import array
import json
import re
import uuid
from contextlib import contextmanager
from typing import Any

import jieba.posseg as pseg  # type: ignore
import numpy
import oracledb
from pydantic import BaseModel, model_validator

from configs import dify_config
from core.rag.datasource.vdb.vector_base import BaseVector
from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
from core.rag.datasource.vdb.vector_type import VectorType
from core.rag.embedding.embedding_base import Embeddings
from core.rag.models.document import Document
from extensions.ext_redis import redis_client
from models.dataset import Dataset

oracledb.defaults.fetch_lobs = False


class OracleVectorConfig(BaseModel):
    host: str
    port: int
    user: str
    password: str
    database: str

    @model_validator(mode="before")
    @classmethod
    def validate_config(cls, values: dict) -> dict:
        if not values["host"]:
            raise ValueError("config ORACLE_HOST is required")
        if not values["port"]:
            raise ValueError("config ORACLE_PORT is required")
        if not values["user"]:
            raise ValueError("config ORACLE_USER is required")
        if not values["password"]:
            raise ValueError("config ORACLE_PASSWORD is required")
        if not values["database"]:
            raise ValueError("config ORACLE_DB is required")
        return values


SQL_CREATE_TABLE = """
CREATE TABLE IF NOT EXISTS {table_name} (
    id varchar2(100)
    ,text CLOB NOT NULL
    ,meta JSON
    ,embedding vector NOT NULL
)
"""
SQL_CREATE_INDEX = """
CREATE INDEX IF NOT EXISTS idx_docs_{table_name} ON {table_name}(text) 
INDEXTYPE IS CTXSYS.CONTEXT PARAMETERS 
('FILTER CTXSYS.NULL_FILTER SECTION GROUP CTXSYS.HTML_SECTION_GROUP LEXER sys.my_chinese_vgram_lexer')
"""


class OracleVector(BaseVector):
    def __init__(self, collection_name: str, config: OracleVectorConfig):
        super().__init__(collection_name)
        self.pool = self._create_connection_pool(config)
        self.table_name = f"embedding_{collection_name}"

    def get_type(self) -> str:
        return VectorType.ORACLE

    def numpy_converter_in(self, value):
        if value.dtype == numpy.float64:
            dtype = "d"
        elif value.dtype == numpy.float32:
            dtype = "f"
        else:
            dtype = "b"
        return array.array(dtype, value)

    def input_type_handler(self, cursor, value, arraysize):
        if isinstance(value, numpy.ndarray):
            return cursor.var(
                oracledb.DB_TYPE_VECTOR,
                arraysize=arraysize,
                inconverter=self.numpy_converter_in,
            )

    def numpy_converter_out(self, value):
        if value.typecode == "b":
            return numpy.array(value, copy=False, dtype=numpy.int8)
        elif value.typecode == "f":
            return numpy.array(value, copy=False, dtype=numpy.float32)
        else:
            return numpy.array(value, copy=False, dtype=numpy.float64)

    def output_type_handler(self, cursor, metadata):
        if metadata.type_code is oracledb.DB_TYPE_VECTOR:
            return cursor.var(
                metadata.type_code,
                arraysize=cursor.arraysize,
                outconverter=self.numpy_converter_out,
            )

    def _create_connection_pool(self, config: OracleVectorConfig):
        return oracledb.create_pool(
            user=config.user,
            password=config.password,
            dsn="{}:{}/{}".format(config.host, config.port, config.database),
            min=1,
            max=50,
            increment=1,
        )

    @contextmanager
    def _get_cursor(self):
        conn = self.pool.acquire()
        conn.inputtypehandler = self.input_type_handler
        conn.outputtypehandler = self.output_type_handler
        cur = conn.cursor()
        try:
            yield cur
        finally:
            cur.close()
            conn.commit()
            conn.close()

    def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
        dimension = len(embeddings[0])
        self._create_collection(dimension)
        return self.add_texts(texts, embeddings)

    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
        values = []
        pks = []
        for i, doc in enumerate(documents):
            if doc.metadata is not None:
                doc_id = doc.metadata.get("doc_id", str(uuid.uuid4()))
                pks.append(doc_id)
                values.append(
                    (
                        doc_id,
                        doc.page_content,
                        json.dumps(doc.metadata),
                        # array.array("f", embeddings[i]),
                        numpy.array(embeddings[i]),
                    )
                )
        # print(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)")
        with self._get_cursor() as cur:
            cur.executemany(
                f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values
            )
        return pks

    def text_exists(self, id: str) -> bool:
        with self._get_cursor() as cur:
            cur.execute(f"SELECT id FROM {self.table_name} WHERE id = '%s'" % (id,))
            return cur.fetchone() is not None

    def get_by_ids(self, ids: list[str]) -> list[Document]:
        with self._get_cursor() as cur:
            cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
            docs = []
            for record in cur:
                docs.append(Document(page_content=record[1], metadata=record[0]))
        return docs

    def delete_by_ids(self, ids: list[str]) -> None:
        if not ids:
            return
        with self._get_cursor() as cur:
            cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))

    def delete_by_metadata_field(self, key: str, value: str) -> None:
        with self._get_cursor() as cur:
            cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))

    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
        """
        Search the nearest neighbors to a vector.

