fix(needs_fixes): 1 исправлений, 0 отстояно — main.py

main.py

@@ -2,92 +2,93 @@ import os
 import asyncio
 from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import PydanticOutputParser
 from pydantic import BaseModel, Field
+from langchain_core.messages import HumanMessage
 from langchain.tools import tool
 from deepagents import create_deep_agent
 from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
 
-# Load environment variables (OPENAI_API_KEY)
+# Load environment variables
 load_dotenv()
 
-# 1. Define the Pydantic model for the task card
+# ---------- LLM INITIALIZATION ----------
-class TaskCard(BaseModel):
+# Using OpenRouter via langchain-openai as required
-    title: str = Field(description="Краткое название задания")
-    subject: str = Field(description="Предмет или тема задания")
-    deadline_hint: str = Field(description="Краткая подсказка о сроке выполнения, например 'к пятнице'")
-    deliverable_type: str = Field(description="Тип сдачи: отчёт, код, презентация и т.п.")
-    grading_hints: list[str] = Field(description="Список критериев оценки, упомянутых в тексте")
-
-# 2. Create the parser that will enforce the structure
-parser = PydanticOutputParser(pydantic_object=TaskCard)
-
-# 3. Prompt template that asks the model to output the data in the required format
-prompt = PromptTemplate(
-    template="""
-Ниже приведено описание задания. Ваша задача — извлечь из него следующую информацию и вернуть в формате JSON, соответствующем модели TaskCard:
-
-{input_text}
-
-{format_instructions}
-""",
-    input_variables=["input_text"],
-    partial_variables={"format_instructions": parser.get_format_instructions()},
-)
-
-# 4. LLM configuration (OpenRouter)
 llm = ChatOpenAI(
-    model="openai/gpt-oss-20b:free",
+    model_name="gpt-4o-mini",  # example model available on OpenRouter
-    base_url="https://openrouter.ai/api/v1",
+    base_url="https://openrouter.ai/api/v1/chat/completions",
-    api_key=os.getenv("OPENAI_API_KEY"),
+    api_key=os.getenv("OPENROUTER_API_KEY"),
     temperature=0.0,
 )
 
-# 5. Define a tool that performs the parsing chain
+# ---------- Pydantic MODEL ----------
+class TaskCard(BaseModel):
+    title: str = Field(description="Краткое название задания")
+    subject: str = Field(description="Предмет или тема задания")
+    deadline_hint: str = Field(description="Краткая подсказка о сроке сдачи, например 'к пятнице'")
+    deliverable_type: str = Field(description="Тип сдаваемого материала: отчёт, код, презентация и т.д.")
+    grading_hints: list[str] = Field(description="Список критериев оценки, упомянутых в тексте")
+
+# ---------- PROMPT AND PARSER ----------
+parser = PydanticOutputParser(pydantic_object=TaskCard)
+
+prompt_template = """You are an assistant that extracts structured information from a short task description.
+
+Input: {task_text}
+
+Output must be a JSON object with the following fields:
+{format_instructions}
+
+Respond ONLY with the JSON object.
+"""
+
+prompt = PromptTemplate(
+    template=prompt_template,
+    input_variables=["task_text"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+# ---------- TOOL THAT RUNS THE CHAIN ----------
 @tool
-def parse_task(input_text: str) -> str:
+def parse_task(task_text: str) -> str:
-    """Parse a raw task description into a structured JSON string."""
+    """Parse a task description into a structured JSON object."""
     chain = prompt | llm | parser
-    result = chain.invoke({"input_text": input_text})
+    result = chain.invoke({"task_text": task_text})
-    # parser returns a dict; convert to JSON string for the agent to return
+    # Return the validated object as JSON string for the agent to forward
     return result.model_dump_json()
 
-# 6. Backend for the agent (filesystem + local shell, though not used here)
+# ---------- BACKEND AND AGENT ----------
 backend = CompositeBackend([
     LocalShellBackend(workspace_dir="./workspace"),
     FilesystemBackend(),
 ])
 
-# 7. Create the deep agent with the parsing tool
 agent = create_deep_agent(
     model=llm,
     tools=[parse_task],
     backend=backend,
-    system_prompt="You are a task‑card extractor. Use the provided tool to parse the input and return the JSON string.",
+    system_prompt="You are a helpful assistant that parses a single task description into structured data using the provided tool.",
 )
 
-# 8. Main entry point
+# ---------- MAIN EXECUTION ----------
 async def main():
-    # Example input – replace with any user text
+    # Example input – single line, no dialogue
-    user_text = "Сдайте к пятнице мини‑отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
+    task_description = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
     result = await agent.ainvoke(
-        {"messages": [HumanMessage(content=user_text)]},
+        {"messages": [HumanMessage(content=task_description)]},
         {"configurable": {"thread_id": "session-1"}},
     )
-    # The agent returns the tool output as the last message
+    # The agent will return the tool output as the last message
-    output = result["messages"][-1].content
+    output_json = result["messages"][-1].content
-    print("Parsed JSON:")
+    print("\n--- Parsed JSON ---")
-    print(output)
+    print(output_json)
-    # Pretty‑print the parsed object
+    # Load into Pydantic for pretty printing and summary
-    card = TaskCard.parse_raw(output)
+    card = TaskCard.parse_raw(output_json)
-    print("\nHuman‑readable summary:")
+    print("\n--- Validated Object ---")
-    print(f"Title: {card.title}")
+    print(card.model_dump())
-    print(f"Subject: {card.subject}")
+    print("\n--- Summary ---")
-    print(f"Deadline hint: {card.deadline_hint}")
+    print(f"Title: {card.title}\nSubject: {card.subject}\nDeadline: {card.deadline_hint}\nDeliverable: {card.deliverable_type}\nGrading: {', '.join(card.grading_hints)}")
-    print(f"Deliverable type: {card.deliverable_type}")
-    print(f"Grading hints: {', '.join(card.grading_hints)}")
 
 if __name__ == "__main__":
     asyncio.run(main())