Improved prompts to encourage better investigative flow
Improved abstraction
This commit is contained in:
Fahad
104
tools/debug.py
104
tools/debug.py
@@ -21,52 +21,48 @@ logger = logging.getLogger(__name__)
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# Field descriptions for the investigation steps
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DEBUG_INVESTIGATION_FIELD_DESCRIPTIONS = {
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"step": (
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"Your current investigation step. For the first step, describe the issue/error to investigate. "
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"For subsequent steps, describe what you're investigating, what code you're examining, "
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"what patterns you're looking for, or what hypothesis you're testing."
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"Describe what you're currently investigating. In step 1, clearly state the issue to investigate and begin "
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"thinking deeply about where the problem might originate. In all subsequent steps, continue uncovering relevant "
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"code, examining patterns, and formulating hypotheses with deliberate attention to detail."
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),
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"step_number": "Current step number in the investigation sequence (starts at 1)",
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"total_steps": "Current estimate of total investigation steps needed (can be adjusted as investigation progresses)",
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"next_step_required": "Whether another investigation step is required",
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"step_number": "Current step number in the investigation sequence (starts at 1).",
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"total_steps": "Estimate of total investigation steps expected (adjustable as the process evolves).",
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"next_step_required": "Whether another investigation step is needed after this one.",
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"findings": (
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"Current findings from this investigation step. Include code patterns discovered, "
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"potential causes identified, hypotheses formed, or evidence gathered."
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"Summarize discoveries in this step. Think critically and include relevant code behavior, suspicious patterns, "
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"evidence collected, and any partial conclusions or leads."
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),
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"files_checked": (
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"List of files you've examined so far in the investigation (cumulative list). "
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"Include all files you've looked at, even if they turned out to be irrelevant."
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"List all files examined during the investigation so far. Include even files ruled out, as this tracks your exploration path."
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),
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"relevant_files": (
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"List of files that are definitely related to the issue (subset of files_checked). "
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"Only include files that contain code directly related to the problem."
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"Subset of files_checked that contain code directly relevant to the issue. Only list those that are directly tied to the root cause or its effects."
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),
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"relevant_methods": (
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"List of specific methods/functions that are involved in the issue. "
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"Format: 'ClassName.methodName' or 'functionName'"
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"List specific methods/functions clearly tied to the issue. Use 'ClassName.methodName' or 'functionName' format."
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),
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"hypothesis": (
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"Your current working hypothesis about the root cause. This can be updated/revised "
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"as the investigation progresses."
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"Formulate your current best guess about the underlying cause. This is a working theory and may evolve based on further evidence."
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),
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"confidence": "Your confidence level in the current hypothesis: 'low', 'medium', or 'high'",
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"backtrack_from_step": "If you need to revise a previous finding, which step number to backtrack from",
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"continuation_id": "Thread continuation ID for multi-turn investigation sessions",
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"confidence": "How confident you are in the current hypothesis: 'low', 'medium', or 'high'.",
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"backtrack_from_step": "If a previous step needs revision, specify the step number to backtrack from.",
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"continuation_id": "Continuation token used for linking multi-step investigations.",
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"images": (
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"Optional images showing error screens, UI issues, logs displays, or visual debugging information "
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"that help understand the issue (must be FULL absolute paths - DO NOT SHORTEN)"
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"Optional. Include full absolute paths to visual debugging images (UI issues, logs, error screens) that help clarify the issue."
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),
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}
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# Field descriptions for the final debug request
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DEBUG_FIELD_DESCRIPTIONS = {
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"initial_issue": "The original issue description that started the investigation",
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"investigation_summary": "Complete summary of the systematic investigation performed",
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"findings": "Consolidated findings from all investigation steps",
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"files": "Essential files identified during investigation (must be FULL absolute paths - DO NOT SHORTEN)",
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"error_context": "Stack trace, logs, or error context discovered during investigation",
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"relevant_methods": "List of methods/functions identified as involved in the issue",
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"hypothesis": "Final hypothesis about the root cause after investigation",
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"images": "Optional images showing error screens, UI issues, or visual debugging information",
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"initial_issue": "Describe the original problem that triggered the investigation.",
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"investigation_summary": (
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"Full overview of the systematic investigation process. Reflect deep thinking and each step's contribution to narrowing down the issue."
