add: main.py — Повторный экзамен: Исследовательский бриф (план → шаги → сводка)

main.py

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+import os
+import json
+import asyncio
+from dotenv import load_dotenv
+from typing import TypedDict, List, Optional
+
+from langchain_openai import ChatOpenAI
+from langchain_tavily import TavilySearchResults
+from langchain.tools import tool
+
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+from langgraph.graph import StateGraph, START, END
+
+# Load environment variables
+load_dotenv()
+
+# LLM configuration
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# Backend for deepagents
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# TypedDict for graph state
+class BriefState(TypedDict):
+    topic: str
+    outline: List[str] | None
+    step_index: int
+    notes: List[str]
+    final_brief: str | None
+
+# Tool: Web search using Tavily
+@tool
+def tavily_search(query: str) -> str:
+    """Search the web for the query and return results."""
+    search = TavilySearchResults()
+    results = search.run(query)
+    return results
+
+# Outline node: generate research plan
+def outline_node(state: BriefState) -> BriefState:
+    if state["outline"] is None:
+        prompt = (
+            f"Generate 4-5 bullet points outlining a research plan for the topic: "
+            f"{state['topic']}. Return as a JSON array of strings."
+        )
+        response = llm.invoke(prompt)
+        try:
+            outline = json.loads(response.content)
+            if isinstance(outline, list):
+                state["outline"] = outline
+        except Exception:
+            state["outline"] = []
+    return state
+
+# Research step node: one search per iteration
+def research_step_node(state: BriefState) -> BriefState:
+    if state["step_index"] < len(state["outline"] or []):
+        current_step = state["outline"][state["step_index"]]
+        # Perform web search
+        search_results = tavily_search(current_step)
+        # Summarize results
+        summary_prompt = (
+            f"Summarize the following search results into 5-8 sentences:\n{search_results}"
+        )
+        summary = llm.invoke(summary_prompt).content.strip()
+        state["notes"].append(summary)
+        state["step_index"] += 1
+    return state
+
+# Synthesize node: combine notes into final brief
+def synthesize_node(state: BriefState) -> BriefState:
+    if state["step_index"] >= len(state["outline"] or []):
+        notes_text = "\n\n".join(state["notes"])
+        synth_prompt = (
+            f"Combine the following notes into a concise research brief (about one page). "
+            f"Use headings for each point.\n\n{notes_text}"
+        )
+        brief = llm.invoke(synth_prompt).content.strip()
+        state["final_brief"] = brief
+    return state
+
+# Build the LangGraph
+graph = StateGraph(BriefState)
+graph.add_node("outline", outline_node)
+graph.add_node("research_step", research_step_node)
+graph.add_node("synthesize", synthesize_node)
+
+graph.set_entry_point("outline")
+graph.add_conditional_edges(
+    "outline",
+    lambda state: "research_step" if state["outline"] is not None else END,
+)
+graph.add_conditional_edges(
+    "research_step",
+    lambda state: (
+        "research_step"
+        if state["step_index"] < len(state["outline"] or [])
+        else "synthesize"
+    ),
+)
+graph.add_edge("synthesize", END)
+
+compiled_graph = graph.compile()
+
+# Tool: Generate brief using the graph
+@tool
+def generate_brief(topic: str) -> str:
+    """Generate a research brief for the given topic."""
+    initial_state: BriefState = {
+        "topic": topic,
+        "outline": None,
+        "step_index": 0,
+        "notes": [],
+        "final_brief": None,
+    }
+    final_state = compiled_graph.invoke(initial_state)
+    return final_state["final_brief"] or ""
+
+# Create deepagents agent
+agent = create_deep_agent(
+    model=llm,
+    tools=[generate_brief, tavily_search],
+    backend=backend,
+    system_prompt="You are a helpful research assistant. Use the provided tools to generate a research brief.",
+)
+
+# Demo execution
+async def main():
+    topic = "Как студенту безопасно подключать MCP к LangChain"
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=f"Generate a brief on: {topic}")]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    print(result["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())