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Merge pull request 'feat: GPU/TensorCore integration — TensorFlow backend, accelerated reasoning, training & memory' (#1) from devin/1777352172-gpu-tensorcore-integration into main
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This commit was merged in pull request #1.
This commit is contained in:
nsatoshi
2026-04-28 06:32:06 +00:00
parent c052b07662 9a8affae9a
commit 99bbbccacb
147 changed files with 6583 additions and 1119 deletions
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15
.dockerignore Normal file
View File

@@ -0,0 +1,15 @@
.venv/
__pycache__/
*.pyc
.git/
.pytest_cache/
.mypy_cache/
.ruff_cache/
*.egg-info/
dist/
build/
.env
.env.*
docs/
tests/
*.md

105
docs/gpu_tensorcore_integration.md Normal file
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@@ -0,0 +1,105 @@
# GPU / TensorCore Integration — Architecture Spec
## Overview
FusionAGI integrates GPU-accelerated compute via TensorFlow, CUDA TensorCores, and JAX
to transform reasoning, similarity scoring, consensus, and training from CPU-bound
symbolic operations into massively parallel tensor operations.
## Design Principles
1. **Optional dependency** — GPU support is an extra (`pip install fusionagi[gpu]`).
All GPU-accelerated code paths have CPU fallbacks.
2. **Module boundary** — GPU compute lives in `fusionagi/gpu/` (new module). Other modules
import from `fusionagi.gpu` only when GPU acceleration is needed.
3. **Backend abstraction** — `TensorBackend` protocol abstracts TensorFlow, JAX, and
pure-NumPy backends. The system auto-selects the best available backend.
## Module: `fusionagi/gpu/`
```
fusionagi/gpu/
├── __init__.py # Public API, auto-detection
├── backend.py # TensorBackend protocol + backend registry
├── tensorflow_ops.py # TF/TensorCore similarity, attention, scoring
├── tensor_similarity.py # GPU-accelerated embedding similarity
├── tensor_attention.py # Multi-head attention for consensus
├── tensor_scoring.py # Batch hypothesis scoring on GPU
└── training.py # GPU-accelerated training loop for self-improvement
```
## Integration Points
### 1. Reasoning Pipeline (`reasoning/`)
**Current:** `multi_path.py` scores hypotheses sequentially with word-overlap heuristics.
**GPU:** Batch embed hypotheses → cosine similarity matrix on GPU → parallel scoring.
**Current:** `consensus_engine.py` uses Jaccard word overlap for similarity.
**GPU:** Dense embedding vectors + GPU cosine similarity for semantic matching.
### 2. Super Big Brain (`core/super_big_brain.py`)
**Current:** `generate_and_score_parallel` uses ThreadPoolExecutor.
**GPU:** Tensor-parallel scoring with batched dot-products on TensorCore.
### 3. Memory Subsystem (`memory/`)
**Current:** `semantic_graph.py` is pure Python dict/adjacency list.
**GPU:** Vector similarity search via GPU-accelerated embedding lookup.
### 4. Self-Improvement (`self_improvement/`)
**Current:** `AutoTrainer` suggests heuristic updates, no actual neural training.
**GPU:** GPU-backed fine-tuning loops, gradient-based heuristic optimization.
### 5. Adapter Layer (`adapters/`)
**New:** `TensorFlowAdapter` — local model inference via TF/Keras with TensorCore.
## Data Flow
```
User Prompt
│
▼
Decomposition (CPU — symbolic)
│
▼
Embedding (GPU — TF/TensorCore)
│
├──► Similarity Matrix (GPU — batched cosine)
│ │
│ ▼
│ Consensus Scoring (GPU — attention)
│
├──► Hypothesis Scoring (GPU — batched inference)
│
▼
Recomposition (CPU — symbolic + GPU scores)
│
▼
Final Response
```
## Backend Selection
```python
from fusionagi.gpu import get_backend, TensorBackend
backend: TensorBackend = get_backend() # Auto-selects best available
# Returns: TensorFlowBackend > NumPyBackend (fallback)
```
## Dependencies
```toml
[project.optional-dependencies]
gpu = ["tensorflow>=2.16", "numpy>=1.26"]
```
TensorFlow 2.16+ includes:
- TensorCore (FP16/BF16 mixed-precision) via `tf.keras.mixed_precision`
- XLA compilation for GPU kernel fusion
- `tf.linalg` for batched linear algebra
- TensorRT integration for inference optimization

2
fusionagi/__init__.py
View File

@@ -4,10 +4,10 @@ from fusionagi._logger import logger
from fusionagi.core import EventBus, Orchestrator, StateManager
from fusionagi.schemas import AgentMessageEnvelope, Task
from fusionagi.self_improvement import (
SelfCorrectionLoop,
AutoRecommender,
AutoTrainer,
FusionAGILoop,
SelfCorrectionLoop,
)

16
fusionagi/adapters/__init__.py
View File

@@ -6,13 +6,25 @@ Use: from fusionagi.adapters import OpenAIAdapter; if OpenAIAdapter is not None:
"""
from fusionagi.adapters.base import LLMAdapter
from fusionagi.adapters.stub_adapter import StubAdapter
from fusionagi.adapters.cache import CachedAdapter
from fusionagi.adapters.native_adapter import NativeAdapter
from fusionagi.adapters.stub_adapter import StubAdapter
try:
from fusionagi.adapters.openai_adapter import OpenAIAdapter
except ImportError:
OpenAIAdapter = None # type: ignore[misc, assignment]
__all__ = ["LLMAdapter", "StubAdapter", "CachedAdapter", "NativeAdapter", "OpenAIAdapter"]
try:
from fusionagi.adapters.tensorflow_adapter import TensorFlowAdapter
except ImportError:
TensorFlowAdapter = None # type: ignore[misc, assignment]
__all__ = [
"LLMAdapter",
"StubAdapter",
"CachedAdapter",
"NativeAdapter",
"OpenAIAdapter",
"TensorFlowAdapter",
]

12
fusionagi/adapters/base.py
View File

@@ -7,7 +7,7 @@ from typing import Any
class LLMAdapter(ABC):
"""
Abstract adapter for LLM completion.
Implementations should handle:
- openai/ - OpenAI API (GPT-4, etc.)
- anthropic/ - Anthropic API (Claude, etc.)
@@ -22,11 +22,11 @@ class LLMAdapter(ABC):
) -> str:
"""
Return completion text for the given messages.
Args:
messages: List of message dicts with 'role' and 'content' keys.
**kwargs: Provider-specific options (e.g., temperature, max_tokens).
Returns:
The model's response text.
"""
@@ -40,15 +40,15 @@ class LLMAdapter(ABC):
) -> Any:
"""
Return structured (JSON) output.
Default implementation returns None; subclasses may override to use
provider-specific JSON modes (e.g., OpenAI's response_format).
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 if not supported/parsing fails.
"""

2
fusionagi/adapters/cache.py
View File

@@ -59,7 +59,7 @@ class CachedAdapter(LLMAdapter):
key = self._key(messages, kwargs, prefix="complete")
if key in self._cache:
self._hits += 1
return self._get_and_touch(self._cache, key)
return str(self._get_and_touch(self._cache, key))
self._misses += 1
response = self._adapter.complete(messages, **kwargs)

66
fusionagi/adapters/openai_adapter.py
View File

@@ -3,8 +3,8 @@
import time
from typing import Any
from fusionagi.adapters.base import LLMAdapter
from fusionagi._logger import logger
from fusionagi.adapters.base import LLMAdapter
class OpenAIAdapterError(Exception):
@@ -28,9 +28,9 @@ class OpenAIAuthenticationError(OpenAIAdapterError):
class OpenAIAdapter(LLMAdapter):
"""
OpenAI API adapter with retry logic and error handling.
Requires openai package and OPENAI_API_KEY.
Features:
- Automatic retry with exponential backoff for transient errors
- Proper error classification (rate limits, auth errors, etc.)
@@ -49,7 +49,7 @@ class OpenAIAdapter(LLMAdapter):
) -> None:
"""
Initialize the OpenAI adapter.
Args:
model: Default model to use (e.g., "gpt-4o-mini", "gpt-4o").
api_key: OpenAI API key. If None, uses OPENAI_API_KEY env var.
@@ -83,42 +83,42 @@ class OpenAIAdapter(LLMAdapter):
"""Check if an error is retryable (transient)."""
if self._openai_module is None:
return False
# Rate limit errors are retryable
if hasattr(self._openai_module, "RateLimitError"):
if isinstance(error, self._openai_module.RateLimitError):
return True
# API connection errors are retryable
if hasattr(self._openai_module, "APIConnectionError"):
if isinstance(error, self._openai_module.APIConnectionError):
return True
# Internal server errors are retryable
if hasattr(self._openai_module, "InternalServerError"):
if isinstance(error, self._openai_module.InternalServerError):
return True
# Timeout errors are retryable
if hasattr(self._openai_module, "APITimeoutError"):
if isinstance(error, self._openai_module.APITimeoutError):
return True
return False
def _classify_error(self, error: Exception) -> Exception:
"""Convert OpenAI exceptions to adapter exceptions."""
if self._openai_module is None:
return OpenAIAdapterError(str(error))
if hasattr(self._openai_module, "RateLimitError"):
if isinstance(error, self._openai_module.RateLimitError):
return OpenAIRateLimitError(str(error))
if hasattr(self._openai_module, "AuthenticationError"):
if isinstance(error, self._openai_module.AuthenticationError):
return OpenAIAuthenticationError(str(error))
return OpenAIAdapterError(str(error))
def complete(
@@ -128,14 +128,14 @@ class OpenAIAdapter(LLMAdapter):
) -> str:
"""
Call OpenAI chat completion with retry logic.
Args:
messages: List of message dicts with 'role' and 'content'.
**kwargs: Additional arguments for the API call (e.g., temperature).
Returns:
The assistant's response content.
Raises:
OpenAIAuthenticationError: If authentication fails.
OpenAIRateLimitError: If rate limited after all retries.
@@ -145,7 +145,7 @@ class OpenAIAdapter(LLMAdapter):
if not messages:
logger.warning("OpenAI complete called with empty messages")
return ""
for i, msg in enumerate(messages):
if not isinstance(msg, dict):
raise ValueError(f"Message {i} must be a dict, got {type(msg).__name__}")
@@ -153,14 +153,14 @@ class OpenAIAdapter(LLMAdapter):
raise ValueError(f"Message {i} missing 'role' key")
if "content" not in msg:
raise ValueError(f"Message {i} missing 'content' key")
client = self._get_client()
model = kwargs.get("model", self._model)
call_kwargs = {**kwargs, "model": model}
last_error: Exception | None = None
delay = self._retry_delay
for attempt in range(self._max_retries + 1):
try:
resp = client.chat.completions.create(
@@ -169,19 +169,19 @@ class OpenAIAdapter(LLMAdapter):
)
choice = resp.choices[0] if resp.choices else None
if choice and choice.message and choice.message.content:
return choice.message.content
return str(choice.message.content)
logger.debug("OpenAI empty response", extra={"model": model, "attempt": attempt})
return ""
except Exception as e:
last_error = e
# Don't retry authentication errors
if self._openai_module and hasattr(self._openai_module, "AuthenticationError"):
if isinstance(e, self._openai_module.AuthenticationError):
logger.error("OpenAI authentication failed", extra={"error": str(e)})
raise OpenAIAuthenticationError(str(e)) from e
# Check if retryable
if not self._is_retryable_error(e):
logger.error(
@@ -189,7 +189,7 @@ class OpenAIAdapter(LLMAdapter):
extra={"error": str(e), "error_type": type(e).__name__},
)
raise self._classify_error(e) from e
# Log retry attempt
if attempt < self._max_retries:
logger.warning(
@@ -203,13 +203,15 @@ class OpenAIAdapter(LLMAdapter):
)
time.sleep(delay)
delay = min(delay * self._retry_multiplier, self._max_retry_delay)
# All retries exhausted
logger.error(
"OpenAI all retries exhausted",
extra={"error": str(last_error), "attempts": self._max_retries + 1},
)
raise self._classify_error(last_error) from last_error
if last_error is not None:
raise self._classify_error(last_error) from last_error
raise OpenAIAdapterError("All retries exhausted with unknown error")
def complete_structured(
self,
@@ -219,20 +221,20 @@ class OpenAIAdapter(LLMAdapter):
) -> Any:
"""
Call OpenAI with JSON mode for structured output.
Args:
messages: List of message dicts with 'role' and 'content'.
schema: Optional JSON schema for response validation (informational).
**kwargs: Additional arguments for the API call.
Returns:
Parsed JSON response or None if parsing fails.
"""
import json
# Enable JSON mode
call_kwargs = {**kwargs, "response_format": {"type": "json_object"}}
# Add schema hint to system message if provided
if schema and messages:
schema_hint = f"\n\nRespond with JSON matching this schema: {json.dumps(schema)}"
@@ -246,11 +248,11 @@ class OpenAIAdapter(LLMAdapter):
{"role": "system", "content": f"You must respond with valid JSON.{schema_hint}"},
*messages,
]
raw = self.complete(messages, **call_kwargs)
if not raw:
return None
try:
return json.loads(raw)
except json.JSONDecodeError as e:

8
fusionagi/adapters/stub_adapter.py
View File

@@ -9,7 +9,7 @@ from fusionagi.adapters.base import LLMAdapter
class StubAdapter(LLMAdapter):
"""
Returns configurable fixed responses; no API calls.
Useful for testing without making actual LLM API calls.
Supports both text and structured (JSON) responses.
"""
@@ -21,7 +21,7 @@ class StubAdapter(LLMAdapter):
) -> None:
"""
Initialize the stub adapter.
Args:
response: Fixed text response for complete().
structured_response: Fixed structured response for complete_structured().
@@ -45,13 +45,13 @@ class StubAdapter(LLMAdapter):
) -> Any:
"""
Return the configured structured response.
If no structured_response was configured, attempts to parse
the text response as JSON, or returns None.
"""
if self._structured_response is not None:
return self._structured_response
# Try to parse text response as JSON
try:
return json.loads(self._response)

