From 7fcfe77bbe7b32b0051c5a93c6ece2fc69c8353b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=90=D1=80=D1=82=D0=B5=D0=BC=20=D0=92=D0=BB=D0=B0=D0=B4?=
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 =?UTF-8?q?=D0=B1=D0=B0=D0=B9=D0=BA=D0=B8=D0=BD?= <ababaykin2016@litsey2.ru>
Date: Thu, 28 May 2026 16:37:33 +0000
Subject: [PATCH] feat: solution for 6a1865008a94f887e50d471c

---
 .../6a1865008a94f887e50d471c/solution.py      | 82 +++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 solutions/6a1865008a94f887e50d471c/solution.py

diff --git a/solutions/6a1865008a94f887e50d471c/solution.py b/solutions/6a1865008a94f887e50d471c/solution.py
new file mode 100644
index 0000000..43953a5
--- /dev/null
+++ b/solutions/6a1865008a94f887e50d471c/solution.py
@@ -0,0 +1,82 @@
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field, SecretStr
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import PromptTemplate
+import sys
+
+# LLM placeholder
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b",
+    base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
+    api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
+    temperature=0.7,
+)
+
+class PersonInfo(BaseModel):
+    name: str = Field(description="Full name")
+    age: int | None = Field(default=None, description="Age in years")
+    profession: str = Field(description="Job title")
+    skills: list[str] = Field(description="List of technical skills")
+
+class MeetingNotes(BaseModel):
+    date: str = Field(description="Meeting date in ISO format")
+    participants: list[str] = Field(description="Names of attendees")
+    topics: list[str] = Field(description="Discussion topics")
+    decisions: list[str] = Field(description="Decisions made")
+    next_steps: list[str] = Field(description="Action items")
+
+# Prompt templates
+person_prompt = PromptTemplate(
+    input_variables=["text"],
+    template=(
+        "Extract a PersonInfo object from the following text. "
+        "Return only JSON matching the schema.\n\n"
+        "{format_instructions}\n\nText: {text}"
+    ),
+)
+meeting_prompt = PromptTemplate(
+    input_variables=["text"],
+    template=(
+        "Extract a MeetingNotes object from the following text. "
+        "Return only JSON matching the schema.\n\n"
+        "{format_instructions}\n\nText: {text}"
+    ),
+)
+
+# Parsers
+person_parser = PydanticOutputParser(pydantic_object=PersonInfo)
+meeting_parser = PydanticOutputParser(pydantic_object=MeetingNotes)
+
+def route_and_parse(text: str):
+    # Simple heuristic: presence of "meeting" or date-like pattern
+    if "meeting" in text.lower() or any(c.isdigit() for c in text[:10]):
+        prompt = meeting_prompt.partial(
+            format_instructions=meeting_parser.get_format_instructions()
+        )
+        chain = prompt | llm | meeting_parser
+        result = chain.invoke({"text": text})
+        return MeetingNotes(**result)
+    else:
+        prompt = person_prompt.partial(
+            format_instructions=person_parser.get_format_instructions()
+        )
+        chain = prompt | llm | person_parser
+        result = chain.invoke({"text": text})
+        return PersonInfo(**result)
+
+def main():
+    examples = [
+        "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.",
+        "Meeting on 2024-05-27 with Alice and Bob. Topics: budget, timeline. Decisions: approve Q3 plan. Next steps: send email to stakeholders."
+    ]
+    if len(sys.argv) > 1:
+        inputs = [" ".join(sys.argv[1:])]
+    else:
+        inputs = examples
+    for txt in inputs:
+        print("\nInput:", txt)
+        obj = route_and_parse(txt)
+        print(obj.model_dump())
+
+if __name__ == "__main__":
+    main()
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