Feature/37 topic model poc

.gitignore

@@ -160,3 +160,5 @@ cython_debug/
 #.idea/
 
 .vscode/settings.json
+
+.DS_Store

pyproject.toml

@@ -30,7 +30,8 @@ dependencies = [
     "sqlalchemy",
     "pydantic_settings",
     "fastapi",
-    "JSON-log-formatter"
+    "JSON-log-formatter",
+    "openai"
 ]
 
 [project.urls]

scripts/add_topic_poc.py

@@ -0,0 +1,84 @@
+import json
+import os
+from argparse import ArgumentParser
+from typing import Dict, List
+
+from dotenv import load_dotenv
+from openai import OpenAI
+
+response_sample = """
+{
+  "1700958646329": {
+    "topics": ["医療", "福祉", "政治"],
+    "language": "en"
+  }
+}
+"""
+
+def get_topic(client: OpenAI, note_id: int, tweet: str, note: str) -> Dict[str, List[str]]:
+    print(f"note id: {note_id}")
+    with open(os.path.join(os.path.dirname(__file__), "fewshot_sample.json"), "r") as f:
+        fewshot_sample = json.load(f)
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": f"""以下はツイートと、それに追加されたコミュニティノートです。
+ツイート:
+```
+{fewshot_sample["tweet"]}
+```
+コミュニティノート:
+```
+{fewshot_sample["note"]}
+```
+このセットに対してのトピックは「{" ".join(fewshot_sample["topics"])}」です。
+これを踏まえて、以下のセットに対して同じ粒度で複数のトピック(少なくとも3つ)を提示してください。形式はJSONで、キーをtopicsとして値にトピックを配列で格納してください。また、ツイートに用いられている言語も推定し、キーをlanguageとしてiso 639-1に準拠した言語コードを格納してください。topicsとlanguageを格納するオブジェクトはnote idをキーとした値に格納してください
+レスポンスの例 (1700958646329はnote id):
+```
+{response_sample}
+```
+""",
+            },
+            {
+                "role": "user",
+                "content": f"""
+note id: {note_id}
+ツイート:
+```
+{tweet}
+```
+コミュニティノート:
+```
+{note}
+```
+""",
+            },
+        ],
+        model="gpt-3.5-turbo",
+        temperature=0.0,
+    )
+
+    return json.loads(chat_completion.choices[0].message.content)
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("input_file")
+    parser.add_argument("output_file")
+    args = parser.parse_args()
+    load_dotenv()
+    client = OpenAI()
+    with open(args.input_file, "r") as f:
+        notes = json.load(f)
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        json.dump(
+            [
+                get_topic(client, note["noteId"], note["tweetBody"], note["noteBody"])
+                for note in notes
+            ],
+            f,
+            ensure_ascii=False,
+            indent=2,
+        )
+

scripts/fewshot_sample.json

@@ -0,0 +1,7 @@
+{
+  "tweet": "For those that care — 432 hz improves mental clarity, removes emotional blockages, reduces stress and anxiety, better sleep quality, increases creativity & inspiration, and strengthens the immune system. Play it while you sleep & watch these areas improve!",
+  "note": "There are no placebo controlled studies which support this. There is no evidence that this frequency has different effects from any other arbitrary frequency. https://ask.audio/articles/music-theory-432-hz-tuning-separating-fact-from-fiction",
+  "topics": [
+    "医療", "福祉"
+  ]
+}