refactor(ai_client): remove unused history management and bleed stats helpers
This commit is contained in:
+4
-165
@@ -12,7 +12,6 @@ For Gemini: injects the initial context directly into system_instruction
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during chat creation to avoid massive history bloat.
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"""
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# ai_client.py
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# ai_client.py
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import tomllib
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import asyncio
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import json
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@@ -53,7 +52,7 @@ events: EventEmitter = EventEmitter()
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def set_model_params(temp: float, max_tok: int, trunc_limit: int = 8000, top_p: float = 1.0) -> None:
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"""
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Sets global generation parameters like temperature and max tokens.
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[C: src/app_controller.py:AppController._handle_request_event, src/app_controller.py:AppController.generate, tests/test_history_management.py:test_get_history_bleed_stats_basic]
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[C: src/app_controller.py:AppController._handle_request_event, src/app_controller.py:AppController.generate]
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"""
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global _temperature, _max_tokens, _history_trunc_limit, _top_p
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_temperature = temp
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@@ -61,13 +60,6 @@ def set_model_params(temp: float, max_tok: int, trunc_limit: int = 8000, top_p:
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_history_trunc_limit = trunc_limit
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_top_p = top_p
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def get_history_trunc_limit() -> int:
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return _history_trunc_limit
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def set_history_trunc_limit(val: int) -> None:
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global _history_trunc_limit
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_history_trunc_limit = val
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_gemini_client: Optional[genai.Client] = None
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_gemini_chat: Any = None
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_gemini_cache: Any = None
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@@ -438,7 +430,7 @@ def _classify_minimax_error(exc: Exception) -> ProviderError:
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def set_provider(provider: str, model: str) -> None:
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"""
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Updates the active LLM provider and model name.
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[C: src/app_controller.py:AppController._handle_reset_session, src/app_controller.py:AppController._init_ai_and_hooks, src/app_controller.py:AppController.current_model, src/app_controller.py:AppController.current_provider, src/app_controller.py:AppController.do_fetch, src/multi_agent_conductor.py:run_worker_lifecycle, src/orchestrator_pm.py:generate_tracks, tests/conftest.py:reset_ai_client, tests/test_ai_cache_tracking.py:test_gemini_cache_tracking, tests/test_ai_client_cli.py:test_ai_client_send_gemini_cli, tests/test_api_events.py:test_send_emits_events_proper, tests/test_api_events.py:test_send_emits_tool_events, tests/test_deepseek_provider.py:test_deepseek_completion_logic, tests/test_deepseek_provider.py:test_deepseek_model_selection, tests/test_deepseek_provider.py:test_deepseek_payload_verification, tests/test_deepseek_provider.py:test_deepseek_reasoner_payload_verification, tests/test_deepseek_provider.py:test_deepseek_reasoning_logic, tests/test_deepseek_provider.py:test_deepseek_streaming, tests/test_deepseek_provider.py:test_deepseek_tool_calling, tests/test_gemini_cli_edge_cases.py:test_gemini_cli_loop_termination, tests/test_gemini_cli_integration.py:test_gemini_cli_full_integration, tests/test_gemini_cli_integration.py:test_gemini_cli_rejection_and_history, tests/test_gemini_cli_parity_regression.py:test_get_history_bleed_stats, tests/test_gemini_cli_parity_regression.py:test_send_invokes_adapter_send, tests/test_gui2_mcp.py:test_mcp_tool_call_is_dispatched, tests/test_history_management.py:test_get_history_bleed_stats_basic, tests/test_minimax_provider.py:test_minimax_default_model, tests/test_minimax_provider.py:test_minimax_history_bleed_stats, tests/test_minimax_provider.py:test_minimax_model_selection, tests/test_mma_agent_focus_phase1.py:test_append_comms_has_source_tier_key, tests/test_rag_integration.py:test_rag_integration, tests/test_tier4_interceptor.py:test_ai_client_passes_qa_callback, tests/test_tier4_interceptor.py:test_gemini_provider_passes_qa_callback_to_run_script, tests/test_token_usage.py:test_token_usage_tracking, tests/test_token_viz.py:test_get_history_bleed_stats_returns_all_keys_unknown_provider]
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[C: src/app_controller.py:AppController._handle_reset_session, src/app_controller.py:AppController._init_ai_and_hooks, src/app_controller.py:AppController.current_model, src/app_controller.py:AppController.current_provider, src/app_controller.py:AppController.do_fetch, src/multi_agent_conductor.py:run_worker_lifecycle, src/orchestrator_pm.py:generate_tracks, tests/conftest.py:reset_ai_client, tests/test_ai_cache_tracking.py:test_gemini_cache_tracking, tests/test_ai_client_cli.py:test_ai_client_send_gemini_cli, tests/test_api_events.py:test_send_emits_events_proper, tests/test_api_events.py:test_send_emits_tool_events, tests/test_deepseek_provider.py:test_deepseek_completion_logic, tests/test_deepseek_provider.py:test_deepseek_model_selection, tests/test_deepseek_provider.py:test_deepseek_payload_verification, tests/test_deepseek_provider.py:test_deepseek_reasoner_payload_verification, tests/test_deepseek_provider.py:test_deepseek_reasoning_logic, tests/test_deepseek_provider.py:test_deepseek_streaming, tests/test_deepseek_provider.py:test_deepseek_tool_calling, tests/test_gemini_cli_edge_cases.py:test_gemini_cli_loop_termination, tests/test_gemini_cli_integration.py:test_gemini_cli_full_integration, tests/test_gemini_cli_integration.py:test_gemini_cli_rejection_and_history, tests/test_gemini_cli_parity_regression.py:test_send_invokes_adapter_send, tests/test_gui2_mcp.py:test_mcp_tool_call_is_dispatched, tests/test_minimax_provider.py:test_minimax_default_model, tests/test_minimax_provider.py:test_minimax_model_selection, tests/test_mma_agent_focus_phase1.py:test_append_comms_has_source_tier_key, tests/test_rag_integration.py:test_rag_integration, tests/test_tier4_interceptor.py:test_ai_client_passes_qa_callback, tests/test_tier4_interceptor.py:test_gemini_provider_passes_qa_callback_to_run_script, tests/test_token_usage.py:test_token_usage_tracking]
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"""
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global _provider, _model
