mirror of
https://github.com/ijaric/voice_assistant.git
synced 2025-05-24 14:33:26 +00:00
Changes by ijaric
This commit is contained in:
parent
b3e480886e
commit
288af57ab5
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@ -1,9 +1,6 @@
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import asyncio
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import logging
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import typing
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import uuid
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import fastapi
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import langchain.agents
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import langchain.agents.format_scratchpad
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import langchain.agents.output_parsers
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@ -25,23 +22,15 @@ class AgentService:
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self,
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settings: app_settings.Settings,
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chat_repository: _chat_repository.ChatHistoryRepository,
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tools: lib_agent_repositories.OpenAIFunctions | None = None,
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) -> None:
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self.settings = settings
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self.tools = tools
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self.llm = langchain.chat_models.ChatOpenAI(
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temperature=self.settings.openai.agent_temperature,
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openai_api_key=self.settings.openai.api_key.get_secret_value()
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)
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self.chat_repository = chat_repository
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self.logger = logging.getLogger(__name__)
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async def get_chat_session_id(self, request: models.RequestLastSessionId) -> uuid.UUID:
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session_id = self.chat_repository.get_last_session_id(request)
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if not session_id:
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session_id = uuid.uuid4()
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return session_id
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async def artem_process_request(self, request: models.AgentCreateRequestModel) -> models.AgentCreateResponseModel:
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async def process_request(self, request: models.AgentCreateRequestModel) -> models.AgentCreateResponseModel:
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# Get session ID
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session_request = models.RequestLastSessionId(
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channel=request.channel,
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@ -50,11 +39,9 @@ class AgentService:
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)
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session_id = await self.chat_repository.get_last_session_id(session_request)
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if not session_id:
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print("NO PREVIOUS CHATS")
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session_id = uuid.uuid4()
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print("FOUND CHAT:", )
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print("SID:", session_id)
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# Declare tools (OpenAI functions)
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tools = [
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langchain.tools.Tool(
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name="GetMovieByDescription",
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@ -66,22 +53,17 @@ class AgentService:
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llm = langchain.chat_models.ChatOpenAI(temperature=self.settings.openai.agent_temperature, openai_api_key=self.settings.openai.api_key.get_secret_value())
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# chat_history = langchain.memory.ChatMessageHistory()
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chat_history = []
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chat_history_name = f"{chat_history=}".partition("=")[0]
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request_chat_history = models.RequestChatHistory(session_id=session_id)
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chat_history_source = await self.chat_repository.get_messages_by_sid(request_chat_history)
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for entry in chat_history_source:
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print("ENTRY: ", entry)
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if entry.role == "user":
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chat_history.append(langchain.schema.HumanMessage(content=entry.content))
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elif entry.role == "agent":
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chat_history.append(langchain.schema.AIMessage(content=entry.content))
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# memory = langchain.memory.ConversationBufferMemory(memory_key=chat_history_name,chat_memory=chat_history)
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print("CHAT HISTORY:", chat_history)
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prompt = langchain.prompts.ChatPromptTemplate.from_messages(
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[
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@ -112,13 +94,10 @@ class AgentService:
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| langchain.agents.output_parsers.OpenAIFunctionsAgentOutputParser()
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)
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print("AGENT:", agent)
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agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=True)
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print("CH:", type(chat_history), chat_history)
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agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=False)
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chat_history = [] # temporary disable chat_history
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response = await agent_executor.ainvoke({"input": request.text, "chat_history": chat_history})
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print("AI RESPONSE:", response)
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user_request = models.RequestChatMessage(
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session_id=session_id,
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@ -137,108 +116,3 @@ class AgentService:
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await self.chat_repository.add_message(ai_response)
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return response
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# TODO: Добавить запрос для процессинга запроса с памятью+
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# TODO: Улучшить промпт+
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# TODO: Возможно, надо добавить Chain на перевод
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async def process_request(self, request: models.AgentCreateRequestModel) -> models.AgentCreateResponseModel:
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result = await self.tools.get_movie_by_description(request.text)
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if len(result) == 0:
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raise fastapi.HTTPException(status_code=404, detail="Movies not found")
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# llm = langchain.chat_models.ChatOpenAI(
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# temperature=self.settings.openai.agent_temperature,
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# openai_api_key=self.settings.openai.api_key.get_secret_value()
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# )
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content_films = "\n".join(film.get_movie_info_line() for film in result)
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system_prompt = (
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"You are a cinema expert. "
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f"Here are the movies I found for you: {content_films}"
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"Listen to the question and answer it based on the information above."
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)
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prompt = langchain.prompts.ChatPromptTemplate.from_messages(
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[
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("system", system_prompt),
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]
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)
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chain = prompt | self.llm
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response = await chain.ainvoke({"input": request.text})
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response_model = models.AgentCreateResponseModel(text=response.content)
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return response_model
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async def main():
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import lib.agent.repositories as agent_repositories
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import lib.clients as clients
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postgres_client = clients.AsyncPostgresClient(app_settings.Settings())
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embedding_repository = agent_repositories.EmbeddingRepository(app_settings.Settings())
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chat_repository = _chat_repository.ChatHistoryRepository(postgres_client.get_async_session())
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agent_service = AgentService(
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settings=app_settings.Settings(),
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tools=openai_functions.OpenAIFunctions(
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repository=embedding_repository,
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pg_async_session=postgres_client.get_async_session(),
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),
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chat_repository=chat_repository
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)
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# question = "What is the movie about a famous country singer meet a talented singer and songwriter who works as a waitress?"
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request_1 = models.AgentCreateRequestModel(
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channel="telegram",
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user_id="123",
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text="What is the movie about a famous country singer meet a talented singer and songwriter who works as a waitress?"
