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chore: cleaning up

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This commit is contained in:
Artem Litvinov 2023-10-15 16:59:17 +01:00
parent 23aada53b2
commit 59175fea38
4 changed files with 1 additions and 10 deletions
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4
src/assistant/alembic/env.py
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@ -19,10 +19,6 @@ if config.config_file_name is not None:
config.set_main_option("sqlalchemy.url", app_settings.Settings().postgres.dsn) 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 target_metadata = orm_models.Base.metadata

1
src/assistant/lib/agent/repositories/chat_repository.py
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@ -54,7 +54,6 @@ class ChatHistoryRepository:
.filter_by(session_id=request.session_id) .filter_by(session_id=request.session_id)
.order_by(orm_models.ChatHistory.created.asc()) .order_by(orm_models.ChatHistory.created.asc())
) )
print("get_messages_by_sid:", statement)
result = await session.execute(statement) result = await session.execute(statement)
for row in result.scalars().all(): for row in result.scalars().all():
# TODO: Было бы интересно понять почему pyright ругается ниже и как правильно вызывать компоненты # TODO: Было бы интересно понять почему pyright ругается ниже и как правильно вызывать компоненты

3
src/assistant/lib/agent/repositories/embedding_repository.py
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@ -33,8 +33,7 @@ class EmbeddingRepository:
response: dict[str, typing.Any] = await openai.Embedding.acreate( response: dict[str, typing.Any] = await openai.Embedding.acreate(
input=text, input=text,
model=model, model=model,
) # type: ignore[reportGeneralTypeIssues] ) # type: ignore[reportGeneralTypeIssues]
# print(response["data"][0]["embedding"])
return models.Embedding(root=response["data"][0]["embedding"]) return models.Embedding(root=response["data"][0]["embedding"])
except openai.error.OpenAIError: except openai.error.OpenAIError:

3
src/assistant/lib/agent/services.py
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@ -78,14 +78,11 @@ class AgentService:
request_chat_history = models.RequestChatHistory(session_id=session_id) request_chat_history = models.RequestChatHistory(session_id=session_id)
chat_history = await self.chat_repository.get_messages_by_sid(request_chat_history) chat_history = await self.chat_repository.get_messages_by_sid(request_chat_history)
for entry in chat_history: for entry in chat_history:
print("ENTRY: ", entry)
if entry.role == "user": if entry.role == "user":
memory.chat_memory.add_user_message(entry.content) memory.chat_memory.add_user_message(entry.content)
elif entry.role == "agent": elif entry.role == "agent":
memory.chat_memory.add_ai_message(entry.content) 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 = langchain.agents.OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
agent_executor: langchain.agents.AgentExecutor = langchain.agents.AgentExecutor.from_agent_and_tools( agent_executor: langchain.agents.AgentExecutor = langchain.agents.AgentExecutor.from_agent_and_tools(
tools=tools, tools=tools,