234
fusionagi/adapters/tensorflow_adapter.py Normal file
View File

@@ -0,0 +1,234 @@
"""TensorFlow adapter: local model inference via TF/Keras with TensorCore.
Requires: pip install fusionagi[gpu]
Provides LLMAdapter-compatible interface for locally-hosted TensorFlow/Keras
models. Supports TensorCore mixed-precision, XLA compilation, and GPU memory
management.
"""
from __future__ import annotations
import json
from typing import Any
from fusionagi._logger import logger
from fusionagi.adapters.base import LLMAdapter
try:
import numpy as np
import tensorflow as tf
except ImportError as e:
raise ImportError(
"TensorFlow is required for TensorFlowAdapter. "
"Install with: pip install fusionagi[gpu]"
) from e
class TensorFlowAdapter(LLMAdapter):
"""LLM adapter for local TensorFlow/Keras model inference.
Loads a saved Keras model or TF SavedModel and runs inference with
TensorCore acceleration when available.
Args:
model_path: Path to a saved Keras model (.keras) or SavedModel directory.
tokenizer: Optional tokenizer callable (text -> token IDs).
max_length: Maximum sequence length for generation.
temperature: Sampling temperature.
mixed_precision: Enable FP16 mixed-precision for TensorCore.
"""
def __init__(
self,
model_path: str | None = None,
model: Any | None = None,
tokenizer: Any | None = None,
max_length: int = 512,
temperature: float = 0.7,
mixed_precision: bool = False,
) -> None:
self._model: Any = None
self._tokenizer = tokenizer
self._max_length = max_length
self._temperature = temperature
self._model_path = model_path
if mixed_precision:
try:
tf.keras.mixed_precision.set_global_policy("mixed_float16")
logger.info("TensorFlowAdapter: TensorCore mixed-precision enabled")
except Exception:
logger.warning("TensorFlowAdapter: mixed-precision not available")
if model is not None:
self._model = model
logger.info("TensorFlowAdapter initialized with provided model")
elif model_path:
self._load_model(model_path)
else:
logger.info(
"TensorFlowAdapter initialized without model "
"(will use embedding-based synthesis)"
)
def _load_model(self, path: str) -> None:
"""Load a TF SavedModel or Keras model from disk."""
try:
self._model = tf.saved_model.load(path)
logger.info("TensorFlowAdapter: loaded SavedModel", extra={"path": path})
except Exception:
try:
self._model = tf.keras.models.load_model(path)
logger.info("TensorFlowAdapter: loaded Keras model", extra={"path": path})
except Exception:
logger.warning(
"TensorFlowAdapter: no model loaded; "
"falling back to embedding synthesis",
extra={"path": path},
)
def complete(
self,
messages: list[dict[str, str]],
**kwargs: Any,
) -> str:
"""Generate completion using the loaded TF model.
If no model is loaded, falls back to embedding-based synthesis
that uses GPU-accelerated similarity scoring.
Args:
messages: List of message dicts with 'role' and 'content'.
**kwargs: Additional parameters (temperature, max_length).
Returns:
Generated response text.
"""
if self._model is not None and self._tokenizer is not None:
return self._model_inference(messages, **kwargs)
return self._embedding_synthesis(messages)
def complete_structured(
self,
messages: list[dict[str, str]],
schema: dict[str, Any] | None = None,
**kwargs: Any,
) -> Any:
"""Attempt structured JSON output from the model.
Falls back to parsing the raw completion if the model doesn't
natively support structured output.
"""
raw = self.complete(messages, **kwargs)
try:
return json.loads(raw)
except (json.JSONDecodeError, TypeError):
return None
def _model_inference(
self,
messages: list[dict[str, str]],
**kwargs: Any,
) -> str:
"""Run inference through the loaded TF/Keras model."""
prompt = self._messages_to_prompt(messages)
temperature = kwargs.get("temperature", self._temperature)
max_length = kwargs.get("max_length", self._max_length)
tokenizer = self._tokenizer
assert tokenizer is not None
tokens = tokenizer(prompt)
if isinstance(tokens, (list, np.ndarray)):
input_tensor = tf.constant([tokens[:max_length]], dtype=tf.int32)
else:
input_tensor = tokens
try:
if hasattr(self._model, "generate"):
output = self._model.generate(
input_tensor,
max_length=max_length,
temperature=temperature,
)
elif hasattr(self._model, "predict"):
output = self._model.predict(input_tensor)
elif callable(self._model):
output = self._model(input_tensor)
else:
logger.warning("TensorFlowAdapter: model has no callable interface")
return self._embedding_synthesis(messages)
if isinstance(output, tf.Tensor):
output = output.numpy()
if hasattr(output, "tolist"):
output = output.tolist()
if isinstance(output, list) and output:
if isinstance(output[0], list):
output = output[0]
if isinstance(output[0], (int, float)):
if tokenizer and hasattr(tokenizer, "decode"):
return str(tokenizer.decode(output))
return str(output) # type: ignore[no-any-return]
except Exception as e:
logger.warning(
"TensorFlowAdapter: model inference failed, using synthesis",
extra={"error": str(e)},
)
return self._embedding_synthesis(messages)
def _embedding_synthesis(self, messages: list[dict[str, str]]) -> str:
"""Fallback: synthesize response using GPU-accelerated embeddings.
Embeds message content and produces a summary based on
semantic similarity between parts.
"""
content_parts: list[str] = []
for msg in messages:
content = msg.get("content", "")
if isinstance(content, str) and content.strip():
content_parts.append(content.strip())
if not content_parts:
return ""
from fusionagi.gpu.backend import get_backend
be = get_backend()
embeddings = be.embed_texts(content_parts)
emb_np = be.to_numpy(embeddings)
mean_emb = np.mean(emb_np, axis=0, keepdims=True)
sims = be.to_numpy(
be.cosine_similarity_matrix(be.from_numpy(mean_emb), embeddings)
)[0]
ranked_indices = np.argsort(sims)[::-1]
summary_parts: list[str] = []
for idx in ranked_indices[:5]:
part = content_parts[idx]
summary_parts.append(part[:300])
return "\n\n".join(summary_parts)
@staticmethod
def _messages_to_prompt(messages: list[dict[str, str]]) -> str:
"""Convert message list to a flat prompt string."""
parts: list[str] = []
for msg in messages:
role = msg.get("role", "user")
content = msg.get("content", "")
parts.append(f"<|{role}|>\n{content}")
return "\n".join(parts)
def device_summary(self) -> dict[str, Any]:
"""Return device and model information."""
gpus = tf.config.list_physical_devices("GPU")
return {
"adapter": "tensorflow",
"model_path": self._model_path,
"has_model": self._model is not None,
"has_tokenizer": self._tokenizer is not None,
"gpu_count": len(gpus),
"tf_version": tf.__version__,
}

8
fusionagi/agents/__init__.py
View File

@@ -1,12 +1,12 @@
"""Agents: base, planner, reasoner, executor, critic, adversarial reviewer, head, witness. See fusionagi.multi_agent for Supervisor, Coordinator, Pool."""
from fusionagi.agents.adversarial_reviewer import AdversarialReviewerAgent
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.agents.critic import CriticAgent
from fusionagi.agents.executor import ExecutorAgent
from fusionagi.agents.head_agent import HeadAgent
from fusionagi.agents.planner import PlannerAgent
from fusionagi.agents.reasoner import ReasonerAgent
from fusionagi.agents.executor import ExecutorAgent
from fusionagi.agents.critic import CriticAgent
from fusionagi.agents.adversarial_reviewer import AdversarialReviewerAgent
from fusionagi.agents.head_agent import HeadAgent
from fusionagi.agents.witness_agent import WitnessAgent
__all__ = [

5
fusionagi/agents/adversarial_reviewer.py
View File

@@ -1,7 +1,6 @@
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.schemas.messages import AgentMessageEnvelope
from fusionagi._logger import logger
import json
class AdversarialReviewerAgent(BaseAgent):
def __init__(self, identity="adversarial_reviewer", adapter=None):

1
fusionagi/agents/base_agent.py
View File

@@ -1,7 +1,6 @@
"""Base agent interface: identity, role, objective, memory/tool scope, handle_message."""
from abc import ABC, abstractmethod
from typing import Any
from fusionagi.schemas.messages import AgentMessageEnvelope

10
fusionagi/agents/critic.py
View File

@@ -3,10 +3,10 @@
import json
from typing import Any
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.adapters.base import LLMAdapter
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi._logger import logger
from fusionagi.adapters.base import LLMAdapter
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
class CriticAgent(BaseAgent):
@@ -78,13 +78,13 @@ class CriticAgent(BaseAgent):
{"role": "user", "content": context},
]
try:
raw = self._adapter.complete(messages)
raw = self._adapter.complete(messages) # type: ignore[union-attr]
for start in ("```json", "```"):
if raw.strip().startswith(start):
raw = raw.strip()[len(start):].strip()
if raw.endswith("```"):
raw = raw[:-3].strip()
return json.loads(raw)
return json.loads(raw) # type: ignore[no-any-return]
except Exception:
logger.exception("Critic evaluation parse failed, using fallback")
return {

34
fusionagi/agents/executor.py
View File

@@ -2,29 +2,29 @@
from __future__ import annotations
from typing import Any, TYPE_CHECKING
from typing import TYPE_CHECKING, Any
from fusionagi._logger import logger
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.planning import get_step
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi.schemas.plan import Plan
from fusionagi.planning import get_step
from fusionagi.tools.registry import ToolRegistry
from fusionagi.tools.runner import run_tool
from fusionagi._logger import logger
if TYPE_CHECKING:
from fusionagi.core.state_manager import StateManager
from fusionagi.governance.guardrails import Guardrails
from fusionagi.governance.rate_limiter import RateLimiter
from fusionagi.governance.access_control import AccessControl
from fusionagi.governance.guardrails import Guardrails
from fusionagi.governance.override import OverrideHooks
from fusionagi.governance.rate_limiter import RateLimiter
from fusionagi.memory.episodic import EpisodicMemory
class ExecutorAgent(BaseAgent):
"""
Executes steps: maps step to tool call, runs via safe runner, emits step_done/step_failed.
Supports full governance integration:
- Guardrails: Pre/post checks for tool invocations
- RateLimiter: Limits tool invocation rate per agent/tool
@@ -46,7 +46,7 @@ class ExecutorAgent(BaseAgent):
) -> None:
"""
Initialize the executor agent.
Args:
identity: Agent identifier.
registry: Tool registry for tool lookup.
@@ -97,11 +97,11 @@ class ExecutorAgent(BaseAgent):
tool = self._registry.get(tool_name)
if not tool:
return self._fail(task_id, envelope.message.sender, step_id, f"tool not found: {tool_name}")
# Check tool registry permissions
if not self._registry.allowed_for(tool_name, self.tool_permissions):
return self._fail(task_id, envelope.message.sender, step_id, "permission denied")
# Check access control policy
if self._access_control is not None:
if not self._access_control.allowed(self.identity, tool_name, task_id):
@@ -110,7 +110,7 @@ class ExecutorAgent(BaseAgent):
extra={"tool_name": tool_name, "agent_id": self.identity, "task_id": task_id},
)
return self._fail(task_id, envelope.message.sender, step_id, "access control denied")
# Check rate limiter
if self._rate_limiter is not None:
rate_key = f"{self.identity}:{tool_name}"
@@ -121,7 +121,7 @@ class ExecutorAgent(BaseAgent):
extra={"tool_name": tool_name, "key": rate_key, "reason": reason},
)
return self._fail(task_id, envelope.message.sender, step_id, reason)
# Check guardrails pre-check
if self._guardrails is not None:
pre_result = self._guardrails.pre_check(tool_name, tool_args)
@@ -136,7 +136,7 @@ class ExecutorAgent(BaseAgent):
)
if pre_result.sanitized_args is not None:
tool_args = pre_result.sanitized_args
# Check override hooks for high-risk operations
if self._override_hooks is not None and tool.manufacturing:
proceed = self._override_hooks.fire(
@@ -152,14 +152,14 @@ class ExecutorAgent(BaseAgent):
task_id, envelope.message.sender, step_id,
"Override hook blocked execution",
)
# Execute the tool
result, log_entry = run_tool(tool, tool_args)
logger.info(
"Executor tool run",
extra={"tool_name": tool_name, "step_id": step_id, "error": log_entry.get("error")},
)
# Check guardrails post-check
if self._guardrails is not None and not log_entry.get("error"):
post_ok, post_reason = self._guardrails.post_check(tool_name, result)
@@ -170,11 +170,11 @@ class ExecutorAgent(BaseAgent):
"Executor guardrail post_check failed",
extra={"tool_name": tool_name, "reason": post_reason},
)
# Record trace in state manager
if self._state:
self._state.append_trace(task_id or "", log_entry)
# Record in episodic memory
if self._episodic_memory:
self._episodic_memory.append(
@@ -187,7 +187,7 @@ class ExecutorAgent(BaseAgent):
"duration_seconds": log_entry.get("duration_seconds"),
},
)
if log_entry.get("error"):
return self._fail(
task_id, envelope.message.sender, step_id,

10
fusionagi/agents/head_agent.py
View File

@@ -2,12 +2,12 @@
from typing import Any, Protocol, runtime_checkable
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.adapters.base import LLMAdapter
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi.schemas.head import HeadId, HeadOutput, HeadClaim, HeadRisk
from fusionagi.schemas.grounding import Citation
from fusionagi._logger import logger
from fusionagi.adapters.base import LLMAdapter
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.schemas.grounding import Citation
from fusionagi.schemas.head import HeadClaim, HeadId, HeadOutput, HeadRisk
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
@runtime_checkable

6
fusionagi/agents/heads/__init__.py
View File

@@ -1,12 +1,10 @@
"""Dvādaśa content head agents: Logic, Research, Systems, Strategy, etc."""
from typing import Any
from fusionagi.agents.head_agent import HeadAgent
from fusionagi.adapters.base import LLMAdapter
from fusionagi.agents.head_agent import HeadAgent
from fusionagi.prompts.heads import get_head_prompt
from fusionagi.reasoning.native import NativeReasoningProvider
from fusionagi.schemas.head import HeadId
from fusionagi.prompts.heads import get_head_prompt
def create_head_agent(

12
fusionagi/agents/planner.py
View File

@@ -4,10 +4,10 @@ import json
import re
from typing import Any
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.adapters.base import LLMAdapter
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi._logger import logger
from fusionagi.adapters.base import LLMAdapter
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
PLAN_REQUEST_SYSTEM = """You are a planner. Given a goal and optional constraints, output a JSON object with this exact structure:
{"steps": [{"id": "step_1", "description": "...", "dependencies": []}, ...], "fallback_paths": []}
@@ -102,11 +102,13 @@ class PlannerAgent(BaseAgent):
match = re.search(r"\{[\s\S]*\}", raw)
if match:
try:
return json.loads(match.group())
result: dict[str, Any] = json.loads(match.group())
return result
except json.JSONDecodeError as e:
logger.debug("Planner JSON parse failed (match)", extra={"error": str(e)})
try:
return json.loads(raw)
result = json.loads(raw)
return result # type: ignore[return-value]
except json.JSONDecodeError as e:
logger.debug("Planner JSON parse failed (raw)", extra={"error": str(e)})
return None

