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opengradient / types

Package opengradient.types ​

OpenGradient Specific Types

Classes ​

Abi ​

Constructor ​

python
def __init__(functions: List[`AbiFunction`])

Static methods ​

from_json() ​

python
static def from_json(abi_json)

Variables ​

  • static functions : List[AbiFunction]

AbiFunction ​

Constructor ​

python
def __init__(name: str, inputs: List[Union[str, ForwardRef('`AbiFunction`')]], outputs: List[Union[str, ForwardRef('`AbiFunction`')]], state_mutability: str)

Variables ​

  • static inputs : List[Union[str, AbiFunction]]
  • static name : str
  • static outputs : List[Union[str, AbiFunction]]
  • static state_mutability : str

CandleOrder ​

Enum where members are also (and must be) ints

Variables ​

  • static ASCENDING
  • static DESCENDING

CandleType ​

Enum where members are also (and must be) ints

Variables ​

  • static CLOSE
  • static HIGH
  • static LOW
  • static OPEN
  • static VOLUME

FileUploadResult ​

Constructor ​

python
def __init__(modelCid: str, size: int)

Variables ​

  • static modelCid : str
  • static size : int

HistoricalInputQuery ​

Constructor ​

python
def __init__(base: str, quote: str, total_candles: int, candle_duration_in_mins: int, order: `CandleOrder`, candle_types: List[`CandleType`])

Methods ​

to_abi_format() ​

python
def to_abi_format(self) ‑> tuple

Convert to format expected by contract ABI

Variables ​

  • static base : str
  • static candle_duration_in_mins : int
  • static candle_types : List[CandleType]
  • static order : CandleOrder
  • static quote : str
  • static total_candles : int

InferenceMode ​

Enum for the different inference modes available for inference (VANILLA, ZKML, TEE)

Variables ​

  • static TEE
  • static VANILLA
  • static ZKML

InferenceResult ​

Output for ML inference requests. This class has two fields transaction_hash (str): Blockchain hash for the transaction model_output (Dict[str, np.ndarray]): Output of the ONNX model

Constructor ​

python
def __init__(transaction_hash: str, model_output: Dict[str, `ndarray`])

Variables ​

  • static model_output : Dict[str, ndarray]
  • static transaction_hash : str

ModelInput ​

A collection of tensor inputs required for ONNX model inference.

Attributes

  • numbers: Collection of numeric tensors for the model.
  • strings: Collection of string tensors for the model.

Constructor ​

python
def __init__(numbers: List[`NumberTensor`], strings: List[`StringTensor`])

Variables ​

  • static numbers : List[NumberTensor]
  • static strings : List[StringTensor]

ModelOutput ​

Model output struct based on translations from smart contract.

Constructor ​

python
def __init__(numbers: Dict[str, `ndarray`], strings: Dict[str, `ndarray`], jsons: Dict[str, `ndarray`], is_simulation_result: bool)

Variables ​

  • static is_simulation_result : bool
  • static jsons : Dict[str, ndarray]
  • static numbers : Dict[str, ndarray]
  • static strings : Dict[str, ndarray]

ModelRepository ​

Constructor ​

python
def __init__(name: str, initialVersion: str)

Variables ​

  • static initialVersion : str
  • static name : str

Number ​

Constructor ​

python
def __init__(value: int, decimals: int)

Variables ​

  • static decimals : int
  • static value : int

NumberTensor ​

A container for numeric tensor data used as input for ONNX models.

Attributes

  • name: Identifier for this tensor in the model.
  • values: List of integer tuples representing the tensor data.

Constructor ​

python
def __init__(name: str, values: List[Tuple[int, int]])

Variables ​

  • static name : str
  • static values : List[Tuple[int, int]]

SchedulerParams ​

Constructor ​

python
def __init__(frequency: int, duration_hours: int)

Static methods ​

from_dict() ​

python
static def from_dict(data: Optional[Dict[str, int]]) ‑> Optional[`SchedulerParams`]

Variables ​

  • static duration_hours : int
  • static frequency : int
  • end_time : int

StreamChoice ​

Represents a choice in a streaming response.

Attributes

  • delta: The incremental changes in this chunk
  • index: Choice index (usually 0)
  • finish_reason: Reason for completion (appears in final chunk)

Constructor ​

python
def __init__(delta: `StreamDelta`, index: int = 0, finish_reason: Optional[str] = None)

Variables ​

  • static delta : StreamDelta
  • static finish_reason : Optional[str]
  • static index : int

StreamChunk ​

Represents a single chunk in a streaming LLM response.

This follows the OpenAI streaming format but is provider-agnostic. Each chunk contains incremental data, with the final chunk including usage information.

Attributes

  • choices: List of streaming choices (usually contains one choice)
  • model: Model identifier
  • usage: Token usage information (only in final chunk)
  • is_final: Whether this is the final chunk (before [DONE])

Constructor ​

python
def __init__(choices: List[`StreamChoice`], model: str, usage: Optional[`StreamUsage`] = None, is_final: bool = False)

Static methods ​

from_sse_data() ​

python
static def from_sse_data(data: Dict) ‑> `StreamChunk`

Parse a StreamChunk from SSE data dictionary.

