feat: working langchain memory

README.md

@@ -8,6 +8,7 @@
 ## Что удалось реализовать?
 
 ###
+
 - Организация кодовой базы по [шаблону DDD](https://github.com/yp-middle-python-24/python-service-example/)
 - Speech To Text на базе [Whisper](https://openai.com/research/whisper) от OpenAI
 - LLM:
@@ -25,10 +26,12 @@
 - [Алексей](https://github.com/grucshetskyaleksei)
 
 ## Как запустить проект?
+
 1. Скачать [файл базы данных](https://disk.yandex.ru/d/ZAKDDg8lP9DHBQ) с `embeddings` и поместить её по пути `src/assistant/data/dump.sql`.
 
 2. В директории `src/assistant` файл `.env.example` переименовать в `.env` и заполнить переменные окружения.
-Пример заполнения переменных окружения:
+   Пример заполнения переменных окружения:
+
 ```
 POSTGRES_DRIVER=postgresql+asyncpg # Драйвер для работы с базой данных
 POSTGRES_HOST=db # Хост базы данных
@@ -68,7 +71,7 @@ TTS_ELEVEN_LABS_DEFAULT_VOICE_ID=EXAVITQu4vr4xnSDxMaL # ID голоса по у
 ```
 
 3. В директории `src/bot_aiogram` файл `.env.example` переименовать в `.env` и заполнить переменные окружения.
-Пример заполнения переменных окружения:
+   Пример заполнения переменных окружения:
 
 ```
 BOT_CONTAINER_NAME=bot_container_name # Название контейнера
@@ -88,5 +91,6 @@ REDIS_PORT=6379 # Порт Redis
 3. Запустить проект командой `docker-compose up -d`
 
 ### Важно!
+
 Для работы с Telegram-ботом необходимо предварительно начать с ним диалог и отключить в параметрах конфиденциальности
 вашего аккаунта запрет на голосовые сообщения.

src/assistant/lib/agent/services.py

@@ -30,29 +30,12 @@ class AgentService:
         self.chat_repository = chat_repository
         self.logger = logging.getLogger(__name__)
 
-    async def send_message_request(self, request: str, system_prompt: str):
-        prompt = langchain.prompts.ChatPromptTemplate.from_messages(
-            [
-                ("system", system_prompt),
-            ]
-        )
-        llm = langchain.chat_models.ChatOpenAI(
-            temperature=self.settings.openai.agent_temperature,
-            openai_api_key=self.settings.openai.api_key.get_secret_value(),
-        )
-        chain = langchain.chains.LLMChain(llm=llm, prompt=prompt)
-        result = await chain.ainvoke({"input": request})
-        return result["text"]
-
     async def process_request(self, request: models.AgentCreateRequestModel) -> models.AgentCreateResponseModel:
         # Get session ID
-        request_text = request.text
-        translate_text = await self.send_message_request(request=request_text, system_prompt="Translation into English")
         session_request = models.RequestLastSessionId(channel=request.channel, user_id=request.user_id, minutes_ago=3)
         session_id = await self.chat_repository.get_last_session_id(session_request)
         if not session_id:
             session_id = uuid.uuid4()
-        await self.send_message_request(request="test", system_prompt="test")
 
         # Declare tools (OpenAI functions)
         tools = [
@@ -64,59 +47,56 @@ class AgentService:
             ),
         ]
 
-        llm = langchain.chat_models.ChatOpenAI(
+        template = """
-            temperature=self.settings.openai.agent_temperature,
+        1. You are movie expert with a vast knowledge base about movies and their related aspects.
-            openai_api_key=self.settings.openai.api_key.get_secret_value(),
+        2. Use functions to get an additional data about movies.        
-        )
+        3. Translate each inbound request into English language. Before calling any functions.
-
+        4. Answer always in Russian language.
-        chat_history = []
+        5. Be very concise. You answer must be no longer than 100 words."""
-        chat_history_name = f"{chat_history=}".partition("=")[0]
-
-        request_chat_history = models.RequestChatHistory(session_id=session_id)
-        chat_history_source = await self.chat_repository.get_messages_by_sid(request_chat_history)
-        if not chat_history_source:
-            for entry in chat_history_source:
-                if entry.role == "user":
-                    chat_history.append(langchain.schema.HumanMessage(content=entry.content))
-                elif entry.role == "agent":
-                    chat_history.append(langchain.schema.AIMessage(content=entry.content))
 
         prompt = langchain.prompts.ChatPromptTemplate.from_messages(
             [
-                (
+                ("system", template),
-                    "system",
