quivr/backend/modules/brain/knowledge_brain_qa.py

import json
from typing import AsyncIterable, List, Optional
from uuid import UUID

from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
from logger import get_logger
from models import BrainSettings
from modules.brain.entity.brain_entity import BrainEntity
from modules.brain.qa_interface import QAInterface
from modules.brain.rags.quivr_rag import QuivrRAG
from modules.brain.rags.rag_interface import RAGInterface
from modules.brain.service.brain_service import BrainService
from modules.brain.service.utils.format_chat_history import format_chat_history
from modules.brain.service.utils.get_prompt_to_use_id import get_prompt_to_use_id
from modules.chat.controller.chat.utils import (
    find_model_and_generate_metadata,
    update_user_usage,
)
from modules.chat.dto.chats import ChatQuestion, Sources
from modules.chat.dto.inputs import CreateChatHistory
from modules.chat.dto.outputs import GetChatHistoryOutput
from modules.chat.service.chat_service import ChatService
from modules.prompt.service.get_prompt_to_use import get_prompt_to_use
from modules.upload.service.generate_file_signed_url import generate_file_signed_url
from modules.user.service.user_usage import UserUsage
from pydantic import BaseModel, ConfigDict
from pydantic_settings import BaseSettings

logger = get_logger(__name__)
QUIVR_DEFAULT_PROMPT = "Your name is Quivr. You're a helpful assistant.  If you don't know the answer, just say that you don't know, don't try to make up an answer."


brain_service = BrainService()
chat_service = ChatService()


def is_valid_uuid(uuid_to_test, version=4):
    try:
        uuid_obj = UUID(uuid_to_test, version=version)
    except ValueError:
        return False

    return str(uuid_obj) == uuid_to_test


def generate_source(source_documents, brain_id, citations: List[int] = None):
    """
    Generate the sources list for the answer
    It takes in a list of sources documents and citations that points to the docs index that was used in the answer
    """
    # Initialize an empty list for sources
    sources_list: List[Sources] = []

    # Initialize a dictionary for storing generated URLs
    generated_urls = {}

    # remove duplicate sources with same name and create a list of unique sources
    sources_url_cache = {}

    # Get source documents from the result, default to an empty list if not found

    # If source documents exist
    if source_documents:
        logger.info(f"Citations {citations}")
        # Iterate over each document
        for doc, index in zip(source_documents, range(len(source_documents))):
            logger.info(f"Processing source document {doc.metadata['file_name']}")
            if citations is not None:
                if index not in citations:
                    logger.info(f"Skipping source document {doc.metadata['file_name']}")
                    continue
            # Check if 'url' is in the document metadata
            is_url = (
                "original_file_name" in doc.metadata
                and doc.metadata["original_file_name"] is not None
                and doc.metadata["original_file_name"].startswith("http")
            )

            # Determine the name based on whether it's a URL or a file
            name = (
                doc.metadata["original_file_name"]
                if is_url
                else doc.metadata["file_name"]
            )

            # Determine the type based on whether it's a URL or a file
            type_ = "url" if is_url else "file"

            # Determine the source URL based on whether it's a URL or a file
            if is_url:
                source_url = doc.metadata["original_file_name"]
            else:
                file_path = f"{brain_id}/{doc.metadata['file_name']}"
                # Check if the URL has already been generated
                if file_path in generated_urls:
                    source_url = generated_urls[file_path]
                else:
                    # Generate the URL
                    if file_path in sources_url_cache:
                        source_url = sources_url_cache[file_path]
                    else:
                        generated_url = generate_file_signed_url(file_path)
                        if generated_url is not None:
                            source_url = generated_url.get("signedURL", "")
                        else:
                            source_url = ""
                    # Store the generated URL
                    generated_urls[file_path] = source_url

            # Append a new Sources object to the list
            sources_list.append(
                Sources(
                    name=name,
                    type=type_,
                    source_url=source_url,
                    original_file_name=name,
                    citation=doc.page_content,
                )
            )
    else:
        logger.info("No source documents found or source_documents is not a list.")
    return sources_list


class KnowledgeBrainQA(BaseModel, QAInterface):
    """
    Main class for the Brain Picking functionality.
    It allows to initialize a Chat model, generate questions and retrieve answers using ConversationalRetrievalChain.
    It has two main methods: `generate_question` and `generate_stream`.
    One is for generating questions in a single request, the other is for generating questions in a streaming fashion.
    Both are the same, except that the streaming version streams the last message as a stream.
    Each have the same prompt template, which is defined in the `prompt_template` property.
    """

    model_config = ConfigDict(arbitrary_types_allowed=True)

