import json from typing import Optional from uuid import UUID import jq import requests from fastapi import HTTPException from litellm import completion from llm.utils.call_brain_api import call_brain_api from llm.utils.get_api_brain_definition_as_json_schema import ( get_api_brain_definition_as_json_schema, ) from logger import get_logger from modules.brain.knowledge_brain_qa import KnowledgeBrainQA from modules.brain.qa_interface import QAInterface from modules.brain.service.brain_service import BrainService from modules.chat.dto.chats import ChatQuestion from modules.chat.dto.inputs import CreateChatHistory from modules.chat.dto.outputs import GetChatHistoryOutput from modules.chat.service.chat_service import ChatService brain_service = BrainService() chat_service = ChatService() logger = get_logger(__name__) class UUIDEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, UUID): # if the object is uuid, we simply return the value of uuid return str(obj) return super().default(obj) class APIBrainQA(KnowledgeBrainQA, QAInterface): user_id: UUID raw: bool = False jq_instructions: Optional[str] = None def __init__( self, model: str, brain_id: str, chat_id: str, streaming: bool = False, prompt_id: Optional[UUID] = None, raw: bool = False, jq_instructions: Optional[str] = None, **kwargs, ): user_id = kwargs.get("user_id") if not user_id: raise HTTPException(status_code=400, detail="Cannot find user id") super().__init__( model=model, brain_id=brain_id, chat_id=chat_id, streaming=streaming, prompt_id=prompt_id, **kwargs, ) self.user_id = user_id self.raw = raw self.jq_instructions = jq_instructions def get_api_call_response_as_text( self, method, api_url, params, search_params, secrets ) -> str: headers = {} api_url_with_search_params = api_url if search_params: api_url_with_search_params += "?" for search_param in search_params: api_url_with_search_params += ( f"{search_param}={search_params[search_param]}&" ) for secret in secrets: headers[secret] = secrets[secret] try: if method in ["GET", "DELETE"]: response = requests.request( method, url=api_url_with_search_params, params=params or None, headers=headers or None, ) elif method in ["POST", "PUT", "PATCH"]: response = requests.request( method, url=api_url_with_search_params, json=params or None, headers=headers or None, ) else: raise ValueError(f"Invalid method: {method}") return response.text except Exception as e: logger.error(f"Error calling API: {e}") return None def log_steps(self, message: str, type: str): if "api" not in self.metadata: self.metadata["api"] = {} if "steps" not in self.metadata["api"]: self.metadata["api"]["steps"] = [] self.metadata["api"]["steps"].append( { "number": len(self.metadata["api"]["steps"]), "type": type, "message": message, } ) async def make_completion( self, messages, functions, brain_id: UUID, recursive_count=0, should_log_steps=True, ) -> str | None: if recursive_count > 5: self.log_steps( "The assistant is having issues and took more than 5 calls to the API. Please try again later or an other instruction.", "error", ) return if "api" not in self.metadata: self.metadata["api"] = {} if "raw" not in self.metadata["api"]: self.metadata["api"]["raw_enabled"] = self.raw response = completion( model=self.model, temperature=self.temperature, max_tokens=self.max_tokens, messages=messages, functions=functions, stream=True, function_call="auto", ) function_call = { "name": None, "arguments": "", } for chunk in response: finish_reason = chunk.choices[0].finish_reason if finish_reason == "stop": self.log_steps("Quivr has finished", "info") break if ( "function_call" in chunk.choices[0].delta and chunk.choices[0].delta["function_call"] ): if chunk.choices[0].delta["function_call"].name: function_call["name"] = chunk.choices[0].delta["function_call"].name if chunk.choices[0].delta["function_call"].arguments: function_call["arguments"] += ( chunk.choices[0].delta["function_call"].arguments ) elif finish_reason == "function_call": try: arguments = json.loads(function_call["arguments"]) except Exception: self.log_steps(f"Issues with {arguments}", "error") arguments = {} self.log_steps(f"Calling {brain_id} with arguments {arguments}", "info") try: api_call_response = call_brain_api( brain_id=brain_id, user_id=self.user_id, arguments=arguments, ) except Exception as e: logger.info(f"Error while calling API: {e}") api_call_response = f"Error while calling API: {e}" function_name = function_call["name"] self.log_steps("Quivr has called the API", "info") messages.append( { "role": "function", "name": function_call["name"], "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. If an error, display it to the user in raw.", } ) self.metadata["api"]["raw_response"] = json.loads(api_call_response) if self.raw: # Yield the raw response in a format that can then be catched by the generate_stream function response_to_yield = f"````raw_response: {api_call_response}````" yield response_to_yield return async for value in self.make_completion( messages=messages, functions=functions, brain_id=brain_id, recursive_count=recursive_count + 1, should_log_steps=should_log_steps, ): yield value else: if ( hasattr(chunk.choices[0], "delta") and chunk.choices[0].delta and hasattr(chunk.choices[0].delta, "content") ): content = chunk.choices[0].delta.content yield content else: # pragma: no cover yield "**...