feat: complete all 19 tasks — liquid networks, quantum backend, embodiment, self-model, ASI rubric, plugin system, auth/rate-limit middleware, async adapters, CI/CD, Dockerfile, benchmarks, module boundary fix, TTS adapter, lifespan migration, OpenAPI docs, code cleanup

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>

fusionagi/adapters/base.py

@@ -5,8 +5,7 @@ from typing import Any
 
 
 class LLMAdapter(ABC):
-    """
-    Abstract adapter for LLM completion.
+    """Abstract adapter for LLM completion.
 
     Implementations should handle:
     - openai/ - OpenAI API (GPT-4, etc.)
@@ -20,8 +19,7 @@ class LLMAdapter(ABC):
         messages: list[dict[str, str]],
         **kwargs: Any,
     ) -> str:
-        """
-        Return completion text for the given messages.
+        """Return completion text for the given messages.
 
         Args:
             messages: List of message dicts with 'role' and 'content' keys.
@@ -38,8 +36,7 @@ class LLMAdapter(ABC):
         schema: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Any:
-        """
-        Return structured (JSON) output.
+        """Return structured (JSON) output.
 
         Default implementation returns None; subclasses may override to use
         provider-specific JSON modes (e.g., OpenAI's response_format).
@@ -53,3 +50,48 @@ class LLMAdapter(ABC):
             Parsed JSON response or None if not supported/parsing fails.
         """
         return None
+
+    async def acomplete(
+        self,
+        messages: list[dict[str, str]],
+        **kwargs: Any,
+    ) -> str:
+        """Async completion — default wraps sync ``complete()`` in a thread.
+
+        Subclasses with native async support (e.g., httpx-based providers)
+        should override this for true non-blocking I/O.
+
+        Args:
+            messages: List of message dicts with 'role' and 'content' keys.
+            **kwargs: Provider-specific options.
+
+        Returns:
+            The model's response text.
+        """
+        import asyncio
+
+        loop = asyncio.get_running_loop()
+        return await loop.run_in_executor(None, lambda: self.complete(messages, **kwargs))
+
+    async def acomplete_structured(
+        self,
+        messages: list[dict[str, str]],
+        schema: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Async structured completion — default wraps sync version.
+
+        Args:
+            messages: List of message dicts with 'role' and 'content' keys.
+            schema: Optional JSON schema for response validation.
+            **kwargs: Provider-specific options.
+
+        Returns:
+            Parsed JSON response or None.
+        """
+        import asyncio
+
+        loop = asyncio.get_running_loop()
+        return await loop.run_in_executor(
+            None, lambda: self.complete_structured(messages, schema=schema, **kwargs)
+        )

fusionagi/adapters/tts_adapter.py

@@ -0,0 +1,122 @@
+"""TTS adapter protocol and implementations for speech synthesis."""
+
+from __future__ import annotations
+
+import base64
+from abc import ABC, abstractmethod
+from typing import Any
+
+from fusionagi._logger import logger
+
+
+class TTSAdapter(ABC):
+    """Abstract adapter for text-to-speech synthesis.
+
+    Implementations handle provider-specific API calls (ElevenLabs,
+    Azure Cognitive Services, Google Cloud TTS, etc.).
+    """
+
+    @abstractmethod
+    async def synthesize(
+        self,
+        text: str,
+        *,
+        voice_id: str | None = None,
+        language: str = "en",
+        **kwargs: Any,
+    ) -> bytes | None:
+        """Synthesize text to audio bytes.
+
+        Args:
+            text: Text to synthesize.
+            voice_id: Provider-specific voice identifier.
+            language: Language code (BCP-47).
+            **kwargs: Provider-specific options.
+
+        Returns:
+            Raw audio bytes (mp3/wav) or None on failure.
+        """
+        ...
+
+
+class StubTTSAdapter(TTSAdapter):
+    """Stub TTS adapter for testing; returns empty audio."""
+
+    async def synthesize(
+        self,
+        text: str,
+        *,
+        voice_id: str | None = None,
+        language: str = "en",
+        **kwargs: Any,
+    ) -> bytes | None:
+        """Return empty bytes for testing."""
+        logger.debug("StubTTS: synthesize called", extra={"text": text[:50], "voice_id": voice_id})
+        return b""
+
+
+class ElevenLabsTTSAdapter(TTSAdapter):
+    """ElevenLabs TTS adapter.
+
+    Requires the ``httpx`` package and an ElevenLabs API key.
+    """
+
+    API_BASE = "https://api.elevenlabs.io/v1"
+    DEFAULT_VOICE = "21m00Tcm4TlvDq8ikWAM"  # Rachel
+
+    def __init__(
+        self,
+        api_key: str,
+        *,
+        default_voice_id: str | None = None,
+        model_id: str = "eleven_monolingual_v1",
+    ) -> None:
+        self._api_key = api_key
+        self._default_voice = default_voice_id or self.DEFAULT_VOICE
+        self._model_id = model_id
+
+    async def synthesize(
+        self,
+        text: str,
+        *,
+        voice_id: str | None = None,
+        language: str = "en",
+        **kwargs: Any,
+    ) -> bytes | None:
+        """Call ElevenLabs TTS API."""
+        try:
+            import httpx
+        except ImportError:
+            logger.error("httpx not installed; pip install httpx")
+            return None
+
+        vid = voice_id or self._default_voice
+        url = f"{self.API_BASE}/text-to-speech/{vid}"
+        headers = {"xi-api-key": self._api_key, "Content-Type": "application/json"}
+        payload = {
+            "text": text,
+            "model_id": self._model_id,
+            "voice_settings": {"stability": 0.5, "similarity_boost": 0.75},
+        }
+
+        try:
+            async with httpx.AsyncClient() as client:
+                resp = await client.post(url, json=payload, headers=headers, timeout=30.0)
+                resp.raise_for_status()
+                return resp.content
+        except Exception as e:
+            logger.error("ElevenLabs TTS failed", extra={"error": str(e)})
+            return None
+
+
+def audio_to_base64(audio_bytes: bytes) -> str:
+    """Encode raw audio bytes to base64 string."""
+    return base64.b64encode(audio_bytes).decode()
+
+
+__all__ = [
+    "TTSAdapter",
+    "StubTTSAdapter",
+    "ElevenLabsTTSAdapter",
+    "audio_to_base64",
+]