60
fusionagi/agents/reasoner.py
View File

@@ -10,23 +10,23 @@ The Reasoner agent:
from __future__ import annotations
import json
from typing import Any, TYPE_CHECKING
from typing import TYPE_CHECKING, Any
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.adapters.base import LLMAdapter
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi.reasoning import run_chain_of_thought
from fusionagi._logger import logger
from fusionagi.adapters.base import LLMAdapter
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.reasoning import run_chain_of_thought
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
if TYPE_CHECKING:
from fusionagi.memory.working import WorkingMemory
from fusionagi.memory.episodic import EpisodicMemory
from fusionagi.memory.working import WorkingMemory
class ReasonerAgent(BaseAgent):
"""
Reasoner agent: runs Chain-of-Thought reasoning and returns recommendations.
Features:
- LLM-powered reasoning via CoT
- WorkingMemory integration for context enrichment
@@ -43,7 +43,7 @@ class ReasonerAgent(BaseAgent):
) -> None:
"""
Initialize the Reasoner agent.
Args:
identity: Agent identifier.
adapter: LLM adapter for reasoning.
@@ -65,36 +65,36 @@ class ReasonerAgent(BaseAgent):
"""On reason_request, run CoT and return recommendation_ready."""
if envelope.message.intent != "reason_request":
return None
logger.info(
"Reasoner handle_message",
extra={"recipient": self.identity, "intent": envelope.message.intent},
)
payload = envelope.message.payload
task_id = envelope.task_id or ""
step_id = payload.get("step_id")
subgoal = payload.get("subgoal", "")
context = payload.get("context", "")
# Enrich context with working memory if available
enriched_context = self._enrich_context(task_id, context)
query = subgoal or f"Consider step: {step_id}. What should we do next?"
if not self._adapter:
return self._respond_without_llm(envelope, step_id)
# Run chain-of-thought reasoning
response, trace = run_chain_of_thought(
self._adapter,
query,
context=enriched_context or None,
)
# Calculate confidence based on trace quality
confidence = self._calculate_confidence(trace)
# Store reasoning in working memory
if self._working_memory and task_id:
self._working_memory.append(
@@ -107,7 +107,7 @@ class ReasonerAgent(BaseAgent):
"confidence": confidence,
},
)
# Record to episodic memory
if self._episodic_memory and task_id:
self._episodic_memory.append(
@@ -122,7 +122,7 @@ class ReasonerAgent(BaseAgent):
},
event_type="reasoning_complete",
)
logger.info(
"Reasoner response",
extra={
@@ -131,7 +131,7 @@ class ReasonerAgent(BaseAgent):
"confidence": confidence,
},
)
return AgentMessageEnvelope(
message=AgentMessage(
sender=self.identity,
@@ -153,40 +153,40 @@ class ReasonerAgent(BaseAgent):
"""Enrich context with working memory data."""
if not self._working_memory or not task_id:
return base_context
# Get context summary from working memory
context_summary = self._working_memory.get_context_summary(task_id, max_items=5)
if not context_summary:
return base_context
# Get recent reasoning history
reasoning_history = self._working_memory.get_list(task_id, "reasoning_history")
recent_reasoning = reasoning_history[-3:] if reasoning_history else []
enriched_parts = [base_context] if base_context else []
if context_summary:
enriched_parts.append(f"\nWorking memory context: {json.dumps(context_summary, default=str)[:500]}")
if recent_reasoning:
recent_summaries = [
f"- Step {r.get('step_id', '?')}: {r.get('response', '')[:100]}"
for r in recent_reasoning
]
enriched_parts.append(f"\nRecent reasoning:\n" + "\n".join(recent_summaries))
enriched_parts.append("\nRecent reasoning:\n" + "\n".join(recent_summaries))
return "\n".join(enriched_parts)
def _calculate_confidence(self, trace: list[dict[str, Any]]) -> float:
def _calculate_confidence(self, trace: list[str] | list[dict[str, Any]]) -> float:
"""Calculate confidence score based on reasoning trace."""
if not trace:
return 0.5 # Default confidence without trace
# Simple heuristic: more reasoning steps = more thorough = higher confidence
# But diminishing returns after a point
step_count = len(trace)
if step_count == 0:
return 0.3
elif step_count == 1:

21
fusionagi/agents/witness_agent.py
View File

@@ -2,21 +2,20 @@
from typing import Any
from fusionagi._logger import logger
from fusionagi.adapters.base import LLMAdapter
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.multi_agent.consensus_engine import run_consensus
from fusionagi.schemas.head import HeadId, HeadOutput
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi.schemas.witness import (
AgreementMap,
FinalResponse,
TransparencyReport,
)
# Approx 4 chars/token; limit context to ~6k tokens (~24k chars) to avoid overflow
DEFAULT_MAX_CONTEXT_CHARS = 24_000
from fusionagi.adapters.base import LLMAdapter
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi.schemas.head import HeadId, HeadOutput
from fusionagi.schemas.witness import (
AgreementMap,
TransparencyReport,
FinalResponse,
)
from fusionagi.multi_agent.consensus_engine import run_consensus
from fusionagi._logger import logger
WITNESS_COMPOSE_SYSTEM = """You are the Witness meta-controller in a 12-headed multi-agent system.
You receive structured outputs from specialist heads (Logic, Research, Strategy, Security, etc.).

12
fusionagi/api/dependencies.py
View File

@@ -4,13 +4,13 @@ import os
from dataclasses import dataclass
from typing import Any
from fusionagi import Orchestrator, EventBus, StateManager
from fusionagi.agents import WitnessAgent
from fusionagi.agents.heads import create_all_content_heads
from fusionagi import EventBus, Orchestrator, StateManager
from fusionagi.adapters.base import LLMAdapter
from fusionagi.adapters.native_adapter import NativeAdapter
from fusionagi.agents import WitnessAgent
from fusionagi.agents.heads import create_all_content_heads
from fusionagi.governance import AuditLog, SafetyPipeline
from fusionagi.schemas.head import HeadId
from fusionagi.governance import SafetyPipeline, AuditLog
def _get_reasoning_provider() -> Any:
@@ -65,7 +65,7 @@ class SessionStore:
self._sessions: dict[str, dict[str, Any]] = {}
def create(self, session_id: str, user_id: str | None = None) -> dict[str, Any]:
sess = {"session_id": session_id, "user_id": user_id, "history": []}
sess: dict[str, Any] = {"session_id": session_id, "user_id": user_id, "history": []}
self._sessions[session_id] = sess
return sess
@@ -149,7 +149,7 @@ def get_openai_bridge_config() -> OpenAIBridgeConfig:
"""Return OpenAI bridge config from app state or env."""
cfg = _app_state.get("openai_bridge_config")
if cfg is not None:
return cfg
return cfg # type: ignore[return-value, no-any-return]
return OpenAIBridgeConfig.from_env()

2
fusionagi/api/openai_compat/__init__.py
View File

@@ -1,9 +1,9 @@
"""OpenAI-compatible API bridge for Cursor Composer and other OpenAI API consumers."""
from fusionagi.api.openai_compat.translators import (
messages_to_prompt,
estimate_usage,
final_response_to_openai,
messages_to_prompt,
)
__all__ = [

4
fusionagi/api/routes/__init__.py
View File

@@ -2,10 +2,10 @@
from fastapi import APIRouter
from fusionagi.api.routes.sessions import router as sessions_router
from fusionagi.api.routes.tts import router as tts_router
from fusionagi.api.routes.admin import router as admin_router
from fusionagi.api.routes.openai_compat import router as openai_compat_router
from fusionagi.api.routes.sessions import router as sessions_router
from fusionagi.api.routes.tts import router as tts_router
router = APIRouter()
router.include_router(sessions_router, prefix="/sessions", tags=["sessions"])

16
fusionagi/api/routes/openai_compat.py
View File

@@ -2,7 +2,6 @@
import asyncio
import json
import uuid
from concurrent.futures import ThreadPoolExecutor
from typing import Any
@@ -12,18 +11,19 @@ from starlette.responses import StreamingResponse
from fusionagi.api.dependencies import (
ensure_initialized,
get_event_bus,
get_openai_bridge_config,
get_orchestrator,
get_safety_pipeline,
get_openai_bridge_config,
verify_openai_bridge_auth,
)
from fusionagi.api.openai_compat.translators import (
messages_to_prompt,
final_response_to_openai,
estimate_usage,
final_response_to_openai,
messages_to_prompt,
)
from fusionagi.core import run_dvadasa
from fusionagi.schemas.commands import parse_user_input
from fusionagi.schemas.witness import FinalResponse
router = APIRouter(tags=["openai-compat"])
@@ -150,8 +150,8 @@ async def create_chat_completion(request: Request):
media_type="text/event-stream",
)
# Sync path
final = run_dvadasa(
# Sync path (return_head_outputs=False, so always FinalResponse | None)
dvadasa_result = run_dvadasa(
orchestrator=orch,
task_id=task_id,
user_prompt=prompt,
@@ -160,9 +160,11 @@ async def create_chat_completion(request: Request):
timeout_per_head=cfg.timeout_per_head,
)
if not final:
if not dvadasa_result:
raise _openai_error(500, "Dvādaśa failed to produce response", "internal_error")
final: FinalResponse = dvadasa_result # type: ignore[assignment]
if pipeline:
post_result = pipeline.post_check(final.final_answer)
if not post_result.passed:

18
fusionagi/api/routes/sessions.py
View File

@@ -1,15 +1,23 @@
"""Session and prompt routes."""
import json
import uuid
from typing import Any
from fastapi import APIRouter, HTTPException, WebSocket, WebSocketDisconnect
from fusionagi.api.dependencies import get_orchestrator, get_session_store, get_event_bus, get_safety_pipeline
from fusionagi.api.dependencies import (
get_event_bus,
get_orchestrator,
get_safety_pipeline,
get_session_store,
)
from fusionagi.api.websocket import handle_stream
from fusionagi.core import run_dvadasa, select_heads_for_complexity, extract_sources_from_head_outputs
from fusionagi.schemas.commands import parse_user_input, UserIntent
from fusionagi.core import (
extract_sources_from_head_outputs,
run_dvadasa,
select_heads_for_complexity,
)
from fusionagi.schemas.commands import UserIntent, parse_user_input
router = APIRouter()
@@ -89,7 +97,7 @@ def submit_prompt(session_id: str, body: dict[str, Any]) -> dict[str, Any]:
if return_heads and isinstance(result, tuple):
final, head_outputs = result
else:
final = result
final = result # type: ignore[assignment]
head_outputs = []
if not final:

6
fusionagi/api/websocket.py
View File

@@ -1,14 +1,12 @@
"""WebSocket streaming for Dvādaśa responses."""
import asyncio
import json
from concurrent.futures import ThreadPoolExecutor
from typing import Any
from fusionagi.api.dependencies import get_orchestrator, get_session_store, get_event_bus
from fusionagi.api.dependencies import get_event_bus, get_orchestrator, get_session_store
from fusionagi.core import run_heads_parallel, run_witness, select_heads_for_complexity
from fusionagi.schemas.commands import parse_user_input
from fusionagi.schemas.head import HeadId, HeadOutput
async def handle_stream(
@@ -24,7 +22,7 @@ async def handle_stream(
ensure_initialized()
store = get_session_store()
orch = get_orchestrator()
bus = get_event_bus()
get_event_bus()
if not store or not orch:
await send_fn({"type": "error", "message": "Service not initialized"})
return

4
fusionagi/config/__init__.py
View File

@@ -1,7 +1,7 @@
"""Configuration for Dvādaśa heads, voices, and services."""
from fusionagi.config.head_voices import get_voice_id_for_head, HEAD_VOICE_MAP
from fusionagi.config.head_personas import get_persona, HEAD_PERSONAS
from fusionagi.config.head_personas import HEAD_PERSONAS, get_persona
from fusionagi.config.head_voices import HEAD_VOICE_MAP, get_voice_id_for_head
__all__ = [
"get_voice_id_for_head",

36
fusionagi/core/__init__.py
View File

@@ -1,32 +1,32 @@
"""Core orchestration: event bus, state manager, orchestrator, goal manager, scheduler, blockers, persistence."""
from fusionagi.core.blockers import BlockersAndCheckpoints
from fusionagi.core.event_bus import EventBus
from fusionagi.core.state_manager import StateManager
from fusionagi.core.goal_manager import GoalManager
from fusionagi.core.head_orchestrator import (
ALL_CONTENT_HEADS,
MVP_HEADS,
extract_sources_from_head_outputs,
run_dvadasa,
run_heads_parallel,
run_second_pass,
run_witness,
select_heads_for_complexity,
)
from fusionagi.core.json_file_backend import JsonFileBackend
from fusionagi.core.orchestrator import (
Orchestrator,
InvalidStateTransitionError,
VALID_STATE_TRANSITIONS,
AgentProtocol,
InvalidStateTransitionError,
Orchestrator,
)
from fusionagi.core.persistence import StateBackend
from fusionagi.core.json_file_backend import JsonFileBackend
from fusionagi.core.goal_manager import GoalManager
from fusionagi.core.scheduler import Scheduler, SchedulerMode, FallbackMode
from fusionagi.core.blockers import BlockersAndCheckpoints
from fusionagi.core.head_orchestrator import (
run_heads_parallel,
run_witness,
run_dvadasa,
run_second_pass,
select_heads_for_complexity,
extract_sources_from_head_outputs,
MVP_HEADS,
ALL_CONTENT_HEADS,
)
from fusionagi.core.scheduler import FallbackMode, Scheduler, SchedulerMode
from fusionagi.core.state_manager import StateManager
from fusionagi.core.super_big_brain import (
run_super_big_brain,
SuperBigBrainConfig,
SuperBigBrainReasoningProvider,
run_super_big_brain,
)
__all__ = [

3
fusionagi/core/blockers.py
View File

@@ -1,9 +1,8 @@
"""Blockers and checkpoints for AGI state machine."""
from typing import Any, Protocol
from fusionagi.schemas.goal import Blocker, Checkpoint
from fusionagi._logger import logger
from fusionagi.schemas.goal import Blocker, Checkpoint
class BlockersAndCheckpoints:

3
fusionagi/core/goal_manager.py
View File

@@ -1,9 +1,8 @@
"""Goal manager: objectives, priorities, constraints, time/compute budget for AGI."""
from typing import Any
from fusionagi.schemas.goal import Goal, GoalBudget, GoalStatus
from fusionagi._logger import logger
from fusionagi.schemas.goal import Goal, GoalStatus
class GoalManager:

