add main.py

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()