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)
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request_2 = models.AgentCreateRequestModel(
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channel="telegram",
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user_id="123",
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text="So what is the rating of the movie? Do you recommend it?"
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)
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request_3 = models.AgentCreateRequestModel(
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channel="telegram",
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user_id="123",
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text="What are the similar movies?"
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)
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response = await agent_service.artem_process_request(request_1)
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response = await agent_service.artem_process_request(request_2)
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response = await agent_service.artem_process_request(request_3)
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# response = await agent_service.artem_process_request(question)
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# question = "Highly Rated Titanic Movies"
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# request = models.AgentCreateRequestModel(text=question)
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# film_results = await agent_service.process_request(request=request)
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# result = [agent_service.tools.get_movie_by_id(id=film.id) for film in film_results]
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# agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=True)
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#
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# # first_question = "What is the movie where halfling bring the ring to the volcano?"
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# first_question = (
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# "What is the movie about a famous country singer meet a talented singer and songwriter who works as a waitress?"
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# )
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# second_question = "So what is the rating of the movie? Do you recommend it?"
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# third_question = "What are the similar movies?"
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# first_result = await agent_executor.ainvoke({"input": first_question, "chat_history": chat_history})
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# chat_history.append(langchain.schema.messages.HumanMessage(content=first_question))
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# chat_history.append(langchain.schema.messages.AIMessage(content=first_result["output"]))
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# second_result = await agent_executor.ainvoke({"input": second_question, "chat_history": chat_history})
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# chat_history.append(langchain.schema.messages.HumanMessage(content=second_question))
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# chat_history.append(langchain.schema.messages.AIMessage(content=second_result["output"]))
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# final_result = await agent_executor.ainvoke({"input": third_question, "chat_history": chat_history})
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if __name__ == "__main__":
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asyncio.run(main())
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@ -2,7 +2,7 @@ import uuid
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import fastapi
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import lib.agent as agent
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import lib.agent.repositories as agent_repositories
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import lib.models as models
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@ -4,7 +4,7 @@ import io
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import fastapi
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import lib.stt.services as stt_services
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import lib.agent.services as agent_service
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import lib.tts.services as tts_service
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import lib.models as models
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@ -14,9 +14,11 @@ class VoiceResponseHandler:
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self,
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stt: stt_services.SpeechService,
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tts: tts_service.TTSService,
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# agent: agent_service.AgentService,
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):
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self.stt = stt
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self.tts = tts
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# self.agent = agent
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self.router = fastapi.APIRouter()
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self.router.add_api_route(
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"/",
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@ -35,12 +37,13 @@ class VoiceResponseHandler:
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voice_text: str = await self.stt.recognize(voice)
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if voice_text == "":
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raise fastapi.HTTPException(status_code=http.HTTPStatus.BAD_REQUEST, detail="Speech recognition failed")
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# TODO: Добавить обработку текста через клиента openai
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# TODO: Добавить синтез речи через клиента tts
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# TODO: Заменить заглушку на реальный ответ
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# agent_request = models.AgentCreateRequestModel(channel=channel, user_id=user_id, text=voice_text)
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# reply_text = await self.agent.process_request(agent_request)
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reply_text = "hi there"
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response = await self.tts.get_audio_as_bytes(
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models.TTSCreateRequestModel(
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text=voice_text,
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text=reply_text,
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)
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)
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return fastapi.responses.StreamingResponse(io.BytesIO(response.audio_content), media_type="audio/ogg")
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@ -14,6 +14,9 @@ import lib.clients as clients
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import lib.models as models
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import lib.stt as stt
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import lib.tts as tts
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import lib.agent.repositories as agent_repositories
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import lib.agent.repositories.openai_functions as agent_functions
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import lib.agent.services as agent_services
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logger = logging.getLogger(__name__)
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@ -89,7 +92,8 @@ class Application:
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logger.info("Initializing repositories")
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stt_repository: stt.STTProtocol = stt.OpenaiSpeechRepository(settings=settings)
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chat_history_repository = agent.ChatHistoryRepository(pg_async_session=postgres_client.get_async_session())
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chat_history_repository = agent_repositories.ChatHistoryRepository(pg_async_session=postgres_client.get_async_session())
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embedding_repository = agent_repositories.EmbeddingRepository(settings)
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tts_yandex_repository = tts.TTSYandexRepository(
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tts_settings=app_split_settings.TTSYandexSettings(),
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@ -101,14 +105,21 @@ class Application:
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is_models_from_api=True,
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)
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# Caches
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logger.info("Initializing caches")
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# Tools
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agent_tools = agent_functions.OpenAIFunctions(repository=embedding_repository, pg_async_session=postgres_client.get_async_session())
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# Services
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logger.info("Initializing services")
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stt_service: stt.SpeechService = stt.SpeechService(repository=stt_repository) # type: ignore
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stt_service: stt.SpeechService = stt.SpeechService(repository=stt_repository) # type: ignore
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tts_service: tts.TTSService = tts.TTSService( # type: ignore
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repositories={
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@ -117,6 +128,9 @@ class Application:
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},
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)
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agent_service: agent_services.AgentService(settings=settings, chat_repository=chat_history_repository)
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# agent_service: agent_services.AgentService(settings=settings, chat_repository=chat_history_repository, tools=agent_tools)
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# Handlers
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logger.info("Initializing handlers")
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@ -127,6 +141,7 @@ class Application:
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voice_response_handler = api_v1_handlers.VoiceResponseHandler(
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stt=stt_service,
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tts=tts_service,
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agent=agent_services,
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).router
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logger.info("Creating application")
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