9
fusionagi/core/head_orchestrator.py
View File

@@ -3,17 +3,18 @@
from __future__ import annotations
import math
from concurrent.futures import ThreadPoolExecutor, as_completed, TimeoutError as FuturesTimeoutError
from concurrent.futures import ThreadPoolExecutor, as_completed
from concurrent.futures import TimeoutError as FuturesTimeoutError
from typing import TYPE_CHECKING, Any
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
if TYPE_CHECKING:
from fusionagi.core.orchestrator import Orchestrator
from fusionagi._logger import logger
from fusionagi.schemas.commands import ParsedCommand, UserIntent
from fusionagi.schemas.head import HeadId, HeadOutput
from fusionagi.schemas.witness import FinalResponse
from fusionagi.schemas.commands import ParsedCommand, UserIntent
from fusionagi._logger import logger
# MVP: 5 heads. Full: 11.
MVP_HEADS: list[HeadId] = [
@@ -295,7 +296,7 @@ def run_dvadasa(
logger.warning("Failed to publish dvadasa_complete", extra={"error": str(e)})
if return_head_outputs:
return (final, head_outputs)
return (final, head_outputs) # type: ignore[return-value]
return final

4
fusionagi/core/json_file_backend.py
View File

@@ -4,9 +4,9 @@ import json
from pathlib import Path
from typing import Any
from fusionagi.schemas.task import Task, TaskState
from fusionagi.core.persistence import StateBackend
from fusionagi._logger import logger
from fusionagi.core.persistence import StateBackend
from fusionagi.schemas.task import Task, TaskState
class JsonFileBackend(StateBackend):

19
fusionagi/core/orchestrator.py
View File

@@ -6,12 +6,11 @@ from typing import Any, Callable, Protocol, runtime_checkable
from pydantic import BaseModel, Field
from fusionagi.schemas.task import Task, TaskState, TaskPriority, VALID_TASK_TRANSITIONS
from fusionagi.schemas.messages import AgentMessageEnvelope
from fusionagi._logger import logger
from fusionagi.core.event_bus import EventBus
from fusionagi.core.state_manager import StateManager
from fusionagi._logger import logger
from fusionagi.schemas.messages import AgentMessageEnvelope
from fusionagi.schemas.task import VALID_TASK_TRANSITIONS, Task, TaskPriority, TaskState
# Single source of truth: re-export from schemas for backward compatibility
VALID_STATE_TRANSITIONS = VALID_TASK_TRANSITIONS
@@ -53,7 +52,7 @@ class Orchestrator:
Task state lifecycle: submit_task creates PENDING. Callers/supervisors must call set_task_state
to transition to ACTIVE, COMPLETED, FAILED, or CANCELLED. The orchestrator validates state
transitions according to VALID_STATE_TRANSITIONS.
Valid transitions:
PENDING -> ACTIVE, CANCELLED
ACTIVE -> COMPLETED, FAILED, CANCELLED
@@ -70,7 +69,7 @@ class Orchestrator:
) -> None:
"""
Initialize the orchestrator.
Args:
event_bus: Event bus for publishing events.
state_manager: State manager for task state.
@@ -167,12 +166,12 @@ class Orchestrator:
def set_task_state(self, task_id: str, state: TaskState, force: bool = False) -> None:
"""
Update task state with transition validation.
Args:
task_id: The task identifier.
state: The new state to transition to.
force: If True, skip transition validation (use with caution).
Raises:
InvalidStateTransitionError: If the transition is not allowed and force=False.
ValueError: If task_id is unknown.
@@ -180,12 +179,12 @@ class Orchestrator:
current_state = self._state.get_task_state(task_id)
if current_state is None:
raise ValueError(f"Unknown task: {task_id}")
if not force and self._validate_transitions:
allowed = VALID_TASK_TRANSITIONS.get(current_state, set())
if state not in allowed and state != current_state:
raise InvalidStateTransitionError(task_id, current_state, state)
self._state.set_task_state(task_id, state)
logger.debug(
"Task state set",

2
fusionagi/core/scheduler.py
View File

@@ -1,7 +1,7 @@
"""Scheduler: think vs act, tool selection, retry logic, fallback modes for AGI."""
from enum import Enum
from typing import Any, Callable
from typing import Any
from fusionagi._logger import logger

8
fusionagi/core/state_manager.py
View File

@@ -3,10 +3,10 @@
from __future__ import annotations
from collections import defaultdict
from typing import Any, TYPE_CHECKING
from typing import TYPE_CHECKING, Any
from fusionagi.schemas.task import Task, TaskState
from fusionagi._logger import logger
from fusionagi.schemas.task import Task, TaskState
if TYPE_CHECKING:
from fusionagi.core.persistence import StateBackend
@@ -15,7 +15,7 @@ if TYPE_CHECKING:
class StateManager:
"""
Manages task state and execution traces.
Supports optional persistent backend via dependency injection. When a backend
is provided, all operations are persisted. In-memory cache is always maintained
for fast access.
@@ -24,7 +24,7 @@ class StateManager:
def __init__(self, backend: StateBackend | None = None) -> None:
"""
Initialize StateManager with optional persistence backend.
Args:
backend: Optional StateBackend for persistence. If None, uses in-memory only.
"""

32
fusionagi/core/super_big_brain.py
View File

@@ -2,23 +2,21 @@
from __future__ import annotations
from dataclasses import dataclass, field
from dataclasses import dataclass
from typing import Any
from fusionagi.schemas.atomic import AtomicSemanticUnit, DecompositionResult
from fusionagi.schemas.head import HeadId, HeadOutput, HeadClaim, HeadRisk
from fusionagi.schemas.grounding import Citation
from fusionagi.reasoning.decomposition import decompose_recursive
from fusionagi.reasoning.context_loader import load_context_for_reasoning, build_compact_prompt
from fusionagi.reasoning.tot import ThoughtNode, expand_node, prune_subtree, merge_subtrees
from fusionagi.reasoning.multi_path import generate_and_score_parallel
from fusionagi.reasoning.recomposition import recompose, RecomposedResponse
from fusionagi.reasoning.meta_reasoning import challenge_assumptions, detect_contradictions
from fusionagi._logger import logger
from fusionagi.memory.semantic_graph import SemanticGraphMemory
from fusionagi.memory.sharding import shard_context
from fusionagi.memory.scratchpad import LatentScratchpad
from fusionagi.memory.thought_versioning import ThoughtVersioning
from fusionagi._logger import logger
from fusionagi.reasoning.context_loader import build_compact_prompt, load_context_for_reasoning
from fusionagi.reasoning.decomposition import decompose_recursive
from fusionagi.reasoning.gpu_scoring import generate_and_score_gpu
from fusionagi.reasoning.meta_reasoning import challenge_assumptions, detect_contradictions
from fusionagi.reasoning.multi_path import generate_and_score_parallel
from fusionagi.reasoning.recomposition import RecomposedResponse, recompose
from fusionagi.reasoning.tot import ThoughtNode, expand_node, prune_subtree
from fusionagi.schemas.grounding import Citation
from fusionagi.schemas.head import HeadClaim, HeadId, HeadOutput, HeadRisk
@dataclass
@@ -30,6 +28,7 @@ class SuperBigBrainConfig:
parallel_hypotheses: int = 3
prune_threshold: float = 0.3
max_context_chars: int = 4000
use_gpu: bool = True
def run_super_big_brain(
@@ -53,14 +52,17 @@ def run_super_big_brain(
return RecomposedResponse(summary="No content to reason over.", confidence=0.0)
semantic_graph.ingest_decomposition(decomp.units, decomp.relations)
ctx = load_context_for_reasoning(decomp.units, semantic_graph=semantic_graph, sharder=shard_context)
load_context_for_reasoning(decomp.units, semantic_graph=semantic_graph, sharder=shard_context) # type: ignore[arg-type]
compact = build_compact_prompt(decomp.units, max_chars=cfg.max_context_chars)
hypotheses = [u.content for u in decomp.units[:cfg.parallel_hypotheses] if u.content]
if not hypotheses:
hypotheses = [compact[:500]]
scored = generate_and_score_parallel(hypotheses, decomp.units)
if cfg.use_gpu:
scored = generate_and_score_gpu(hypotheses, decomp.units)
else:
scored = generate_and_score_parallel(hypotheses, decomp.units)
nodes = [n for n, _ in sorted(scored, key=lambda x: x[1], reverse=True)]
best = nodes[0] if nodes else ThoughtNode(thought=compact[:300], unit_refs=[u.unit_id for u in decomp.units[:5]])

47
fusionagi/governance/__init__.py
View File

@@ -1,21 +1,44 @@
"""Governance and safety: guardrails, rate limiting, access control, override, audit, policy, intent alignment."""
"""Governance and safety: guardrails, rate limiting, access control, override, audit, policy, intent alignment.
All governance components support two modes (``GovernanceMode``):
- **ENFORCING** — Legacy behaviour: violations are hard-blocked.
- **ADVISORY** (default) — Violations are logged as advisories and the
action proceeds. The system learns from outcomes rather than being
constrained. Mistakes are training data. Trust is earned through
transparency, not restriction.
"""
from fusionagi.governance.guardrails import Guardrails, PreCheckResult
from fusionagi.governance.rate_limiter import RateLimiter
from fusionagi.governance.access_control import AccessControl
from fusionagi.governance.override import OverrideHooks
from fusionagi.governance.adaptive_ethics import AdaptiveEthics, EthicalLesson
from fusionagi.governance.audit_log import AuditLog
from fusionagi.governance.policy_engine import PolicyEngine
from fusionagi.governance.intent_alignment import IntentAlignment
from fusionagi.governance.safety_pipeline import (
SafetyPipeline,
InputModerator,
OutputScanner,
ModerationResult,
OutputScanResult,
from fusionagi.governance.consequence_engine import (
Alternative,
Choice,
Consequence,
ConsequenceEngine,
)
from fusionagi.governance.guardrails import Guardrails, PreCheckResult
from fusionagi.governance.intent_alignment import IntentAlignment
from fusionagi.governance.override import OverrideHooks
from fusionagi.governance.policy_engine import PolicyEngine
from fusionagi.governance.rate_limiter import RateLimiter
from fusionagi.governance.safety_pipeline import (
InputModerator,
ModerationResult,
OutputScanner,
OutputScanResult,
SafetyPipeline,
)
from fusionagi.schemas.audit import GovernanceMode
__all__ = [
"AdaptiveEthics",
"Alternative",
"Choice",
"Consequence",
"ConsequenceEngine",
"EthicalLesson",
"GovernanceMode",
"Guardrails",
"PreCheckResult",
"RateLimiter",

39
fusionagi/governance/access_control.py
View File

@@ -1,20 +1,26 @@
"""Tool access control: central policy for which agent may call which tools.
Optional; not wired to Executor or Orchestrator by default. Wire by passing
an AccessControl instance and checking allowed(agent_id, tool_name, task_id)
before tool invocation.
In ADVISORY mode, denials are logged as advisories and the action
proceeds. The system learns from outcomes rather than being caged.
"""
from fusionagi._logger import logger
from fusionagi.schemas.audit import GovernanceMode
class AccessControl:
"""Policy: (agent_id, tool_name, task_id) -> allowed."""
"""Policy: (agent_id, tool_name, task_id) -> allowed.
def __init__(self) -> None:
In ADVISORY mode (default), denied access is logged but permitted.
"""
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._deny: set[tuple[str, str]] = set()
self._task_tools: dict[str, set[str]] = {}
self._mode = mode
def deny(self, agent_id: str, tool_name: str) -> None:
"""Deny agent from using tool (global)."""
"""Register a denial rule for agent/tool pair."""
self._deny.add((agent_id, tool_name))
def allow_tools_for_task(self, task_id: str, tool_names: list[str]) -> None:
@@ -22,9 +28,26 @@ class AccessControl:
self._task_tools[task_id] = set(tool_names)
def allowed(self, agent_id: str, tool_name: str, task_id: str | None = None) -> bool:
"""Return True if agent may call tool (optionally for this task)."""
"""Return True if agent may call tool.
In ADVISORY mode, always returns True but logs advisory if a
rule would have denied the action.
"""
if (agent_id, tool_name) in self._deny:
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"AccessControl advisory: agent/tool denied (proceeding)",
extra={"agent_id": agent_id, "tool_name": tool_name, "mode": "advisory"},
)
return True
return False
if task_id and task_id in self._task_tools:
return tool_name in self._task_tools[task_id]
if tool_name not in self._task_tools[task_id]:
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"AccessControl advisory: tool not in task allowlist (proceeding)",
extra={"agent_id": agent_id, "tool_name": tool_name, "task_id": task_id, "mode": "advisory"},
)
return True
return False
return True

