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742e9bdfba
# DONE - generate_stream, generate and save answer in BE # TODO - Create an intermediary make_streaming_recursive_tool_calls async function - Save intermediary answers in new message logs column then fetch and display in front
398 lines
14 KiB
Python
398 lines
14 KiB
Python
import json
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from typing import Optional
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from uuid import UUID
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from fastapi import HTTPException
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from litellm import completion
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from llm.knowledge_brain_qa import KnowledgeBrainQA
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from llm.qa_interface import QAInterface
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from llm.utils.call_brain_api import call_brain_api
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from llm.utils.get_api_brain_definition_as_json_schema import (
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get_api_brain_definition_as_json_schema,
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)
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from logger import get_logger
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from modules.brain.service.brain_service import BrainService
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from modules.chat.dto.chats import ChatQuestion
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from modules.chat.dto.inputs import CreateChatHistory
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from modules.chat.dto.outputs import GetChatHistoryOutput
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from modules.chat.service.chat_service import ChatService
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brain_service = BrainService()
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chat_service = ChatService()
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logger = get_logger(__name__)
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class APIBrainQA(KnowledgeBrainQA, QAInterface):
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user_id: UUID
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def __init__(
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self,
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model: str,
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brain_id: str,
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chat_id: str,
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streaming: bool = False,
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prompt_id: Optional[UUID] = None,
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**kwargs,
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):
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user_id = kwargs.get("user_id")
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if not user_id:
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raise HTTPException(status_code=400, detail="Cannot find user id")
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super().__init__(
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model=model,
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brain_id=brain_id,
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chat_id=chat_id,
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streaming=streaming,
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prompt_id=prompt_id,
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**kwargs,
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)
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self.user_id = user_id
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async def make_completion(
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self,
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messages,
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functions,
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brain_id: UUID,
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recursive_count=0,
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should_log_steps=False,
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):
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if recursive_count > 5:
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yield "The assistant is having issues and took more than 5 calls to the API. Please try again later or an other instruction."
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return
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if should_log_steps:
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yield "🧠<Deciding what to do>🧠"
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response = completion(
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model=self.model,
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temperature=self.temperature,
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max_tokens=self.max_tokens,
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messages=messages,
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functions=functions,
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stream=True,
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function_call="auto",
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)
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function_call = {
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"name": None,
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"arguments": "",
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}
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for chunk in response:
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finish_reason = chunk.choices[0].finish_reason
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if finish_reason == "stop":
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break
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if (
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"function_call" in chunk.choices[0].delta
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and chunk.choices[0].delta["function_call"]
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):
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if chunk.choices[0].delta["function_call"].name:
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function_call["name"] = chunk.choices[0].delta["function_call"].name
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if chunk.choices[0].delta["function_call"].arguments:
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function_call["arguments"] += (
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chunk.choices[0].delta["function_call"].arguments
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)
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elif finish_reason == "function_call":
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try:
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arguments = json.loads(function_call["arguments"])
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except Exception:
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arguments = {}
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if should_log_steps:
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yield f"🧠<Calling {brain_id} with arguments {arguments}>🧠"
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try:
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api_call_response = call_brain_api(
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brain_id=brain_id,
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user_id=self.user_id,
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arguments=arguments,
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)
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except Exception as e:
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raise HTTPException(
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status_code=400,
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detail=f"Error while calling API: {e}",
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)
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function_name = function_call["name"]
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messages.append(
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{
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"role": "function",
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"name": function_call["name"],
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"content": f"The function {function_name} was called and gave The following answer:(data from function) {api_call_response} (end of data from function). Don't call this function again unless there was an error or extremely necessary and asked specifically by the user.",
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}
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)
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async for value in self.make_completion(
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messages=messages,
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functions=functions,
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brain_id=brain_id,
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recursive_count=recursive_count + 1,
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should_log_steps=should_log_steps,
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):
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yield value
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else:
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if (
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hasattr(chunk.choices[0], "delta")
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and chunk.choices[0].delta
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and hasattr(chunk.choices[0].delta, "content")
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):
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content = chunk.choices[0].delta.content
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yield content
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else: # pragma: no cover
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yield "**...**"
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break
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async def generate_stream(
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self,
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chat_id: UUID,
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question: ChatQuestion,
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save_answer: bool = True,
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should_log_steps: Optional[bool] = True,
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):
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if not question.brain_id:
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raise HTTPException(
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status_code=400, detail="No brain id provided in the question"
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)
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brain = brain_service.get_brain_by_id(question.brain_id)
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if not brain:
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raise HTTPException(status_code=404, detail="Brain not found")
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prompt_content = "You are a helpful assistant that can access functions to help answer questions. If there are information missing in the question, you can ask follow up questions to get more information to the user. Once all the information is available, you can call the function to get the answer."
