Create cli

cli.py

@@ -0,0 +1,59 @@
+import argparse
+from datetime import datetime
+from typing import List, Optional
+
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+# 1. Models
+class PersonInfo(BaseModel):
+    name: str = Field(..., description="Имя человека")
+    age: Optional[int] = Field(None, description="Возраст (необязательно)")
+    profession: str = Field(..., description="Профессия")
+    skills: List[str] = Field(..., description="Список навыков")
+
+class MeetingNotes(BaseModel):
+    date: datetime = Field(..., description="Дата встречи")
+    participants: List[str] = Field(..., description="Участники")
+    topics: List[str] = Field(..., description="Темы обсуждения")
+    decisions: List[str] = Field(..., description="Принятые решения")
+    next_steps: List[str] = Field(..., description="Следующие шаги")
+
+# 2. Prompt and parser
+prompt_template = """
+Extract structured data from the following text.
+Use the appropriate schema:
+- PersonInfo for a person description
+- MeetingNotes for meeting notes
+Return only JSON matching the chosen schema.
+Text: {text}
+"""
+prompt = PromptTemplate.from_template(prompt_template)
+parser_person = PydanticOutputParser(pydantic_object=PersonInfo)
+parser_meeting = PydanticOutputParser(pydantic_object=MeetingNotes)
+
+# 3. Simple type guesser (very naive)
+def choose_parser(text: str):
+    if "профессия" in text.lower() or "навыки" in text.lower():
+        return parser_person, PersonInfo
+    else:
+        return parser_meeting, MeetingNotes
+
+# 4. Main chain
+llm = ChatOpenAI(temperature=0)
+
+def extract(text: str):
+    parser, model_cls = choose_parser(text)
+    chain = prompt | llm | parser
+    result = chain.invoke({"text": text})
+    return result, model_cls
+
+# 5. CLI
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(description="Extract structured data from raw text")
+    ap.add_argument("--text", required=True, help="Raw input text")
+    args = ap.parse_args()
+    out, model_cls = extract(args.text)
+    print(out.model_dump(indent=2))