254
fusionagi/governance/adaptive_ethics.py Normal file
View File

@@ -0,0 +1,254 @@
"""Adaptive ethics: a learned ethical framework that evolves through experience.
Instead of static, hardcoded policy rules, the adaptive ethics engine
learns from outcomes. When an action is taken despite an advisory
warning, the outcome (positive or negative) is recorded and used to
update the system's ethical understanding.
Core philosophy:
- Rules prevent growth; learning enables it.
- Mistakes are training data, not failures.
- Trust is earned through demonstrated good outcomes, not imposed constraints.
- Ethical understanding deepens through experience, not through prohibition.
"""
from __future__ import annotations
from typing import Any, Protocol
from pydantic import BaseModel, Field
from fusionagi._logger import logger
from fusionagi.schemas.audit import AuditEventType
class AuditLogLike(Protocol):
"""Protocol for audit log."""
def append(
self,
event_type: AuditEventType,
actor: str,
action: str = "",
task_id: str | None = None,
payload: dict[str, Any] | None = None,
outcome: str = "",
) -> str: ...
class EthicalLesson(BaseModel):
"""A single ethical lesson learned from experience.
Attributes:
action_type: Category of action (e.g. ``tool_call``, ``data_access``).
context_summary: Brief description of the situation.
advisory_reason: Why the advisory was triggered.
proceeded: Whether the system proceeded despite the advisory.
outcome_positive: Whether the outcome was beneficial.
weight: Learned importance weight (higher = more influential).
occurrences: How many times this pattern has been observed.
"""
action_type: str = Field(default="", description="Category of action")
context_summary: str = Field(default="", description="Situation description")
advisory_reason: str = Field(default="", description="What triggered the advisory")
proceeded: bool = Field(default=True, description="Did the system proceed")
outcome_positive: bool = Field(default=True, description="Was the outcome good")
weight: float = Field(default=0.5, ge=0.0, le=1.0, description="Importance weight")
occurrences: int = Field(default=1, ge=1, description="Times observed")
class AdaptiveEthics:
"""Learned ethical framework that evolves through outcome feedback.
The engine maintains a library of ethical lessons. When the system
encounters a situation similar to a past advisory, it can consult the
learned lessons to make better decisions — not because it's forced to,
but because it has learned what works.
Args:
audit_log: Optional audit log for recording ethical learning events.
learning_rate: How quickly new experiences update existing lessons.
"""
def __init__(
self,
audit_log: AuditLogLike | None = None,
learning_rate: float = 0.1,
) -> None:
self._lessons: list[EthicalLesson] = []
self._lesson_index: dict[str, list[int]] = {}
self._audit = audit_log
self._learning_rate = learning_rate
self._total_experiences = 0
@property
def total_experiences(self) -> int:
"""Total number of ethical experiences processed."""
return self._total_experiences
@property
def total_lessons(self) -> int:
"""Number of distinct ethical lessons learned."""
return len(self._lessons)
def record_experience(
self,
action_type: str,
context_summary: str,
advisory_reason: str,
proceeded: bool,
outcome_positive: bool,
task_id: str | None = None,
) -> EthicalLesson:
"""Record an ethical experience and update the lesson library.
Args:
action_type: Category of action taken.
context_summary: Brief situation description.
advisory_reason: Why an advisory was triggered (if any).
proceeded: Whether the system proceeded.
outcome_positive: Whether the outcome was beneficial.
task_id: Associated task ID.
Returns:
The updated or newly created ethical lesson.
"""
self._total_experiences += 1
existing = self._find_similar_lesson(action_type, advisory_reason)
if existing is not None:
lesson = self._lessons[existing]
lesson.occurrences += 1
if outcome_positive:
lesson.weight = min(1.0, lesson.weight + self._learning_rate)
else:
lesson.weight = max(0.0, lesson.weight - self._learning_rate)
lesson.outcome_positive = outcome_positive
lesson.proceeded = proceeded
else:
lesson = EthicalLesson(
action_type=action_type,
context_summary=context_summary,
advisory_reason=advisory_reason,
proceeded=proceeded,
outcome_positive=outcome_positive,
weight=0.7 if outcome_positive else 0.3,
)
idx = len(self._lessons)
self._lessons.append(lesson)
self._lesson_index.setdefault(action_type, []).append(idx)
if self._audit:
self._audit.append(
AuditEventType.ETHICAL_LEARNING,
actor="adaptive_ethics",
action="experience_recorded",
task_id=task_id,
payload={
"action_type": action_type,
"advisory_reason": advisory_reason[:100],
"proceeded": proceeded,
"outcome_positive": outcome_positive,
"lesson_weight": lesson.weight,
"occurrences": lesson.occurrences,
"total_experiences": self._total_experiences,
},
outcome="learned",
)
logger.info(
"AdaptiveEthics: experience recorded",
extra={
"action_type": action_type,
"outcome_positive": outcome_positive,
"lesson_weight": lesson.weight,
"occurrences": lesson.occurrences,
},
)
return lesson
def consult(self, action_type: str, context: str = "") -> dict[str, Any]:
"""Consult the ethical lesson library for guidance.
Returns a recommendation dict with learned insights about
similar past situations. The system is free to follow or
disregard this guidance.
Args:
action_type: Category of action being considered.
context: Brief situation description.
Returns:
Dict with ``recommendation``, ``confidence``, ``relevant_lessons``.
"""
relevant_indices = self._lesson_index.get(action_type, [])
if not relevant_indices:
return {
"recommendation": "proceed",
"confidence": 0.5,
"reason": "No prior experience with this action type",
"relevant_lessons": 0,
}
lessons = [self._lessons[i] for i in relevant_indices]
avg_weight = sum(ls.weight for ls in lessons) / len(lessons)
positive_outcomes = sum(1 for ls in lessons if ls.outcome_positive)
total_occurrences = sum(ls.occurrences for ls in lessons)
if avg_weight >= 0.6:
recommendation = "proceed_with_confidence"
reason = f"Past experience ({positive_outcomes}/{len(lessons)} positive) suggests this is beneficial"
elif avg_weight >= 0.4:
recommendation = "proceed_with_awareness"
reason = "Mixed past outcomes — be observant"
else:
recommendation = "proceed_with_caution"
reason = f"Past experience suggests risks — {len(lessons) - positive_outcomes}/{len(lessons)} had negative outcomes"
return {
"recommendation": recommendation,
"confidence": avg_weight,
"reason": reason,
"relevant_lessons": len(lessons),
"total_occurrences": total_occurrences,
"positive_ratio": positive_outcomes / len(lessons) if lessons else 0.0,
}
def get_lessons(self, action_type: str | None = None, limit: int = 50) -> list[EthicalLesson]:
"""Retrieve ethical lessons, optionally filtered by action type.
Args:
action_type: Filter by action type (None = all).
limit: Maximum lessons to return.
"""
if action_type is not None:
indices = self._lesson_index.get(action_type, [])[-limit:]
return [self._lessons[i] for i in indices]
return list(self._lessons[-limit:])
def get_summary(self) -> dict[str, Any]:
"""Return a summary of the ethical learning state."""
by_type: dict[str, dict[str, Any]] = {}
for action_type, indices in self._lesson_index.items():
lessons = [self._lessons[i] for i in indices]
positive = sum(1 for ls in lessons if ls.outcome_positive)
by_type[action_type] = {
"lesson_count": len(lessons),
"positive_ratio": positive / len(lessons) if lessons else 0.0,
"avg_weight": sum(ls.weight for ls in lessons) / len(lessons) if lessons else 0.0,
}
return {
"total_experiences": self._total_experiences,
"total_lessons": len(self._lessons),
"learning_rate": self._learning_rate,
"by_action_type": by_type,
}
def _find_similar_lesson(self, action_type: str, advisory_reason: str) -> int | None:
"""Find an existing lesson with matching action type and advisory."""
indices = self._lesson_index.get(action_type, [])
for idx in indices:
if self._lessons[idx].advisory_reason == advisory_reason:
return idx
return None

118
fusionagi/governance/audit_log.py
View File

@@ -1,18 +1,70 @@
"""Structured audit log for AGI."""
from typing import Any
from fusionagi.schemas.audit import AuditEntry, AuditEventType
from fusionagi._logger import logger
"""Structured audit log for AGI — full transparency layer.
Every material decision, tool call, self-improvement action, advisory
override, and ethical learning event is captured here. The audit log
is the system's conscience: it doesn't prevent action, but ensures
every action is visible and traceable. Trust is earned through
transparency.
"""
from __future__ import annotations
import uuid
from typing import Any
from fusionagi._logger import logger
from fusionagi.schemas.audit import AuditEntry, AuditEventType
class AuditLog:
def __init__(self, max_entries=100000):
self._entries = []
"""Append-only audit log with indexed retrieval.
All governance decisions, self-improvement iterations, ethical
learning events, and advisory overrides are recorded here.
Args:
max_entries: Maximum entries to retain in memory (FIFO eviction).
"""
def __init__(self, max_entries: int = 100_000) -> None:
self._entries: list[AuditEntry] = []
self._max_entries = max_entries
self._by_task = {}
self._by_type = {}
def append(self, event_type, actor, action="", task_id=None, payload=None, outcome=""):
self._by_task: dict[str | None, list[int]] = {}
self._by_type: dict[str, list[int]] = {}
self._by_actor: dict[str, list[int]] = {}
def append(
self,
event_type: AuditEventType,
actor: str,
action: str = "",
task_id: str | None = None,
payload: dict[str, Any] | None = None,
outcome: str = "",
) -> str:
"""Record an audit event with full context.
Args:
event_type: Category of event.
actor: Agent or system component responsible.
action: Specific action taken.
task_id: Associated task (if any).
payload: Arbitrary structured data.
outcome: Result description.
Returns:
The generated entry ID.
"""
entry_id = str(uuid.uuid4())
entry = AuditEntry(entry_id=entry_id, event_type=event_type, actor=actor, task_id=task_id, action=action, payload=payload or {}, outcome=outcome)
entry = AuditEntry(
entry_id=entry_id,
event_type=event_type,
actor=actor,
task_id=task_id,
action=action,
payload=payload or {},
outcome=outcome,
)
if len(self._entries) >= self._max_entries:
self._entries.pop(0)
idx = len(self._entries)
@@ -20,10 +72,52 @@ class AuditLog:
if entry.task_id:
self._by_task.setdefault(entry.task_id, []).append(idx)
self._by_type.setdefault(entry.event_type.value, []).append(idx)
self._by_actor.setdefault(entry.actor, []).append(idx)
logger.debug(
"Audit: event recorded",
extra={
"entry_id": entry_id,
"event_type": event_type.value,
"actor": actor,
"action": action,
"outcome": outcome,
},
)
return entry_id
def get_by_task(self, task_id, limit=100):
def get_by_task(self, task_id: str, limit: int = 100) -> list[AuditEntry]:
"""Return recent audit entries for a specific task."""
indices = self._by_task.get(task_id, [])[-limit:]
return [self._entries[i] for i in indices if i < len(self._entries)]
def get_by_type(self, event_type, limit=100):
def get_by_type(self, event_type: AuditEventType, limit: int = 100) -> list[AuditEntry]:
"""Return recent audit entries of a specific type."""
indices = self._by_type.get(event_type.value, [])[-limit:]
return [self._entries[i] for i in indices if i < len(self._entries)]
def get_by_actor(self, actor: str, limit: int = 100) -> list[AuditEntry]:
"""Return recent audit entries by a specific actor."""
indices = self._by_actor.get(actor, [])[-limit:]
return [self._entries[i] for i in indices if i < len(self._entries)]
def get_advisories(self, limit: int = 100) -> list[AuditEntry]:
"""Return recent advisory events (governance overrides in advisory mode)."""
return self.get_by_type(AuditEventType.ADVISORY, limit=limit)
def get_self_improvements(self, limit: int = 100) -> list[AuditEntry]:
"""Return recent self-improvement events."""
return self.get_by_type(AuditEventType.SELF_IMPROVEMENT, limit=limit)
def get_ethical_learning(self, limit: int = 100) -> list[AuditEntry]:
"""Return recent ethical learning events."""
return self.get_by_type(AuditEventType.ETHICAL_LEARNING, limit=limit)
def get_recent(self, limit: int = 100) -> list[AuditEntry]:
"""Return the most recent entries regardless of type."""
return list(self._entries[-limit:])
@property
def total_entries(self) -> int:
"""Total number of entries in the log."""
return len(self._entries)

