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Simplify research verbose logging

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Yiorgis Gozadinos 2025-09-19 09:44:46 +03:00
parent 5f102c8475
commit ccaae7cca1
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3 changed files with 74 additions and 90 deletions
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7
docs/agents.md
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@ -70,14 +70,17 @@ from haiku.rag.client import HaikuRAG
from haiku.rag.research import ResearchOrchestrator
client = HaikuRAG(path_to_db)
orchestrator = ResearchOrchestrator(provider="openai", model="gpt-4o-mini")
orchestrator = ResearchOrchestrator(
provider="ollama",
model="gpt-oss",
verbose=True
)
report = await orchestrator.conduct_research(
question="What are the main drivers and recent trends of global temperature anomalies since 1990?",
client=client,
max_iterations=2,
confidence_threshold=0.8,
verbose=False,
)
print(report.title)

4
src/haiku/rag/app.py
View file

@ -86,7 +86,7 @@ class HaikuRAGApp:
async with HaikuRAG(db_path=self.db_path) as client:
try:
# Create orchestrator with default config or fallback to QA
orchestrator = ResearchOrchestrator()
orchestrator = ResearchOrchestrator(verbose=verbose)
if verbose:
self.console.print(
@ -100,8 +100,6 @@ class HaikuRAGApp:
question=question,
client=client,
max_iterations=max_iterations,
verbose=verbose,
console=self.console if verbose else None,
)
# Display the report

