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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/base.py
wave_lang.runtime.op_reg.base.IntArg
from typing import Any, Callable, Optional, Sequence, Type, Union, cast from torch import Tensor class IntArg: __slots__ = ['ir_arity', 'spec_value', 'v'] maybe_tensor_value: Optional[Tensor] = None is_list: bool = False def __init__(self, v: int): self.v = v self.spec_value: Optional[...
class IntArg: def __init__(self, v: int): pass def __repr__(self): pass def generate_meta(self) -> int: pass @property def spec_key(self) -> str: '''Generates a key that will be the same for all specializations.''' pass @property def mlir_type_asm(...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/base.py
wave_lang.runtime.op_reg.base.KernelBuilder
from wave_lang.support.ir_imports import Block, Context, FunctionType, IndexType, InsertionPoint, IntegerAttr, IrType, Location, StringAttr, SymbolTable, Value, arith_d, builtin_d, func_d from typing import Any, Callable, Optional, Sequence, Type, Union, cast from abc import ABC, abstractmethod class KernelBuilder(ABC...
class KernelBuilder(ABC): '''Support class for building a kernel.''' def __init__(self, ksel: KernelSelection, arg_bindings: list[Union[Value, list[Value]]], *, ip: InsertionPoint, module_body: Block, symbol_table: SymbolTable): pass def arg_value(self, index: int) -> Union[list[Value], Value]: ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/base.py
wave_lang.runtime.op_reg.base.KernelSelection
from typing import Any, Callable, Optional, Sequence, Type, Union, cast from abc import ABC, abstractmethod import torch from torch import Tensor import textwrap class KernelSelection(ABC): """Represents a selected kernel based on a concrete signature. The `CustomOp.select` method must yield an instance of th...
class KernelSelection(ABC): '''Represents a selected kernel based on a concrete signature. The `CustomOp.select` method must yield an instance of this, and it will be done for every invocation. At this point, the kernel has not yet been generated, but we have selected a generation strategy based on...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/base.py
wave_lang.runtime.op_reg.base.TensorArg
from torch import Tensor from wave_lang.support.conversions import TORCH_DTYPE_TO_IREE_TYPE_ASM class TensorArg: __slots__ = ['t', 'spec_dims', 'maybe_tensor_value'] ir_arity: int = 1 is_list: bool = False def __init__(self, t: Tensor): self.t = t self.spec_dims = len(t.shape) * [_None...
class TensorArg: def __init__(self, t: Tensor): pass def specialize_all_dims(self): '''Marks all dimensions as specialized.''' pass def specialize_dims(self, *indices: int): '''Specializes individual dimensions. `i` can have negative indexing. ''' ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/base.py
wave_lang.runtime.op_reg.base.TensorListArg
from torch import Tensor from typing import Any, Callable, Optional, Sequence, Type, Union, cast from wave_lang.support.conversions import TORCH_DTYPE_TO_IREE_TYPE_ASM class TensorListArg: __slots__ = ['ts', 'spec_dims', 'ir_arity', 'maybe_tensor_value'] is_list: bool = True def __init__(self, ts: list[Te...
class TensorListArg: def __init__(self, ts: list[Tensor]): pass def __repr__(self): pass def generate_meta(self) -> list[Tensor]: pass @property def spec_key(self) -> str: '''Generates a key that will be the same for all specializations.''' pass @prope...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/impl_helper.py
wave_lang.runtime.op_reg.impl_helper.JinjaTemplateLoader
from wave_lang.support.ir_imports import FlatSymbolRefAttr, FunctionType, MLIRError, Operation, StringAttr, TypeAttr, Value from .base import KernelBuilder class JinjaTemplateLoader(TemplateLoader): """Template loader based on jinja templates. Usage: _templates = JinjaTemplateLoader(__name__) By de...
class JinjaTemplateLoader(TemplateLoader): '''Template loader based on jinja templates. Usage: _templates = JinjaTemplateLoader(__name__) By default, this will resolve a template like "foo" from templates/foo.mlir in the package directory. ''' def __init__(self, package_name: str, packag...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/impl_helper.py
wave_lang.runtime.op_reg.impl_helper.StrFormatTemplateLoader
from wave_lang.support.ir_imports import FlatSymbolRefAttr, FunctionType, MLIRError, Operation, StringAttr, TypeAttr, Value from .base import KernelBuilder class StrFormatTemplateLoader(TemplateLoader): """Template loader that uses str.format. Usage: _templates = StrFromatTemplateLoader(__name__) B...
class StrFormatTemplateLoader(TemplateLoader): '''Template loader that uses str.format. Usage: _templates = StrFromatTemplateLoader(__name__) By default, this will resolve a template like "foo" from templates/foo.mlir in the package directory. ''' def __init__(self, package_name: str, pa...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/runtime/op_reg/impl_helper.py
wave_lang.runtime.op_reg.impl_helper.TemplateLoader
from abc import ABC, abstractmethod import textwrap from .base import KernelBuilder from wave_lang.support.logging import runtime_logger as logger from wave_lang.transforms.merger import Merger from wave_lang.support.ir_imports import FlatSymbolRefAttr, FunctionType, MLIRError, Operation, StringAttr, TypeAttr, Value im...
class TemplateLoader(ABC): '''Base class for templates that can be loaded by name.''' @abstractmethod def load_template(self, kb: KernelBuilder, name: str, **kwargs) -> Operation: '''Loads a template by name and kwargs, returning the module operation.''' pass def _parse_module_asm(self...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/support/conversions.py
wave_lang.support.conversions.UnknownDTypeError
class UnknownDTypeError(ValueError): def __init__(self, dtype): self.dtype = dtype super().__init__(f'Unable to map torch dtype {dtype} to Turbine')
class UnknownDTypeError(ValueError): def __init__(self, dtype): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/support/debugging.py
wave_lang.support.debugging.DebugFlags
import re import logging import sys from dataclasses import dataclass import os from typing import Callable, Optional @dataclass class DebugFlags: log_level: int = logging.WARNING asserts: bool = False runtime_trace_dir: Optional[str] = None def set(self, part: str): m = re.match(SETTING_PART_...
