robotgpt 0.0.14

RobotGPT LLM 支持Langchain

RobotGPT LLM

RobotGPT 支持langchain-0.1.4

Quick Install

pip install robotgpt

使用样例

agent块式输出:

from robotgpt.robotgpt import RobotGPTLLM
from langchain.agents import AgentType, initialize_agent
from langchain.tools import BaseTool, StructuredTool, Tool, tool
class CoffeeMaking:
    def inference(self):
        return "Making coffee requires a coffee machine, coffee beans, sugar packets, and paper cups."

class ImageObjectDetect:
    def inference(self, obj):
        if obj == "sugar packets":
            return "No "+obj
        return "There is a "+obj

class AskCustomer:
    def inference(self,):
        return "Hello! We don’t have any sugar packets at the moment. Do you need to add milk?"

robotgpt_api_url = "https://dataai.harix.iamidata.com/llm/api/ask"  #流式智能问答统一适配服务，从用户控制台购买https://console.openai.iamidata.com/api/apiList
model_name = "openai/gpt-3.5-turbo-0613"
robotgpt_api_token = "Your token" #https://dataai-doc.dataarobotics.com/docs/getting-started/authentication
llm = RobotGPTLLM(temperature=0, model_name=model_name,robotgpt_api_token=robotgpt_api_token,robotgpt_api_url=robotgpt_api_url)

imgObjDetect = ImageObjectDetect()
tools = [
    Tool.from_function(
        func=CoffeeMaking.inference,
        name="Coffee making",
        description="useful for when the user needs to make coffee."
        # coroutine= ... <- you can specify an async method if desired as well
    ),
    Tool.from_function(
        func=imgObjDetect.inference,
        name="Determine whether the object exists in the picture",
        description="useful for when you want to know what is inside the photo. receives object as input. The input to this tool should be a string, representing the object. "
        # coroutine= ... <- you can specify an async method if desired as well
    ),
    Tool.from_function(
        func=AskCustomer.inference,
        name="Ask the customer whether to add milk",
        description="useful for when making coffee without sugar packets, you can ask the customer whether you need to add milk. The input to this tool should be a bool, represents whether there is a sugar packet."
        # coroutine= ... <- you can specify an async method if desired as well
    ),
]
agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run(
    "给我做杯咖啡"
)

块式输出:

from robotgpt.robotgpt import RobotGPTLLM
from langchain.schema import HumanMessage

robotgpt_api_url = "https://dataai.harix.iamidata.com/llm/api/ask"  #流式智能问答统一适配服务
model_name = "openai/gpt-3.5-turbo-0613"
robotgpt_api_token = "Your token" #https://dataai-doc.dataarobotics.com/docs/getting-started/authentication

llm = RobotGPTLLM(temperature=0, model_name=model_name,robotgpt_api_token=robotgpt_api_token,robotgpt_api_url=robotgpt_api_url)
resp = llm([HumanMessage(content="Write me a song about sparkling water.")])
print(resp)

流式输出:

from langchain.callbacks import StreamingStdOutCallbackHandler
from langchain.schema import HumanMessage
from robotgpt.robotgpt import RobotGPTLLM
robotgpt_api_url = "https://dataai.harix.iamidata.com/llm/api/ask"  #流式智能问答统一适配服务
model_name = "openai/gpt-3.5-turbo-0613"
robotgpt_api_token = "Your token" #https://dataai-doc.dataarobotics.com/docs/getting-started/authentication
chat = RobotGPTLLM(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0,model_name=model_name,robotgpt_api_token=robotgpt_api_token,robotgpt_api_url=robotgpt_api_url)
resp = chat([HumanMessage(content="Write me a song about sparkling water.")])
print(resp)