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from langchain_core.utils.function_calling import convert_to_openai_function
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.memory import ConversationBufferWindowMemory
from langchain.schema.runnable import RunnablePassthrough
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.agents import AgentExecutor
from langchain_groq import ChatGroq
from functions import book_slot , check_slots , suggest_specialization , reschedule_event,delete_event
def create_agent(general_prompt_template):
# print("get user Id**********************",user_id)
API_KEY = "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"
tools = [book_slot , delete_event , check_slots , suggest_specialization , reschedule_event]
functions = [convert_to_openai_function(f) for f in tools]
llm = ChatGroq(
model="llama-3.1-70b-versatile",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=API_KEY
).bind_functions(functions=functions)
prompt = ChatPromptTemplate.from_messages([("system", general_prompt_template),
MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad")])
memory = ConversationBufferWindowMemory(memory_key="chat_history" , return_messages=True, k=5)
chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | llm | OpenAIFunctionsAgentOutputParser()
agent_executor = AgentExecutor(
agent=chain, tools=tools, memory=memory, verbose=True)
return agent_executor
# give me available slots at 17 september 2024 , book schedule at 10:30 AM 17 september 2024 , |