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| from __future__ import annotations | |
| from typing import Literal, cast | |
| from pydantic import BaseModel, Field, ValidationError | |
| from openhands.core import logger | |
| from openhands.core.config.llm_config import LLMConfig | |
| class NoOpCondenserConfig(BaseModel): | |
| """Configuration for NoOpCondenser.""" | |
| type: Literal['noop'] = Field('noop') | |
| model_config = {'extra': 'forbid'} | |
| class ObservationMaskingCondenserConfig(BaseModel): | |
| """Configuration for ObservationMaskingCondenser.""" | |
| type: Literal['observation_masking'] = Field('observation_masking') | |
| attention_window: int = Field( | |
| default=100, | |
| description='The number of most-recent events where observations will not be masked.', | |
| ge=1, | |
| ) | |
| model_config = {'extra': 'forbid'} | |
| class BrowserOutputCondenserConfig(BaseModel): | |
| """Configuration for the BrowserOutputCondenser.""" | |
| type: Literal['browser_output_masking'] = Field('browser_output_masking') | |
| attention_window: int = Field( | |
| default=1, | |
| description='The number of most recent browser output observations that will not be masked.', | |
| ge=1, | |
| ) | |
| class RecentEventsCondenserConfig(BaseModel): | |
| """Configuration for RecentEventsCondenser.""" | |
| type: Literal['recent'] = Field('recent') | |
| # at least one event by default, because the best guess is that it is the user task | |
| keep_first: int = Field( | |
| default=1, | |
| description='The number of initial events to condense.', | |
| ge=0, | |
| ) | |
| max_events: int = Field( | |
| default=100, description='Maximum number of events to keep.', ge=1 | |
| ) | |
| model_config = {'extra': 'forbid'} | |
| class LLMSummarizingCondenserConfig(BaseModel): | |
| """Configuration for LLMCondenser.""" | |
| type: Literal['llm'] = Field('llm') | |
| llm_config: LLMConfig = Field( | |
| ..., description='Configuration for the LLM to use for condensing.' | |
| ) | |
| # at least one event by default, because the best guess is that it's the user task | |
| keep_first: int = Field( | |
| default=1, | |
| description='Number of initial events to always keep in history.', | |
| ge=0, | |
| ) | |
| max_size: int = Field( | |
| default=100, | |
| description='Maximum size of the condensed history before triggering forgetting.', | |
| ge=2, | |
| ) | |
| max_event_length: int = Field( | |
| default=10_000, | |
| description='Maximum length of the event representations to be passed to the LLM.', | |
| ) | |
| model_config = {'extra': 'forbid'} | |
| class AmortizedForgettingCondenserConfig(BaseModel): | |
| """Configuration for AmortizedForgettingCondenser.""" | |
| type: Literal['amortized'] = Field('amortized') | |
| max_size: int = Field( | |
| default=100, | |
| description='Maximum size of the condensed history before triggering forgetting.', | |
| ge=2, | |
| ) | |
| # at least one event by default, because the best guess is that it's the user task | |
| keep_first: int = Field( | |
| default=1, | |
| description='Number of initial events to always keep in history.', | |
| ge=0, | |
| ) | |
| model_config = {'extra': 'forbid'} | |
| class LLMAttentionCondenserConfig(BaseModel): | |
| """Configuration for LLMAttentionCondenser.""" | |
| type: Literal['llm_attention'] = Field('llm_attention') | |
| llm_config: LLMConfig = Field( | |
| ..., description='Configuration for the LLM to use for attention.' | |
| ) | |
| max_size: int = Field( | |
| default=100, | |
| description='Maximum size of the condensed history before triggering forgetting.', | |
| ge=2, | |
| ) | |
| # at least one event by default, because the best guess is that it's the user task | |
| keep_first: int = Field( | |
| default=1, | |
| description='Number of initial events to always keep in history.', | |
| ge=0, | |
| ) | |
| model_config = {'extra': 'forbid'} | |
| class StructuredSummaryCondenserConfig(BaseModel): | |
| """Configuration for StructuredSummaryCondenser instances.""" | |
| type: Literal['structured'] = Field('structured') | |
| llm_config: LLMConfig = Field( | |
| ..., description='Configuration for the LLM to use for condensing.' | |
| ) | |
| # at least one event by default, because the best guess is that it's the user task | |
| keep_first: int = Field( | |
| default=1, | |
| description='Number of initial events to always keep in history.', | |
| ge=0, | |
| ) | |
| max_size: int = Field( | |
| default=100, | |
| description='Maximum size of the condensed history before triggering forgetting.', | |
| ge=2, | |
| ) | |
| max_event_length: int = Field( | |
| default=10_000, | |
| description='Maximum length of the event representations to be passed to the LLM.', | |
| ) | |
| model_config = {'extra': 'forbid'} | |
| class CondenserPipelineConfig(BaseModel): | |
| """Configuration for the CondenserPipeline. | |
| Not currently supported by the TOML or ENV_VAR configuration strategies. | |
| """ | |
| type: Literal['pipeline'] = Field('pipeline') | |
