#
#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#


from typing import Any, NotRequired, Optional, TYPE_CHECKING, TypedDict
from .base import Base
from .session import Session

if TYPE_CHECKING:
    from ..ragflow import RAGFlow

__all__ = 'Chat',

class Variable(TypedDict):
    key: str
    optional: NotRequired[bool]

LLMUpdateMessage = TypedDict('LLMUpdateMessage', {
    "model_name": NotRequired[str],
    "temperature": NotRequired[float],
    "top_p": NotRequired[float],
    "presence_penalty": NotRequired[float],
    "frequency penalty": NotRequired[float],
})

class PromptUpdateMessage(TypedDict):
    similarity_threshold: NotRequired[float]
    keywords_similarity_weight: NotRequired[float]
    top_n: NotRequired[int]
    variables: NotRequired[list[Variable]]
    rerank_model: NotRequired[str]
    empty_response: NotRequired[str]
    opener: NotRequired[str]
    show_quote: NotRequired[bool]
    prompt: NotRequired[str]

class UpdateMessage(TypedDict):
    name: NotRequired[str]
    avatar: NotRequired[str]
    dataset_ids: NotRequired[list[str]]
    llm: NotRequired[LLMUpdateMessage]
    prompt: NotRequired[PromptUpdateMessage]


class Chat(Base):
    __slots__ = (
        'id',
        'name',
        'avatar',
        'llm',
        'prompt',
    )

    id: str
    name: str
    avatar: str
    llm: "Chat.LLM"
    prompt: "Chat.Prompt"

    def __init__(self, rag: "RAGFlow", res_dict: dict[str, Any]) -> None:
        self.id = ""
        self.name = "assistant"
        self.avatar = "path/to/avatar"
        self.llm = Chat.LLM(rag, {})
        self.prompt = Chat.Prompt(rag, {})
        super().__init__(rag, res_dict)

    class LLM(Base):
        __slots__ = (
            'model_name',
            'temperature',
            'top_p',
            'presence_penalty',
            'frequency_penalty',
            'max_tokens',
        )

        model_name: Optional[str]
        temperature: float
        top_p: float
        presence_penalty: float
        frequency_penalty: float
        max_tokens: int

        def __init__(self, rag: "RAGFlow", res_dict: dict[str, Any]) -> None:
            self.model_name = None
            self.temperature = 0.1
            self.top_p = 0.3
            self.presence_penalty = 0.4
            self.frequency_penalty = 0.7
            self.max_tokens = 512
            super().__init__(rag, res_dict)

    class Prompt(Base):
        __slots__ = (
            'similarity_threshold',
            'keywords_similarity_weight',
            'top_n',
            'top_k',
            'variables',
            'rerank_model',
            'empty_response',
            'opener',
            'show_quote',
            'prompt',
        )

        similarity_threshold: float
        keywords_similarity_weight: float
        top_n: int
        top_k: int
        variables: list[Variable]
        rerank_model: str
        empty_response: Optional[str]
        opener: str
        show_quote: bool
        prompt: str

        def __init__(self, rag: "RAGFlow", res_dict: dict[str, Any]) -> None:
            self.similarity_threshold = 0.2
            self.keywords_similarity_weight = 0.7
            self.top_n = 8
            self.top_k = 1024
            self.variables = [{"key": "knowledge", "optional": True}]
            self.rerank_model = ""
            self.empty_response = None
            self.opener = "Hi! I'm your assistant. What can I do for you?"
            self.show_quote = True
            self.prompt = (
                "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. "
                "Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, "
                "your answer must include the sentence 'The answer you are looking for is not found in the knowledge base!' "
                "Answers need to consider chat history.\nHere is the knowledge base:\n{knowledge}\nThe above is the knowledge base."
            )
            super().__init__(rag, res_dict)

    def update(self, update_message: UpdateMessage) -> None:
        res = self.put(f"/chats/{self.id}", update_message)
        res = res.json()
        if res.get("code") != 0:
            raise Exception(res["message"])

    def create_session(self, name: str = "New session") -> Session:
        res = self.post(f"/chats/{self.id}/sessions", {"name": name})
        res = res.json()
        if res.get("code") == 0:
            return Session(self.rag, res["data"])
        raise Exception(res["message"])

    def list_sessions(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, id: Optional[str] = None, name: Optional[str] = None) -> list[Session]:
        res = self.get(f"/chats/{self.id}/sessions", {"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
        res = res.json()
        if res.get("code") == 0:
            result_list = []
            for data in res["data"]:
                result_list.append(Session(self.rag, data))
            return result_list
        raise Exception(res["message"])

    def delete_sessions(self, ids: list[str] | None = None) -> None:
        res = self.rm(f"/chats/{self.id}/sessions", {"ids": ids})
        res = res.json()
        if res.get("code") != 0:
            raise Exception(res.get("message"))