**" break async def generate_stream( self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True, should_log_steps: Optional[bool] = True, ): brain = brain_service.get_brain_by_id(self.brain_id) if not brain: raise HTTPException(status_code=404, detail="Brain not found") 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." if self.prompt_to_use: prompt_content += self.prompt_to_use.content messages = [{"role": "system", "content": prompt_content}] history = chat_service.get_chat_history(self.chat_id) for message in history: formatted_message = [ {"role": "user", "content": message.user_message}, {"role": "assistant", "content": message.assistant}, ] messages.extend(formatted_message) messages.append({"role": "user", "content": question.question}) if save_answer: streamed_chat_history = chat_service.update_chat_history( CreateChatHistory( **{ "chat_id": chat_id, "user_message": question.question, "assistant": "", "brain_id": self.brain_id, "prompt_id": self.prompt_to_use_id, } ) ) streamed_chat_history = GetChatHistoryOutput( **{ "chat_id": str(chat_id), "message_id": streamed_chat_history.message_id, "message_time": streamed_chat_history.message_time, "user_message": question.question, "assistant": "", "prompt_title": self.prompt_to_use.title if self.prompt_to_use else None, "brain_name": brain.name if brain else None, "brain_id": str(self.brain_id), "metadata": self.metadata, } ) else: streamed_chat_history = GetChatHistoryOutput( **{ "chat_id": str(chat_id), "message_id": None, "message_time": None, "user_message": question.question, "assistant": "", "prompt_title": self.prompt_to_use.title if self.prompt_to_use else None, "brain_name": brain.name if brain else None, "brain_id": str(self.brain_id), "metadata": self.metadata, } ) response_tokens = [] async for value in self.make_completion( messages=messages, functions=[get_api_brain_definition_as_json_schema(brain)], brain_id=self.brain_id, should_log_steps=should_log_steps, ): # Look if the value is a raw response if value.startswith("````raw_response:"): raw_value_cleaned = value.replace("````raw_response: ", "").replace( "````", "" ) logger.info(f"Raw response: {raw_value_cleaned}") if self.jq_instructions: json_raw_value_cleaned = json.loads(raw_value_cleaned) raw_value_cleaned = ( jq.compile(self.jq_instructions) .input_value(json_raw_value_cleaned) .first() ) streamed_chat_history.assistant = raw_value_cleaned response_tokens.append(raw_value_cleaned) yield f"data: {json.dumps(streamed_chat_history.dict())}" else: streamed_chat_history.assistant = value response_tokens.append(value) yield f"data: {json.dumps(streamed_chat_history.dict())}" if save_answer: chat_service.update_message_by_id( message_id=str(streamed_chat_history.message_id), user_message=question.question, assistant="".join(str(token) for token in response_tokens), metadata=self.metadata, ) def make_completion_without_streaming( self, messages, functions, brain_id: UUID, recursive_count=0, should_log_steps=False, ): if recursive_count > 5: print( "The assistant is having issues and took more than 5 calls to the API. Please try again later or an other instruction." ) return if should_log_steps: print("🧠<Deciding what to do>🧠") response = completion( model=self.model, temperature=self.temperature, max_tokens=self.max_tokens, messages=messages, functions=functions, stream=False, function_call="auto", ) response_message = response.choices[0].message finish_reason = response.choices[0].finish_reason if finish_reason == "function_call": function_call = response_message.function_call try: arguments = json.loads(function_call.arguments) except Exception: arguments = {} if should_log_steps: self.log_steps(f"Calling {brain_id} with arguments {arguments}", "info") try: api_call_response = call_brain_api( brain_id=brain_id, user_id=self.user_id, arguments=arguments, ) except Exception as e: raise HTTPException( status_code=400, detail=f"Error while calling API: {e}", ) function_name = function_call.name messages.append( { "role": "function", "name": function_call.name, "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.", } ) return self.make_completion_without_streaming( messages=messages, functions=functions, brain_id=brain_id, recursive_count=recursive_count + 1, should_log_steps=should_log_steps, ) if finish_reason == "stop": return response_message else: print("Never ending completion") def generate_answer( self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True, raw: bool = True, ): if not self.brain_id: raise HTTPException( status_code=400, detail="No brain id provided in the question" ) brain = brain_service.get_brain_by_id(self.brain_id) if not brain: raise HTTPException(status_code=404, detail="Brain not found") 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." if self.prompt_to_use: prompt_content += self.prompt_to_use.content messages = [{"role": "system", "content": prompt_content}] history = chat_service.get_chat_history(self.chat_id) for message in history: formatted_message = [ {"role": "user", "content": message.user_message}, {"role": "assistant", "content": message.assistant}, ] messages.extend(formatted_message) messages.append({"role": "user", "content": question.question}) response = self.make_completion_without_streaming( messages=messages, functions=[get_api_brain_definition_as_json_schema(brain)], brain_id=self.brain_id, should_log_steps=False, raw=raw, ) answer = response.content if save_answer: new_chat = chat_service.update_chat_history( CreateChatHistory( **{ "chat_id": chat_id, "user_message": question.question, "assistant": answer, "brain_id": self.brain_id, "prompt_id": self.prompt_to_use_id, } ) ) return GetChatHistoryOutput( **{ "chat_id": chat_id, "user_message": question.question, "assistant": answer, "message_time": new_chat.message_time, "prompt_title": self.prompt_to_use.title if self.prompt_to_use else None, "brain_name": brain.name if brain else None, "message_id": new_chat.message_id, "metadata": self.metadata, "brain_id": str(self.brain_id), } ) return GetChatHistoryOutput( **{ "chat_id": chat_id, "user_message": question.question, "assistant": answer, "message_time": "123", "prompt_title": None, "brain_name": brain.name, "message_id": None, "metadata": self.metadata, "brain_id": str(self.brain_id), } )