Formatting and dead code

tests/test_tsellm.py

@@ -103,51 +103,6 @@ def test_embed_default_hazo(self):
             out,
         )
 
-#
-# def test_cli_prompt(existing_db_path):
-#     db = Database(existing_db_path)
-#
-#     assert db.execute("select prompt from prompts").fetchall() == [
-#         ("hello world!",),
-#         ("how are you?",),
-#         ("is this real life?",),
-#         ("1+1=?",),
-#     ]
-#
-#     cli(
-#         [
-#             existing_db_path,
-#             "UPDATE prompts SET generated_markov=prompt(prompt, 'markov')",
-#         ]
-#     )
-#
-#     for prompt, generated_markov in db.execute(
-#         "select prompt, generated_markov from prompts"
-#     ).fetchall():
-#         words = generated_markov.strip().split()
-#         # Every word should be one of the original prompt (see https://github.com/simonw/llm-markov/blob/657ca504bcf9f0bfc1c6ee5fe838cde9a8976381/tests/test_llm_markov.py#L20)
-#         prompt_words = prompt.split()
-#         for word in words:
-#             assert word in prompt_words
-#
-#     # cli(
-#     #     [
-#     #         existing_db_path,
-#     #         "UPDATE prompts SET embedding=prompt(embed, 'dummy model')",
-#     #     ]
-#     # )
-#
-#
-# def test_cli_embed(existing_db_path):
-#     db = Database(existing_db_path)
-#
-#     assert db.execute("select prompt from prompts").fetchall() == [
-#         ("hello world!",),
-#         ("how are you?",),
-#         ("is this real life?",),
-#         ("1+1=?",),
-#     ]
-
 
 if __name__ == "__main__":
     unittest.main()

tsellm/core.py

@@ -17,14 +17,20 @@
 def _prompt_model(prompt, model):
     return llm.get_model(model).prompt(prompt).text()
 
+
 def _prompt_model_default(prompt):
     return llm.get_model("markov").prompt(prompt).text()
 
+
 def _embed_model(text, model):
     return json.dumps(llm.get_embedding_model(model).embed(text))
 
+
 def _embed_model_default(text):
-    return json.dumps(llm.get_embedding_model(llm_cli.get_default_embedding_model()).embed(text))
+    return json.dumps(
+        llm.get_embedding_model(llm_cli.get_default_embedding_model()).embed(text)
+    )
+
 
 def _tsellm_init(con):
     """Entry-point for tsellm initialization."""