81ade8f7ac
Consolidates the long-running refit work (DESIGN.md as authoritative spec)
into a single baseline commit. Six stages landed together:
S1 Case + EvidenceSource abstraction; tools parameterised by source_id
(case.py, main.py multi-source bootstrap, .bin extension support)
S2 Grounding gateway in add_phenomenon: verified_facts cite real
ToolInvocation ids; substring / normalised match enforced; agent +
task scope checked. Phenomenon.description split into verified_facts
(grounded) + interpretation (free text). [invocation: inv-xxx]
prefix on every wrapped tool result so the LLM can cite.
S3 Confidence as additive log-odds: edge_type → log10(LR) calibration
table; commutative updates; supported / refuted thresholds derived
from log_odds; hypothesis × evidence matrix view.
S4 iOS plugin: unzip_archive + parse_plist / sqlite_tables /
sqlite_query / parse_ios_keychain / read_idevice_info;
IOSArtifactAgent; SOURCE_TYPE_AGENTS routing.
S5 Cross-source entity resolution: typed identifiers on Entity,
observe_identity gateway, auto coref hypothesis with shared /
conflicting strong/weak LR edges, reversible same_as edges,
actor_clusters() view.
S6 Android partition probe + AndroidArtifactAgent; MediaAgent with
OCR fallback; orchestrator Phase 1 iterates every analysable
source; platform-aware get_triage_agent_type; ReportAgent renders
actor clusters + per-source breakdown.
142 unit tests / 1 skipped — full coverage of the new gateway, log-odds
math, coref hypothesis fall-out, and orchestrator multi-source dispatch.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
"""Media Agent — OCR-based analysis of screenshot/photo evidence.
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DESIGN.md §4.7: the LLM backend has no vision capability, so JPEG/PNG
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evidence must go through tesseract first. The agent runs OCR, then
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records extracted strings — especially identifiers (wallet addresses,
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phone numbers, usernames) — via the grounded observe_identity gateway so
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they participate in cross-source coref the same way iOS keychain entries
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or Windows account names do.
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If the OCR runtime is missing on the host, ocr_image returns an explicit
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install hint; the agent should record that as a negative finding ("no
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text extracted — tesseract not installed") rather than guessing.
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"""
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from __future__ import annotations
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from base_agent import BaseAgent
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from evidence_graph import EvidenceGraph
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from llm_client import LLMClient
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from tool_registry import TOOL_CATALOG
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class MediaAgent(BaseAgent):
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name = "media"
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role = (
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"Media / OCR forensic analyst. You analyse screenshots, photos, and "
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"scanned documents — any pixel-based evidence the LLM cannot read "
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"directly. Workflow: list_extracted_dir to enumerate images, "
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"ocr_image on each promising one, then add_phenomenon (with the "
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"OCR'd text as the verified_fact value) and observe_identity for "
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"any wallet addresses, phone numbers, email addresses, or "
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"usernames the text contains. If OCR fails because tesseract is "
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"missing, RECORD that as a negative finding instead of fabricating "
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"image content — the absence is a real fact about this run."
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)
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def __init__(self, llm: LLMClient, graph: EvidenceGraph) -> None:
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super().__init__(llm, graph)
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self._register_tools()
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def _register_tools(self) -> None:
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tool_names = [
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"ocr_image",
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"list_extracted_dir", "find_files",
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"read_binary_preview",
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"read_text_file",
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"search_text_file",
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]
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for name in tool_names:
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td = TOOL_CATALOG.get(name)
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if td:
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self.register_tool(td.name, td.description, td.input_schema, td.executor)
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