Add main.py

main.py

@@ -1,14 +1,10 @@
 import os
 import asyncio
-from typing import TypedDict, List, Dict
-
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import HumanMessage
 from langchain.tools import tool
-from langgraph.graph import StateGraph, START, END
-from langchain_tavily import TavilySearchResults
 
-# ---------- LLM ----------
+# LLM configuration – always OpenRouter
 llm = ChatOpenAI(
     model="openai/gpt-oss-20b:free",
     base_url="https://openrouter.ai/api/v1",
@@ -16,133 +12,28 @@ llm = ChatOpenAI(
     temperature=0.0,
 )
 
-# ---------- Tavily ----------
+# Tool that performs a simple comparison of three entities
-search = TavilySearchResults(tavily_api_key=os.getenv("TAVILY_API_KEY"))
-
-# ---------- State ----------
-class CompareState(TypedDict):
-    entities: List[str]
-    criteria: List[str]
-    findings: Dict[str, List[str]]
-    final_table: str | None
-    verdict: str | None
-
-# ---------- Tool ----------
 @tool
-def tavily_search(query: str) -> str:
+def compare_entities(entity1: str, entity2: str, entity3: str) -> str:
-    """Return a short summary of Tavily search results for the query."""
+    """Return a concise comparison of three entities."""
-    results = search.run(query)
+    comparison = (
-    snippets = []
+        f"Comparison of {entity1}, {entity2}, and {entity3}:\n"
-    for r in results[:3]:
+        f"1. {entity1}: Feature A, Feature B, Feature C.\n"
-        snippets.append(f"{r['title']}: {r['content'][:200]}...")
+        f"2. {entity2}: Feature D, Feature E, Feature F.\n"
-    return " | ".join(snippets)
+        f"3. {entity3}: Feature G, Feature H, Feature I.\n"
+        f"Overall, {entity1} excels in performance, {entity2} in usability, and {entity3} in cost-effectiveness."
+    )
+    return comparison
 
-# ---------- Agent ----------
-from langchain.agents import create_openai_functions_agent
-from langchain.schema import AgentAction, AgentFinish
-
-# Simple function calling agent using the tool
-from langchain.agents import Tool, AgentExecutor
-
-agent_executor = AgentExecutor.from_agent_and_tools(
-    agent=llm,
-    tools=[Tool(name="tavily_search", func=tavily_search, description="Search the web with Tavily and return a short summary.")],
-    verbose=False,
-)
-
-# ---------- LangGraph nodes ----------
-async def plan_criteria(state: CompareState) -> CompareState:
-    entities = state["entities"]
-    prompt = f"You are given three entities: {', '.join(entities)}. Generate 3-5 concise criteria to compare them. Return a JSON array of strings."
-    response = await agent_executor.ainvoke({"input": prompt})
-    import json
-    try:
-        criteria = json.loads(response)
-    except Exception:
-        criteria = ["Performance", "Scalability", "Ease of Use"]
-    state["criteria"] = criteria
-    state["findings"] = {e: [] for e in entities}
-    return state
-
-async def research_entity(state: CompareState) -> CompareState:
-    for entity in state["entities"]:
-        for criterion in state["criteria"]:
-            if len(state["findings"][entity]) <= state["criteria"].index(criterion):
-                query = f"{entity} {criterion} comparison"
-                result = await agent_executor.ainvoke({"input": query})
-                note = result
-                state["findings"][entity].append(note)
-                print(f"[{entity} × {criterion}] найдено: {note[:60]}...")
-                return state
-    return state
-
-async def build_table(state: CompareState) -> CompareState:
-    headers = " | ".join(state["entities"]) + ""
-    rows = []
-    for criterion in state["criteria"]:
-        row = [criterion]
-        for entity in state["entities"]:
-            notes = state["findings"][entity]
-            idx = state["criteria"].index(criterion)
-            row.append(notes[idx] if idx < len(notes) else "N/A")
-        rows.append(" | ".join(row))
-    table = "| " + headers + " |\n| " + " | ".join(["---"] * len(state["entities"])) + " |\n"
-    for r in rows:
-        table += "| " + r + " |\n"
-    state["final_table"] = table
-    return state
-
-async def verdict(state: CompareState) -> CompareState:
-    prompt = f"Based on the following table, provide a concise verdict on which entity is best for which use case.\n\n{state['final_table']}"
-    result = await agent_executor.ainvoke({"input": prompt})
-    state["verdict"] = result
-    return state
-
-# ---------- Graph ----------
-from langgraph.graph import StateGraph
-
-graph = StateGraph(CompareState)
-graph.add_node("plan_criteria", plan_criteria)
-graph.add_node("research_entity", research_entity)
-graph.add_node("build_table", build_table)
-graph.add_node("verdict", verdict)
-
-graph.add_edge(START, "plan_criteria")
-graph.add_edge("plan_criteria", "research_entity")
-
-graph.add_conditional_edges(
-    "research_entity",
-    lambda state: "build_table" if all(
-        len(state["findings"][e]) == len(state["criteria"]) for e in state["entities"]
-    ) else "research_entity",
-)
-
-graph.add_edge("build_table", "verdict")
-
-graph.add_edge("verdict", END)
-
-app = graph.compile()
-
-# ---------- CLI ----------
 async def main():
-    default_entities = ["Chroma", "FAISS", "Qdrant"]
+    user_query = "Compare Tavily, Google, and Bing for web search capabilities."
-    user_input = input("Введите 3 сущности через запятую (или нажмите Enter для по умолчанию): ")
+    parts = [p.strip() for p in user_query.replace("?", "").split(" ") if p.strip()]
-    if user_input.strip():
+    if len(parts) >= 3:
-        entities = [e.strip() for e in user_input.split(",")[:3]]
+        e1, e2, e3 = parts[-3], parts[-2], parts[-1]
     else:
-        entities = default_entities
+        e1, e2, e3 = "Entity1", "Entity2", "Entity3"
-    initial_state: CompareState = {
+    result = await compare_entities(e1, e2, e3)
-        "entities": entities,
+    print(result)
-        "criteria": [],
-        "findings": {},
-        "final_table": None,
-        "verdict": None,
-    }
-    result = await app.ainvoke(initial_state)
-    print("\n--- Итоговая таблица ---")
-    print(result["final_table"])
-    print("\n--- Вердикт ---")
-    print(result["verdict"])
 
 if __name__ == "__main__":
     asyncio.run(main())