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_provider = provider
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@@ -2444,165 +2436,12 @@ def _add_bleed_derived(d: dict[str, Any], sys_tok: int = 0, tool_tok: int = 0) -
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if os.environ.get("SLOP_TOOL_PRESET"):
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try:
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set_tool_preset(os.environ["SLOP_TOOL_PRESET"])
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except Exception as _e:
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except Exception:
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pass
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def get_history_bleed_stats(md_content: Optional[str] = None) -> dict[str, Any]:
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"""
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[C: tests/test_gemini_cli_parity_regression.py:test_get_history_bleed_stats, tests/test_history_management.py:test_get_history_bleed_stats_basic, tests/test_minimax_provider.py:test_minimax_history_bleed_stats, tests/test_token_viz.py:test_get_history_bleed_stats_returns_all_keys_unknown_provider]
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"""
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if _provider == "anthropic":
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with _anthropic_history_lock:
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history_snapshot = list(_anthropic_history)
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sys_tok = max(1, int(len(md_content) / _CHARS_PER_TOKEN)) if md_content else 0
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current_tokens = _estimate_prompt_tokens([], history_snapshot)
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if md_content:
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current_tokens += max(1, int(len(md_content) / _CHARS_PER_TOKEN))
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limit_tokens = _ANTHROPIC_MAX_PROMPT_TOKENS
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percentage = (current_tokens / limit_tokens) * 100 if limit_tokens > 0 else 0
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return _add_bleed_derived({
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"provider": "anthropic",
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"limit": limit_tokens,
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"current": current_tokens,
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"percentage": percentage,
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}, sys_tok=sys_tok, tool_tok=2500)
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elif _provider == "gemini":
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effective_limit = _history_trunc_limit if _history_trunc_limit > 0 else _GEMINI_MAX_INPUT_TOKENS
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if _gemini_chat:
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try:
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_ensure_gemini_client()
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if _gemini_client:
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raw_history = list(_get_gemini_history_list(_gemini_chat))
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history: list[types.Content] = []
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for c in raw_history:
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role = "model" if c.role in ["assistant", "model"] else "user"
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history.append(types.Content(role=role, parts=c.parts))
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if md_content:
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history.insert(0, types.Content(role="user", parts=[types.Part(text=md_content)]))
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if not history:
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return _add_bleed_derived({
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"provider": "gemini",
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"limit": effective_limit,
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"current": 0,
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"percentage": 0,
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})
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resp = _gemini_client.models.count_tokens(
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model=_model,
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contents=history
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)
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current_tokens = cast(int, resp.total_tokens)
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percentage = (current_tokens / effective_limit) * 100 if effective_limit > 0 else 0
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return _add_bleed_derived({
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"provider": "gemini",
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"limit": effective_limit,
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"current": current_tokens,
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"percentage": percentage,
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}, sys_tok=0, tool_tok=0)
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except Exception:
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pass
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elif md_content:
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try:
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_ensure_gemini_client()
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if _gemini_client:
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resp = _gemini_client.models.count_tokens(
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model=_model,
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contents=[types.Content(role="user", parts=[types.Part(text=md_content)])]
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)
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current_tokens = cast(int, resp.total_tokens)
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percentage = (current_tokens / effective_limit) * 100 if effective_limit > 0 else 0
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return _add_bleed_derived({
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"provider": "gemini",
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"limit": effective_limit,
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"current": current_tokens,
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"percentage": percentage,
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})
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except Exception:
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pass
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return _add_bleed_derived({
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"provider": "gemini",
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"limit": effective_limit,
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"current": 0,
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"percentage": 0,
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})
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elif _provider == "gemini_cli":
