chore: cleaning up

src/assistant/alembic/env.py

@@ -19,10 +19,6 @@ if config.config_file_name is not None:
 
 config.set_main_option("sqlalchemy.url", app_settings.Settings().postgres.dsn)
 
-print("BASE: ", orm_models.Base.metadata.schema)
-for t in orm_models.Base.metadata.sorted_tables:
-    print(t.name)
-
 target_metadata = orm_models.Base.metadata
 
 

src/assistant/lib/agent/repositories/chat_repository.py

@@ -54,7 +54,6 @@ class ChatHistoryRepository:
                     .filter_by(session_id=request.session_id)
                     .order_by(orm_models.ChatHistory.created.asc())
                 )
-                print("get_messages_by_sid:", statement)
                 result = await session.execute(statement)
                 for row in result.scalars().all():
                     # TODO: Было бы интересно понять почему pyright ругается ниже и как правильно вызывать компоненты

src/assistant/lib/agent/repositories/embedding_repository.py

@@ -33,8 +33,7 @@ class EmbeddingRepository:
             response: dict[str, typing.Any] = await openai.Embedding.acreate(
                 input=text,
                 model=model,
-            )  # type: ignore[reportGeneralTypeIssues]
-            # print(response["data"][0]["embedding"])
+            )  # type: ignore[reportGeneralTypeIssues]            
             return models.Embedding(root=response["data"][0]["embedding"])
 
         except openai.error.OpenAIError:

src/assistant/lib/agent/services.py

@@ -78,14 +78,11 @@ class AgentService:
         request_chat_history = models.RequestChatHistory(session_id=session_id)
         chat_history = await self.chat_repository.get_messages_by_sid(request_chat_history)
         for entry in chat_history:
-            print("ENTRY: ", entry)
             if entry.role == "user":
                 memory.chat_memory.add_user_message(entry.content)
             elif entry.role == "agent":
                 memory.chat_memory.add_ai_message(entry.content)
         
-        print("MEMORY: ", memory.load_memory_variables({}))
-
         agent = langchain.agents.OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
         agent_executor: langchain.agents.AgentExecutor = langchain.agents.AgentExecutor.from_agent_and_tools(
             tools=tools,