Arguments

  • data: Dictionary parsed from SSE data line

Returns

StreamChunk instance

Variables ​

  • static choices : List[StreamChoice]
  • static is_final : bool
  • static model : str
  • static usage : Optional[StreamUsage]

StreamDelta ​

Represents a delta (incremental change) in a streaming response.

Attributes

  • content: Incremental text content (if any)
  • role: Message role (appears in first chunk)
  • tool_calls: Tool call information (if function calling is used)

Constructor ​

python
def __init__(content: Optional[str] = None, role: Optional[str] = None, tool_calls: Optional[List[Dict]] = None)

Variables ​

  • static content : Optional[str]
  • static role : Optional[str]
  • static tool_calls : Optional[List[Dict]]

StreamUsage ​

Token usage information for a streaming response.

Attributes

  • prompt_tokens: Number of tokens in the prompt
  • completion_tokens: Number of tokens in the completion
  • total_tokens: Total tokens used

Constructor ​

python
def __init__(prompt_tokens: int, completion_tokens: int, total_tokens: int)

Variables ​

  • static completion_tokens : int
  • static prompt_tokens : int
  • static total_tokens : int

StringTensor ​

A container for string tensor data used as input for ONNX models.

Attributes

  • name: Identifier for this tensor in the model.
  • values: List of strings representing the tensor data.

Constructor ​

python
def __init__(name: str, values: List[str])

Variables ​

  • static name : str
  • static values : List[str]

TEE_LLM ​

Enum for LLM models available for TEE (Trusted Execution Environment) execution.

TEE mode provides cryptographic verification that inference was performed correctly in a secure enclave. Use this for applications requiring auditability and tamper-proof AI inference.

Variables ​

  • static CLAUDE_3_5_HAIKU
  • static CLAUDE_3_7_SONNET
  • static CLAUDE_4_0_SONNET
  • static GEMINI_2_0_FLASH
  • static GEMINI_2_5_FLASH
  • static GEMINI_2_5_FLASH_LITE
  • static GEMINI_2_5_PRO
  • static GPT_4O
  • static GPT_4_1_2025_04_14
  • static GROK_2_1212
  • static GROK_2_VISION_LATEST
  • static GROK_3_BETA
  • static GROK_3_MINI_BETA
  • static GROK_4_1_FAST
  • static GROK_4_1_FAST_NON_REASONING
  • static O4_MINI

TextGenerationOutput ​

Output structure for text generation requests.

Constructor ​

python
def __init__(transaction_hash: str, finish_reason: Optional[str] = None, chat_output: Optional[Dict] = None, completion_output: Optional[str] = None, payment_hash: Optional[str] = None)

Variables ​

  • static chat_output : Optional[Dict] - Dictionary of chat response containing role, message content, tool call parameters, etc.. Empty dict if not applicable.
  • static completion_output : Optional[str] - Raw text output from completion-style generation. Empty string if not applicable.
  • static finish_reason : Optional[str] - Reason for completion (e.g., 'tool_call', 'stop', 'error'). Empty string if not applicable.
  • static payment_hash : Optional[str] - Payment hash for x402 transaction
  • static transaction_hash : str - Blockchain hash for the transaction.

TextGenerationStream ​

Iterator wrapper for streaming text generation responses.

Provides a clean interface for iterating over stream chunks with automatic parsing of SSE format.

Constructor ​

python
def __init__(_iterator: Union[Iterator[str], AsyncIterator[str]])

x402SettlementMode ​

Settlement modes for x402 payment protocol transactions.

These modes control how inference data is recorded on-chain for payment settlement and auditability. Each mode offers different trade-offs between data completeness, privacy, and transaction costs.

Attributes

  • SETTLE: Individual settlement with input/output hashes only. Also known as SETTLE_INDIVIDUAL in some documentation. Records cryptographic hashes of the inference input and output. Most privacy-preserving option - actual data is not stored on-chain. Suitable for applications where only proof of execution is needed. CLI usage: --settlement-mode settle
  • SETTLE_METADATA: Individual settlement with full metadata. Also known as SETTLE_INDIVIDUAL_WITH_METADATA in some documentation. Records complete model information, full input and output data, and all inference metadata on-chain. Provides maximum transparency and auditability. Higher gas costs due to larger data storage. CLI usage: --settlement-mode settle-metadata
  • SETTLE_BATCH: Batch settlement for multiple inferences. Aggregates multiple inference requests into a single settlement transaction using batch hashes. Most cost-efficient for high-volume applications. Reduced per-inference transaction overhead. CLI usage: --settlement-mode settle-batch

Variables ​

  • static SETTLE
  • static SETTLE_BATCH
  • static SETTLE_INDIVIDUAL
  • static SETTLE_INDIVIDUAL_WITH_METADATA
  • static SETTLE_METADATA