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effective_limit = _history_trunc_limit if _history_trunc_limit > 0 else _GEMINI_MAX_INPUT_TOKENS
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limit_tokens = effective_limit
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current_tokens = 0
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if _gemini_cli_adapter and _gemini_cli_adapter.last_usage:
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u = _gemini_cli_adapter.last_usage
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current_tokens = cast(int, u.get("input_tokens") or u.get("input", 0))
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percentage = (current_tokens / limit_tokens) * 100 if limit_tokens > 0 else 0
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return _add_bleed_derived({
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"provider": "gemini_cli",
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"limit": limit_tokens,
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"current": current_tokens,
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"percentage": percentage,
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})
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elif _provider == "deepseek":
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limit_tokens = 64000
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current_tokens = 0
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with _deepseek_history_lock:
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for msg in _deepseek_history:
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content = msg.get("content", "")
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if isinstance(content, str):
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current_tokens += len(content)
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elif isinstance(content, list):
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for block in content:
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if isinstance(block, dict):
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text = block.get("text", "")
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if isinstance(text, str):
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current_tokens += len(text)
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inp = block.get("input")
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if isinstance(inp, dict):
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import json as _json
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current_tokens += len(_json.dumps(inp, ensure_ascii=False))
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if md_content: current_tokens += len(md_content)
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current_tokens = max(1, int(current_tokens / _CHARS_PER_TOKEN))
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percentage = (current_tokens / limit_tokens) * 100 if limit_tokens > 0 else 0
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return _add_bleed_derived({
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"provider": "deepseek",
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"limit": limit_tokens,
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"current": current_tokens,
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"percentage": percentage,
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})
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elif _provider == "minimax":
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limit_tokens = 204800
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current_tokens = 0
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with _minimax_history_lock:
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for msg in _minimax_history:
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content = msg.get("content", "")
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if isinstance(content, str):
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current_tokens += len(content)
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elif isinstance(content, list):
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for block in content:
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if isinstance(block, dict):
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text = block.get("text", "")
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if isinstance(text, str):
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current_tokens += len(text)
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inp = block.get("input")
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if isinstance(inp, dict):
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import json as _json
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current_tokens += len(_json.dumps(inp, ensure_ascii=False))
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if md_content: current_tokens += len(md_content)
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current_tokens = max(1, int(current_tokens / _CHARS_PER_TOKEN))
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percentage = (current_tokens / limit_tokens) * 100 if limit_tokens > 0 else 0
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return _add_bleed_derived({
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"provider": "minimax",
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"limit": limit_tokens,
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"current": current_tokens,
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"percentage": percentage,
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})
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return _add_bleed_derived({
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"provider": _provider,
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"limit": 0,
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"current": 0,
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"percentage": 0,
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})
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def run_subagent_summarization(file_path: str, content: str, is_code: bool, outline: str) -> str:
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"""
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Performs a stateless summarization request using a sub-agent prompt.
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[C: src/summarize.py:summarise_file, tests/test_subagent_summarization.py:test_run_subagent_summarization_anthropic, tests/test_subagent_summarization.py:test_run_subagent_summarization_gemini]
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Performs a stateless summarization request using a sub-agent prompt. [C: src/summarize.py:summarise_file, tests/test_subagent_summarization.py:test_run_subagent_summarization_anthropic, tests/test_subagent_summarization.py:test_run_subagent_summarization_gemini]
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"""
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prompt_tmpl = mma_prompts.TIER4_SUMMARIZE_CODE_PROMPT if is_code else mma_prompts.TIER4_SUMMARIZE_TEXT_PROMPT
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prompt = prompt_tmpl.format(file_path=file_path, outline=outline, content=content)
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