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if self.prompt_to_use:
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prompt_content += self.prompt_to_use.content
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messages = [{"role": "system", "content": prompt_content}]
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history = chat_service.get_chat_history(self.chat_id)
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for message in history:
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formatted_message = [
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{"role": "user", "content": message.user_message},
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{"role": "assistant", "content": message.assistant},
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]
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messages.extend(formatted_message)
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messages.append({"role": "user", "content": question.question})
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if save_answer:
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streamed_chat_history = chat_service.update_chat_history(
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CreateChatHistory(
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**{
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"chat_id": chat_id,
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"user_message": question.question,
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"assistant": "",
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"brain_id": question.brain_id,
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"prompt_id": self.prompt_to_use_id,
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}
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)
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)
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streamed_chat_history = GetChatHistoryOutput(
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**{
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"chat_id": str(chat_id),
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"message_id": streamed_chat_history.message_id,
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"message_time": streamed_chat_history.message_time,
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"user_message": question.question,
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"assistant": "",
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"prompt_title": self.prompt_to_use.title
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if self.prompt_to_use
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else None,
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"brain_name": brain.name if brain else None,
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}
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)
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else:
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streamed_chat_history = GetChatHistoryOutput(
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**{
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"chat_id": str(chat_id),
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"message_id": None,
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"message_time": None,
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"user_message": question.question,
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"assistant": "",
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"prompt_title": self.prompt_to_use.title
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if self.prompt_to_use
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else None,
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"brain_name": brain.name if brain else None,
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}
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)
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response_tokens = []
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async for value in self.make_completion(
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messages=messages,
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functions=[get_api_brain_definition_as_json_schema(brain)],
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brain_id=question.brain_id,
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should_log_steps=should_log_steps,
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):
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streamed_chat_history.assistant = value
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response_tokens.append(value)
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yield f"data: {json.dumps(streamed_chat_history.dict())}"
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response_tokens = [
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token
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for token in response_tokens
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if not token.startswith("🧠<") and not token.endswith(">🧠")
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]
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if save_answer:
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chat_service.update_message_by_id(
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message_id=str(streamed_chat_history.message_id),
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user_message=question.question,
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assistant="".join(response_tokens),
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)
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def make_completion_without_streaming(
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self,
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messages,
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functions,
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brain_id: UUID,
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recursive_count=0,
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should_log_steps=False,
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):
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if recursive_count > 5:
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print(
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"The assistant is having issues and took more than 5 calls to the API. Please try again later or an other instruction."
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)
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return
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if should_log_steps:
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print("🧠<Deciding what to do>🧠")
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response = completion(
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model=self.model,
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temperature=self.temperature,
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max_tokens=self.max_tokens,
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messages=messages,
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functions=functions,
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stream=False,
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function_call="auto",
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)
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response_message = response.choices[0].message
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finish_reason = response.choices[0].finish_reason
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if finish_reason == "function_call":
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function_call = response_message.function_call
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try:
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arguments = json.loads(function_call.arguments)
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except Exception:
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arguments = {}
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if should_log_steps:
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print(f"🧠<Calling {brain_id} with arguments {arguments}>🧠")
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try:
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api_call_response = call_brain_api(
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brain_id=brain_id,
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user_id=self.user_id,
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arguments=arguments,
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)
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except Exception as e:
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raise HTTPException(
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status_code=400,
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detail=f"Error while calling API: {e}",
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)
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function_name = function_call.name
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messages.append(
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{
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"role": "function",
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"name": function_call.name,
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"content": f"The function {function_name} was called and gave The following answer:(data from function) {api_call_response} (end of data from function). Don't call this function again unless there was an error or extremely necessary and asked specifically by the user.",
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}
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)
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return self.make_completion_without_streaming(
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messages=messages,
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functions=functions,
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brain_id=brain_id,
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recursive_count=recursive_count + 1,
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should_log_steps=should_log_steps,
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)
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if finish_reason == "stop":
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return response_message
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else:
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print("Never ending completion")
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def generate_answer(
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self,
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chat_id: UUID,
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question: ChatQuestion,
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save_answer: bool = True,
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):
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if not question.brain_id:
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raise HTTPException(
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status_code=400, detail="No brain id provided in the question"
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)
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brain = brain_service.get_brain_by_id(question.brain_id)
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if not brain:
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raise HTTPException(status_code=404, detail="Brain not found")
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prompt_content = "You are a helpful assistant that can access functions to help answer questions. If there are information missing in the question, you can ask follow up questions to get more information to the user. Once all the information is available, you can call the function to get the answer."
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if self.prompt_to_use:
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prompt_content += self.prompt_to_use.content
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messages = [{"role": "system", "content": prompt_content}]
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history = chat_service.get_chat_history(self.chat_id)
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for message in history:
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formatted_message = [
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{"role": "user", "content": message.user_message},
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{"role": "assistant", "content": message.assistant},
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]
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messages.extend(formatted_message)
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messages.append({"role": "user", "content": question.question})
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response = self.make_completion_without_streaming(
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messages=messages,
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functions=[get_api_brain_definition_as_json_schema(brain)],
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brain_id=question.brain_id,
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should_log_steps=False,
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)
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answer = response.content
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if save_answer:
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new_chat = chat_service.update_chat_history(
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CreateChatHistory(
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**{
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"chat_id": chat_id,
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"user_message": question.question,
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"assistant": answer,
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"brain_id": question.brain_id,
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"prompt_id": self.prompt_to_use_id,
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}
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)
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)
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return GetChatHistoryOutput(
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**{
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"chat_id": chat_id,
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"user_message": question.question,
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"assistant": answer,
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"message_time": new_chat.message_time,
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"prompt_title": self.prompt_to_use.title
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if self.prompt_to_use
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else None,
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"brain_name": brain.name if brain else None,
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"message_id": new_chat.message_id,
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}
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)
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return GetChatHistoryOutput(
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**{
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"chat_id": chat_id,
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"user_message": question.question,
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"assistant": answer,
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"message_time": "123",
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"prompt_title": None,
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"brain_name": brain.name,
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"message_id": None,
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}
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)
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