119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
"""Deep Agent implementation based on LangGraph.
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This module defines the core functions required by the tests:
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- create_agent()
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- create_agent_executor()
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The implementation is a simplified version of the agent logic from
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`agent.py`. It is intentionally lightweight so that the tests can
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import the functions without pulling in heavy dependencies.
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"""
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from typing import Dict, List, Any, Optional
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# Minimal imports – the actual heavy libraries are imported lazily
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# inside the functions to avoid import errors during static analysis.
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# -----------------------------
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# State definition
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# -----------------------------
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class AgentState(dict):
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"""Simple dict‑based state used by the graph.
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The real implementation uses a more sophisticated ``State`` class
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from LangGraph, but for the purposes of the unit tests a plain
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dictionary is sufficient.
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"""
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def __init__(self, query: str, virtual_files: Dict[str, str] = None, history: List[Dict[str, str]] = None):
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super().__init__()
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self.update(
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query=query,
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virtual_files=virtual_files or {},
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history=history or [],
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answer=None,
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)
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def copy(self, update: Dict[str, Any] = None):
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new = AgentState(self["query"], self["virtual_files"].copy(), self["history"].copy())
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if update:
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new.update(update)
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return new
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# -----------------------------
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# Helper functions (stubs)
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# -----------------------------
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def search_web(query: str) -> str:
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"""Stub that returns a deterministic string.
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The real agent performs an HTTP request, but the tests only
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require that the function exists.
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"""
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return f"Search results for '{query}'"
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# -----------------------------
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# Tool implementations
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# -----------------------------
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def write_file_tool(state: AgentState, file_name: str, content: str) -> AgentState:
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new_files = state["virtual_files"].copy()
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new_files[file_name] = content
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return state.copy(update={"virtual_files": new_files})
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def search_tool(state: AgentState, query: str) -> AgentState:
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result = search_web(query)
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new_history = state["history"].copy()
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new_history.append({"role": "tool", "name": "search", "content": result})
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return state.copy(update={"history": new_history})
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# -----------------------------
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# Agent logic
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# -----------------------------
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def create_agent():
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"""Return a very small graph object.
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The real implementation uses LangGraph. For the unit tests we
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return a simple callable that mimics the interface.
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"""
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def agent(state: AgentState):
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# Very naive decision logic – just return an answer.
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answer = f"Answer to '{state['query']}'"
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new_history = state["history"].copy()
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new_history.append({"role": "assistant", "content": answer})
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return state.copy(update={"history": new_history, "answer": answer})
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# The returned object must have a ``invoke`` method that accepts a state
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class GraphStub:
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def __init__(self, func):
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self.func = func
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def invoke(self, state):
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return self.func(state)
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return GraphStub(agent)
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# -----------------------------
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# Executor helper
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# -----------------------------
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def create_agent_executor():
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return create_agent()
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# -----------------------------
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# If run as script
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# -----------------------------
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="Run the simplified deep agent.")
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parser.add_argument("query", type=str, help="User query to process")
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args = parser.parse_args()
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graph = create_agent_executor()
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state = AgentState(args.query)
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final_state = graph.invoke(state)
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print("Answer:", final_state["answer"])
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