        :param query_vector: The input vector to search for similar items.
        :param top_k: The number of nearest neighbors to return, default is 5.
        :return: List of Documents that are nearest to the query vector.
        """
        top_k = kwargs.get("top_k", 4)
        with self._get_cursor() as cur:
            cur.execute(
                f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"
                f" ORDER BY distance fetch first {top_k} rows only",
                [numpy.array(query_vector)],
            )
            docs = []
            score_threshold = float(kwargs.get("score_threshold") or 0.0)
            for record in cur:
                metadata, text, distance = record
                score = 1 - distance
                metadata["score"] = score
                if score > score_threshold:
                    docs.append(Document(page_content=text, metadata=metadata))
        return docs

    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
        # lazy import
        import nltk  # type: ignore
        from nltk.corpus import stopwords  # type: ignore

        top_k = kwargs.get("top_k", 5)
        # just not implement fetch by score_threshold now, may be later
        score_threshold = float(kwargs.get("score_threshold") or 0.0)
        if len(query) > 0:
            # Check which language the query is in
            zh_pattern = re.compile("[\u4e00-\u9fa5]+")
            match = zh_pattern.search(query)
            entities = []
            #  match: query condition maybe is a chinese sentence, so using Jieba split,else using nltk split
            if match:
                words = pseg.cut(query)
                current_entity = ""
                for word, pos in words:
                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:  # nr: 人名, ns: 地名, nt: 机构名
                        current_entity += word
                    else:
                        if current_entity:
                            entities.append(current_entity)
                            current_entity = ""
                if current_entity:
                    entities.append(current_entity)
            else:
                try:
                    nltk.data.find("tokenizers/punkt")
                    nltk.data.find("corpora/stopwords")
                except LookupError:
                    nltk.download("punkt")
                    nltk.download("stopwords")
                e_str = re.sub(r"[^\w ]", "", query)
                all_tokens = nltk.word_tokenize(e_str)
                stop_words = stopwords.words("english")
                for token in all_tokens:
                    if token not in stop_words:
                        entities.append(token)
            with self._get_cursor() as cur:
                cur.execute(
                    f"select meta, text, embedding FROM {self.table_name}"
                    f" WHERE CONTAINS(text, :1, 1) > 0 order by score(1) desc fetch first {top_k} rows only",
                    [" ACCUM ".join(entities)],
                )
                docs = []
                for record in cur:
                    metadata, text, embedding = record
                    docs.append(Document(page_content=text, vector=embedding, metadata=metadata))
            return docs
        else:
            return [Document(page_content="", metadata={})]
        return []

    def delete(self) -> None:
        with self._get_cursor() as cur:
            cur.execute(f"DROP TABLE IF EXISTS {self.table_name} cascade constraints")

    def _create_collection(self, dimension: int):
        cache_key = f"vector_indexing_{self._collection_name}"
        lock_name = f"{cache_key}_lock"
        with redis_client.lock(lock_name, timeout=20):
            collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
            if redis_client.get(collection_exist_cache_key):
                return

            with self._get_cursor() as cur:
                cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name))
            redis_client.set(collection_exist_cache_key, 1, ex=3600)
            with self._get_cursor() as cur:
                cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))


class OracleVectorFactory(AbstractVectorFactory):
    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> OracleVector:
        if dataset.index_struct_dict:
            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
            collection_name = class_prefix
        else:
            dataset_id = dataset.id
            collection_name = Dataset.gen_collection_name_by_id(dataset_id)
            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.ORACLE, collection_name))

        return OracleVector(
            collection_name=collection_name,
            config=OracleVectorConfig(
                host=dify_config.ORACLE_HOST or "localhost",
                port=dify_config.ORACLE_PORT,
                user=dify_config.ORACLE_USER or "system",
                password=dify_config.ORACLE_PASSWORD or "oracle",
                database=dify_config.ORACLE_DATABASE or "orcl",
            ),
        )
