# 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, StrictStr from typing import Any, ClassVar, Dict, List, Optional from typing_extensions import Annotated from lance_namespace_urllib3_client.models.identity import Identity from typing import Optional, Set from typing_extensions import Self class CreateTableIndexRequest(BaseModel): """ CreateTableIndexRequest """ # noqa: E501 identity: Optional[Identity] = None context: Optional[Dict[str, StrictStr]] = Field(default=None, description="Arbitrary context as key-value pairs. How to use the context is custom to the specific implementation. On a request, it carries caller-provided context to the implementation. On a response, it carries implementation-provided context back to the caller. REST NAMESPACE ONLY Context entries are mapped to and from HTTP headers using the `header.` prefix: - On a request, any entry whose key starts with `header.` is sent as an HTTP request header with the prefix stripped. For example, the entry `{\"header.Authorization\": \"Bearer abc\"}` is sent as the request header `Authorization: Bearer abc`. - On a response, every HTTP response header is returned as an entry whose key is the header name prefixed with `header.`. For example, the response header `x-request-id: abc123` is returned as the entry `{\"header.x-request-id\": \"abc123\"}`. ") id: Optional[List[StrictStr]] = None branch: Optional[StrictStr] = Field(default=None, description="Branch to target. When not specified, the main branch is used. ") column: Annotated[str, Field(min_length=1, strict=True)] = Field(description="Lance field path to create the index on. Nested fields use dot-separated segments; use backtick-quoted segments for literal dots and double backticks inside quoted segments. Use canonical full paths for display and errors; leaf names alone only identify top-level fields; invalid or unresolved paths should return InvalidInput or TableColumnNotFound.") index_type: StrictStr = Field(description="Type of index to create (e.g., BTREE, BITMAP, LABEL_LIST, IVF_FLAT, IVF_PQ, IVF_HNSW_SQ, FTS)") name: Optional[StrictStr] = Field(default=None, description="Optional name for the index. If not provided, a name will be auto-generated.") distance_type: Optional[StrictStr] = Field(default=None, description="Distance metric type for vector indexes (e.g., l2, cosine, dot)") num_partitions: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the number of IVF partitions. Applies to all IVF index types.") num_sub_vectors: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the number of PQ sub-vectors. Applies to IVF_PQ only.") num_bits: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the number of bits used by the quantizer.") sample_rate: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the per-partition sample rate used during IVF training.") max_iterations: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the maximum number of IVF k-means training iterations.") target_partition_size: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the target partition size. Alternative to num_partitions.") m: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the number of edges per node in the HNSW graph. Applies to HNSW index types.") ef_construction: Optional[Annotated[int, Field(strict=True, ge=1)]] = Field(default=None, description="Optional vector index parameter for the number of candidates evaluated during HNSW graph construction. Applies to HNSW index types.") with_position: Optional[StrictBool] = Field(default=None, description="Optional FTS parameter for position tracking") base_tokenizer: Optional[StrictStr] = Field(default=None, description="Optional FTS parameter for base tokenizer") language: Optional[StrictStr] = Field(default=None, description="Optional FTS parameter for language") max_token_length: Optional[Annotated[int, Field(strict=True, ge=0)]] = Field(default=None, description="Optional FTS parameter for maximum token length") lower_case: Optional[StrictBool] = Field(default=None, description="Optional FTS parameter for lowercase conversion") stem: Optional[StrictBool] = Field(default=None, description="Optional FTS parameter for stemming") remove_stop_words: Optional[StrictBool] = Field(default=None, description="Optional FTS parameter for stop word removal") ascii_folding: Optional[StrictBool] = Field(default=None, description="Optional FTS parameter for ASCII folding") __properties: ClassVar[List[str]] = ["identity", "context", "id", "branch", "column", "index_type", "name", "distance_type", "num_partitions", "num_sub_vectors", "num_bits", "sample_rate", "max_iterations", "target_partition_size", "m", "ef_construction", "with_position", "base_tokenizer", "language", "max_token_length", "lower_case", "stem", "remove_stop_words", "ascii_folding"] 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 CreateTableIndexRequest 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() return _dict @classmethod def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]: """Create an instance of CreateTableIndexRequest 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"), "branch": obj.get("branch"), "column": obj.get("column"), "index_type": obj.get("index_type"), "name": obj.get("name"), "distance_type": obj.get("distance_type"), "num_partitions": obj.get("num_partitions"), "num_sub_vectors": obj.get("num_sub_vectors"), "num_bits": obj.get("num_bits"), "sample_rate": obj.get("sample_rate"), "max_iterations": obj.get("max_iterations"), "target_partition_size": obj.get("target_partition_size"), "m": obj.get("m"), "ef_construction": obj.get("ef_construction"), "with_position": obj.get("with_position"), "base_tokenizer": obj.get("base_tokenizer"), "language": obj.get("language"), "max_token_length": obj.get("max_token_length"), "lower_case": obj.get("lower_case"), "stem": obj.get("stem"), "remove_stop_words": obj.get("remove_stop_words"), "ascii_folding": obj.get("ascii_folding") }) return _obj