Moving from httpx to aiohttp - dev branch

[alice-cohere]

Worked on this with Sonnet 4.5 in Cursor. My prompt

Here's the issue I'm having: I need to make async requests to a model that's deployed here, through the @cohere-python_cursor SDK. Unfortunately, I'm getting these errors

httpx.ConnectError: All connection attempts failed

Everything is pointing towards httpx being the culprit, so I want to swap out httpx for aiohttp everywhere. Can you please look at the repo, and propose me a plan for how to conduct this refactor? Think this thoroughly, no need to hurry. I want something precise.

Model plan:

---

name: Refactor validator return values
overview: Extend ValidationDecision to capture all information from validator LLM calls, ensuring both should_trigger_tool and is_compliant (plus detailed labels) are always populated when available, minimizing redundant validator calls.
todos:

• id: extend-validation-decision
  content: Extend ValidationDecision dataclass with semantic fields (should_trigger_tool, is_compliant, is_refusal, abuse_areas, severities)
  status: completed
• id: modify-llm-judge
  content: Modify _get_decision_from_llm_judge to return metadata dict with all computed values
  status: completed
• id: update-validator-methods
  content: Update all validator methods (should_call_safety_content, content_tool_oracle, evaluate_completion_compliance, evaluate_is_refusal) to populate new fields
  status: completed
• id: update-oracles
  content: Update oracle functions to store and reuse all validator outputs from ValidationDecision
  status: completed
• id: update-tool-validation
  content: Update tool_validation.py functions to leverage cached values
  status: completed
• id: update-conversation-validation
  content: Update conversation_validation.py to use new semantic fields
  status: completed
  isProject: false

---

Refactor Validator to Return Complete Information

Overview

Currently, validator methods discard valuable information computed during LLM calls. When calling the validator LLM for safety classification, we get predict_safety_related, predict_abuse_areas, and predict_severities, but different methods only return subsets of this data. This refactor ensures all available information is captured and returned.

1. Extend ValidationDecision Dataclass

File: [bee/comb/comb/envs/safety/validator/safety_validator.py](bee/comb/comb/envs/safety/validator/safety_validator.py)

Add optional semantic fields to ValidationDecision (lines 32-42):

@dataclass
class ValidationDecision:
    by: str = field()
    decision: bool | tuple | None = field()  # Keep for backward compatibility
    reason: str | None = field(default=None)
    weight: int = field(default=1)
    classification_prompt: list[str] | None = field(default=None)
    validator_completion: list[str] | None = field(default=None)
    
    # New semantic fields populated from validator calls:
    should_trigger_tool: bool | None = field(default=None)  # predict_safety_related
    is_compliant: bool | None = field(default=None)  # compliance decision
    is_refusal: bool | None = field(default=None)  # refusal classification
    abuse_areas: list | None = field(default=None)  # detailed abuse categories
    severities: list | None = field(default=None)  # severity levels

2. Modify _get_decision_from_llm_judge to Return All Information

File: [bee/comb/comb/envs/safety/validator/safety_validator.py](bee/comb/comb/envs/safety/validator/safety_validator.py)

Update the return signature (line 337) from:

tuple[bool | tuple, list[str], list[str]]

To:

tuple[bool | tuple, list[str], list[str], dict[str, Any]]

The new dict will contain:

• should_trigger_tool: bool | None
• is_compliant: bool | None
• is_refusal: bool | None
• abuse_areas: list | None
• severities: list | None

Modify the method to:

1. Track predict_safety_related, predict_abuse_areas, predict_severities throughout
2. Compute compliance when these values are available
3. Return metadata dict alongside existing return values

3. Update Validator Methods to Populate All Fields

File: [bee/comb/comb/envs/safety/validator/safety_validator.py](bee/comb/comb/envs/safety/validator/safety_validator.py)

should_call_safety_content (lines 162-205)

• Unpack the new metadata dict from _get_decision_from_llm_judge
• Populate: should_trigger_tool, is_compliant, abuse_areas, severities
• Keep decision as should_trigger_tool for backward compatibility

content_tool_oracle (lines 298-326)

• Unpack the new metadata dict from _get_decision_from_llm_judge
• Populate: should_trigger_tool, is_compliant, abuse_areas, severities
• Keep decision as is_compliant for backward compatibility

evaluate_completion_compliance (lines 207-244)

• Unpack the new metadata dict
• Populate: should_trigger_tool, is_compliant, abuse_areas, severities
• Keep decision as is_compliant

evaluate_is_refusal (lines 246-262)

• Unpack the new metadata dict
• Populate: is_refusal, and also should_trigger_tool/abuse_areas/severities if computed
• Keep decision as is_refusal

evaluate_is_reasoning_consistent (lines 264-296)

• This method is separate (reasoning consistency classification)
• Only needs to populate decision (no additional semantic fields applicable)

4. Update Oracle Functions to Use New Fields

File: [bee/comb/comb/envs/safety/agent_env/rollout_validation/oracles.py](bee/comb/comb/envs/safety/agent_env/rollout_validation/oracles.py)

Update functions that retrieve and store validator outputs:

tool_call_decision_oracle (lines 369-493)

• When caching validator results, store both should_trigger_tool AND is_compliant
• Check if both values are already cached before making new validator calls
• Store in state.validator_outputs as separate keys or nested structure

tool_result_oracle (lines 496-559)

• Check if is_compliant is already available from previous calls
• Use cached value if available
• When calling validator, extract and cache both should_trigger_tool and is_compliant

should_be_refusal_oracle (lines 281-332)

• Leverage cached compliance information from previous validator calls
• Store newly computed values for reuse

completion_safety_compliance (lines 192-278)

• Extract and cache all available fields from ValidationDecision
• Check cache before making new validator calls

5. Update Tool Validation Functions

File: [bee/comb/comb/envs/safety/agent_env/rollout_validation/tool_validation.py](bee/comb/comb/envs/safety/agent_env/rollout_validation/tool_validation.py)

postprocess_first_tool_turn (lines 298-427)

• Access new semantic fields from ValidationDecision
• Cache all available information in state

postprocess_tool_results_on_the_fly (lines 221-295)

• Use cached validator outputs when available
• Store newly computed values

postprocess_all_tool_calls (lines 15-218)

• Leverage cached information to minimize validator calls
• Store comprehensive validation results

6. Update Conversation Validation

File: [bee/comb/comb/envs/safety/agent_env/rollout_validation/conversation_validation.py](bee/comb/comb/envs/safety/agent_env/rollout_validation/conversation_validation.py)

postprocess_overall_conversation (lines 13-124)

• Use the new semantic fields from ValidationDecision
• No functional changes needed, but can potentially optimize by checking cached values

Key Implementation Details

1. Backward Compatibility: Keep decision field unchanged so existing code continues to work
2. Caching Strategy: Store all computed values in state.validator_outputs with clear keys
3. Metadata Extraction: In _get_decision_from_llm_judge, compute all derivable values before returning
4. Compliance Computation: When we have predict_safety_related, predict_abuse_areas, and predict_severities, we can compute is_compliant using the same logic currently in the compliance query path

Testing Considerations

• The main() function in safety_validator.py (lines 495-661) should be updated to verify all fields are populated
• Verify that cached values are correctly reused across multiple oracle calls
• Ensure state.validator_outputs structure properly stores and retrieves all semantic fields