# coding: utf-8

"""
    RAGsys API

     RAGSys API is an innovative AI retrieval engine developed by Crossing Minds to enhance  Large Language Model (LLM) performance without the need for traditional fine-tuning.  By integrating Retrieval-Augmented Generation (RAG) techniques, RAGSys enables seamless adaptation  of LLMs to various tasks and domains, offering a cost-effective and scalable solution for  enterprises.   Key Features of RAGSys:  - **Seamless Integration**: Compatible with any LLM, including those from Anthropic, OpenAI, and open-source alternatives, allowing effortless switching between models without losing optimizations.  - **Enhanced Retrieval with RAG Embeddings**: Utilizes proprietary RAG embeddings to ensure better understanding and rapid information retrieval, even with massive datasets, resulting in more contextually relevant and accurate LLM outputs.  - **Groundbreaking Accuracy Improvements**: Consistently outperforms traditional fine-tuning, achieving up to a 76% improvement on key benchmarks, with significant enhancements in truthfulness, emotion detection, and commonsense reasoning across various LLMs.  - **Scalable Architecture**: Designed for enterprise-scale deployment, efficiently handling large datasets and complex retrieval tasks, ensuring consistent performance as AI needs grow.   By leveraging RAGSys, organizations can rapidly adapt LLMs to new tasks or domains, saving computational resources and accelerating deployment cycles, all while maintaining high levels of accuracy and relevance in AI outputs.  

    The version of the OpenAPI document: 1.0.0
    Contact: contact@crossingminds.com
    Generated by OpenAPI Generator (https://openapi-generator.tech)

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


from __future__ import annotations
from inspect import getfullargspec
import json
import pprint
import re  # noqa: F401
from pydantic import BaseModel, ConfigDict, Field, StrictStr, ValidationError, field_validator
from typing import List, Optional
from ragsys.models.content_item import ContentItem
from typing import Union, Any, List, Set, TYPE_CHECKING, Optional, Dict
from typing_extensions import Literal, Self
from pydantic import Field

CONTENT_ANY_OF_SCHEMAS = ["List[ContentItem]", "str"]

class Content(BaseModel):
    """
    The content of the messages
    """

    # data type: str
    anyof_schema_1_validator: Optional[StrictStr] = None
    # data type: List[ContentItem]
    anyof_schema_2_validator: Optional[List[ContentItem]] = None
    if TYPE_CHECKING:
        actual_instance: Optional[Union[List[ContentItem], str]] = None
    else:
        actual_instance: Any = None
    any_of_schemas: Set[str] = { "List[ContentItem]", "str" }

    model_config = {
        "validate_assignment": True,
        "protected_namespaces": (),
    }

    def __init__(self, *args, **kwargs) -> None:
        if args:
            if len(args) > 1:
                raise ValueError("If a position argument is used, only 1 is allowed to set `actual_instance`")
            if kwargs:
                raise ValueError("If a position argument is used, keyword arguments cannot be used.")
            super().__init__(actual_instance=args[0])
        else:
            super().__init__(**kwargs)

    @field_validator('actual_instance')
    def actual_instance_must_validate_anyof(cls, v):
        instance = Content.model_construct()
        error_messages = []
        # validate data type: str
        try:
            instance.anyof_schema_1_validator = v
            return v
        except (ValidationError, ValueError) as e:
            error_messages.append(str(e))
        # validate data type: List[ContentItem]
        try:
            instance.anyof_schema_2_validator = v
            return v
        except (ValidationError, ValueError) as e:
            error_messages.append(str(e))
        if error_messages:
            # no match
            raise ValueError("No match found when setting the actual_instance in Content with anyOf schemas: List[ContentItem], str. Details: " + ", ".join(error_messages))
        else:
            return v

    @classmethod
    def from_dict(cls, obj: Dict[str, Any]) -> Self:
        return cls.from_json(json.dumps(obj))

    @classmethod
    def from_json(cls, json_str: str) -> Self:
        """Returns the object represented by the json string"""
        instance = cls.model_construct()
        error_messages = []
        # deserialize data into str
        try:
            # validation
            instance.anyof_schema_1_validator = json.loads(json_str)
            # assign value to actual_instance
            instance.actual_instance = instance.anyof_schema_1_validator
            return instance
        except (ValidationError, ValueError) as e:
            error_messages.append(str(e))
        # deserialize data into List[ContentItem]
        try:
            # validation
            instance.anyof_schema_2_validator = json.loads(json_str)
            # assign value to actual_instance
            instance.actual_instance = instance.anyof_schema_2_validator
            return instance
        except (ValidationError, ValueError) as e:
            error_messages.append(str(e))

        if error_messages:
            # no match
            raise ValueError("No match found when deserializing the JSON string into Content with anyOf schemas: List[ContentItem], str. Details: " + ", ".join(error_messages))
        else:
            return instance

    def to_json(self) -> str:
        """Returns the JSON representation of the actual instance"""
        if self.actual_instance is None:
            return "null"

        if hasattr(self.actual_instance, "to_json") and callable(self.actual_instance.to_json):
            return self.actual_instance.to_json()
        else:
            return json.dumps(self.actual_instance)

    def to_dict(self) -> Optional[Union[Dict[str, Any], List[ContentItem], str]]:
        """Returns the dict representation of the actual instance"""
        if self.actual_instance is None:
            return None

        if hasattr(self.actual_instance, "to_dict") and callable(self.actual_instance.to_dict):
            return self.actual_instance.to_dict()
        else:
            return self.actual_instance

    def to_str(self) -> str:
        """Returns the string representation of the actual instance"""
        return pprint.pformat(self.model_dump())


