，给出的最终输出。

https://github.com/hwchase17/langchain/blob/master/langchain/agents/mrkl/output_parser.py
最终会根据Final Answer 解析出最后的答案
"""

Other tools

whisper 语音转文本

语音合成：Azure认知服务(一定字符内免费)， 百度开源PaddleSpeech TTSExecutor

数字人：https://www.d-id.com/

CLip 图片搜索

Stable Diffusion 图片生成

from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipeline.to("cuda")
image = pipeline("a photograph of an astronaut riding a horse").images[0]
image