# coding: utf-8 """ Lance Namespace Specification This OpenAPI specification is a part of the Lance namespace specification. It contains 2 parts: The `components/schemas`, `components/responses`, `components/examples`, `tags` sections define the request and response shape for each operation in a Lance Namespace across all implementations. See https://lance.org/format/namespace/operations for more details. The `servers`, `security`, `paths`, `components/parameters` sections are for the Lance REST Namespace implementation, which defines a complete REST server that can work with Lance datasets. See https://lance.org/format/namespace/rest for more details. The version of the OpenAPI document: 1.0.0 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, StrictFloat, StrictInt, StrictStr from typing import Any, ClassVar, Dict, List, Optional, Union from typing_extensions import Annotated from lance_namespace_urllib3_client.models.identity import Identity from lance_namespace_urllib3_client.models.query_table_request_columns import QueryTableRequestColumns from lance_namespace_urllib3_client.models.query_table_request_full_text_query import QueryTableRequestFullTextQuery from lance_namespace_urllib3_client.models.query_table_request_vector import QueryTableRequestVector from typing import Optional, Set from typing_extensions import Self class AnalyzeTableQueryPlanRequest(BaseModel): """ AnalyzeTableQueryPlanRequest """ # noqa: E501 identity: Optional[Identity] = None context: Optional[Dict[str, StrictStr]] = Field(default=None, description="Arbitrary context for a request as key-value pairs. How to use the context is custom to the specific implementation. REST NAMESPACE ONLY Context entries are passed via HTTP headers using the naming convention `x-lance-ctx-: `. For example, a context entry `{\"trace_id\": \"abc123\"}` would be sent as the header `x-lance-ctx-trace_id: abc123`. ") id: Optional[List[StrictStr]] = None bypass_vector_index: Optional[StrictBool] = Field(default=None, description="Whether to bypass vector index") columns: Optional[QueryTableRequestColumns] = None distance_type: Optional[StrictStr] = Field(default=None, description="Distance metric to use") ef: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=None, description="Search effort parameter for HNSW index") fast_search: Optional[StrictBool] = Field(default=None, description="Whether to use fast search") filter: Optional[StrictStr] = Field(default=None, description="Optional SQL filter expression") full_text_query: Optional[QueryTableRequestFullTextQuery] = None k: Annotated[int, Field(strict=True, ge=0)] = Field(description="Number of results to return") lower_bound: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Lower bound for search") nprobes: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=None, description="Number of probes for IVF index") offset: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=None, description="Number of results to skip") prefilter: Optional[StrictBool] = Field(default=None, description="Whether to apply filtering before vector search") refine_factor: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=None, description="Refine factor for search") upper_bound: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Upper bound for search") vector: QueryTableRequestVector vector_column: Optional[StrictStr] = Field(default=None, description="Name of the vector column to search") version: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=None, description="Table version to query") with_row_id: Optional[StrictBool] = Field(default=None, description="If true, return the row id as a column called `_rowid`") __properties: ClassVar[List[str]] = ["identity", "context", "id", "bypass_vector_index", "columns", "distance_type", "ef", "fast_search", "filter", "full_text_query", "k", "lower_bound", "nprobes", "offset", "prefilter", "refine_factor", "upper_bound", "vector", "vector_column", "version", "with_row_id"] 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 AnalyzeTableQueryPlanRequest 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, ) # override the default output from pydantic by calling `to_dict()` of identity if self.identity: _dict['identity'] = self.identity.to_dict() # override the default output from pydantic by calling `to_dict()` of columns if self.columns: _dict['columns'] = self.columns.to_dict() # override the default output from pydantic by calling `to_dict()` of full_text_query if self.full_text_query: _dict['full_text_query'] = self.full_text_query.to_dict() # override the default output from pydantic by calling `to_dict()` of vector if self.vector: _dict['vector'] = self.vector.to_dict() return _dict @classmethod def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]: """Create an instance of AnalyzeTableQueryPlanRequest from a dict""" if obj is None: return None if not isinstance(obj, dict): return cls.model_validate(obj) _obj = cls.model_validate({ "identity": Identity.from_dict(obj["identity"]) if obj.get("identity") is not None else None, "context": obj.get("context"), "id": obj.get("id"), "bypass_vector_index": obj.get("bypass_vector_index"), "columns": QueryTableRequestColumns.from_dict(obj["columns"]) if obj.get("columns") is not None else None, "distance_type": obj.get("distance_type"), "ef": obj.get("ef"), "fast_search": obj.get("fast_search"), "filter": obj.get("filter"), "full_text_query": QueryTableRequestFullTextQuery.from_dict(obj["full_text_query"]) if obj.get("full_text_query") is not None else None, "k": obj.get("k"), "lower_bound": obj.get("lower_bound"), "nprobes": obj.get("nprobes"), "offset": obj.get("offset"), "prefilter": obj.get("prefilter"), "refine_factor": obj.get("refine_factor"), "upper_bound": obj.get("upper_bound"), "vector": QueryTableRequestVector.from_dict(obj["vector"]) if obj.get("vector") is not None else None, "vector_column": obj.get("vector_column"), "version": obj.get("version"), "with_row_id": obj.get("with_row_id") }) return _obj