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),
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"findings": "Final list of critical insights and discoveries across all steps.",
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"files": "Essential files referenced during investigation (must be full absolute paths).",
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"error_context": "Logs, tracebacks, or execution details that support the root cause hypothesis.",
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"relevant_methods": "List of all methods/functions identified as directly involved.",
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"hypothesis": "Final, most likely explanation of the root cause based on evidence.",
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"images": "Optional screenshots or visual materials that helped diagnose the issue.",
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}
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@@ -268,7 +264,9 @@ class DebugIssueTool(BaseTool):
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# Create thread for first step
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if not continuation_id and request.step_number == 1:
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continuation_id = create_thread("debug", arguments)
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# Clean arguments to remove non-serializable fields
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clean_args = {k: v for k, v in arguments.items() if k not in ["_model_context", "_resolved_model_name"]}
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continuation_id = create_thread("debug", clean_args)
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# Store initial issue description
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self.initial_issue = request.step
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@@ -356,8 +354,9 @@ class DebugIssueTool(BaseTool):
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final_hypothesis=request.hypothesis,
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error_context=self._extract_error_context(),
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images=list(set(self.consolidated_findings["images"])), # Unique images
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model_info=arguments.get("_model_context"),
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model_override=arguments.get("model"), # Pass model selection from final step
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model_info=arguments.get("_model_context"), # Use pre-resolved model context from server.py
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arguments=arguments, # Pass arguments for model resolution
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request=request, # Pass request for model resolution
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)
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# Combine investigation and expert analysis
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@@ -478,9 +477,36 @@ class DebugIssueTool(BaseTool):
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error_context: Optional[str],
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images: list[str],
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model_info: Optional[Any] = None,
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model_override: Optional[str] = None,
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arguments: Optional[dict] = None,
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request: Optional[Any] = None,
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) -> dict:
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"""Call AI model for expert analysis of the investigation"""
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# Set up model context when we actually need it for expert analysis
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# Use the same model resolution logic as the base class
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if model_info:
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# Use pre-resolved model context from server.py (normal case)
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self._model_context = model_info
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model_name = model_info.model_name
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else:
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# Use centralized model resolution from base class
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if arguments and request:
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try:
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model_name, model_context = self._resolve_model_context(arguments, request)
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self._model_context = model_context
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except ValueError as e:
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# Model resolution failed, return error
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return {"error": f"Model resolution failed: {str(e)}", "status": "model_resolution_error"}
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else:
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# Last resort fallback if no arguments/request provided
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from config import DEFAULT_MODEL
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from utils.model_context import ModelContext
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model_name = DEFAULT_MODEL
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self._model_context = ModelContext(model_name)
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# Store model name for use by other methods
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self._current_model_name = model_name
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provider = self.get_model_provider(model_name)
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# Prepare the debug prompt with all investigation context
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prompt_parts = [
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f"=== ISSUE DESCRIPTION ===\n{initial_issue}\n=== END DESCRIPTION ===",
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@@ -517,16 +543,6 @@ class DebugIssueTool(BaseTool):
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full_prompt = "\n".join(prompt_parts)
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# Get appropriate model and provider
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from config import DEFAULT_MODEL
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from providers.registry import ModelProviderRegistry
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model_name = model_override or DEFAULT_MODEL # Use override if provided
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provider = ModelProviderRegistry.get_provider_for_model(model_name)
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if not provider:
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return {"error": f"No provider available for model {model_name}", "status": "provider_error"}
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# Generate AI response
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try:
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full_analysis_prompt = f"{self.get_system_prompt()}\n\n{full_prompt}\n\nPlease debug this issue following the structured format in the system prompt."
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