feat(brain): Add ProxyBrain integration

backend/modules/brain/integrations/Proxy/Brain.py

@@ -0,0 +1,97 @@
+import json
+from typing import AsyncIterable
+from uuid import UUID
+
+from langchain_community.chat_models import ChatLiteLLM
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from logger import get_logger
+from modules.brain.knowledge_brain_qa import KnowledgeBrainQA
+from modules.chat.dto.chats import ChatQuestion
+from modules.chat.dto.outputs import GetChatHistoryOutput
+from modules.chat.service.chat_service import ChatService
+
+logger = get_logger(__name__)
+
+chat_service = ChatService()
+
+
+class ProxyBrain(KnowledgeBrainQA):
+    """This is the Proxy brain class.
+
+    Args:
+        KnowledgeBrainQA (_type_): A brain that store the knowledge internaly
+    """
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        super().__init__(
+            **kwargs,
+        )
+
+    def get_chain(self):
+
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are Quivr. You are an assistant. {custom_personality}",
+                ),
+                MessagesPlaceholder(variable_name="chat_history"),
+                ("human", "{question}"),
+            ]
+        )
+
+        chain = prompt | ChatLiteLLM(model=self.model, max_tokens=self.max_tokens)
+
+        return chain
+
+    async def generate_stream(
+        self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
+    ) -> AsyncIterable:
+        conversational_qa_chain = self.get_chain()
+        transformed_history, streamed_chat_history = (
+            self.initialize_streamed_chat_history(chat_id, question)
+        )
+        response_tokens = []
+
+        async for chunk in conversational_qa_chain.astream(
+            {
+                "question": question.question,
+                "chat_history": transformed_history,
+                "custom_personality": (
+                    self.prompt_to_use.content if self.prompt_to_use else None
+                ),
+            }
+        ):
+            response_tokens.append(chunk.content)
+            streamed_chat_history.assistant = chunk.content
+            yield f"data: {json.dumps(streamed_chat_history.dict())}"
+
+        self.save_answer(question, response_tokens, streamed_chat_history, save_answer)
+
+    def generate_answer(
+        self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
+    ) -> GetChatHistoryOutput:
+        conversational_qa_chain = self.get_chain()
+        transformed_history, streamed_chat_history = (
+            self.initialize_streamed_chat_history(chat_id, question)
+        )
+        model_response = conversational_qa_chain.invoke(
+            {
+                "question": question.question,
+                "chat_history": transformed_history,
+                "custom_personality": (
+                    self.prompt_to_use.content if self.prompt_to_use else None
+                ),
+            }
+        )
+
+        answer = model_response.content
+
+        return self.save_non_streaming_answer(
+            chat_id=chat_id,
+            question=question,
+            answer=answer,
+        )

backend/modules/chat/controller/chat/brainful_chat.py

@@ -4,6 +4,7 @@
 from modules.brain.integrations.Big.Brain import BigBrain
 from modules.brain.integrations.GPT4.Brain import GPT4Brain
 from modules.brain.integrations.Notion.Brain import NotionBrain
+from modules.brain.integrations.Proxy.Brain import ProxyBrain
 from modules.brain.integrations.SQL.Brain import SQLBrain
 from modules.brain.knowledge_brain_qa import KnowledgeBrainQA
 from modules.brain.service.api_brain_definition_service import ApiBrainDefinitionService
@@ -41,6 +42,7 @@
     "sql": SQLBrain,
     "big": BigBrain,
     "doc": KnowledgeBrainQA,
+    "proxy": ProxyBrain,
 }
 
 brain_service = BrainService()