mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
"""Helpers for translating OpenAI-style tool schemas to Gemini's schema subset."""
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from __future__ import annotations
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from typing import Any, Dict
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# Gemini's ``FunctionDeclaration.parameters`` field accepts the ``Schema``
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# object, which is only a subset of OpenAPI 3.0 / JSON Schema. Strip fields
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# outside that subset before sending Hermes tool schemas to Google.
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_GEMINI_SCHEMA_ALLOWED_KEYS = {
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"type",
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"format",
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"title",
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"description",
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"nullable",
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"enum",
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"maxItems",
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"minItems",
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"properties",
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"required",
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"minProperties",
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"maxProperties",
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"minLength",
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"maxLength",
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"pattern",
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"example",
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"anyOf",
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"propertyOrdering",
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"default",
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"items",
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"minimum",
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"maximum",
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}
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def sanitize_gemini_schema(schema: Any) -> Dict[str, Any]:
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"""Return a Gemini-compatible copy of a tool parameter schema.
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Hermes tool schemas are OpenAI-flavored JSON Schema and may contain keys
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such as ``$schema`` or ``additionalProperties`` that Google's Gemini
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``Schema`` object rejects. This helper preserves the documented Gemini
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subset and recursively sanitizes nested ``properties`` / ``items`` /
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``anyOf`` definitions.
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"""
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if not isinstance(schema, dict):
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return {}
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cleaned: Dict[str, Any] = {}
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for key, value in schema.items():
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if key not in _GEMINI_SCHEMA_ALLOWED_KEYS:
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continue
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if key == "properties":
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if not isinstance(value, dict):
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continue
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props: Dict[str, Any] = {}
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for prop_name, prop_schema in value.items():
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if not isinstance(prop_name, str):
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continue
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props[prop_name] = sanitize_gemini_schema(prop_schema)
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cleaned[key] = props
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continue
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if key == "items":
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cleaned[key] = sanitize_gemini_schema(value)
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continue
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if key == "anyOf":
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if not isinstance(value, list):
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continue
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cleaned[key] = [
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sanitize_gemini_schema(item)
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for item in value
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if isinstance(item, dict)
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]
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continue
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cleaned[key] = value
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# Gemini's Schema validator requires every ``enum`` entry to be a string,
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# even when the parent ``type`` is ``integer`` / ``number`` / ``boolean``.
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# OpenAI / OpenRouter / Anthropic accept typed enums (e.g. Discord's
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# ``auto_archive_duration: {type: integer, enum: [60, 1440, 4320, 10080]}``),
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# so we only drop the ``enum`` when it would collide with Gemini's rule.
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# Keeping ``type: integer`` plus the human-readable description gives the
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# model enough guidance; the tool handler still validates the value.
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enum_val = cleaned.get("enum")
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type_val = cleaned.get("type")
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if isinstance(enum_val, list) and type_val in {"integer", "number", "boolean"}:
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if any(not isinstance(item, str) for item in enum_val):
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cleaned.pop("enum", None)
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return cleaned
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def sanitize_gemini_tool_parameters(parameters: Any) -> Dict[str, Any]:
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"""Normalize tool parameters to a valid Gemini object schema."""
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cleaned = sanitize_gemini_schema(parameters)
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if not cleaned:
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return {"type": "object", "properties": {}}
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return cleaned
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