From ab1f222c443d5ccbf1c7db9deb953c9791db0f36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=93=D0=BB=D0=B5=D0=B1=20=D0=9D=D0=B8=D0=BA=D0=B8=D1=88?= =?UTF-8?q?=D0=B8=D0=BD?= Date: Thu, 28 May 2026 17:35:44 +0000 Subject: [PATCH] add main.py --- main.py | 161 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 161 insertions(+) create mode 100644 main.py diff --git a/main.py b/main.py new file mode 100644 index 0000000..3af7527 --- /dev/null +++ b/main.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python +"""Structured output extraction with LangChain and Pydantic. + +This script demonstrates how to parse a free‑text description of either a person +or a meeting into a validated Pydantic model using LangChain's structured +output facilities. It can be run from the command line with a sample text or +with user input. +""" + +import os +import sys +from typing import List, Union + +from langchain_core.output_parsers import PydanticOutputParser +from langchain_core.prompts import PromptTemplate +from langchain_openai import ChatOpenAI +from pydantic import BaseModel, Field + +# --------------------------------------------------------------------------- +# 1. Pydantic models +# --------------------------------------------------------------------------- + +class PersonInfo(BaseModel): + name: str = Field(..., description="Full name of the person") + age: int | None = Field(None, description="Age in years, optional") + profession: str = Field(..., description="Current profession or job title") + skills: List[str] = Field(..., description="List of professional skills") + +class MeetingNotes(BaseModel): + date: str = Field(..., description="Date of the meeting in ISO format or natural language") + participants: List[str] = Field(..., description="Names of participants") + topics: List[str] = Field(..., description="Main discussion topics") + decisions: List[str] = Field(..., description="Decisions made during the meeting") + next_steps: List[str] = Field(..., description="Action items to be completed after the meeting") + +# --------------------------------------------------------------------------- +# 2. LangChain LLM setup +# --------------------------------------------------------------------------- + +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, +) + +# --------------------------------------------------------------------------- +# 3. Prompt templates +# --------------------------------------------------------------------------- + +person_prompt = PromptTemplate( + input_variables=["text"], + template=""" +You are an assistant that extracts structured data about a person from the following text. + +Text: {text} + +Return the data as a JSON object that matches the PersonInfo schema. + +{format_instructions} +""", +) + +meeting_prompt = PromptTemplate( + input_variables=["text"], + template=""" +You are an assistant that extracts structured data about a meeting from the following text. + +Text: {text} + +Return the data as a JSON object that matches the MeetingNotes schema. + +{format_instructions} +""", +) + +# --------------------------------------------------------------------------- +# 4. Output parsers +# --------------------------------------------------------------------------- + +person_parser = PydanticOutputParser(pydantic_object=PersonInfo) +meeting_parser = PydanticOutputParser(pydantic_object=MeetingNotes) + +# --------------------------------------------------------------------------- +# 5. Heuristic to decide which schema to use +# --------------------------------------------------------------------------- + +def choose_schema(text: str) -> str: + """Return 'person' or 'meeting' based on simple keyword heuristics. + + The heuristic is intentionally simple: if the text contains words that + are more likely to appear in a meeting description (e.g. "meeting", "agenda", + "participants", "decisions", "action items") we choose the meeting schema. + Otherwise we default to the person schema. + """ + meeting_keywords = { + "meeting", + "agenda", + "participants", + "decisions", + "action items", + "next steps", + "topics", + "date", + } + text_lower = text.lower() + if any(word in text_lower for word in meeting_keywords): + return "meeting" + return "person" + +# --------------------------------------------------------------------------- +# 6. Main extraction function +# --------------------------------------------------------------------------- + +def extract(text: str) -> Union[PersonInfo, MeetingNotes]: + schema_type = choose_schema(text) + if schema_type == "person": + prompt = person_prompt + parser = person_parser + else: + prompt = meeting_prompt + parser = meeting_parser + + chain = prompt | llm | parser + result = chain.invoke({"text": text}) + return result + +# --------------------------------------------------------------------------- +# 7. CLI +# --------------------------------------------------------------------------- + +def main(): + if len(sys.argv) > 1: + # First argument is the text to parse + input_text = " ".join(sys.argv[1:]) + else: + # Provide two built‑in examples + examples = { + "person": "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.", + "meeting": "Встреча 12.09.2026. Участники: Иван, Мария. Темы: проект X, бюджет. Решения: утвердить бюджет. Next steps: подготовить презентацию.", + } + print("Choose example: 1 - person, 2 - meeting, or type your own text.") + choice = input("> ").strip() + if choice == "1": + input_text = examples["person"] + elif choice == "2": + input_text = examples["meeting"] + else: + input_text = choice + + print("\nInput text:\n" + input_text + "\n") + try: + obj = extract(input_text) + print("\nParsed object (model_dump):") + print(obj.model_dump(indent=2)) + print("\nSummary: ", obj) + except Exception as e: + print("Error during parsing:", e) + +if __name__ == "__main__": + main()