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
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
"""Abstract base for provider transports.
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A transport owns the data path for one api_mode:
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convert_messages → convert_tools → build_kwargs → normalize_response
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It does NOT own: client construction, streaming, credential refresh,
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prompt caching, interrupt handling, or retry logic. Those stay on AIAgent.
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"""
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional
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from agent.transports.types import NormalizedResponse
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class ProviderTransport(ABC):
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"""Base class for provider-specific format conversion and normalization."""
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@property
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@abstractmethod
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def api_mode(self) -> str:
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"""The api_mode string this transport handles (e.g. 'anthropic_messages')."""
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...
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@abstractmethod
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def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
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"""Convert OpenAI-format messages to provider-native format.
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Returns provider-specific structure (e.g. (system, messages) for Anthropic,
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or the messages list unchanged for chat_completions).
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"""
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...
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@abstractmethod
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def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
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"""Convert OpenAI-format tool definitions to provider-native format.
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Returns provider-specific tool list (e.g. Anthropic input_schema format).
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"""
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...
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@abstractmethod
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def build_kwargs(
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self,
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model: str,
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messages: List[Dict[str, Any]],
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tools: Optional[List[Dict[str, Any]]] = None,
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**params,
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) -> Dict[str, Any]:
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"""Build the complete API call kwargs dict.
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This is the primary entry point — it typically calls convert_messages()
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and convert_tools() internally, then adds model-specific config.
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Returns a dict ready to be passed to the provider's SDK client.
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"""
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...
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@abstractmethod
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def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
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"""Normalize a raw provider response to the shared NormalizedResponse type.
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This is the only method that returns a transport-layer type.
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"""
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...
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def validate_response(self, response: Any) -> bool:
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"""Optional: check if the raw response is structurally valid.
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Returns True if valid, False if the response should be treated as invalid.
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Default implementation always returns True.
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"""
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return True
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def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
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"""Optional: extract provider-specific cache hit/creation stats.
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Returns dict with 'cached_tokens' and 'creation_tokens', or None.
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Default returns None.
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"""
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return None
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def map_finish_reason(self, raw_reason: str) -> str:
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"""Optional: map provider-specific stop reason to OpenAI equivalent.
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Default returns the raw reason unchanged. Override for providers
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with different stop reason vocabularies.
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"""
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return raw_reason
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