Merge pull request #23 from AierLab/update

Comprehensive Update to Bot, DAO, and Data Model Architecture

Author: yhbcode000

.gitignore

@@ -161,4 +161,5 @@ cython_debug/
 # option (not recommended) you can uncomment the following to ignore the entire idea folder.
 .idea/
 
-history_store
+history
+

backend/app/__init__.py

@@ -1 +1 @@
-pass
+from .main import main

backend/app/chatBot.py

@@ -0,0 +1,9 @@
+from .openaiBot import OpenAIBot
+from .petalsBot import PetalsBot
+from .wenxinBot import WenxinBot
+
+# Optional: Initialize or configure models here if necessary
+# For example, you could load bot configurations or API keys from environment variables
+
+# Export the models for easy import elsewhere in your application
+__all__ = ['OpenAIBot', 'PetalsBot', 'WenxinBot']

backend/app/control/__init__.py

@@ -0,0 +1,24 @@
+from abc import ABC, abstractmethod
+
+from app.model.dataModel import MessageModel, SessionModel, AgentModel, VectorStoreModel
+
+
+class BaseBot(ABC):
+
+ @abstractmethod
+ def ask(self,
+ message: MessageModel,
+ agent: AgentModel,
+ session: SessionModel,
+ vector_store_model: VectorStoreModel) -> MessageModel:
+ """
+ Process input message and session context to return bot output.
+
+ Args:
+ message (MessageModel): The message to be processed by the bot.
+ session (SessionModel): The session context in which the message is being processed, if applicable.
+
+ Returns:
+ str: The output generated by the bot in response to the message.
+ """
+ pass

backend/app/control/bot/__init__.py

@@ -0,0 +1,34 @@
+from openai import OpenAI
+
+from app.model.dataModel import MessageModel, SessionModel, AgentModel, VectorStoreModel, RoleEnum
+from .baseBot import BaseBot
+
+
+class OpenAIBot(BaseBot):
+ def __init__(self, api_key):
+ self.client = OpenAI(api_key=api_key)
+
+ def ask(self,
+ message: MessageModel,
+ session: SessionModel,
+ agent: AgentModel = None,
+ vector_store_model: VectorStoreModel = None) -> MessageModel:
+ if agent:
+ fake_message = MessageModel(content=agent.generate_prompt(content=message.content, self_name=agent.name,
+ target_name="Open AI Assistant"),
+ role=message.role)
+ else:
+ fake_message = message
+
+ completion = self.client.chat.completions.create(
+ model="gpt-4o-mini",
+ messages=session.serialize()["message_list"] + [fake_message.serialize()]
+ )
+
+ # Retrieve the bot's response
+ content_response = completion.choices[0].message.content
+
+ message_response = MessageModel(content=content_response, role=RoleEnum.ASSISTANT)
+
+ # Return the last assistant message and the updated context
+ return message_response

backend/app/control/bot/baseBot.py

@@ -0,0 +1,12 @@
+# Manages interactions with the Petals framework, enabling decentralized bot operations as described earlier.
+from .baseBot import BaseBot
+from app.model.dataModel import MessageModel, AgentModel, SessionModel, VectorStoreModel
+
+
+class PetalsBot(BaseBot):
+ def ask(self,
+ message: MessageModel,
+ agent: AgentModel,
+ session: SessionModel,
+ vector_store_model: VectorStoreModel) -> MessageModel: # Interaction with Petals decentralized network
+ pass # TODO

backend/app/control/bot/openaiBot.py

@@ -0,0 +1,12 @@
+# Interfaces with the Wenxin API (likely another AI or ML service), handling specific functionalities provided by this service.
+from .baseBot import BaseBot
+from app.model.dataModel import MessageModel, AgentModel, SessionModel, VectorStoreModel
+
+
+class WenxinBot(BaseBot):
+ def ask(self,
+ message: MessageModel,
+ agent: AgentModel,
+ session: SessionModel,
+ vector_store_model: VectorStoreModel) -> MessageModel: # Call to Wenxin API
+ pass # TODO