@dataclass class DebugFlags: def set(self, part: str): pass @staticmethod def parse(settings: str) -> 'DebugFlags': pass @staticmethod def parse_from_env() -> 'DebugFlags': pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/support/logging.py
wave_lang.support.logging.DefaultFormatter
import logging class DefaultFormatter(logging.Formatter): def __init__(self): super().__init__('%(levelname)s %(asctime)s [%(filename)s:%(lineno)d] %(message)s', '%m-%d %H:%M:%S')
class DefaultFormatter(logging.Formatter): def __init__(self): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/tools/interpreter.py
wave_lang.tools.interpreter.Interpreter
import numpy as np import re from typing import Callable from wave_lang.support.ir_imports import Context, F16Type, F32Type, IndexType, IntegerAttr, IntegerType, Module, Operation, Value, VectorType, amdgpu_d, arith_d, builtin_d, flow_d, func_d, gpu_d, llvm_d, memref_d, scf_d, stream_d, vector_d import torch class Int...
class Interpreter: ''' Python interpreter for MLIR. Uses torch for tensor operations. ''' def __init__(self, workgroup_ids: list[int], thread_ids: list[int]) -> None: pass def get_dtype(self, dtype): pass def callback(self, op: Operation) -> None: pass def wa...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/scripts/benchmark.py
benchmark.BenchmarkDataset
from enum import Enum class BenchmarkDataset(Enum): MATH500 = 'math500' AIME_2024 = 'aime-2024'
class BenchmarkDataset(Enum): pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/scripts/benchmark.py
benchmark.ScalingAlgorithm
from enum import Enum class ScalingAlgorithm(Enum): SELF_CONSISTENCY = 'self-consistency' BEAM_SEARCH = 'beam-search' PARTICLE_FILTERING = 'particle-filtering'
class ScalingAlgorithm(Enum): pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/__init__.py
its_hub.algorithms.MetropolisHastings
from its_hub.lms import StepGeneration from its_hub.base import AbstractLanguageModel, AbstractOutcomeRewardModel, AbstractScalingAlgorithm, AbstractScalingResult class MetropolisHastings(AbstractScalingAlgorithm): def __init__(self, step_generation: StepGeneration, orm: AbstractOutcomeRewardModel): self....
class MetropolisHastings(AbstractScalingAlgorithm): def __init__(self, step_generation: StepGeneration, orm: AbstractOutcomeRewardModel): pass def infer(self, lm: AbstractLanguageModel, prompt: str, budget: int, show_progress: bool=False, return_response_only: bool=True) -> str | MetropolisHastingsRe...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/__init__.py
its_hub.algorithms.MetropolisHastingsResult
from its_hub.base import AbstractLanguageModel, AbstractOutcomeRewardModel, AbstractScalingAlgorithm, AbstractScalingResult class MetropolisHastingsResult(AbstractScalingResult): pass
class MetropolisHastingsResult(AbstractScalingResult): pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/beam_search.py
its_hub.algorithms.beam_search.BeamSearch
import numpy as np from its_hub.base import AbstractLanguageModel, AbstractProcessRewardModel, AbstractScalingAlgorithm, AbstractScalingResult from its_hub.lms import StepGeneration class BeamSearch(AbstractScalingAlgorithm): def __init__(self, sg: StepGeneration, prm: AbstractProcessRewardModel, beam_width: int)...
class BeamSearch(AbstractScalingAlgorithm): def __init__(self, sg: StepGeneration, prm: AbstractProcessRewardModel, beam_width: int): pass def _search_one_level(self, lm: AbstractLanguageModel, candidates: list[Path], prompt: str, batched: bool=False) -> list[Path]: pass def infer(self, ...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/beam_search.py
its_hub.algorithms.beam_search.BeamSearchResult
from pydantic.dataclasses import dataclass from its_hub.base import AbstractLanguageModel, AbstractProcessRewardModel, AbstractScalingAlgorithm, AbstractScalingResult @dataclass class BeamSearchResult(AbstractScalingResult): responses: list[str] scores: list[float] selected_index: int steps_used: list[...
@dataclass class BeamSearchResult(AbstractScalingResult): @property def the_one(self) -> str: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/beam_search.py
its_hub.algorithms.beam_search.Path
from pydantic.dataclasses import dataclass import copy @dataclass class Path: steps: list[str] is_stopped: bool score: float def deepcopy(self): return Path(steps=copy.deepcopy(self.steps), is_stopped=self.is_stopped, score=self.score)
@dataclass class Path: def deepcopy(self): pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/bon.py
its_hub.algorithms.bon.BestOfN
from its_hub.base import AbstractLanguageModel, AbstractOutcomeRewardModel, AbstractScalingAlgorithm, AbstractScalingResult from its_hub.types import ChatMessage class BestOfN(AbstractScalingAlgorithm): def __init__(self, orm: AbstractOutcomeRewardModel): self.orm = orm def infer(self, lm: AbstractLa...
class BestOfN(AbstractScalingAlgorithm): def __init__(self, orm: AbstractOutcomeRewardModel): pass def infer(self, lm: AbstractLanguageModel, prompt: str, budget: int, return_response_only: bool=True) -> str | BestOfNResult: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/bon.py
its_hub.algorithms.bon.BestOfNResult
from pydantic.dataclasses import dataclass from its_hub.base import AbstractLanguageModel, AbstractOutcomeRewardModel, AbstractScalingAlgorithm, AbstractScalingResult @dataclass class BestOfNResult(AbstractScalingResult): responses: list[str] scores: list[float] selected_index: int @property def t...
@dataclass class BestOfNResult(AbstractScalingResult): @property def the_one(self) -> str: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/particle_gibbs.py
its_hub.algorithms.particle_gibbs.Particle
from pydantic.dataclasses import dataclass import copy @dataclass class Particle: steps: list[str] is_stopped: bool partial_log_weights: list[float] @property def log_weight(self) -> float: """Return the most recent log weight.""" if self.partial_log_weights: return sel...
@dataclass class Particle: @property def log_weight(self) -> float: '''Return the most recent log weight.''' pass def deepcopy(self): pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/particle_gibbs.py
its_hub.algorithms.particle_gibbs.ParticleFiltering
from its_hub.base import AbstractLanguageModel, AbstractProcessRewardModel, AbstractScalingAlgorithm, AbstractScalingResult from its_hub.lms import StepGeneration class ParticleFiltering(ParticleGibbs): """ Particle filtering being a special case of particle Gibbs with num_iterations=1 """ def __init_...
class ParticleFiltering(ParticleGibbs): ''' Particle filtering being a special case of particle Gibbs with num_iterations=1 ''' def __init__(self, sg: StepGeneration, prm: AbstractProcessRewardModel, selection_method: str | SelectionMethod=SelectionMethod.ARGMAX): pass def infer(self, lm:...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/particle_gibbs.py
its_hub.algorithms.particle_gibbs.ParticleFilteringResult
from pydantic.dataclasses import dataclass from its_hub.base import AbstractLanguageModel, AbstractProcessRewardModel, AbstractScalingAlgorithm, AbstractScalingResult @dataclass class ParticleFilteringResult(AbstractScalingResult): responses: list[str] log_weights_lst: list[float] selected_index: int s...
@dataclass class ParticleFilteringResult(AbstractScalingResult): @property def the_one(self) -> str: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/particle_gibbs.py
its_hub.algorithms.particle_gibbs.ParticleGibbs
from its_hub.base import AbstractLanguageModel, AbstractProcessRewardModel, AbstractScalingAlgorithm, AbstractScalingResult from its_hub.lms import StepGeneration import numpy as np import random class ParticleGibbs(AbstractScalingAlgorithm): """ Particle-based Monte Carlo methods for inference time scaling. ...
class ParticleGibbs(AbstractScalingAlgorithm): ''' Particle-based Monte Carlo methods for inference time scaling. It supports the following variants: - Particle Filtering (PF): num_iterations = 1 - Particle Gibbs (PG): num_iterations > 1 - PG with ancestor sampling (PGAS): num_iterations > 1 an...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/particle_gibbs.py
its_hub.algorithms.particle_gibbs.ParticleGibbsResult
from pydantic.dataclasses import dataclass from its_hub.base import AbstractLanguageModel, AbstractProcessRewardModel, AbstractScalingAlgorithm, AbstractScalingResult @dataclass class ParticleGibbsResult(AbstractScalingResult): responses_lst: list[list[str]] log_weights_lst: list[list[float]] ref_indices_l...
@dataclass class ParticleGibbsResult(AbstractScalingResult): @property def the_one(self) -> str: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/particle_gibbs.py
its_hub.algorithms.particle_gibbs.SelectionMethod
from enum import Enum class SelectionMethod(Enum): SAMPLE = 'sample' ARGMAX = 'argmax'
class SelectionMethod(Enum): pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/planning_wrapper.py
its_hub.algorithms.planning_wrapper.ApproachPromptTemplate
class ApproachPromptTemplate: """Template for generating approach-specific prompts.""" APPROACH_TEMPLATE = 'Using the {approach} method from your plan, solve this problem step by step:\n\nProblem: {problem}\n\nApproach to use: {approach}\n\nPlease solve the problem following this specific approach and show your...
class ApproachPromptTemplate: '''Template for generating approach-specific prompts.''' @classmethod def create_approach_prompt(cls, problem: str, approach: str) -> str: '''Create an approach-specific prompt.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/planning_wrapper.py
its_hub.algorithms.planning_wrapper.PlanParser
import re class PlanParser: """Parser to extract approaches from planning output.""" @staticmethod def extract_approaches(plan: str) -> list[str]: """Extract approaches from the planning output.""" approaches = [] approach_pattern = 'APPROACH\\s+(\\d+):\\s*([^\\n]+(?:\\n(?!APPROACH...
class PlanParser: '''Parser to extract approaches from planning output.''' @staticmethod def extract_approaches(plan: str) -> list[str]: '''Extract approaches from the planning output.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/planning_wrapper.py
its_hub.algorithms.planning_wrapper.PlanningPromptTemplate
class PlanningPromptTemplate: """Template for generating planning prompts.""" PLANNING_TEMPLATE = 'Before solving this problem, I want you to first create a plan with different approaches to explore. This will help generate diverse solution strategies.\n\nProblem: {problem}\n\nPlease provide a plan with 3 disti...
class PlanningPromptTemplate: '''Template for generating planning prompts.''' @classmethod def create_planning_prompt(cls, problem: str) -> str: '''Create a planning prompt for the given problem.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/planning_wrapper.py
its_hub.algorithms.planning_wrapper.PlanningWrappedResult
from dataclasses import dataclass from its_hub.base import AbstractLanguageModel, AbstractScalingAlgorithm, AbstractScalingResult @dataclass class PlanningWrappedResult(AbstractScalingResult): """Result object for Planning-Enhanced algorithms.""" plan: str approaches: list[str] approach_results: dict[s...
@dataclass class PlanningWrappedResult(AbstractScalingResult): '''Result object for Planning-Enhanced algorithms.''' @property def the_one(self) -> str: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/planning_wrapper.py
its_hub.algorithms.planning_wrapper.PlanningWrapper
from its_hub.types import ChatMessage from its_hub.base import AbstractLanguageModel, AbstractScalingAlgorithm, AbstractScalingResult class PlanningWrapper(AbstractScalingAlgorithm): """ Planning Wrapper that can enhance any ITS algorithm with a planning phase. This wrapper adds a planning step before run...
class PlanningWrapper(AbstractScalingAlgorithm): ''' Planning Wrapper that can enhance any ITS algorithm with a planning phase. This wrapper adds a planning step before running the base algorithm, where: 1. Model generates a plan with distinct approaches/hypotheses 2. Budget is divided equally acro...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/self_consistency.py
its_hub.algorithms.self_consistency.SelfConsistency
from its_hub.base import AbstractLanguageModel, AbstractScalingAlgorithm, AbstractScalingResult from collections.abc import Callable from its_hub.types import ChatMessage class SelfConsistency(AbstractScalingAlgorithm): def __init__(self, consistency_space_projection_func: Callable): self.consistency_spac...
class SelfConsistency(AbstractScalingAlgorithm): def __init__(self, consistency_space_projection_func: Callable): pass def infer(self, lm: AbstractLanguageModel, prompt: str, budget: int, return_response_only: bool=True) -> str | SelfConsistencyResult: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/algorithms/self_consistency.py
its_hub.algorithms.self_consistency.SelfConsistencyResult
from its_hub.base import AbstractLanguageModel, AbstractScalingAlgorithm, AbstractScalingResult from collections import Counter from pydantic.dataclasses import dataclass @dataclass class SelfConsistencyResult(AbstractScalingResult): responses: list[str] response_counts: Counter[str] selected_index: int ...
@dataclass class SelfConsistencyResult(AbstractScalingResult): @property def the_one(self) -> str: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/base.py
its_hub.base.AbstractLanguageModel
from abc import ABC, abstractmethod from .types import ChatMessage class AbstractLanguageModel(ABC): """abstract base class for (autoregressive) language models""" @abstractmethod def generate(self, messages: list[ChatMessage] | list[list[ChatMessage]], stop: str | None=None) -> str | list[str]: "...
class AbstractLanguageModel(ABC): '''abstract base class for (autoregressive) language models''' @abstractmethod def generate(self, messages: list[ChatMessage] | list[list[ChatMessage]], stop: str | None=None) -> str | list[str]: ''' generate a response from the model Args: ...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/base.py
its_hub.base.AbstractOutcomeRewardModel
from abc import ABC, abstractmethod class AbstractOutcomeRewardModel(ABC): """abstract base class for outcome reward models""" @abstractmethod def score(self, prompt: str, response: str) -> float: """the score for a given prompt and response""" pass
class AbstractOutcomeRewardModel(ABC): '''abstract base class for outcome reward models''' @abstractmethod def score(self, prompt: str, response: str) -> float: '''the score for a given prompt and response''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/base.py
its_hub.base.AbstractProcessRewardModel
from abc import ABC, abstractmethod class AbstractProcessRewardModel(ABC): """abstract base class for process reward models""" @abstractmethod def score(self, prompt: str, steps: list[str]) -> list[float]: """the score for a given prompt and steps""" pass
class AbstractProcessRewardModel(ABC): '''abstract base class for process reward models''' @abstractmethod def score(self, prompt: str, steps: list[str]) -> list[float]: '''the score for a given prompt and steps''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/base.py
its_hub.base.AbstractScalingAlgorithm
from abc import ABC, abstractmethod class AbstractScalingAlgorithm(ABC): """abstract base class for inference-time scaling algorithms""" @abstractmethod def infer(self, lm: AbstractLanguageModel, prompt: str, budget: int, return_response_only: bool=True) -> str | AbstractScalingResult: """ ...
class AbstractScalingAlgorithm(ABC): '''abstract base class for inference-time scaling algorithms''' @abstractmethod def infer(self, lm: AbstractLanguageModel, prompt: str, budget: int, return_response_only: bool=True) -> str | AbstractScalingResult: ''' run inference with the given languag...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/base.py
its_hub.base.AbstractScalingResult
from abc import ABC, abstractmethod class AbstractScalingResult(ABC): """abstract base class for scaling result""" @property @abstractmethod def the_one(self) -> str: """the selected response""" pass
class AbstractScalingResult(ABC): '''abstract base class for scaling result''' @property @abstractmethod def the_one(self) -> str: '''the selected response''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/error_handling.py
its_hub.error_handling.APIConnectionError
class APIConnectionError(APIError): """Network/connection issues - retryable.""" pass
class APIConnectionError(APIError): '''Network/connection issues - retryable.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/error_handling.py
its_hub.error_handling.APIError
from typing import Any class APIError(Exception): """Base class for API-related errors.""" def __init__(self, message: str, status_code: int | None=None, error_details: dict[str, Any] | None=None): self.message = message self.status_code = status_code self.error_details = error_details...
class APIError(Exception): '''Base class for API-related errors.''' def __init__(self, message: str, status_code: int | None=None, error_details: dict[str, Any] | None=None): pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/error_handling.py
its_hub.error_handling.AuthenticationError
class AuthenticationError(APIError): """Authentication failed - not retryable.""" pass
class AuthenticationError(APIError): '''Authentication failed - not retryable.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/error_handling.py
its_hub.error_handling.BadRequestError
class BadRequestError(APIError): """Bad request (invalid parameters) - not retryable.""" pass
class BadRequestError(APIError): '''Bad request (invalid parameters) - not retryable.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/error_handling.py
its_hub.error_handling.ContextLengthError
class ContextLengthError(APIError): """Context length exceeded - not retryable.""" pass
class ContextLengthError(APIError): '''Context length exceeded - not retryable.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/error_handling.py
its_hub.error_handling.InternalServerError
class InternalServerError(APIError): """Server error - retryable.""" pass
class InternalServerError(APIError): '''Server error - retryable.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/error_handling.py
its_hub.error_handling.RateLimitError
class RateLimitError(APIError): """Rate limit exceeded - retryable.""" pass
class RateLimitError(APIError): '''Rate limit exceeded - retryable.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/integration/iaas.py
its_hub.integration.iaas.ChatCompletionChoice
from pydantic import BaseModel, Field, field_validator from its_hub.types import ChatMessage class ChatCompletionChoice(BaseModel): """Single completion choice.""" index: int = Field(..., description='Choice index') message: ChatMessage = Field(..., description='Generated message') finish_reason: str =...
class ChatCompletionChoice(BaseModel): '''Single completion choice.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/integration/iaas.py
its_hub.integration.iaas.ChatCompletionRequest
from its_hub.types import ChatMessage from pydantic import BaseModel, Field, field_validator class ChatCompletionRequest(BaseModel): """Chat completion request with inference-time scaling support.""" model: str = Field(..., description='Model identifier') messages: list[ChatMessage] = Field(..., descriptio...
class ChatCompletionRequest(BaseModel): '''Chat completion request with inference-time scaling support.''' @field_validator('messages') @classmethod def validate_messages(cls, v): '''Validate message format and constraints.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/integration/iaas.py
its_hub.integration.iaas.ChatCompletionResponse
from pydantic import BaseModel, Field, field_validator class ChatCompletionResponse(BaseModel): """Chat completion response.""" id: str = Field(..., description='Unique response identifier') object: str = Field('chat.completion', description='Object type') created: int = Field(..., description='Creatio...
class ChatCompletionResponse(BaseModel): '''Chat completion response.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/integration/iaas.py
its_hub.integration.iaas.ChatCompletionUsage
from pydantic import BaseModel, Field, field_validator class ChatCompletionUsage(BaseModel): """Token usage information.""" prompt_tokens: int = Field(..., description='Tokens in prompt') completion_tokens: int = Field(..., description='Generated tokens') total_tokens: int = Field(..., description='Tot...
class ChatCompletionUsage(BaseModel): '''Token usage information.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/integration/iaas.py
its_hub.integration.iaas.ConfigRequest
from pydantic import BaseModel, Field, field_validator class ConfigRequest(BaseModel): """Configuration request for setting up the IaaS service.""" endpoint: str = Field(..., description='Language model endpoint URL') api_key: str = Field(..., description='API key for the language model') model: str = ...
class ConfigRequest(BaseModel): '''Configuration request for setting up the IaaS service.''' @field_validator('alg') @classmethod def validate_algorithm(cls, v): '''Validate that the algorithm is supported.''' pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/integration/reward_hub.py
its_hub.integration.reward_hub.LocalVllmProcessRewardModel
from reward_hub.base import AggregationMethod from its_hub.base import AbstractProcessRewardModel class LocalVllmProcessRewardModel(AbstractProcessRewardModel): def __init__(self, model_name: str, device: str, aggregation_method: AggregationMethod): from reward_hub.vllm.reward import VllmProcessRewardMode...
class LocalVllmProcessRewardModel(AbstractProcessRewardModel): def __init__(self, model_name: str, device: str, aggregation_method: AggregationMethod): pass def score(self, prompt: str, response_or_responses: str | list[str]) -> float: pass
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/lms.py
its_hub.lms.LocalVLLMLanguageModel
from .base import AbstractLanguageModel class LocalVLLMLanguageModel(AbstractLanguageModel): pass
class LocalVLLMLanguageModel(AbstractLanguageModel): pass
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Red-Hat-AI-Innovation-Team/its_hub
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its_hub.lms.OpenAICompatibleLanguageModel
from .types import ChatMessage import asyncio import aiohttp from .error_handling import RETRYABLE_ERRORS, APIError, enhanced_on_backoff, format_non_retryable_error, parse_api_error, should_retry import backoff import requests import logging from .base import AbstractLanguageModel class OpenAICompatibleLanguageModel(A...
class OpenAICompatibleLanguageModel(AbstractLanguageModel): def __init__(self, endpoint: str, api_key: str, model_name: str, system_prompt: str | None=None, is_async: bool=False, stop: str | None=None, max_tokens: int | None=None, temperature: float | None=None, max_tries: int=8, max_concurrency: int=-1, replace_...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/lms.py
its_hub.lms.StepGeneration
from .types import ChatMessage from .base import AbstractLanguageModel import logging class StepGeneration: def __init__(self, max_steps: int, step_token: str | list[str] | None=None, stop_token: str | None=None, temperature: float=0.8, include_stop_str_in_output: bool=False, temperature_switch: tuple[float, str,...
class StepGeneration: def __init__(self, max_steps: int, step_token: str | list[str] | None=None, stop_token: str | None=None, temperature: float=0.8, include_stop_str_in_output: bool=False, temperature_switch: tuple[float, str, str] | None=None, tokens_per_step: int | None=None): pass def _post_proc...
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Red-Hat-AI-Innovation-Team/its_hub
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Red-Hat-AI-Innovation-Team_its_hub/its_hub/lms.py
its_hub.lms.TransformersLanguageModel
from .base import AbstractLanguageModel class TransformersLanguageModel(AbstractLanguageModel): pass
class TransformersLanguageModel(AbstractLanguageModel): pass
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Red-Hat-AI-Innovation-Team/its_hub
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its_hub.types.ChatMessage
from typing import Literal from pydantic.dataclasses import dataclass @dataclass class ChatMessage: """A chat message with role and content.""" role: Literal['system', 'user', 'assistant'] content: str
@dataclass class ChatMessage: '''A chat message with role and content.''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/core/src/flux0_core/agent_runners/api.py
api.AgentRunner
from abc import ABC, abstractmethod from flux0_core.agent_runners.context import Context class AgentRunner(ABC): @abstractmethod async def run(self, context: Context, deps: Deps) -> bool: ...
class AgentRunner(ABC): @abstractmethod async def run(self, context: Context, deps: Deps) -> bool: pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/core/src/flux0_core/agent_runners/api.py
api.AgentRunnerFactory
from abc import ABC, abstractmethod from flux0_core.agents import Agent, AgentId, AgentStore, AgentType class AgentRunnerFactory(ABC): @abstractmethod def create_runner(self, agent_type: AgentType) -> AgentRunner: ... @abstractmethod def runner_exists(self, agent_type: AgentType) -> bool: ...
class AgentRunnerFactory(ABC): @abstractmethod def create_runner(self, agent_type: AgentType) -> AgentRunner: pass @abstractmethod def runner_exists(self, agent_type: AgentType) -> bool: pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/core/src/flux0_core/agent_runners/api.py
api.Deps
from flux0_core.sessions import Event, Session, SessionId, SessionStore from typing import Callable, Optional, Sequence, Type, TypeVar from flux0_core.contextual_correlator import ContextualCorrelator from flux0_core.logging import Logger from flux0_stream.emitter.api import EventEmitter from flux0_core.agents import A...
class Deps: def __init__(self, correlator: ContextualCorrelator, logger: Logger, event_emitter: EventEmitter, agent_store: AgentStore, session_store: SessionStore, recording_store: RecordingStore) -> None: pass async def read_session(self, session_id: SessionId) -> Optional[Session]: pass ...
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/stream/src/flux0_stream/emitter/api.py
api.EventEmitter
from abc import ABC, abstractmethod from flux0_stream.types import ChunkEvent, EmittedEvent from flux0_core.sessions import EventId, StatusEventData from typing import Awaitable, Callable, Optional class EventEmitter(ABC): """ABC for event emissions.""" @abstractmethod async def enqueue_status_event(self,...
class EventEmitter(ABC): '''ABC for event emissions.''' @abstractmethod async def enqueue_status_event(self, correlation_id: str, data: StatusEventData, event_id: Optional[EventId]=None) -> None: '''Enqueues a status event for a specific execution (correlation_id).''' pass @abstractmethod a...
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/stream/src/flux0_stream/store/api.py
api.EventStore
from flux0_stream.types import ChunkEvent, EmittedEvent from flux0_core.sessions import EventId from typing import Optional from abc import ABC, abstractmethod class EventStore(ABC): """ABC for event storage and retrieval.""" @abstractmethod async def add_chunk(self, chunk: ChunkEvent) -> None: .....
class EventStore(ABC): '''ABC for event storage and retrieval.''' @abstractmethod async def add_chunk(self, chunk: ChunkEvent) -> None: pass @abstractmethod async def finalize_event(self, correlation_id: str, event_id: EventId) -> Optional[EmittedEvent]: pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/core/src/flux0_core/agent_runners/context.py
context.Context
from flux0_core.sessions import Event, SessionId from flux0_core.agents import AgentId from dataclasses import dataclass @dataclass(frozen=True) class Context: session_id: SessionId agent_id: AgentId
@dataclass(frozen=True) class Context: pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/core/src/flux0_core/agent_runners/context.py
context.InteractionState
from dataclasses import dataclass from typing import Sequence from flux0_core.sessions import Event, SessionId @dataclass(frozen=True) class InteractionState: last_known_event_offset: int history: Sequence[Event]
@dataclass(frozen=True) class InteractionState: pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/auth.py
flux0_api.auth.AuthHandler
from fastapi import Depends, Request from abc import ABC, abstractmethod from flux0_core.users import User, UserStore class AuthHandler(ABC): @abstractmethod async def __call__(self, request: Request) -> User: """Auth handler that returns a user object or raises an HTTPException."""
class AuthHandler(ABC): @abstractmethod async def __call__(self, request: Request) -> User: '''Auth handler that returns a user object or raises an HTTPException.''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/auth.py
flux0_api.auth.AuthType
from enum import Enum class AuthType(Enum): NOOP = 'noop'
class AuthType(Enum): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/auth.py
flux0_api.auth.NoopAuthHandler
from flux0_core.users import User, UserStore from fastapi import Depends, Request class NoopAuthHandler(AuthHandler): _default_sub = NOOP_AUTH_HANDLER_DEFAULT_SUB user_store: UserStore def __init__(self, user_store: UserStore): self.user_store = user_store async def __call__(self, request: Re...
class NoopAuthHandler(AuthHandler): def __init__(self, user_store: UserStore): pass async def __call__(self, request: Request) -> User: '''No-op auth handler that always returns an anonymous user.''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/common.py
flux0_api.common.DefaultBaseEnum
from pydantic_core import CoreSchema from enum import Enum from pydantic import BaseModel, ConfigDict, Field, GetJsonSchemaHandler from pydantic.json_schema import JsonSchemaValue class DefaultBaseEnum(Enum): def __str__(self) -> str: name = self.__class__.__name__ return name.removesuffix('DTO') ...
class DefaultBaseEnum(Enum): def __str__(self) -> str: pass @classmethod def __get_pydantic_json_schema__(cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue: pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/common.py
flux0_api.common.DefaultBaseModel
from pydantic import BaseModel, ConfigDict, Field, GetJsonSchemaHandler class DefaultBaseModel(BaseModel): """ Base class for all flux0 Pydantic models. """ model_config = DEFAULT_MODEL_CONFIG
class DefaultBaseModel(BaseModel): ''' Base class for all flux0 Pydantic models. ''' pass
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flux0-ai/flux0
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flux0_api.session_service.SessionService
import asyncio from flux0_core.types import JSONSerializable from flux0_core.agents import Agent, AgentStore from datetime import datetime, timezone from flux0_core.background_tasks_service import BackgroundTaskService from flux0_core.ids import gen_id from flux0_core.contextual_correlator import ContextualCorrelator f...
class SessionService: def __init__(self, contextual_correlator: ContextualCorrelator, logger: Logger, agent_store: AgentStore, session_store: SessionStore, recording_store: RecordingStore, background_task_service: BackgroundTaskService, agent_runner_factory: AgentRunnerFactory, event_emitter: EventEmitter): ...
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_agents.py
flux0_api.types_agents.AgentCreationParamsDTO
from typing import Annotated, Optional, Sequence, TypeAlias from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseModel, ExampleJson class AgentCreationParamsDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_extra'] = {'example': agent_creation_params_example} ...
class AgentCreationParamsDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_agents.py
flux0_api.types_agents.AgentDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseModel, ExampleJson from typing import Annotated, Optional, Sequence, TypeAlias class AgentDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_extra'] = {'example': agent_example} '\n An agent is a speci...
class AgentDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_agents.py
flux0_api.types_agents.AgentsDTO
from typing import Annotated, Optional, Sequence, TypeAlias from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseModel, ExampleJson class AgentsDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_extra'] = {'example': {'data': [agent_example]}} '\n List o...
class AgentsDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ChunkEventDTO
import time from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO from flux0_api.types_patch import JsonPatchOperationDTO from dataclasses import field from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class Chunk...
class ChunkEventDTO(DefaultBaseModel, json_schema_extra={'example': pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ChunkEventStream
from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class ChunkEventStream(DefaultBaseModel): event: Literal['chunk'] data: ChunkEventDTO
class ChunkEventStream(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ContentPartDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class ContentPartDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json...
class ContentPartDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ControlOptions
from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class ControlOptions(DefaultBaseModel): mode: Literal['auto', 'manual']
class ControlOptions(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.EmittedEventDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO from pydantic import Field, RootModel from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class EmittedEventDTO(DefaultBaseModel, json_schema_extra={'examples': ...
class EmittedEventDTO(DefaultBaseModel, json_schema_extra={'examples': pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.EmittedEventStream
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class EmittedEventStream(DefaultBaseModel): id: EventIdPath event: Literal['status'] data: Emitte...
class EmittedEventStream(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.EventCreationParamsDTO
from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class EventCreationParamsDTO(DefaultBaseModel, json_schema_extra={'example': event_creation_params_example}):...
class EventCreationParamsDTO(DefaultBaseModel, json_schema_extra={'example': '''Parameters for creating a new event within a session.''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.EventDTO
from pydantic import Field, RootModel from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class EventDTO(DefaultBaseModel, json_schema_extra={'example': event_ex...
class EventDTO(DefaultBaseModel, json_schema_extra={'example': pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.EventSourceDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class EventSourceDTO(DefaultBaseEnum): """ Source of the event within a session. Identifies who or what generated the event. """ USER = 'user' AI_AGENT = 'ai_agent' SYSTEM...