    # Instantiate settings
    brain_settings: BaseSettings = BrainSettings()

    # Default class attributes
    model: str = "gpt-3.5-turbo-0125"  # pyright: ignore reportPrivateUsage=none
    temperature: float = 0.1
    chat_id: str = None  # pyright: ignore reportPrivateUsage=none
    brain_id: str = None  # pyright: ignore reportPrivateUsage=none
    max_tokens: int = 2000
    max_input: int = 2000
    streaming: bool = False
    knowledge_qa: Optional[RAGInterface] = None
    brain: Optional[BrainEntity] = None
    user_id: str = None
    user_email: str = None
    user_usage: Optional[UserUsage] = None
    user_settings: Optional[dict] = None
    models_settings: Optional[List[dict]] = None
    metadata: Optional[dict] = None

    callbacks: List[AsyncIteratorCallbackHandler] = (
        None  # pyright: ignore reportPrivateUsage=none
    )

    prompt_id: Optional[UUID] = None

    def __init__(
        self,
        brain_id: str,
        chat_id: str,
        streaming: bool = False,
        prompt_id: Optional[UUID] = None,
        metadata: Optional[dict] = None,
        user_id: str = None,
        user_email: str = None,
        cost: int = 100,
        **kwargs,
    ):
        super().__init__(
            brain_id=brain_id,
            chat_id=chat_id,
            streaming=streaming,
            **kwargs,
        )
        self.prompt_id = prompt_id
        self.user_id = user_id
        self.user_email = user_email
        self.user_usage = UserUsage(
            id=user_id,
            email=user_email,
        )
        self.brain = brain_service.get_brain_by_id(brain_id)

        self.user_settings = self.user_usage.get_user_settings()

        # Get Model settings for the user
        self.models_settings = self.user_usage.get_model_settings()
        self.increase_usage_user()
        self.knowledge_qa = QuivrRAG(
            model=self.brain.model if self.brain.model else self.model,
            brain_id=brain_id,
            chat_id=chat_id,
            streaming=streaming,
            max_input=self.max_input,
            max_tokens=self.max_tokens,
            **kwargs,
        )

    @property
    def prompt_to_use(self):
        if self.brain_id and is_valid_uuid(self.brain_id):
            return get_prompt_to_use(UUID(self.brain_id), self.prompt_id)
        else:
            return None

    @property
    def prompt_to_use_id(self) -> Optional[UUID]:
        # TODO: move to prompt service or instruction or something
        if self.brain_id and is_valid_uuid(self.brain_id):
            return get_prompt_to_use_id(UUID(self.brain_id), self.prompt_id)
        else:
            return None

    def filter_history(
        self, chat_history, max_history: int = 10, max_tokens: int = 2000
    ):
        """
        Filter out the chat history to only include the messages that are relevant to the current question

        Takes in a chat_history= [HumanMessage(content='Qui est Chloé ? '), AIMessage(content="Chloé est une salariée travaillant pour l'entreprise Quivr en tant qu'AI Engineer, sous la direction de son supérieur hiérarchique, Stanislas Girard."), HumanMessage(content='Dis moi en plus sur elle'), AIMessage(content=''), HumanMessage(content='Dis moi en plus sur elle'), AIMessage(content="Désolé, je n'ai pas d'autres informations sur Chloé à partir des fichiers fournis.")]
        Returns a filtered chat_history with in priority: first max_tokens, then max_history where a Human message and an AI message count as one pair
        a token is 4 characters
        """
        chat_history = chat_history[::-1]
        total_tokens = 0
        total_pairs = 0
        filtered_chat_history = []
        for i in range(0, len(chat_history), 2):
            if i + 1 < len(chat_history):
                human_message = chat_history[i]
                ai_message = chat_history[i + 1]
                message_tokens = (
                    len(human_message.content) + len(ai_message.content)
                ) // 4
                if (
                    total_tokens + message_tokens > max_tokens
                    or total_pairs >= max_history
                ):
                    break
                filtered_chat_history.append(human_message)
                filtered_chat_history.append(ai_message)
                total_tokens += message_tokens
                total_pairs += 1
        chat_history = filtered_chat_history[::-1]

        return chat_history

    def increase_usage_user(self):
        # Raises an error if the user has consumed all of of his credits

        update_user_usage(
            usage=self.user_usage,
            user_settings=self.user_settings,
            cost=self.calculate_pricing(),
        )

    def calculate_pricing(self):

        model_to_use = find_model_and_generate_metadata(
            self.chat_id,
            self.brain.model,
            self.user_settings,
            self.models_settings,
        )
        self.model = model_to_use.name
        self.max_input = model_to_use.max_input
        self.max_tokens = model_to_use.max_output
        user_choosen_model_price = 1000

        for model_setting in self.models_settings:
            if model_setting["name"] == self.model:
                user_choosen_model_price = model_setting["price"]

        return user_choosen_model_price

    def generate_answer(
        self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
    ) -> GetChatHistoryOutput:
        conversational_qa_chain = self.knowledge_qa.get_chain()
        transformed_history, streamed_chat_history = (
            self.initialize_streamed_chat_history(chat_id, question)
        )
        metadata = self.metadata or {}
        citations = None
        answer = ""
        config = {"metadata": {"conversation_id": str(chat_id)}}