| condensers: list[CondenserConfig] = Field( | |
| default_factory=list, | |
| description='List of condenser configurations to be used in the pipeline.', | |
| ) | |
| model_config = {'extra': 'forbid'} | |
| # Type alias for convenience | |
| CondenserConfig = ( | |
| NoOpCondenserConfig | |
| | ObservationMaskingCondenserConfig | |
| | BrowserOutputCondenserConfig | |
| | RecentEventsCondenserConfig | |
| | LLMSummarizingCondenserConfig | |
| | AmortizedForgettingCondenserConfig | |
| | LLMAttentionCondenserConfig | |
| | StructuredSummaryCondenserConfig | |
| | CondenserPipelineConfig | |
| ) | |
| def condenser_config_from_toml_section( | |
| data: dict, llm_configs: dict | None = None | |
| ) -> dict[str, CondenserConfig]: | |
| """ | |
| Create a CondenserConfig instance from a toml dictionary representing the [condenser] section. | |
| For CondenserConfig, the handling is different since it's a union type. The type of condenser | |
| is determined by the 'type' field in the section. | |
| Example: | |
| Parse condenser config like: | |
| [condenser] | |
| type = "noop" | |
| For condensers that require an LLM config, you can specify the name of an LLM config: | |
| [condenser] | |
| type = "llm" | |
| llm_config = "my_llm" # References [llm.my_llm] section | |
| Args: | |
| data: The TOML dictionary representing the [condenser] section. | |
| llm_configs: Optional dictionary of LLMConfig objects keyed by name. | |
| Returns: | |
| dict[str, CondenserConfig]: A mapping where the key "condenser" corresponds to the configuration. | |
| """ | |
| # Initialize the result mapping | |
| condenser_mapping: dict[str, CondenserConfig] = {} | |
| # Process config | |
| try: | |
| # Determine which condenser type to use based on 'type' field | |
| condenser_type = data.get('type', 'noop') | |
| # Handle LLM config reference if needed | |
| if ( | |
| condenser_type in ('llm', 'llm_attention') | |
| and 'llm_config' in data | |
| and isinstance(data['llm_config'], str) | |
| ): | |
| llm_config_name = data['llm_config'] | |
| if llm_configs and llm_config_name in llm_configs: | |
| # Replace the string reference with the actual LLMConfig object | |
| data_copy = data.copy() | |
| data_copy['llm_config'] = llm_configs[llm_config_name] | |
| config = create_condenser_config(condenser_type, data_copy) | |
| else: | |
| logger.openhands_logger.warning( | |
| f"LLM config '{llm_config_name}' not found for condenser. Using default LLMConfig." | |
| ) | |
| # Create a default LLMConfig if the referenced one doesn't exist | |
| data_copy = data.copy() | |
| # Try to use the fallback 'llm' config | |
| if llm_configs is not None: | |
| data_copy['llm_config'] = llm_configs.get('llm') | |
| config = create_condenser_config(condenser_type, data_copy) | |
| else: | |
| config = create_condenser_config(condenser_type, data) | |
| condenser_mapping['condenser'] = config | |
| except (ValidationError, ValueError) as e: | |
| logger.openhands_logger.warning( | |
| f'Invalid condenser configuration: {e}. Using NoOpCondenserConfig.' | |
| ) | |
| # Default to NoOpCondenserConfig if config fails | |
| config = NoOpCondenserConfig(type='noop') | |
| condenser_mapping['condenser'] = config | |
| return condenser_mapping | |
| # For backward compatibility | |
| from_toml_section = condenser_config_from_toml_section | |
| def create_condenser_config(condenser_type: str, data: dict) -> CondenserConfig: | |
| """ | |
| Create a CondenserConfig instance based on the specified type. | |
| Args: | |
| condenser_type: The type of condenser to create. | |
| data: The configuration data. | |
| Returns: | |
| A CondenserConfig instance. | |
| Raises: | |
| ValueError: If the condenser type is unknown. | |
| ValidationError: If the provided data fails validation for the condenser type. | |
| """ | |
| # Mapping of condenser types to their config classes | |
| condenser_classes = { | |
| 'noop': NoOpCondenserConfig, | |
| 'observation_masking': ObservationMaskingCondenserConfig, | |
| 'recent': RecentEventsCondenserConfig, | |
| 'llm': LLMSummarizingCondenserConfig, | |
| 'amortized': AmortizedForgettingCondenserConfig, | |
| 'llm_attention': LLMAttentionCondenserConfig, | |
| 'structured': StructuredSummaryCondenserConfig, | |
| } | |
| if condenser_type not in condenser_classes: | |
| raise ValueError(f'Unknown condenser type: {condenser_type}') | |
| # Create and validate the config using direct instantiation | |
| # Explicitly handle ValidationError to provide more context | |
| try: | |
| config_class = condenser_classes[condenser_type] | |
| # Use type casting to help mypy understand the return type | |
| return cast(CondenserConfig, config_class(**data)) | |
| except ValidationError as e: | |
| # Just re-raise with a more descriptive message, but don't try to pass the errors | |
| # which can cause compatibility issues with different pydantic versions | |
| raise ValueError( | |
| f"Validation failed for condenser type '{condenser_type}': {e}" | |
| ) | |