+                langchain.prompts.MessagesPlaceholder(variable_name="chat_history"),
-                    """1. Translate each inbound request into English language. Before calling any functions.
-2. You are movie expert with a vast knowledge base about movies and their related aspects.
-3. Answer always in Russian language.
-4. Be concise. You answer must be within 100-150 words.""",
-                ),
-                langchain.prompts.MessagesPlaceholder(variable_name=chat_history_name),
                 ("user", "{input}"),
                 langchain.prompts.MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
         )
 
-        llm_with_tools = llm.bind(
+        llm = langchain.chat_models.ChatOpenAI(
-            functions=[langchain.tools.render.format_tool_to_openai_function(tool) for tool in tools]
+            temperature=self.settings.openai.agent_temperature,
+            openai_api_key=self.settings.openai.api_key.get_secret_value(),
+            model=self.settings.openai.model,
         )
 
-        agent = (
+        agent_kwargs = {
-            {
+            "extra_prompt_messages": [langchain.prompts.MessagesPlaceholder(variable_name="memory")],
-                "input": lambda _: _["input"],
+        }
-                "agent_scratchpad": lambda _: langchain.agents.format_scratchpad.format_to_openai_functions(
+        memory = langchain.memory.ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-                    _["intermediate_steps"]
+
-                ),
+        # Load chat history from database
-                "chat_history": lambda _: _["chat_history"],
+        request_chat_history = models.RequestChatHistory(session_id=session_id)
-            }
+        chat_history = await self.chat_repository.get_messages_by_sid(request_chat_history)
-            | prompt
+        for entry in chat_history:
-            | llm_with_tools
+            print("ENTRY: ", entry)
-            | langchain.agents.output_parsers.OpenAIFunctionsAgentOutputParser()
+            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,
+            agent=agent,
+            agent_kwargs=agent_kwargs,
+            memory=memory,
         )
 
-        agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=True)
+        response = await agent_executor.arun({"input": request.text})
-        chat_history = []  # temporary disable chat_history
-        response = await agent_executor.ainvoke({"input": translate_text, "chat_history": chat_history})
 
+        # Save user request and AI response to database
         user_request = models.RequestChatMessage(
             session_id=session_id,
             user_id=request.user_id,
@@ -127,15 +107,10 @@ class AgentService:
             session_id=session_id,
             user_id=request.user_id,
             channel=request.channel,
-            message={"role": "assistant", "content": response["output"]},
+            message={"role": "assistant", "content": response},
         )
 
         await self.chat_repository.add_message(user_request)
         await self.chat_repository.add_message(ai_response)
 
-        response_translate = await self.send_message_request(
+        return models.AgentCreateResponseModel(text=response)
-            request=f"Original text: {request_text}. Answer: {response['output']}",
-            system_prompt="Translate the answer into the language of the original text",
-        )
-        print(response_translate)
-        return models.AgentCreateResponseModel(text=response_translate)

src/assistant/lib/app/split_settings/openai.py

@@ -17,3 +17,4 @@ class OpenaiSettings(pydantic_settings.BaseSettings):
     )
     stt_model: str = "whisper-1"
     agent_temperature: float = 0.7
+    model: str = "gpt-3.5-turbo-0613"

src/assistant/lib/app/split_settings/voice.py

@@ -14,7 +14,7 @@ class VoiceSettings(pydantic_settings.BaseSettings):
 
     max_input_seconds: int = 30
     max_input_size: int = 5120  # 5MB
-    available_formats: str = "wav,mp3,ogg"
+    available_formats: str = "wav,mp3,ogg,oga"
 
     @pydantic.field_validator("available_formats")
     def validate_available_formats(cls, v: str) -> list[str]:

src/assistant/poetry.lock

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@@ -1962,19 +1944,25 @@ files = [
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src/bot_aiogram/docker-compose.yml

@@ -26,7 +26,7 @@ services:
       - tg_bot_network
 
 volumes:
-    redis_data:
+  redis_data:
 
 networks:
   tg_bot_network: