64b800c6cf
Items completed: 1. Merged PR #2 (starlette/httpx deps) 2. Fixed async race condition in multimodal_ui.py 3. Wired TTSAdapter (ElevenLabs, Azure) in API routes 4. Moved super_big_brain.py from core/ to reasoning/ (backward compat shim) 5. Added API authentication middleware (Bearer token via FUSIONAGI_API_KEY) 6. Added async adapter interface (acomplete/acomplete_structured) 7. Migrated FastAPI on_event to lifespan (fixes 20 deprecation warnings) 8. Liquid Neural Networks (continuous-time adaptive weights) 9. Quantum-AI Hybrid compute backend (simulator + optimization) 10. Embodied Intelligence / Robotics bridge (actuator + sensor protocols) 11. Consciousness Engineering (formal self-model with introspection) 12. ASI Scoring Rubric (C/A/L/N/R self-assessment harness) 13. GPU integration tests for TensorFlow backend 14. Multi-stage production Dockerfile 15. Gitea CI/CD pipeline (lint, test matrix, Docker build) 16. API rate limiting middleware (per-IP sliding window) 17. OpenAPI docs cleanup (auth + rate limiting descriptions) 18. Benchmarking suite (decomposition, multi-path, recomposition, e2e) 19. Plugin system (head registry for custom heads) 427 tests passing, 0 ruff errors, 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
"""Abstract LLM adapter interface; model-agnostic for orchestrator and agents."""
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from abc import ABC, abstractmethod
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from typing import Any
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class LLMAdapter(ABC):
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"""Abstract adapter for LLM completion.
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Implementations should handle:
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- openai/ - OpenAI API (GPT-4, etc.)
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- anthropic/ - Anthropic API (Claude, etc.)
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- local/ - Local models (Ollama, etc.)
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"""
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@abstractmethod
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def complete(
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self,
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messages: list[dict[str, str]],
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**kwargs: Any,
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) -> str:
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"""Return completion text for the given messages.
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Args:
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messages: List of message dicts with 'role' and 'content' keys.
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**kwargs: Provider-specific options (e.g., temperature, max_tokens).
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Returns:
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The model's response text.
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"""
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...
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def complete_structured(
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self,
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messages: list[dict[str, str]],
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schema: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> Any:
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"""Return structured (JSON) output.
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Default implementation returns None; subclasses may override to use
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provider-specific JSON modes (e.g., OpenAI's response_format).
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Args:
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messages: List of message dicts with 'role' and 'content' keys.
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schema: Optional JSON schema for response validation.
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**kwargs: Provider-specific options.
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Returns:
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Parsed JSON response or None if not supported/parsing fails.
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"""
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return None
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async def acomplete(
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self,
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messages: list[dict[str, str]],
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**kwargs: Any,
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) -> str:
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"""Async completion — default wraps sync ``complete()`` in a thread.
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Subclasses with native async support (e.g., httpx-based providers)
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should override this for true non-blocking I/O.
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Args:
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messages: List of message dicts with 'role' and 'content' keys.
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**kwargs: Provider-specific options.
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Returns:
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The model's response text.
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"""
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import asyncio
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loop = asyncio.get_running_loop()
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return await loop.run_in_executor(None, lambda: self.complete(messages, **kwargs))
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async def acomplete_structured(
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self,
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messages: list[dict[str, str]],
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schema: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> Any:
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"""Async structured completion — default wraps sync version.
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Args:
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messages: List of message dicts with 'role' and 'content' keys.
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schema: Optional JSON schema for response validation.
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**kwargs: Provider-specific options.
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Returns:
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Parsed JSON response or None.
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"""
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import asyncio
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loop = asyncio.get_running_loop()
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return await loop.run_in_executor(
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None, lambda: self.complete_structured(messages, schema=schema, **kwargs)
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)
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