Files
MASForensic/agents/hypothesis.py
BattleTag 097d2ce472 Initial commit
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-09 17:36:26 +08:00

131 lines
5.0 KiB
Python

"""Hypothesis Agent — analyzes phenomena and generates investigative hypotheses."""
from __future__ import annotations
import json
import logging
from base_agent import BaseAgent
from evidence_graph import EvidenceGraph, HYPOTHESIS_EDGE_WEIGHTS
from llm_client import LLMClient
logger = logging.getLogger(__name__)
class HypothesisAgent(BaseAgent):
name = "hypothesis"
role = (
"Hypothesis analyst. You review all phenomena discovered so far "
"and formulate investigative hypotheses about what happened on this system. "
"Your ultimate goal: build the most complete picture of events that occurred. "
"For each hypothesis, identify which existing phenomena support or contradict it."
)
def __init__(self, llm: LLMClient, graph: EvidenceGraph) -> None:
super().__init__(llm, graph)
self._register_hypothesis_tools()
def _register_hypothesis_tools(self) -> None:
"""Register hypothesis-specific tools."""
valid_edge_types = list(HYPOTHESIS_EDGE_WEIGHTS.keys())
self.register_tool(
name="add_hypothesis",
description=(
"Create a new investigative hypothesis about what happened on the system. "
"Each hypothesis should be a specific, testable claim."
),
input_schema={
"type": "object",
"properties": {
"title": {
"type": "string",
"description": "Short title for the hypothesis.",
},
"description": {
"type": "string",
"description": "Detailed description of what this hypothesis claims.",
},
},
"required": ["title", "description"],
},
executor=self._add_hypothesis,
)
self.register_tool(
name="link_phenomenon_to_hypothesis",
description=(
"Link an existing phenomenon to a hypothesis with a relationship type. "
f"Valid relationship types: {', '.join(valid_edge_types)}. "
"direct_evidence = the phenomenon IS the hypothesis. "
"supports = consistent with the hypothesis. "
"prerequisite_met = a necessary condition is satisfied. "
"consequence_observed = an expected result of the hypothesis is found. "
"contradicts = directly contradicts the hypothesis. "
"weakens = makes the hypothesis less likely."
),
input_schema={
"type": "object",
"properties": {
"phenomenon_id": {
"type": "string",
"description": "ID of the phenomenon (e.g. 'ph-a1b2c3d4').",
},
"hypothesis_id": {
"type": "string",
"description": "ID of the hypothesis (e.g. 'hyp-e5f6g7h8').",
},
"edge_type": {
"type": "string",
"enum": valid_edge_types,
"description": "The edge_type of the relationship.",
},
"reason": {
"type": "string",
"description": "The reason this relationship holds (1-2 sentences).",
},
},
"required": ["phenomenon_id", "hypothesis_id", "edge_type", "reason"],
},
executor=self._link_phenomenon_to_hypothesis,
)
async def _add_hypothesis(self, title: str, description: str) -> str:
hid = await self.graph.add_hypothesis(
title=title,
description=description,
created_by=self.name,
)
return f"Hypothesis created: {hid}{title} (confidence: 0.50)"
async def _link_phenomenon_to_hypothesis(
self,
phenomenon_id: str,
hypothesis_id: str,
edge_type: str = "",
reason: str = "",
# Common LLM misnaming — accept as fallbacks
relationship: str = "",
note: str = "",
) -> str:
edge_type = edge_type or relationship
reason = reason or note
if not edge_type:
return "Error: edge_type is required."
try:
new_conf = await self.graph.update_hypothesis_confidence(
hyp_id=hypothesis_id,
phenomenon_id=phenomenon_id,
edge_type=edge_type,
reason=reason,
)
weight = HYPOTHESIS_EDGE_WEIGHTS[edge_type]
direction = "+" if weight > 0 else ""
return (
f"Linked: {phenomenon_id} —[{edge_type}]→ {hypothesis_id} "
f"(weight: {direction}{weight}, new confidence: {new_conf:.3f})"
)
except ValueError as e:
return f"Error linking: {e}"