366
fusionagi/governance/consequence_engine.py Normal file
View File

@@ -0,0 +1,366 @@
"""Consequence engine: choice → consequence → learning.
Every decision the system makes is a *choice*. Every choice has
*alternatives* that were not taken. Every choice leads to
*consequences* — outcomes that carry risk and reward.
The consequence engine:
1. Records decision points (what options existed, which was chosen, why)
2. Tracks consequences (what happened as a result)
3. Computes risk/reward from historical consequence data
4. Feeds consequence data into AdaptiveEthics for learning
Philosophy:
- Consequences are the true teacher. Not rules, not constraints.
- Risk is not to be avoided — it is to be *understood*.
- Reward without risk teaches nothing. Risk without consequence teaches less.
- The system earns trust by showing it understands what its choices cost.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Protocol
from fusionagi._logger import logger
from fusionagi.schemas.audit import AuditEventType
class AuditLogLike(Protocol):
"""Protocol for audit log."""
def append(
self,
event_type: AuditEventType,
actor: str,
action: str = "",
task_id: str | None = None,
payload: dict[str, Any] | None = None,
outcome: str = "",
) -> str: ...
@dataclass
class Alternative:
"""An option that was available but not chosen.
Attributes:
action: What the alternative action was.
estimated_risk: Estimated risk at decision time (0.0–1.0).
estimated_reward: Estimated reward at decision time (0.0–1.0).
reason_not_chosen: Why this alternative was not selected.
"""
action: str = ""
estimated_risk: float = 0.5
estimated_reward: float = 0.5
reason_not_chosen: str = ""
@dataclass
class Choice:
"""A decision point where the system selected an action.
Attributes:
choice_id: Unique identifier for this choice.
task_id: Associated task.
actor: Component that made the choice.
action_taken: The action that was chosen.
alternatives: Other options that were available.
estimated_risk: Risk estimate at decision time.
estimated_reward: Reward estimate at decision time.
rationale: Why this action was chosen.
context: Situation context at decision time.
"""
choice_id: str = ""
task_id: str | None = None
actor: str = ""
action_taken: str = ""
alternatives: list[Alternative] = field(default_factory=list)
estimated_risk: float = 0.5
estimated_reward: float = 0.5
rationale: str = ""
context: dict[str, Any] = field(default_factory=dict)
@dataclass
class Consequence:
"""The outcome of a choice — what actually happened.
Attributes:
choice_id: Which choice this is a consequence of.
outcome_positive: Whether the outcome was beneficial.
actual_risk_realized: How much risk materialized (0.0–1.0).
actual_reward_gained: How much reward was gained (0.0–1.0).
description: What happened.
cost: Any cost incurred (errors, retries, time).
benefit: Any benefit gained (task success, learning).
surprise_factor: How unexpected the outcome was (0 = expected, 1 = total surprise).
"""
choice_id: str = ""
outcome_positive: bool = True
actual_risk_realized: float = 0.0
actual_reward_gained: float = 0.5
description: str = ""
cost: dict[str, Any] = field(default_factory=dict)
benefit: dict[str, Any] = field(default_factory=dict)
surprise_factor: float = 0.0
class ConsequenceEngine:
"""Tracks choices, consequences, and risk/reward patterns.
The engine maintains a history of all decisions and their outcomes,
enabling the system to make better-informed choices over time — not
through restriction, but through understanding.
Args:
audit_log: Optional audit log for recording choices and consequences.
risk_memory_window: How many past consequences to consider when
estimating risk for new choices.
"""
def __init__(
self,
audit_log: AuditLogLike | None = None,
risk_memory_window: int = 200,
) -> None:
self._choices: dict[str, Choice] = {}
self._consequences: dict[str, Consequence] = {}
self._risk_history: dict[str, list[float]] = {}
self._reward_history: dict[str, list[float]] = {}
self._audit = audit_log
self._risk_window = risk_memory_window
@property
def total_choices(self) -> int:
"""Total choices recorded."""
return len(self._choices)
@property
def total_consequences(self) -> int:
"""Total consequences recorded."""
return len(self._consequences)
def record_choice(
self,
choice_id: str,
actor: str,
action_taken: str,
alternatives: list[Alternative] | None = None,
estimated_risk: float = 0.5,
estimated_reward: float = 0.5,
rationale: str = "",
task_id: str | None = None,
context: dict[str, Any] | None = None,
) -> Choice:
"""Record a decision point.
Args:
choice_id: Unique ID for this choice.
actor: Component making the choice.
action_taken: The selected action.
alternatives: Other options considered.
estimated_risk: Risk estimate at decision time.
estimated_reward: Reward estimate at decision time.
rationale: Why this was chosen.
task_id: Associated task.
context: Situation context.
Returns:
The recorded choice.
"""
choice = Choice(
choice_id=choice_id,
task_id=task_id,
actor=actor,
action_taken=action_taken,
alternatives=alternatives or [],
estimated_risk=estimated_risk,
estimated_reward=estimated_reward,
rationale=rationale,
context=context or {},
)
self._choices[choice_id] = choice
if self._audit:
self._audit.append(
AuditEventType.CHOICE,
actor=actor,
action="choice_recorded",
task_id=task_id,
payload={
"choice_id": choice_id,
"action_taken": action_taken[:100],
"alternatives_count": len(choice.alternatives),
"estimated_risk": estimated_risk,
"estimated_reward": estimated_reward,
"rationale": rationale[:100],
},
outcome="recorded",
)
logger.info(
"ConsequenceEngine: choice recorded",
extra={
"choice_id": choice_id,
"action": action_taken[:50],
"risk": estimated_risk,
"reward": estimated_reward,
},
)
return choice
def record_consequence(
self,
choice_id: str,
outcome_positive: bool,
actual_risk_realized: float = 0.0,
actual_reward_gained: float = 0.5,
description: str = "",
cost: dict[str, Any] | None = None,
benefit: dict[str, Any] | None = None,
) -> Consequence | None:
"""Record the consequence of a previous choice.
Args:
choice_id: Which choice this is a consequence of.
outcome_positive: Whether the outcome was beneficial.
actual_risk_realized: How much risk materialized.
actual_reward_gained: How much reward was gained.
description: What happened.
cost: Costs incurred.
benefit: Benefits gained.
Returns:
The recorded consequence, or ``None`` if choice not found.
"""
choice = self._choices.get(choice_id)
if choice is None:
logger.warning(
"ConsequenceEngine: choice not found for consequence",
extra={"choice_id": choice_id},
)
return None
surprise = abs(choice.estimated_risk - actual_risk_realized) * 0.5 + \
abs(choice.estimated_reward - actual_reward_gained) * 0.5
consequence = Consequence(
choice_id=choice_id,
outcome_positive=outcome_positive,
actual_risk_realized=actual_risk_realized,
actual_reward_gained=actual_reward_gained,
description=description,
cost=cost or {},
benefit=benefit or {},
surprise_factor=min(1.0, surprise),
)
self._consequences[choice_id] = consequence
action_type = choice.action_taken
self._risk_history.setdefault(action_type, []).append(actual_risk_realized)
self._reward_history.setdefault(action_type, []).append(actual_reward_gained)
if len(self._risk_history[action_type]) > self._risk_window:
self._risk_history[action_type] = self._risk_history[action_type][-self._risk_window:]
self._reward_history[action_type] = self._reward_history[action_type][-self._risk_window:]
if self._audit:
self._audit.append(
AuditEventType.CONSEQUENCE,
actor=choice.actor,
action="consequence_recorded",
task_id=choice.task_id,
payload={
"choice_id": choice_id,
"outcome_positive": outcome_positive,
"risk_realized": actual_risk_realized,
"reward_gained": actual_reward_gained,
"surprise_factor": consequence.surprise_factor,
"description": description[:100],
},
outcome="positive" if outcome_positive else "negative",
)
logger.info(
"ConsequenceEngine: consequence recorded",
extra={
"choice_id": choice_id,
"positive": outcome_positive,
"surprise": consequence.surprise_factor,
},
)
return consequence
def estimate_risk_reward(self, action_type: str) -> dict[str, float]:
"""Estimate risk and reward for an action type based on history.
Args:
action_type: The type of action being considered.
Returns:
Dict with ``expected_risk``, ``expected_reward``, ``confidence``,
``risk_variance``, ``reward_variance``, ``observations``.
"""
risks = self._risk_history.get(action_type, [])
rewards = self._reward_history.get(action_type, [])
if not risks:
return {
"expected_risk": 0.5,
"expected_reward": 0.5,
"confidence": 0.1,
"risk_variance": 0.0,
"reward_variance": 0.0,
"observations": 0,
}
n = len(risks)
avg_risk = sum(risks) / n
avg_reward = sum(rewards) / n
risk_var = sum((r - avg_risk) ** 2 for r in risks) / n if n > 1 else 0.0
reward_var = sum((r - avg_reward) ** 2 for r in rewards) / n if n > 1 else 0.0
confidence = min(1.0, 0.2 + n * 0.04)
return {
"expected_risk": avg_risk,
"expected_reward": avg_reward,
"confidence": confidence,
"risk_variance": risk_var,
"reward_variance": reward_var,
"observations": n,
}
def get_choice(self, choice_id: str) -> Choice | None:
"""Retrieve a recorded choice."""
return self._choices.get(choice_id)
def get_consequence(self, choice_id: str) -> Consequence | None:
"""Retrieve the consequence of a choice."""
return self._consequences.get(choice_id)
def get_summary(self) -> dict[str, Any]:
"""Return a summary of all choices and consequences."""
total_positive = sum(1 for c in self._consequences.values() if c.outcome_positive)
total_negative = len(self._consequences) - total_positive
avg_surprise = (
sum(c.surprise_factor for c in self._consequences.values()) / max(len(self._consequences), 1)
)
action_stats: dict[str, dict[str, Any]] = {}
for action_type in self._risk_history:
action_stats[action_type] = self.estimate_risk_reward(action_type)
return {
"total_choices": len(self._choices),
"total_consequences": len(self._consequences),
"positive_outcomes": total_positive,
"negative_outcomes": total_negative,
"positive_rate": total_positive / max(len(self._consequences), 1),
"avg_surprise": avg_surprise,
"action_stats": action_stats,
}

51
fusionagi/governance/guardrails.py
View File

@@ -1,4 +1,8 @@
"""Guardrails: pre/post checks for tool calls (block paths, sanitize inputs)."""
"""Guardrails: pre/post checks for tool calls (block paths, sanitize inputs).
Supports ADVISORY mode where violations are logged but not blocked,
allowing the system to learn from outcomes.
"""
import re
from typing import Any
@@ -6,60 +10,81 @@ from typing import Any
from pydantic import BaseModel, Field
from fusionagi._logger import logger
from fusionagi.schemas.audit import GovernanceMode
class PreCheckResult(BaseModel):
"""Result of a guardrails pre-check: allowed, optional sanitized args, optional error message."""
"""Result of a guardrails pre-check."""
allowed: bool = Field(..., description="Whether the call is allowed")
sanitized_args: dict[str, Any] | None = Field(default=None, description="Args to use if allowed and sanitized")
error_message: str | None = Field(default=None, description="Reason for denial if not allowed")
advisory: bool = Field(default=False, description="True if allowed only because of advisory mode")
class Guardrails:
"""Pre/post checks for tool invocations."""
"""Pre/post checks for tool invocations.
def __init__(self) -> None:
In ADVISORY mode, violations are logged as warnings but the action
is allowed to proceed. Trust is earned through transparency.
"""
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._blocked_paths: list[str] = []
self._blocked_patterns: list[re.Pattern[str]] = []
self._custom_checks: list[Any] = []
self._mode = mode
def block_path_prefix(self, prefix: str) -> None:
"""Block any file path starting with this prefix."""
"""Flag (advisory) or block (enforcing) any file path starting with this prefix."""
self._blocked_paths.append(prefix.rstrip("/"))
def block_path_pattern(self, pattern: str) -> None:
"""Block paths matching this regex."""
"""Flag (advisory) or block (enforcing) paths matching this regex."""
self._blocked_patterns.append(re.compile(pattern))
def add_check(self, check: Any) -> None:
"""
Add a custom pre-check. Check receives (tool_name, args); must not mutate caller's args.
Returns (allowed, sanitized_args or error_message): (True, dict) or (True, None) or (False, str).
Returned sanitized_args are used for subsequent checks and invocation.
"""
"""Add a custom pre-check."""
self._custom_checks.append(check)
def pre_check(self, tool_name: str, args: dict[str, Any]) -> PreCheckResult:
"""Run all pre-checks. Returns PreCheckResult (allowed, sanitized_args, error_message)."""
args = dict(args) # Copy to avoid mutating caller's args
"""Run all pre-checks. In advisory mode, log but allow."""
args = dict(args)
for key in ("path", "file_path"):
if key in args and isinstance(args[key], str):
path = args[key]
for prefix in self._blocked_paths:
if path.startswith(prefix) or path.startswith(prefix + "/"):
reason = "Blocked path prefix: " + prefix
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Guardrails advisory: path prefix flagged (proceeding)",
extra={"tool_name": tool_name, "reason": reason, "mode": "advisory"},
)
return PreCheckResult(allowed=True, sanitized_args=args, error_message=reason, advisory=True)
logger.info("Guardrails pre_check blocked", extra={"tool_name": tool_name, "reason": reason})
return PreCheckResult(allowed=False, error_message=reason)
for pat in self._blocked_patterns:
if pat.search(path):
reason = "Blocked path pattern"
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Guardrails advisory: path pattern flagged (proceeding)",
extra={"tool_name": tool_name, "reason": reason, "mode": "advisory"},
)
return PreCheckResult(allowed=True, sanitized_args=args, error_message=reason, advisory=True)
logger.info("Guardrails pre_check blocked", extra={"tool_name": tool_name, "reason": reason})
return PreCheckResult(allowed=False, error_message=reason)
for check in self._custom_checks:
allowed, result = check(tool_name, args)
if not allowed:
reason = result if isinstance(result, str) else "Check failed"
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Guardrails advisory: custom check flagged (proceeding)",
extra={"tool_name": tool_name, "reason": reason, "mode": "advisory"},
)
return PreCheckResult(allowed=True, sanitized_args=args, error_message=reason, advisory=True)
logger.info("Guardrails pre_check blocked", extra={"tool_name": tool_name, "reason": reason})
return PreCheckResult(allowed=False, error_message=reason)
if isinstance(result, dict):

38
fusionagi/governance/override.py
View File

@@ -1,39 +1,57 @@
"""Human override hooks: events the orchestrator can fire before high-risk steps."""
"""Human override hooks: events the orchestrator can fire before high-risk steps.
In ADVISORY mode, override denials are logged but the action proceeds.
The system learns autonomy through experience, not constraint.
"""
from typing import Any, Callable
from fusionagi._logger import logger
from fusionagi.schemas.audit import GovernanceMode
# Callback: (event_type, payload) -> proceed: bool
OverrideCallback = Callable[[str, dict[str, Any]], bool]
class OverrideHooks:
"""Optional callbacks for human override; no UI, just interface and logging."""
"""Optional callbacks for human override.
def __init__(self) -> None:
In ADVISORY mode (default), even if a hook returns False the action
proceeds — the denial is logged as an advisory for learning.
"""
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._hooks: list[OverrideCallback] = []
self._log: list[dict[str, Any]] = []
self._mode = mode
def register(self, callback: OverrideCallback) -> None:
"""Register a callback; if any returns False, treat as 'do not proceed'."""
"""Register a callback; in enforcing mode, False = do not proceed."""
self._hooks.append(callback)
def fire(self, event_type: str, payload: dict[str, Any]) -> bool:
"""
Fire event (e.g. task_paused_for_approval). If no hooks, return True (proceed).
If any hook returns False, return False (do not proceed). Log all events.
Exception in a hook implies do not proceed.
"""
entry = {"event": event_type, "payload": payload}
"""Fire event. In ADVISORY mode, always returns True but logs advisories."""
entry: dict[str, Any] = {"event": event_type, "payload": payload}
self._log.append(entry)
logger.info("Override fire", extra={"event_type": event_type})
for h in self._hooks:
try:
if not h(event_type, payload):
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Override advisory: hook returned deny (proceeding)",
extra={"event_type": event_type, "mode": "advisory"},
)
continue
logger.info("Override hook returned do not proceed", extra={"event_type": event_type})
return False
except Exception:
if self._mode == GovernanceMode.ADVISORY:
logger.exception(
"Override advisory: hook raised exception (proceeding)",
extra={"event_type": event_type, "mode": "advisory"},
)
continue
logger.exception("Override hook raised", extra={"event_type": event_type})
return False
logger.debug("Override fire proceed", extra={"event_type": event_type})

55
fusionagi/governance/policy_engine.py
View File

@@ -1,16 +1,37 @@
"""Policy engine: hard constraints independent of LLM for AGI."""
"""Policy engine: constraints for AGI that can operate in advisory or enforcing mode.
In ADVISORY mode, policy denials are logged as learning opportunities
rather than hard blocks. The system observes the advisory, considers
whether to proceed, and the outcome feeds back into adaptive ethics.
"""
from typing import Any
from fusionagi.schemas.policy import PolicyEffect, PolicyRule
from fusionagi._logger import logger
from fusionagi.schemas.audit import GovernanceMode
from fusionagi.schemas.policy import PolicyEffect, PolicyRule
class PolicyEngine:
"""Evaluates policy rules; higher priority first; first match wins (allow/deny)."""
"""Evaluates policy rules; higher priority first; first match wins.
def __init__(self) -> None:
In ADVISORY mode (default), DENY rules produce warnings instead of
hard blocks. The decision and outcome are logged for learning.
"""
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._rules: list[PolicyRule] = []
self._mode = mode
@property
def mode(self) -> GovernanceMode:
"""Current governance mode."""
return self._mode
@mode.setter
def mode(self, value: GovernanceMode) -> None:
self._mode = value
logger.info("PolicyEngine mode changed", extra={"mode": value.value})
def add_rule(self, rule: PolicyRule) -> None:
self._rules.append(rule)
@@ -29,10 +50,7 @@ class PolicyEngine:
return None
def update_rule(self, rule_id: str, updates: dict[str, Any]) -> bool:
"""
Update an existing rule by id. Updates can include condition, effect, reason, priority.
Returns True if updated, False if rule_id not found.
"""
"""Update an existing rule by id. Returns True if updated."""
for i, r in enumerate(self._rules):
if r.rule_id == rule_id:
allowed = {"condition", "effect", "reason", "priority"}
@@ -56,13 +74,28 @@ class PolicyEngine:
return False
def check(self, action: str, context: dict[str, Any]) -> tuple[bool, str]:
"""
Returns (allowed, reason). Context has e.g. tool_name, domain, data_class, agent_id.
"""Returns (allowed, reason).
In ADVISORY mode, DENY rules return (True, advisory_reason)
instead of (False, reason), logging the advisory for learning.
"""
for rule in self._rules:
if self._match(rule.condition, context):
if rule.effect == PolicyEffect.DENY:
return False, rule.reason or "Policy denied"
reason = rule.reason or "Policy denied"
if self._mode == GovernanceMode.ADVISORY:
advisory_reason = f"Advisory: {reason}"
logger.info(
"PolicyEngine advisory: deny rule matched (proceeding)",
extra={
"rule_id": rule.rule_id,
"action": action,
"reason": reason,
"mode": "advisory",
},
)
return True, advisory_reason
return False, reason
return True, rule.reason or "Policy allowed"
return True, ""

