59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
from langgraph.prebuilt import create_react_agent
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from langchain_ollama import ChatOllama
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from typing import Annotated
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from langgraph.prebuilt import ToolNode, tools_condition
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from typing_extensions import TypedDict
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from langchain_core.messages import HumanMessage, SystemMessage
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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import logging
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import prompt_toolkit
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import prompt_toolkit.history
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import prompt_toolkit.auto_suggest
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logger = logging.getLogger(__name__)
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from . import tools
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cli_history = prompt_toolkit.history.FileHistory('output/cli_history.txt')
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#MODEL = "gemma3:27b"
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MODEL = "qwen3:latest"
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def create_model():
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available_tools = tools.get_tools()
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logger.info("Available tools:")
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for tool in available_tools:
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logger.info("- %s", tool.name)
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llm = ChatOllama(model=MODEL)
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llm.bind_tools(tools=available_tools )
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return create_react_agent(llm, tools=available_tools )
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def main():
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logging.basicConfig(level='INFO')
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messages = [SystemMessage("You are a useful assistant with access to built in system tools.")]
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llm = create_model()
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prev_idx = 0
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while True:
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user_input = prompt_toolkit.prompt("Human: ",
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history=cli_history,
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auto_suggest=prompt_toolkit.auto_suggest.AutoSuggestFromHistory(),
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)
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messages.append(HumanMessage(user_input))
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result = llm.invoke({
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'messages': messages,
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})
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messages = result['messages']
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for msg in messages[prev_idx:]:
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print(f'{msg.type}: {msg.content}')
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del msg
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prev_idx = len(messages)
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if __name__ == '__main__':
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main()
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