# coding: utf-8

"""
    Mixpeek API

    This is the Mixpeek API, providing access to various endpoints for data processing and retrieval.

    The version of the OpenAPI document: 1.3.0
    Contact: info@mixpeek.com
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from pydantic import BaseModel, ConfigDict, Field, StrictBool, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from typing_extensions import Annotated
from typing import Optional, Set
from typing_extensions import Self

class FacetFieldConfig(BaseModel):
    """
    Configuration for a single facet field.  Faceting counts unique values for a field, enabling faceted search interfaces (e.g., \"Show 45 results in 'Sports', 23 in 'Music'\").  ┌─────────────────────────────────────────────────────────────────────────────┐ │ IMPORTANT: INDEX REQUIREMENT                                                │ │                                                                             │ │ The field MUST have a keyword index in Qdrant for faceting to work.         │ │ Fields are automatically indexed during collection creation for common      │ │ metadata fields. For custom fields, ensure indexing is configured.          │ │                                                                             │ │ Without an index, faceting will fail with an error.                         │ └─────────────────────────────────────────────────────────────────────────────┘  Example:     ```json     {         \"key\": \"metadata.category\",         \"limit\": 10,         \"exact\": false     }     ```
    """ # noqa: E501
    key: StrictStr = Field(description="REQUIRED. Field path to facet on (e.g., 'metadata.category', 'status'). Supports nested fields using dot notation. The field MUST have a keyword index in Qdrant - faceting will fail without it. Common indexed fields: metadata.*, status, collection_id, source_object_id.")
    limit: Optional[Annotated[int, Field(le=1000, strict=True, ge=1)]] = Field(default=10, description="OPTIONAL. Maximum number of unique values to return (default: 10). Results are sorted by count descending, then by value ascending. Higher limits increase response size but provide more comprehensive facets. Common values: 5 (compact), 10 (standard), 20-50 (detailed).")
    exact: Optional[StrictBool] = Field(default=False, description="OPTIONAL. Use exact counts instead of approximate (default: False). - False (default): Fast approximate counts, suitable for most UIs.   Approximate counts are accurate enough for display purposes. - True: Slower but precise counts, useful for debugging or analytics.   Use when exact numbers matter (e.g., reports, dashboards).")
    __properties: ClassVar[List[str]] = ["key", "limit", "exact"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
    )


    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))

    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())

    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of FacetFieldConfig from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        excluded_fields: Set[str] = set([
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        return _dict

    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of FacetFieldConfig from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        _obj = cls.model_validate({
            "key": obj.get("key"),
            "limit": obj.get("limit") if obj.get("limit") is not None else 10,
            "exact": obj.get("exact") if obj.get("exact") is not None else False
        })
        return _obj


