#
#  Copyright 2024 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.

import requests

from .modules.agent import Agent
from .modules.chat import Chat
from .modules.chunk import Chunk
from .modules.dataset import DataSet


class CRLLM:
    def __init__(self, api_key, base_url, version="v1"):
        """
        api_url: http://<host_address>/api/v1
        """
        self.user_key = api_key
        self.api_url = f"{base_url}/api/{version}"
        self.authorization_header = {
            "Authorization": "{} {}".format("Bearer", self.user_key)
        }

    def post(self, path, json=None, stream=False, files=None):
        res = requests.post(
            url=self.api_url + path,
            json=json,
            headers=self.authorization_header,
            stream=stream,
            files=files,
        )
        return res

    def get(self, path, params=None, json=None):
        res = requests.get(
            url=self.api_url + path,
            params=params,
            headers=self.authorization_header,
            json=json,
        )
        return res

    def delete(self, path, json):
        res = requests.delete(
            url=self.api_url + path, json=json, headers=self.authorization_header
        )
        return res

    def put(self, path, json):
        res = requests.put(
            url=self.api_url + path, json=json, headers=self.authorization_header
        )
        return res

    def create_dataset(
        self,
        name: str,
        avatar: str = "",
        description: str = "",
        language: str = "English",
        permission: str = "me",
        chunk_method: str = "naive",
        parser_config: DataSet.ParserConfig = None,
    ) -> DataSet:
        if parser_config:
            parser_config = parser_config.to_json()
        res = self.post(
            "/datasets",
            {
                "name": name,
                "avatar": avatar,
                "description": description,
                "language": language,
                "permission": permission,
                "chunk_method": chunk_method,
                "parser_config": parser_config,
            },
        )
        res = res.json()
        if res.get("code") == 0:
            return DataSet(self, res["data"])
        raise Exception(res["message"])

    def delete_datasets(self, ids: list[str] | None = None):
        res = self.delete("/datasets", {"ids": ids})
        res = res.json()
        if res.get("code") != 0:
            raise Exception(res["message"])

    def get_dataset(self, name: str):
        _list = self.list_datasets(name=name)
        if len(_list) > 0:
            return _list[0]
        raise Exception("Dataset %s not found" % name)

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

    def create_chat(
        self,
        name: str,
        avatar: str = "",
        dataset_ids=None,
        llm: Chat.LLM | None = None,
        prompt: Chat.Prompt | None = None,
    ) -> Chat:
        if dataset_ids is None:
            dataset_ids = []
        dataset_list = []
        for id in dataset_ids:
            dataset_list.append(id)

        if llm is None:
            llm = Chat.LLM(
                self,
                {
                    "model_name": None,
                    "temperature": 0.1,
                    "top_p": 0.3,
                    "presence_penalty": 0.4,
                    "frequency_penalty": 0.7,
                    "max_tokens": 512,
                },
            )
        if prompt is None:
            prompt = Chat.Prompt(
                self,
                {
                    "similarity_threshold": 0.2,
                    "keywords_similarity_weight": 0.7,
                    "top_n": 8,
                    "variables": [{"key": "knowledge", "optional": True}],
                    "rerank_model": "",
                    "empty_response": None,
                    "opener": None,
                    "show_quote": True,
                    "prompt": None,
                },
            )
            if prompt.opener is None:
                prompt.opener = "Hi! I'm your assistant, what can I do for you?"
            if prompt.prompt is None:
                prompt.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."
                )

        temp_dict = {
            "name": name,
            "avatar": avatar,
            "dataset_ids": dataset_list,
            "llm": llm.to_json(),
            "prompt": prompt.to_json(),
        }
        res = self.post("/chats", temp_dict)
        res = res.json()
        if res.get("code") == 0:
            return Chat(self, res["data"])
        raise Exception(res["message"])

    def delete_chats(self, ids: list[str] | None = None):
        res = self.delete("/chats", {"ids": ids})
        res = res.json()
        if res.get("code") != 0:
            raise Exception(res["message"])

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

    def retrieve(
        self,
        dataset_ids,
        document_ids=None,
        question="",
        page=1,
        page_size=30,
        similarity_threshold=0.2,
        vector_similarity_weight=0.3,
        top_k=1024,
        rerank_id: str | None = None,
        keyword: bool = False,
    ):
        if document_ids is None:
            document_ids = []
        data_json = {
            "page": page,
            "page_size": page_size,
            "similarity_threshold": similarity_threshold,
            "vector_similarity_weight": vector_similarity_weight,
            "top_k": top_k,
            "rerank_id": rerank_id,
            "keyword": keyword,
            "question": question,
            "dataset_ids": dataset_ids,
            "documents": document_ids,
        }
        # Send a POST request to the backend service (using requests library as an example, actual implementation may vary)
        res = self.post("/retrieval", json=data_json)
        res = res.json()
        if res.get("code") == 0:
            chunks = []
            for chunk_data in res["data"].get("chunks"):
                chunk = Chunk(self, chunk_data)
                chunks.append(chunk)
            return chunks
        raise Exception(res.get("message"))

    def list_agents(
        self,
        page: int = 1,
        page_size: int = 30,
        orderby: str = "update_time",
        desc: bool = True,
        id: str | None = None,
        title: str | None = None,
    ) -> list[Agent]:
        res = self.get(
            "/agents",
            {
                "page": page,
                "page_size": page_size,
                "orderby": orderby,
                "desc": desc,
                "id": id,
                "title": title,
            },
        )
        res = res.json()
        result_list = []
        if res.get("code") == 0:
            for data in res["data"]:
                result_list.append(Agent(self, data))
            return result_list
        raise Exception(res["message"])
