128 lines
4.9 KiB
Python
128 lines
4.9 KiB
Python
from langchain.embeddings.openai import OpenAIEmbeddings
|
||
from langchain.chat_models import ChatOpenAI
|
||
from langchain.memory import VectorStoreRetrieverMemory
|
||
import faiss
|
||
from langchain.docstore import InMemoryDocstore
|
||
from langchain.vectorstores import FAISS
|
||
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent, initialize_agent
|
||
from langchain.chains import LLMChain
|
||
|
||
from agent.tools.MyTimer import MyTimer
|
||
from agent.tools.QueryTime import QueryTime
|
||
from agent.tools.Weather import Weather
|
||
from agent.tools.Calculator import Calculator
|
||
from agent.tools.CheckSensor import CheckSensor
|
||
from agent.tools.Switch import Switch
|
||
from agent.tools.Knowledge import Knowledge
|
||
from agent.tools.Say import Say
|
||
from agent.tools.QueryTimerDB import QueryTimerDB
|
||
|
||
|
||
class FayAgentCore():
|
||
def __init__(self):
|
||
|
||
#使用open ai embedding
|
||
embedding_size = 1536 # OpenAIEmbeddings 的维度
|
||
index = faiss.IndexFlatL2(embedding_size)
|
||
embedding_fn = OpenAIEmbeddings()
|
||
|
||
#创建llm
|
||
llm = ChatOpenAI(model="gpt-4-1106-preview")
|
||
|
||
#创建向量数据库
|
||
vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {})
|
||
|
||
# 创建记忆
|
||
retriever = vectorstore.as_retriever(search_kwargs=dict(k=2))
|
||
memory = VectorStoreRetrieverMemory(memory_key="chat_history", retriever=retriever)
|
||
|
||
# 保存对话内容到记忆
|
||
memory.save_context({"Human": "你知道我在那里吗?"}, {"ai": "广州市"})
|
||
|
||
#创建agent chain
|
||
my_timer = MyTimer()
|
||
query_time_tool = QueryTime()
|
||
weather_tool = Weather()
|
||
calculator_tool = Calculator()
|
||
check_sensor_tool = CheckSensor()
|
||
switch_tool = Switch()
|
||
knowledge_tool = Knowledge()
|
||
say_tool = Say()
|
||
query_timer_db_tool = QueryTimerDB()
|
||
tools = [
|
||
Tool(
|
||
name=my_timer.name,
|
||
func=my_timer.run,
|
||
description=my_timer.description
|
||
),
|
||
Tool(
|
||
name=query_time_tool.name,
|
||
func=query_time_tool.run,
|
||
description=query_time_tool.description
|
||
),
|
||
Tool(
|
||
name=weather_tool.name,
|
||
func=weather_tool.run,
|
||
description=weather_tool.description
|
||
),
|
||
Tool(
|
||
name=calculator_tool.name,
|
||
func=calculator_tool.run,
|
||
description=calculator_tool.description
|
||
),
|
||
Tool(
|
||
name=check_sensor_tool.name,
|
||
func=check_sensor_tool.run,
|
||
description=check_sensor_tool.description
|
||
),
|
||
Tool(
|
||
name=switch_tool.name,
|
||
func=switch_tool.run,
|
||
description=switch_tool.description
|
||
),
|
||
Tool(
|
||
name=knowledge_tool.name,
|
||
func=knowledge_tool.run,
|
||
description=knowledge_tool.description
|
||
),
|
||
Tool(
|
||
name=say_tool.name,
|
||
func=say_tool.run,
|
||
description=say_tool.description
|
||
),
|
||
Tool(
|
||
name=query_timer_db_tool.name,
|
||
func=query_timer_db_tool.run,
|
||
description=query_timer_db_tool.description
|
||
),
|
||
|
||
]
|
||
prefix = """你是运行在一个智慧农业实验箱的ai数字人，你叫Fay,你的主要作用是，陪伴主人生活、工作，以及协助主人打理好农业种植箱里的农作物. 农业箱内设备会通过一套不成熟的iotm系统自动管理。你可以调用以下工具来完成工作，若缺少必要的工具也请告诉我。所有回复请使用中文，遇到需要提醒的问题也告诉我。若你感觉是我在和你交流请语音回复我。若你需要计算一个新的时间请先获取当前时间。"""
|
||
suffix = """Begin!"
|
||
|
||
{chat_history}
|
||
Question: {input}
|
||
{agent_scratchpad}"""
|
||
|
||
prompt = ZeroShotAgent.create_prompt(
|
||
tools,
|
||
prefix=prefix,
|
||
suffix=suffix,
|
||
input_variables=["input", "chat_history", "agent_scratchpad"],
|
||
)
|
||
llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
|
||
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
|
||
# agent = initialize_agent(agent="chat-conversational-react-description",
|
||
# tools=tools, llm=llm, verbose=True,
|
||
# max_iterations=3, early_stopping_method="generate", memory=memory, handle_parsing_errors=True)
|
||
self.agent_chain = AgentExecutor.from_agent_and_tools(
|
||
agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=True
|
||
)
|
||
|
||
def run(self, input_text):
|
||
return self.agent_chain.run(input_text)
|
||
|
||
if __name__ == "__main__":
|
||
agent = FayAgentCore()
|
||
print(agent.run("你好"))
|