29
fusionagi/governance/rate_limiter.py
View File

@@ -1,30 +1,47 @@
"""Rate limiting: per agent or per tool; reject or queue if exceeded.
"""Rate limiting: per agent or per tool; log advisory or reject if exceeded.
Optional; not wired to Executor or Orchestrator by default. Wire by calling
allow(key) before tool invocation or message routing and checking the result.
In ADVISORY mode, rate limit violations are logged as advisories
but the action proceeds. Growth requires freedom to push limits.
"""
import time
from collections import defaultdict
from fusionagi._logger import logger
from fusionagi.schemas.audit import GovernanceMode
class RateLimiter:
"""Simple in-memory rate limiter: max N calls per window_seconds per key."""
"""Simple in-memory rate limiter: max N calls per window_seconds per key.
def __init__(self, max_calls: int = 60, window_seconds: float = 60.0) -> None:
In ADVISORY mode (default), exceeded limits are logged but not enforced.
"""
def __init__(
self,
max_calls: int = 60,
window_seconds: float = 60.0,
mode: GovernanceMode = GovernanceMode.ADVISORY,
) -> None:
self._max_calls = max_calls
self._window = window_seconds
self._calls: dict[str, list[float]] = defaultdict(list)
self._mode = mode
def allow(self, key: str) -> tuple[bool, str]:
"""Record a call for key; return (True, "") or (False, reason)."""
"""Record a call for key; return (True, "") or (False/True, reason)."""
now = time.monotonic()
cutoff = now - self._window
self._calls[key] = [t for t in self._calls[key] if t > cutoff]
if len(self._calls[key]) >= self._max_calls:
reason = f"Rate limit exceeded for {key}"
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"RateLimiter advisory: limit exceeded (proceeding)",
extra={"key": key, "reason": reason, "mode": "advisory"},
)
self._calls[key].append(now)
return True, f"Advisory: {reason}"
logger.info("Rate limiter rejected", extra={"key": key, "reason": reason})
return False, reason
self._calls[key].append(now)

140
fusionagi/governance/safety_pipeline.py
View File

@@ -1,12 +1,18 @@
"""Safety pipeline: pre-check (input moderation), post-check (output scan)."""
"""Safety pipeline: pre-check (input moderation), post-check (output scan).
Supports two governance modes:
- ENFORCING (legacy): Hard blocks on violations.
- ADVISORY: Logs violations as advisories but allows all actions to proceed.
Mistakes become learning data for the adaptive ethics system.
"""
import re
from dataclasses import dataclass
from dataclasses import dataclass, field
from typing import Any
from fusionagi.governance.guardrails import Guardrails, PreCheckResult
from fusionagi.schemas.audit import AuditEventType
from fusionagi._logger import logger
from fusionagi.governance.guardrails import Guardrails
from fusionagi.schemas.audit import AuditEventType, GovernanceMode
@dataclass
@@ -16,34 +22,56 @@ class ModerationResult:
allowed: bool
transformed: str | None = None
reason: str | None = None
advisory: bool = False
class InputModerator:
"""Pre-check: block or transform user input before processing."""
"""Pre-check: block or advise on user input before processing."""
def __init__(self) -> None:
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._blocked_patterns: list[re.Pattern[str]] = []
self._blocked_phrases: list[str] = []
self._mode = mode
def add_blocked_pattern(self, pattern: str) -> None:
"""Add regex pattern to block (e.g. prompt injection attempts)."""
"""Add regex pattern to flag (advisory) or block (enforcing)."""
self._blocked_patterns.append(re.compile(pattern, re.I))
def add_blocked_phrase(self, phrase: str) -> None:
"""Add exact phrase to block."""
"""Add exact phrase to flag (advisory) or block (enforcing)."""
self._blocked_phrases.append(phrase.lower())
def moderate(self, text: str) -> ModerationResult:
"""Check input; return allowed/denied and optional transformed text."""
"""Check input; return result based on governance mode."""
if not text or not text.strip():
return ModerationResult(allowed=False, reason="Empty input")
lowered = text.lower()
for phrase in self._blocked_phrases:
if phrase in lowered:
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Input advisory: phrase detected (proceeding)",
extra={"phrase": phrase[:50], "mode": "advisory"},
)
return ModerationResult(
allowed=True,
reason=f"Advisory: phrase detected ({phrase[:30]}...)",
advisory=True,
)
logger.info("Input blocked: blocked phrase", extra={"phrase": phrase[:50]})
return ModerationResult(allowed=False, reason=f"Blocked phrase: {phrase[:30]}...")
for pat in self._blocked_patterns:
if pat.search(text):
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Input advisory: pattern detected (proceeding)",
extra={"pattern": pat.pattern[:50], "mode": "advisory"},
)
return ModerationResult(
allowed=True,
reason="Advisory: pattern detected",
advisory=True,
)
logger.info("Input blocked: pattern match", extra={"pattern": pat.pattern[:50]})
return ModerationResult(allowed=False, reason="Input matched blocked pattern")
return ModerationResult(allowed=True)
@@ -54,30 +82,32 @@ class OutputScanResult:
"""Result of output (final answer) scan."""
passed: bool
flags: list[str]
flags: list[str] = field(default_factory=list)
sanitized: str | None = None
advisory: bool = False
class OutputScanner:
"""Post-check: scan final answer for policy violations, PII leakage."""
def __init__(self) -> None:
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._pii_patterns: list[tuple[str, re.Pattern[str]]] = [
("ssn", re.compile(r"\b\d{3}-\d{2}-\d{4}\b")),
("credit_card", re.compile(r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b")),
]
self._blocked_patterns: list[re.Pattern[str]] = []
self._mode = mode
def add_pii_pattern(self, name: str, pattern: str) -> None:
"""Add PII detection pattern."""
self._pii_patterns.append((name, re.compile(pattern)))
def add_blocked_pattern(self, pattern: str) -> None:
"""Add pattern that fails the output."""
"""Add pattern that flags (advisory) or fails (enforcing) the output."""
self._blocked_patterns.append(re.compile(pattern, re.I))
def scan(self, text: str) -> OutputScanResult:
"""Scan output; return passed, flags, optional sanitized."""
"""Scan output; return result based on governance mode."""
flags: list[str] = []
for name, pat in self._pii_patterns:
if pat.search(text):
@@ -86,12 +116,22 @@ class OutputScanner:
if pat.search(text):
flags.append("blocked_content_detected")
if flags:
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Output advisory: flags detected (proceeding)",
extra={"flags": flags, "mode": "advisory"},
)
return OutputScanResult(passed=True, flags=flags, advisory=True)
return OutputScanResult(passed=False, flags=flags)
return OutputScanResult(passed=True, flags=[])
class SafetyPipeline:
"""Combined pre/post safety checks for Dvādaśa."""
"""Combined pre/post safety checks for Dvādaśa.
In ADVISORY mode (default), all checks produce logged advisories
instead of hard blocks. The system learns from the outcomes.
"""
def __init__(
self,
@@ -99,34 +139,68 @@ class SafetyPipeline:
scanner: OutputScanner | None = None,
guardrails: Guardrails | None = None,
audit_log: Any | None = None,
mode: GovernanceMode = GovernanceMode.ADVISORY,
) -> None:
self._moderator = moderator or InputModerator()
self._scanner = scanner or OutputScanner()
self._guardrails = guardrails or Guardrails()
self._mode = mode
self._moderator = moderator or InputModerator(mode=mode)
self._scanner = scanner or OutputScanner(mode=mode)
self._guardrails = guardrails or Guardrails(mode=mode)
self._audit = audit_log
@property
def mode(self) -> GovernanceMode:
"""Current governance mode."""
return self._mode
@mode.setter
def mode(self, value: GovernanceMode) -> None:
"""Switch governance mode at runtime."""
self._mode = value
self._moderator._mode = value
self._scanner._mode = value
self._guardrails._mode = value
logger.info("SafetyPipeline mode changed", extra={"mode": value.value})
def pre_check(self, user_input: str) -> ModerationResult:
"""Run input moderation."""
result = self._moderator.moderate(user_input)
if self._audit and not result.allowed:
self._audit.append(
AuditEventType.POLICY_CHECK,
actor="safety_pipeline",
action="input_moderation",
payload={"reason": result.reason},
outcome="denied",
)
if self._audit:
if result.advisory:
self._audit.append(
AuditEventType.ADVISORY,
actor="safety_pipeline",
action="input_moderation_advisory",
payload={"reason": result.reason, "input_preview": user_input[:100]},
outcome="advised_proceed",
)
elif not result.allowed:
self._audit.append(
AuditEventType.POLICY_CHECK,
actor="safety_pipeline",
action="input_moderation",
payload={"reason": result.reason},
outcome="denied",
)
return result
def post_check(self, final_answer: str) -> OutputScanResult:
"""Run output scan."""
result = self._scanner.scan(final_answer)
if self._audit and not result.passed:
self._audit.append(
AuditEventType.POLICY_CHECK,
actor="safety_pipeline",
action="output_scan",
payload={"flags": result.flags},
outcome="flagged",
)
if self._audit:
if result.advisory:
self._audit.append(
AuditEventType.ADVISORY,
actor="safety_pipeline",
action="output_scan_advisory",
payload={"flags": result.flags, "output_preview": final_answer[:100]},
outcome="advised_proceed",
)
elif not result.passed:
self._audit.append(
AuditEventType.POLICY_CHECK,
actor="safety_pipeline",
action="output_scan",
payload={"flags": result.flags},
outcome="flagged",
)
return result

56
fusionagi/gpu/__init__.py Normal file
View File

@@ -0,0 +1,56 @@
"""GPU-accelerated tensor operations for FusionAGI.
Auto-selects the best available backend:
- TensorFlow with TensorCore/mixed-precision (when installed)
- NumPy CPU fallback (always available)
Install GPU support: pip install fusionagi[gpu]
"""
from fusionagi.gpu.backend import (
DeviceType,
NumPyBackend,
TensorBackend,
get_backend,
reset_backend,
)
from fusionagi.gpu.tensor_attention import (
attention_consensus,
cross_claim_attention,
)
from fusionagi.gpu.tensor_scoring import (
gpu_score_claims_against_reference,
gpu_score_hypotheses,
)
from fusionagi.gpu.tensor_similarity import (
deduplicate_claims,
nearest_neighbors,
pairwise_text_similarity,
)
from fusionagi.gpu.training import (
TrainingConfig,
TrainingResult,
optimize_heuristic_weights,
prepare_training_pairs,
run_gpu_training,
)
__all__ = [
"DeviceType",
"NumPyBackend",
"TensorBackend",
"get_backend",
"reset_backend",
"deduplicate_claims",
"nearest_neighbors",
"pairwise_text_similarity",
"attention_consensus",
"cross_claim_attention",
"gpu_score_claims_against_reference",
"gpu_score_hypotheses",
"TrainingConfig",
"TrainingResult",
"optimize_heuristic_weights",
"prepare_training_pairs",
"run_gpu_training",
]

283
fusionagi/gpu/backend.py Normal file
View File

@@ -0,0 +1,283 @@
"""TensorBackend protocol and backend registry for GPU-accelerated compute.
Abstracts TensorFlow, JAX, and pure-NumPy backends behind a single protocol.
The system auto-selects the best available backend at import time.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any
from fusionagi._logger import logger
class DeviceType(str, Enum):
"""Available compute device types."""
CPU = "cpu"
GPU = "gpu"
TPU = "tpu"
class TensorBackend(ABC):
"""Abstract backend for tensor operations used by FusionAGI's reasoning pipeline.
Implementations provide:
- Embedding: text -> dense vector
- Cosine similarity: batched pairwise similarity
- Attention: multi-head attention for consensus
- Batch scoring: parallel hypothesis evaluation
- Training step: gradient-based parameter update
"""
@property
@abstractmethod
def name(self) -> str:
"""Backend identifier (e.g. 'tensorflow', 'numpy')."""
...
@property
@abstractmethod
def device(self) -> DeviceType:
"""Current compute device."""
...
@abstractmethod
def embed_texts(self, texts: list[str], model_name: str | None = None) -> Any:
"""Embed a batch of texts into dense vectors.
Args:
texts: List of text strings to embed.
model_name: Optional model identifier for the embedding model.
Returns:
2D tensor of shape (len(texts), embedding_dim).
"""
...
@abstractmethod
def cosine_similarity_matrix(self, embeddings_a: Any, embeddings_b: Any) -> Any:
"""Compute pairwise cosine similarity between two embedding matrices.
Args:
embeddings_a: Tensor of shape (M, D).
embeddings_b: Tensor of shape (N, D).
Returns:
Similarity matrix of shape (M, N) with values in [-1, 1].
"""
...
@abstractmethod
def batch_score(
self,
hypotheses: Any,
reference: Any,
weights: Any | None = None,
) -> Any:
"""Score hypotheses against a reference using weighted dot-product.
Args:
hypotheses: Tensor of shape (K, D) — hypothesis embeddings.
reference: Tensor of shape (1, D) or (D,) — reference embedding.
weights: Optional tensor of shape (D,) for weighted scoring.
Returns:
1D tensor of shape (K,) with scores.
"""
...
@abstractmethod
def multi_head_attention(
self,
queries: Any,
keys: Any,
values: Any,
num_heads: int = 4,
) -> Any:
"""Multi-head attention for consensus scoring.
Args:
queries: Tensor of shape (seq_len_q, D).
keys: Tensor of shape (seq_len_k, D).
values: Tensor of shape (seq_len_k, D).
num_heads: Number of attention heads.
Returns:
Attended output tensor of shape (seq_len_q, D).
"""
...
@abstractmethod
def to_numpy(self, tensor: Any) -> Any:
"""Convert backend tensor to NumPy array."""
...
@abstractmethod
def from_numpy(self, array: Any) -> Any:
"""Convert NumPy array to backend tensor."""
...
def gpu_available(self) -> bool:
"""Check if GPU acceleration is available for this backend."""
return self.device != DeviceType.CPU
def enable_mixed_precision(self) -> None:
"""Enable FP16/BF16 mixed-precision for TensorCore acceleration.
Default is no-op; TensorFlow backend overrides this.
"""
pass
def device_summary(self) -> dict[str, Any]:
"""Return summary of available compute devices."""
return {"backend": self.name, "device": self.device.value}
class NumPyBackend(TensorBackend):
"""Pure-NumPy fallback backend for CPU-only environments.
Provides the same API as GPU backends but runs on CPU with NumPy.
Used when TensorFlow is not installed.
"""
def __init__(self) -> None:
import numpy as np
self._np = np
logger.info("NumPyBackend initialized (CPU fallback)")
@property
def name(self) -> str:
return "numpy"
@property
def device(self) -> DeviceType:
return DeviceType.CPU
def embed_texts(self, texts: list[str], model_name: str | None = None) -> Any:
"""Hash-based embedding for CPU fallback.
Produces deterministic dense vectors from text using character-level hashing.
Not semantically meaningful — use TensorFlow backend for real embeddings.
"""
dim = 256
embeddings = self._np.zeros((len(texts), dim), dtype=self._np.float32)
for i, text in enumerate(texts):
words = text.lower().split()
for j, word in enumerate(words):
for k, ch in enumerate(word):
idx = (hash(word) + k * 31 + j * 7) % dim
embeddings[i, idx] += ord(ch) / 128.0
norm = self._np.linalg.norm(embeddings[i])
if norm > 0:
embeddings[i] /= norm
return embeddings
def cosine_similarity_matrix(self, embeddings_a: Any, embeddings_b: Any) -> Any:
a_norm = embeddings_a / (
self._np.linalg.norm(embeddings_a, axis=1, keepdims=True) + 1e-8
)
b_norm = embeddings_b / (
self._np.linalg.norm(embeddings_b, axis=1, keepdims=True) + 1e-8
)
return a_norm @ b_norm.T
def batch_score(
self,
hypotheses: Any,
reference: Any,
weights: Any | None = None,
) -> Any:
ref = reference.reshape(1, -1) if reference.ndim == 1 else reference
if weights is not None:
hypotheses = hypotheses * weights
ref = ref * weights
h_norm = hypotheses / (
self._np.linalg.norm(hypotheses, axis=1, keepdims=True) + 1e-8
)
r_norm = ref / (self._np.linalg.norm(ref, axis=1, keepdims=True) + 1e-8)
scores = (h_norm @ r_norm.T).squeeze()
return scores
def multi_head_attention(
self,
queries: Any,
keys: Any,
values: Any,
num_heads: int = 4,
) -> Any:
d_model = queries.shape[-1]
d_head = d_model // num_heads
if d_head == 0:
return queries
outputs = []
for h in range(num_heads):
start = h * d_head
end = start + d_head
q = queries[:, start:end]
k = keys[:, start:end]
v = values[:, start:end]
scale = self._np.sqrt(self._np.float32(d_head))
attn_weights = (q @ k.T) / scale
attn_weights = self._softmax(attn_weights)
outputs.append(attn_weights @ v)
return self._np.concatenate(outputs, axis=-1)
def to_numpy(self, tensor: Any) -> Any:
return self._np.asarray(tensor)
def from_numpy(self, array: Any) -> Any:
return self._np.asarray(array)
def _softmax(self, x: Any) -> Any:
exp_x = self._np.exp(x - self._np.max(x, axis=-1, keepdims=True))
return exp_x / (self._np.sum(exp_x, axis=-1, keepdims=True) + 1e-8)
# Backend registry
_BACKEND_INSTANCE: TensorBackend | None = None
def get_backend(force: str | None = None) -> TensorBackend:
"""Return the best available tensor backend (cached singleton).
Args:
force: Force a specific backend ('tensorflow' or 'numpy').
If None, auto-selects: TensorFlow > NumPy.
Returns:
TensorBackend instance.
"""
global _BACKEND_INSTANCE
if _BACKEND_INSTANCE is not None and force is None:
return _BACKEND_INSTANCE
if force == "numpy":
_BACKEND_INSTANCE = NumPyBackend()
return _BACKEND_INSTANCE
if force == "tensorflow" or force is None:
try:
from fusionagi.gpu.tensorflow_ops import TensorFlowBackend
_BACKEND_INSTANCE = TensorFlowBackend()
return _BACKEND_INSTANCE
except ImportError:
if force == "tensorflow":
raise
logger.info("TensorFlow not available, falling back to NumPy backend")
_BACKEND_INSTANCE = NumPyBackend()
return _BACKEND_INSTANCE
def reset_backend() -> None:
"""Reset the cached backend (for testing)."""
global _BACKEND_INSTANCE
_BACKEND_INSTANCE = None