153
src/haiku/rag/research/orchestrator.py
View file

@ -31,7 +31,10 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
"""Orchestrator agent that coordinates the research workflow."""
def __init__(
self, provider: str | None = Config.RESEARCH_PROVIDER, model: str | None = None
self,
provider: str | None = Config.RESEARCH_PROVIDER,
model: str | None = None,
verbose: bool = False,
):
# Use provided values or fall back to config defaults
provider = provider or Config.RESEARCH_PROVIDER or Config.QA_PROVIDER
@ -49,6 +52,10 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
provider, model
)
self.synthesis_agent: SynthesisAgent = SynthesisAgent(provider, model)
if verbose:
self._console = Console()
self.verbose = verbose
def get_system_prompt(self) -> str:
return ORCHESTRATOR_PROMPT
@ -73,14 +80,30 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
}
return format_as_xml(context_data, root_tag="research_context")
def _should_stop_research(
self,
evaluation_result: AgentRunResult[EvaluationResult],
confidence_threshold: float,
) -> bool:
"""Determine if research should stop based on evaluation."""
result = evaluation_result.output
return result.is_sufficient and result.confidence_score >= confidence_threshold
def _log(self, line="", rule=False):
if not self._console:
return
if rule:
self._console.rule(line)
else:
self._console.print(line)
async def conduct_research(
self,
question: str,
client: Any,
max_iterations: int = 3,
confidence_threshold: float = 0.8,
verbose: bool = False,
console: Console | None = None,
) -> ResearchReport:
"""Conduct comprehensive research on a question.
@ -100,16 +123,10 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
context = ResearchContext(original_question=question)
deps = ResearchDependencies(client=client, context=context)
# Use provided console or create a new one
console = console or Console() if verbose else None
# Create initial research plan
# Run a simple presearch survey to summarize KB context
if console:
console.print("\n[bold cyan]📋 Creating research plan...[/bold cyan]")
console.print(
"\n[bold cyan]🔎 Presearch: summarizing KB context...[/bold cyan]"
)
self._log("\n[bold cyan]📋 Creating research plan...[/bold cyan]")
self._log("\n[bold cyan]🔎 Presearch: summarizing KB context...[/bold cyan]")
presearch_result = await self.presearch_agent.run(question, deps=deps)
@ -126,42 +143,36 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
)
context.sub_questions = plan_result.output.sub_questions
if console:
console.print("\n[bold green]✅ Research Plan Created:[/bold green]")
console.print(
f" [bold]Main Question:[/bold] {plan_result.output.main_question}"
)
console.print(" [bold]Sub-questions:[/bold]")
for i, sq in enumerate(plan_result.output.sub_questions, 1):
console.print(f" {i}. {sq}")
console.print()
self._log("\n[bold green]✅ Research Plan Created:[/bold green]")
self._log(f" [bold]Main Question:[/bold] {plan_result.output.main_question}")
self._log(" [bold]Sub-questions:[/bold]")
for i, sq in enumerate(plan_result.output.sub_questions, 1):
self._log(f" {i}. {sq}")
# Execute research iterations
for iteration in range(max_iterations):
if console:
console.rule(
f"[bold yellow]🔄 Iteration {iteration + 1}/{max_iterations}[/bold yellow]"
)
self._log(
f"[bold yellow]🔄 Iteration {iteration + 1}/{max_iterations}[/bold yellow]",
rule=True,
)
# Check if we have questions to search
if not context.sub_questions:
# No more questions to explore
if console:
console.print(
"[yellow]No more questions to explore. Concluding research.[/yellow]"
)
self._log(
"[yellow]No more questions to explore. Concluding research.[/yellow]"
)
break
# Use current sub-questions for this iteration
questions_to_search = context.sub_questions
# Search phase - answer all questions in this iteration
if console:
console.print(
f"\n[bold cyan]🔍 Searching & Answering {len(questions_to_search)} questions:[/bold cyan]"
)
for i, q in enumerate(questions_to_search, 1):
console.print(f" {i}. {q}")
self._log(
f"\n[bold cyan]🔍 Searching & Answering {len(questions_to_search)} questions:[/bold cyan]"
)
for i, q in enumerate(questions_to_search, 1):
self._log(f" {i}. {q}")
# Run searches for all questions and remove answered ones
answered_questions = []
@ -169,27 +180,22 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
try:
await self.search_agent.run(search_question, deps=deps)
except Exception as e: # pragma: no cover - defensive
if console:
console.print(
f"\n [red]×[/red] Omitting failed question: {search_question} ({e})"
)
self._log(
f"\n [red]×[/red] Omitting failed question: {search_question} ({e})"
)
finally:
answered_questions.append(search_question)
if console and context.qa_responses:
if self._console and context.qa_responses:
# Show the last QA response (which should be for this question)
latest_qa = context.qa_responses[-1]
answer_preview = (
latest_qa.answer[:150] + "..."
latest_qa.answer[:150] + "…"
if len(latest_qa.answer) > 150
else latest_qa.answer
)
console.print(
f"\n [green]✓[/green] {search_question[:50]}..."
if len(search_question) > 50
else f"\n [green]✓[/green] {search_question}"
)
console.print(f" {answer_preview}")
self._log(f"\n [green]✓[/green] {search_question}")
self._log(f" {answer_preview}")
# Remove answered questions from the list
for question in answered_questions:
@ -197,10 +203,9 @@ class ResearchOrchestrator(BaseResearchAgent[ResearchPlan]):
context.sub_questions.remove(question)
# Analysis and Evaluation phase
if console:
console.print(
"\n[bold cyan]📊 Analyzing and evaluating research progress...[/bold cyan]"
)
self._log(
"\n[bold cyan]📊 Analyzing and evaluating research progress...[/bold cyan]"
)
# Format context for the evaluation agent
context_xml = self._format_context_for_prompt(context)
@ -215,19 +220,14 @@ Evaluate the research progress for the original question and identify any remain
deps=deps,
)
if console and evaluation_result.output:
output = evaluation_result.output
if output.key_insights:
console.print(" [bold]Key insights:[/bold]")
for insight in output.key_insights:
console.print(f" • {insight}")
console.print(
f" Confidence: [yellow]{output.confidence_score:.1%}[/yellow]"
)
status = (
"[green]Yes[/green]" if output.is_sufficient else "[red]No[/red]"
)
console.print(f" Sufficient: {status}")
output = evaluation_result.output
if output.key_insights:
self._log(" [bold]Key insights:[/bold]")
for insight in output.key_insights:
self._log(f" • {insight}")
self._log(f" Confidence: [yellow]{output.confidence_score:.1%}[/yellow]")
status = "[green]Yes[/green]" if output.is_sufficient else "[red]No[/red]"
self._log(f" Sufficient: {status}")
# Store insights
for insight in evaluation_result.output.key_insights:
@ -240,17 +240,13 @@ Evaluate the research progress for the original question and identify any remain
# Check if research is sufficient
if self._should_stop_research(evaluation_result, confidence_threshold):
if console:
console.print(
f"\n[bold green]✅ Stopping research:[/bold green] {evaluation_result.output.reasoning}"
)
self._log(
f"\n[bold green]✅ Stopping research:[/bold green] {evaluation_result.output.reasoning}"
)
break
# Generate final report
if console:
console.print(
"\n[bold cyan]📝 Generating final research report...[/bold cyan]"
)
self._log("\n[bold cyan]📝 Generating final research report...[/bold cyan]")
# Format context for the synthesis agent
final_context_xml = self._format_context_for_prompt(context)
@ -264,19 +260,6 @@ Create a detailed report that synthesizes all findings into a coherent response.
synthesis_prompt, deps=deps
)
if console:
console.print("[bold green]✅ Research complete![/bold green]")
self._log("[bold green]✅ Research complete![/bold green]")
return report_result.output
def _should_stop_research(
self,
evaluation_result: AgentRunResult[EvaluationResult],
confidence_threshold: float,
) -> bool:
"""Determine if research should stop based on evaluation."""
result = evaluation_result.output
# Stop if the agent indicates sufficient information AND confidence exceeds threshold
return result.is_sufficient and result.confidence_score >= confidence_threshold