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https://github.com/QuivrHQ/quivr.git
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c5546fd374
now updates # Description Please include a summary of the changes and the related issue. Please also include relevant motivation and context. ## Checklist before requesting a review Please delete options that are not relevant. - [ ] My code follows the style guidelines of this project - [ ] I have performed a self-review of my code - [ ] I have commented hard-to-understand areas - [ ] I have ideally added tests that prove my fix is effective or that my feature works - [ ] New and existing unit tests pass locally with my changes - [ ] Any dependent changes have been merged ## Screenshots (if appropriate):
499 lines
18 KiB
Python
499 lines
18 KiB
Python
import json
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from typing import Optional
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from uuid import UUID
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import jq
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import requests
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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 UUIDEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, uuid.UUID):
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# if the object is uuid, we simply return the value of uuid
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return str(obj)
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return super().default(obj)
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class APIBrainQA(KnowledgeBrainQA, QAInterface):
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user_id: UUID
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raw: bool = False
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jq_instructions: Optional[str] = None
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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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raw: bool = False,
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jq_instructions: Optional[str] = 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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self.raw = raw
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self.jq_instructions = jq_instructions
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def get_api_call_response_as_text(
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self, method, api_url, params, search_params, secrets
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) -> str:
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headers = {}
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api_url_with_search_params = api_url
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if search_params:
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api_url_with_search_params += "?"
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for search_param in search_params:
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api_url_with_search_params += (
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f"{search_param}={search_params[search_param]}&"
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)
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for secret in secrets:
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headers[secret] = secrets[secret]
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try:
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if method in ["GET", "DELETE"]:
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response = requests.request(
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method,
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url=api_url_with_search_params,
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params=params or None,
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headers=headers or None,
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)
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elif method in ["POST", "PUT", "PATCH"]:
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response = requests.request(
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method,
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url=api_url_with_search_params,
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json=params or None,
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headers=headers or None,
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)
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else:
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raise ValueError(f"Invalid method: {method}")
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return response.text
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except Exception as e:
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logger.error(f"Error calling API: {e}")
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return None
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def log_steps(self, message: str, type: str):
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if "api" not in self.metadata:
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self.metadata["api"] = {}
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if "steps" not in self.metadata["api"]:
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self.metadata["api"]["steps"] = []
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self.metadata["api"]["steps"].append(
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{
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"number": len(self.metadata["api"]["steps"]),
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"type": type,
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"message": message,
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}
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)
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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=True,
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) -> str | None:
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if recursive_count > 5:
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self.log_steps(
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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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"error",
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)
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return
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if "api" not in self.metadata:
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self.metadata["api"] = {}
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if "raw" not in self.metadata["api"]:
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self.metadata["api"]["raw_enabled"] = self.raw
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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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self.log_steps("Quivr has finished", "info")
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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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self.log_steps(f"Issues with {arguments}", "error")
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arguments = {}
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self.log_steps(f"Calling {brain_id} with arguments {arguments}", "info")
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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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logger.info(f"Error while calling API: {e}")
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api_call_response = f"Error while calling API: {e}"
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function_name = function_call["name"]
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self.log_steps("Quivr has called the API", "info")
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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. If an error, display it to the user in raw.",
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}
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)
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self.metadata["api"]["raw_response"] = json.loads(api_call_response)
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if self.raw:
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# Yield the raw response in a format that can then be catched by the generate_stream function
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response_to_yield = f"````raw_response: {api_call_response}````"
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yield response_to_yield
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return
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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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brain = brain_service.get_brain_by_id(self.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": self.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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"brain_id": str(self.brain_id),
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"metadata": self.metadata,
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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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"brain_id": str(self.brain_id),
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"metadata": self.metadata,
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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=self.brain_id,
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should_log_steps=should_log_steps,
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):
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# Look if the value is a raw response
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if value.startswith("````raw_response:"):
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raw_value_cleaned = value.replace("````raw_response: ", "").replace(
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"````", ""
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)
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logger.info(f"Raw response: {raw_value_cleaned}")
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if self.jq_instructions:
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json_raw_value_cleaned = json.loads(raw_value_cleaned)
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raw_value_cleaned = (
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jq.compile(self.jq_instructions)
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.input_value(json_raw_value_cleaned)
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.first()
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)
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streamed_chat_history.assistant = raw_value_cleaned
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response_tokens.append(raw_value_cleaned)
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yield f"data: {json.dumps(streamed_chat_history.dict())}"
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else:
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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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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(str(token) for token in response_tokens),
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metadata=self.metadata,
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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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self.log_steps(f"Calling {brain_id} with arguments {arguments}", "info")
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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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raw: bool = True,
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):
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if not self.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(self.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=self.brain_id,
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should_log_steps=False,
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raw=raw,
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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": self.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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"metadata": self.metadata,
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"brain_id": str(self.brain_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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"metadata": self.metadata,
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"brain_id": str(self.brain_id),
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}
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)
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