        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
                ),
            },
            config=config,
        )

        if self.model_compatible_with_function_calling(model=self.model):
            if model_response["answer"].tool_calls:
                citations = model_response["answer"].tool_calls[-1]["args"]["citations"]
                if citations:
                    citations = citations
                answer = model_response["answer"].tool_calls[-1]["args"]["answer"]
        else:
            answer = model_response["answer"].content
        sources = model_response["docs"] or []
        if len(sources) > 0:
            sources_list = generate_source(sources, self.brain_id, citations=citations)
            serialized_sources_list = [source.dict() for source in sources_list]
            metadata["sources"] = serialized_sources_list

        return self.save_non_streaming_answer(
            chat_id=chat_id, question=question, answer=answer, metadata=metadata
        )

    async def generate_stream(
        self, chat_id: UUID, question: ChatQuestion, save_answer: bool = True
    ) -> AsyncIterable:
        conversational_qa_chain = self.knowledge_qa.get_chain()
        transformed_history, streamed_chat_history = (
            self.initialize_streamed_chat_history(chat_id, question)
        )
        response_tokens = ""
        sources = []
        citations = []
        first = True
        config = {"metadata": {"conversation_id": str(chat_id)}}

        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
                ),
            },
            config=config,
        ):
            if not streamed_chat_history.metadata:
                streamed_chat_history.metadata = {}
            if self.model_compatible_with_function_calling(model=self.model):
                if chunk.get("answer"):
                    if first:
                        gathered = chunk["answer"]
                        first = False
                    else:
                        gathered = gathered + chunk["answer"]
                        if (
                            gathered.tool_calls
                            and gathered.tool_calls[-1].get("args")
                            and "answer" in gathered.tool_calls[-1]["args"]
                        ):
                            # Only send the difference between answer and response_tokens which was the previous answer
                            answer = gathered.tool_calls[-1]["args"]["answer"]
                            difference = answer[len(response_tokens) :]
                            streamed_chat_history.assistant = difference
                            response_tokens = answer

                            yield f"data: {json.dumps(streamed_chat_history.dict())}"
                        if (
                            gathered.tool_calls
                            and gathered.tool_calls[-1].get("args")
                            and "citations" in gathered.tool_calls[-1]["args"]
                        ):
                            citations = gathered.tool_calls[-1]["args"]["citations"]
            else:
                if chunk.get("answer"):
                    response_tokens += chunk["answer"].content
                    streamed_chat_history.assistant = chunk["answer"].content
                    yield f"data: {json.dumps(streamed_chat_history.dict())}"

            if chunk.get("docs"):
                sources = chunk["docs"]

        sources_list = generate_source(sources, self.brain_id, citations)

        # Serialize the sources list
        serialized_sources_list = [source.dict() for source in sources_list]
        streamed_chat_history.metadata["sources"] = serialized_sources_list
        yield f"data: {json.dumps(streamed_chat_history.dict())}"
        self.save_answer(question, response_tokens, streamed_chat_history, save_answer)

    def initialize_streamed_chat_history(self, chat_id, question):
        history = chat_service.get_chat_history(self.chat_id)
        transformed_history = format_chat_history(history)
        brain = brain_service.get_brain_by_id(self.brain_id)

        streamed_chat_history = chat_service.update_chat_history(
            CreateChatHistory(
                **{
                    "chat_id": chat_id,
                    "user_message": question.question,
                    "assistant": "",
                    "brain_id": brain.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(brain.brain_id) if brain else None,
                "metadata": self.metadata,
            }
        )

        return transformed_history, streamed_chat_history

    def save_answer(
        self, question, response_tokens, streamed_chat_history, save_answer
    ):
        assistant = "".join(response_tokens)

        try:
            if save_answer:
                chat_service.update_message_by_id(
                    message_id=str(streamed_chat_history.message_id),
                    user_message=question.question,
                    assistant=assistant,
                    metadata=streamed_chat_history.metadata,
                )
        except Exception as e:
            logger.error("Error updating message by ID: %s", e)

    def save_non_streaming_answer(self, chat_id, question, answer, metadata):
        new_chat = chat_service.update_chat_history(
            CreateChatHistory(
                **{
                    "chat_id": chat_id,
                    "user_message": question.question,
                    "assistant": answer,
                    "brain_id": self.brain.brain_id,
                    "prompt_id": self.prompt_to_use_id,
                    "metadata": metadata,
                }
            )
        )

        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": self.brain.name if self.brain else None,
                "message_id": new_chat.message_id,
                "brain_id": str(self.brain.brain_id) if self.brain else None,
                "metadata": metadata,
            }
        )