class EventSourceDTO(DefaultBaseEnum): ''' Source of the event within a session. Identifies who or what generated the event. ''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.EventTypeDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class EventTypeDTO(DefaultBaseEnum): """ Type of event that occurred within a session. Represents different types of interactions that can occur within a conversation. """ MESSAGE...
class EventTypeDTO(DefaultBaseEnum): ''' Type of event that occurred within a session. Represents different types of interactions that can occur within a conversation. ''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.EventsDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class EventsDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_sche...
class EventsDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.MessageEventDataDTO
from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class MessageEventDataDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config[...
class MessageEventDataDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ReasoningPartDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class ReasoningPartDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['js...
class ReasoningPartDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.SessionStream
from pydantic import Field, RootModel from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class SessionStream(RootModel[Union[ChunkEventStream, EmittedEventStream]]): root: Union[ChunkEventStream, EmittedEventStream] = Field(..., discriminator='event')
class SessionStream(RootModel[Union[ChunkEventStream, EmittedEventStream]]): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.StatusEventDataDTO
from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class StatusEventDataDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['...
class StatusEventDataDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.StatusEventDataStatusField
from enum import Enum class StatusEventDataStatusField(Enum): """ Status of the event. """ ACKNOWLEDGE = 'acknowledged' CANCELLED = 'cancelled' PROCESSING = 'processing' READY = 'ready' TYPING = 'typing' ERROR = 'error' COMPLETED = 'completed'
class StatusEventDataStatusField(Enum): ''' Status of the event. ''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ToolCallDTO
from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class ToolCallDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_sc...
class ToolCallDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ToolEventDataDTO
from flux0_core.sessions import EventId, ToolCallPartType, ToolCallResultType from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class ToolEventDataDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_ext...
class ToolEventDataDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ToolPartDTO
from flux0_core.sessions import EventId, ToolCallPartType, ToolCallResultType from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO class ToolPartDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_extra'] ...
class ToolPartDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_events.py
flux0_api.types_events.ToolResultDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseEnum, DefaultBaseModel, ExampleJson, JSONSerializableDTO from typing import Annotated, List, Literal, Mapping, Optional, Sequence, TypeAlias, Union class ToolResultDTO(DefaultBaseModel): data: JSONSerializableDTO metadata: Mapping[str, JSONSerializa...
class ToolResultDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_patch.py
flux0_api.types_patch.JsonPatchOperationDTO
from flux0_api.common import DefaultBaseModel, JSONSerializableDTO class JsonPatchOperationDTO(DefaultBaseModel, json_schema_extra={'example': {'op': 'add', 'path': '/a/b', 'value': 1}}): op: PatchOpOpField path: PatchOpPathField value: JSONSerializableDTO
class JsonPatchOperationDTO(DefaultBaseModel, json_schema_extra={'example': pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_session.py
flux0_api.types_session.ConsumptionOffsetsDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseModel, ExampleJson, JSONSerializableDTO from typing import Annotated, Literal, Mapping, Optional, Sequence, TypeAlias class ConsumptionOffsetsDTO(DefaultBaseModel, json_schema_extra={'example': consumption_offsets_example}): """Tracks the state of messa...
class ConsumptionOffsetsDTO(DefaultBaseModel, json_schema_extra={'example': '''Tracks the state of message consumption.''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_session.py
flux0_api.types_session.Moderation
from enum import Enum class Moderation(Enum): """Content moderation settings.""" NONE = 'none'
class Moderation(Enum): '''Content moderation settings.''' pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_session.py
flux0_api.types_session.SessionCreationParamsDTO
from typing import Annotated, Literal, Mapping, Optional, Sequence, TypeAlias from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseModel, ExampleJson, JSONSerializableDTO class SessionCreationParamsDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_extra'] = {'...
class SessionCreationParamsDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_session.py
flux0_api.types_session.SessionDTO
from typing import Annotated, Literal, Mapping, Optional, Sequence, TypeAlias from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseModel, ExampleJson, JSONSerializableDTO class SessionDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_extra'] = {'example': sess...
class SessionDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/api/src/flux0_api/types_session.py
flux0_api.types_session.SessionsDTO
from flux0_api.common import DEFAULT_MODEL_CONFIG, DefaultBaseModel, ExampleJson, JSONSerializableDTO from typing import Annotated, Literal, Mapping, Optional, Sequence, TypeAlias class SessionsDTO(DefaultBaseModel): model_config = DEFAULT_MODEL_CONFIG.copy() model_config['json_schema_extra'] = {'example': {'d...
class SessionsDTO(DefaultBaseModel): pass
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flux0-ai/flux0
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/flux0-ai_flux0/packages/cli/src/flux0_cli/main.py
flux0_cli.main.CLIGroup
import click from typing import Any, Callable, TypeVar, Union, overload class CLIGroup(click.Group): @overload def command(self, __func: Callable[..., Any]) -> click.Command: ... @overload def command(self, *args: Any, **kwargs: Any) -> Callable[[Callable[..., Any]], click.Command]: ....
class CLIGroup(click.Group): @overload def command(self, __func: Callable[..., Any]) -> click.Command: pass @overload def command(self, __func: Callable[..., Any]) -> click.Command: pass def command(self, __func: Callable[..., Any]) -> click.Command: pass def decor...
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