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from langchain.agents import Tool, AgentType, initialize_agent
from langchain.memory import ConversationBufferMemory
# from langchain.utilities import DuckDuckGoSearchAPIWrapper
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import AgentExecutor
from langchain import hub
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import ReActSingleInputOutputParser
from langchain.tools.render import render_text_description
import os
from tools.kg_search import lookup_kg
from dotenv import load_dotenv
from langchain.agents import Tool
from langchain_core.prompts import PromptTemplate
load_dotenv()
os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY")
llm = ChatGoogleGenerativeAI(
model= "gemini-1.5-flash-latest",
temperature = 0
)
# search = DuckDuckGoSearchAPIWrapper()
#
# search_tool = Tool(name="Current Search",
# func=search.run,
# description="Useful when you need to answer questions about detail jobs information or search a job."
# )
kg_query = Tool(
name = 'Query Knowledge Graph',
func = lookup_kg,
description='Useful for when you need to answer questions about job posts.'
)
tools = [kg_query]
# memory = ConversationBufferMemory(memory_key="chat_history")
#
# agent_chain = initialize_agent(tools,
# llm,
# agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
# memory=memory,
# verbose=True)
# agent_prompt = hub.pull("hwchase17/react-chat")
with open("prompts/react_prompt_v2.txt", "r") as file:
react_template = file.read()
react_prompt = PromptTemplate(
input_variables = ["tools", "tool_names", "input", "agent_scratchpad", "chat_history"],
template = react_template
)
prompt = react_prompt.partial(
tools = render_text_description(tools),
tool_names = ", ".join([t.name for t in tools]),
)
llm_with_stop = llm.bind(stop=["\nObservation"])
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
"chat_history": lambda x: x["chat_history"],
}
| prompt
| llm_with_stop
| ReActSingleInputOutputParser()
)
memory = ConversationBufferMemory(memory_key="chat_history")
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, memory=memory)
# result = agent_executor.invoke({"input": "Have any company recruit Machine Learning jobs?"})
# print(result)
# result = agent_chain.run(input = "Have any company recruit Machine Learning jobs?")
# print(result)
# question = {
# "input": "What did I just ask?"
# }
#
# result = agent_executor.invoke(question)
# print(result)
if __name__ == "__main__":
while True:
try:
question = input("> ")
result = agent_executor.invoke({
"input": question
})
except:
break
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