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)
Metadata
Release files for robotgpt 0.0.14
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| robotgpt-0.0.14.tar.gz | 19.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| robotgpt-0.0.14-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 42.6 kB
Release files / robotgpt-0.0.14.tar.gz
| Download URL | robotgpt-0.0.14.tar.gz |
|---|---|
| Size | 19.3 kB |
| Tags | Source |
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