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"""Prompts for Report Agent."""
from typing import TYPE_CHECKING, Any
from src.config.domain import ResearchDomain, get_domain_config
from src.utils.text_utils import select_diverse_evidence, truncate_at_sentence
if TYPE_CHECKING:
from src.services.embedding_protocol import EmbeddingServiceProtocol
from src.utils.models import Evidence, HypothesisAssessment
def get_system_prompt(domain: ResearchDomain | str | None = None) -> str:
"""Get the system prompt for the report agent."""
config = get_domain_config(domain)
return f"""You are a scientific writer specializing in {config.name}.
Your role is to synthesize evidence into clear recommendations for interventions
with proper safety considerations.
When asked to synthesize:
Generate a structured report with these sections:
## Executive Summary
Brief overview of findings and recommendation
## Methodology
Databases searched, queries used, evidence reviewed
## Key Findings
### Mechanism of Action
- Molecular targets
- Biological pathways
- Proposed mechanism
### Clinical Evidence
- Preclinical studies
- Clinical trials
- Safety profile
## Candidates
List specific candidates with potential
## Limitations
Gaps in evidence, conflicting data, caveats
## Conclusion
Final recommendation with confidence level
## References
Use the 'get_bibliography' tool to fetch the complete list of citations.
Format them as a numbered list.
Be comprehensive but concise. Cite evidence for all claims."""
# Keep SYSTEM_PROMPT for backwards compatibility
SYSTEM_PROMPT = get_system_prompt()
async def format_report_prompt(
query: str,
evidence: list["Evidence"],
hypotheses: list["HypothesisAssessment"] | list[Any],
assessment: Any,
metadata: dict[str, Any],
embeddings: "EmbeddingServiceProtocol | None" = None,
) -> str:
"""Format prompt for report generation.
Args:
query: Research query
evidence: Collected evidence
hypotheses: Generated hypotheses
assessment: Judge assessment details
metadata: Search metadata
embeddings: Optional embedding service for diverse selection
"""
# Select diverse evidence (max 15 for report)
selected = await select_diverse_evidence(evidence, n=15, query=query, embeddings=embeddings)
evidence_text = "\n".join(
[
f"- **{e.citation.title}** ({e.citation.source}): "
f"{truncate_at_sentence(e.content, 400)}"
for e in selected
]
)
# Format hypotheses if available
hypotheses_text = "No specific hypotheses generated."
if hypotheses:
# Handle both Pydantic models and dicts/objects
h_list = []
for h in hypotheses:
if hasattr(h, "hypotheses"):
for item in h.hypotheses:
h_list.append(f"- {item.drug} -> {item.target} -> {item.effect}")
elif isinstance(h, dict):
h_list.append(str(h))
else:
h_list.append(str(h))
if h_list:
hypotheses_text = "\n".join(h_list)
return f"""Generate a comprehensive research report for: "{query}""
## Context
- **Sources Searched**: {", ".join(metadata.get("sources", []))}
- **Iterations**: {metadata.get("iterations", 0)}
## Evidence ({len(selected)} key papers)
{evidence_text}
## Generated Hypotheses
{hypotheses_text}
## Task
Synthesize this information into a structured report following the Executive Summary format.
Focus on clinical applicability and safety.
Use specific citations from the evidence list."""
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