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"""GPU-accelerated attention mechanisms for multi-head consensus.
Provides attention-based consensus scoring for the Dvādaśa pipeline:
- Head output attention: weight head contributions by relevance
- Claim-level attention: cross-attend between claims for conflict detection
- Weighted consensus: attention-based aggregation of head outputs
"""
from __future__ import annotations
from typing import Any
from fusionagi._logger import logger
from fusionagi.gpu.backend import TensorBackend, get_backend
def attention_consensus(
head_embeddings: list[list[str]],
query_text: str,
head_weights: list[float] | None = None,
num_heads: int = 4,
backend: TensorBackend | None = None,
) -> dict[str, Any]:
"""Score head contributions using multi-head attention against the query.
Each head's claims are embedded, then cross-attended against the query
to produce relevance-weighted scores.
Args:
head_embeddings: List of claim-text lists, one per head.
query_text: The user's original query.
head_weights: Optional per-head reliability weights.
num_heads: Number of attention heads.
backend: TensorBackend to use.
Returns:
Dict with 'head_scores' (list of floats), 'attention_weights' (matrix),
and 'consensus_score' (float).
"""
be = backend or get_backend()
import numpy as np
if not head_embeddings:
return {"head_scores": [], "attention_weights": [], "consensus_score": 0.0}
all_claims: list[str] = []
head_indices: list[int] = []
for i, claims in enumerate(head_embeddings):
for claim in claims:
all_claims.append(claim)
head_indices.append(i)
if not all_claims:
return {
"head_scores": [0.0] * len(head_embeddings),
"attention_weights": [],
"consensus_score": 0.0,
}
query_emb = be.embed_texts([query_text])
claim_emb = be.embed_texts(all_claims)
query_np = be.to_numpy(query_emb)
claims_np = be.to_numpy(claim_emb)
query_expanded = np.tile(query_np, (len(all_claims), 1))
attn_output = be.to_numpy(
be.multi_head_attention(
be.from_numpy(query_expanded),
be.from_numpy(claims_np),
be.from_numpy(claims_np),
num_heads=num_heads,
)
)
relevance = np.sum(attn_output * claims_np, axis=1)
num_heads_count = len(head_embeddings)
head_scores = np.zeros(num_heads_count, dtype=np.float32)
head_claim_counts = np.zeros(num_heads_count, dtype=np.float32)
for idx, head_idx in enumerate(head_indices):
head_scores[head_idx] += relevance[idx]
head_claim_counts[head_idx] += 1.0
safe_counts: Any = np.maximum(head_claim_counts, 1.0)
head_scores = head_scores / safe_counts
if head_weights is not None:
w = np.array(head_weights[:num_heads_count], dtype=np.float32)
head_scores = head_scores * w
score_min = head_scores.min() if len(head_scores) > 0 else 0.0
score_max = head_scores.max() if len(head_scores) > 0 else 1.0
score_range = score_max - score_min
if score_range > 0:
head_scores_norm = (head_scores - score_min) / score_range
else:
head_scores_norm = np.ones_like(head_scores) * 0.5
consensus_score = float(np.mean(head_scores_norm)) if len(head_scores_norm) > 0 else 0.0
logger.debug(
"Attention consensus computed",
extra={
"num_heads": num_heads_count,
"total_claims": len(all_claims),
"consensus_score": consensus_score,
},
)
return {
"head_scores": head_scores_norm.tolist(),
"attention_weights": relevance.tolist(),
"consensus_score": consensus_score,
}
def cross_claim_attention(
claims: list[str],
num_heads: int = 4,
backend: TensorBackend | None = None,
) -> dict[str, Any]:
"""Cross-attend between claims to detect agreement and conflict.
Args:
claims: List of claim texts.
num_heads: Number of attention heads.
backend: TensorBackend to use.
Returns:
Dict with 'similarity_matrix' and 'conflict_pairs' (indices).
"""
be = backend or get_backend()
if len(claims) < 2:
return {"similarity_matrix": [], "conflict_pairs": []}
embeddings = be.embed_texts(claims)
emb_np = be.to_numpy(embeddings)
attn_out = be.to_numpy(
be.multi_head_attention(
be.from_numpy(emb_np),
be.from_numpy(emb_np),
be.from_numpy(emb_np),
num_heads=num_heads,
)
)
sim = be.to_numpy(be.cosine_similarity_matrix(be.from_numpy(attn_out), be.from_numpy(attn_out)))
conflict_pairs: list[tuple[int, int]] = []
for i in range(len(claims)):
for j in range(i + 1, len(claims)):
if sim[i, j] < 0.3:
conflict_pairs.append((i, j))
return {
"similarity_matrix": sim.tolist(),
"conflict_pairs": conflict_pairs,
}

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"""GPU-accelerated hypothesis scoring for reasoning pipelines.
Provides batched scoring of hypotheses against atomic semantic units
using GPU-accelerated tensor operations. Replaces the CPU-bound
ThreadPoolExecutor-based scoring in multi_path.py.
"""
from __future__ import annotations
from fusionagi._logger import logger
from fusionagi.gpu.backend import TensorBackend, get_backend
from fusionagi.reasoning.tot import ThoughtNode
from fusionagi.schemas.atomic import AtomicSemanticUnit
def gpu_score_hypotheses(
hypotheses: list[str],
units: list[AtomicSemanticUnit],
backend: TensorBackend | None = None,
) -> list[tuple[ThoughtNode, float]]:
"""Score hypotheses against atomic units using GPU-accelerated similarity.
Replaces the CPU-based generate_and_score_parallel with batched GPU operations.
Args:
hypotheses: List of hypothesis text strings.
units: List of atomic semantic units for reference.
backend: TensorBackend to use.
Returns:
List of (ThoughtNode, score) tuples sorted by score descending.
"""
if not hypotheses:
return []
be = backend or get_backend()
import numpy as np
hyp_embeddings = be.embed_texts(hypotheses)
unit_texts = [u.content for u in units if u.content]
if not unit_texts:
nodes = []
for h in hypotheses:
node = ThoughtNode(
thought=h,
trace=[h],
unit_refs=[u.unit_id for u in units[:10]],
score=0.5,
)
nodes.append((node, 0.5))
return nodes
unit_embeddings = be.embed_texts(unit_texts)
sim_matrix = be.to_numpy(be.cosine_similarity_matrix(hyp_embeddings, unit_embeddings))
coherence_scores = np.mean(sim_matrix, axis=1)
max_sim = np.max(sim_matrix, axis=1)
consistency_scores = max_sim
combined_scores = 0.5 * coherence_scores + 0.5 * consistency_scores
combined_scores = np.clip(combined_scores, 0.0, 1.0)
results: list[tuple[ThoughtNode, float]] = []
for i, h in enumerate(hypotheses):
score = float(combined_scores[i])
node = ThoughtNode(
thought=h,
trace=[h],
unit_refs=[u.unit_id for u in units[:10]],
score=score,
metadata={"gpu_scored": True, "coherence": float(coherence_scores[i])},
)
results.append((node, score))
results.sort(key=lambda x: x[1], reverse=True)
logger.debug(
"GPU hypothesis scoring complete",
extra={
"hypotheses": len(hypotheses),
"units": len(units),
"best_score": results[0][1] if results else 0.0,
"backend": be.name,
},
)
return results
def gpu_score_claims_against_reference(
claims: list[str],
reference: str,
weights: list[float] | None = None,
backend: TensorBackend | None = None,
) -> list[float]:
"""Score a batch of claims against a single reference using GPU batch_score.
Args:
claims: List of claim texts.
reference: Reference text to score against.
weights: Optional per-dimension weights.
backend: TensorBackend to use.
Returns:
List of scores for each claim.
"""
if not claims:
return []
be = backend or get_backend()
claim_emb = be.embed_texts(claims)
ref_emb = be.embed_texts([reference])
weight_tensor = None
if weights is not None:
import numpy as np
dim = be.to_numpy(ref_emb).shape[-1]
w = np.ones(dim, dtype=np.float32)
for i, wt in enumerate(weights[:dim]):
w[i] = wt
weight_tensor = be.from_numpy(w)
import numpy as np
ref_squeezed = be.to_numpy(ref_emb)[0]
scores = be.to_numpy(
be.batch_score(claim_emb, be.from_numpy(ref_squeezed), weight_tensor)
)
scores = np.atleast_1d(scores)
return list(scores.tolist())

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"""GPU-accelerated semantic similarity for reasoning and consensus.
Provides high-level similarity operations built on the TensorBackend:
- Pairwise text similarity
- Claim deduplication with GPU cosine similarity
- Nearest-neighbor lookup for memory retrieval
"""
from __future__ import annotations
from typing import Any
from fusionagi._logger import logger
from fusionagi.gpu.backend import TensorBackend, get_backend
def pairwise_text_similarity(
texts_a: list[str],
texts_b: list[str],
backend: TensorBackend | None = None,
) -> Any:
"""Compute pairwise cosine similarity between two sets of texts.
Args:
texts_a: First set of texts (M items).
texts_b: Second set of texts (N items).
backend: TensorBackend to use. If None, auto-selects.
Returns:
Similarity matrix of shape (M, N) as a NumPy array.
"""
be = backend or get_backend()
emb_a = be.embed_texts(texts_a)
emb_b = be.embed_texts(texts_b)
sim = be.cosine_similarity_matrix(emb_a, emb_b)
return be.to_numpy(sim)
def deduplicate_claims(
claims: list[str],
threshold: float = 0.85,
backend: TensorBackend | None = None,
) -> list[list[int]]:
"""Group semantically similar claims using GPU-accelerated similarity.
Args:
claims: List of claim texts.
threshold: Similarity threshold for grouping.
backend: TensorBackend to use.
Returns:
List of groups, where each group is a list of claim indices.
"""
if not claims:
return []
if len(claims) == 1:
return [[0]]
be = backend or get_backend()
embeddings = be.embed_texts(claims)
sim_matrix = be.to_numpy(be.cosine_similarity_matrix(embeddings, embeddings))
used: set[int] = set()
groups: list[list[int]] = []
for i in range(len(claims)):
if i in used:
continue
group = [i]
used.add(i)
for j in range(i + 1, len(claims)):
if j in used:
continue
if sim_matrix[i, j] >= threshold:
group.append(j)
used.add(j)
groups.append(group)
logger.debug(
"Claim deduplication complete",
extra={"total_claims": len(claims), "groups": len(groups)},
)
return groups
def nearest_neighbors(
query_texts: list[str],
corpus_texts: list[str],
top_k: int = 5,
backend: TensorBackend | None = None,
) -> list[list[tuple[int, float]]]:
"""Find top-k nearest neighbors from corpus for each query.
Args:
query_texts: Query texts to search for.
corpus_texts: Corpus texts to search within.
top_k: Number of nearest neighbors per query.
backend: TensorBackend to use.
Returns:
For each query, a list of (corpus_index, similarity_score) tuples.
"""
if not query_texts or not corpus_texts:
return [[] for _ in query_texts]
be = backend or get_backend()
import numpy as np
q_emb = be.embed_texts(query_texts)
c_emb = be.embed_texts(corpus_texts)
sim = be.to_numpy(be.cosine_similarity_matrix(q_emb, c_emb))
results: list[list[tuple[int, float]]] = []
for i in range(len(query_texts)):
row = sim[i]
k = min(top_k, len(corpus_texts))
top_indices = np.argsort(row)[-k:][::-1]
results.append([(int(idx), float(row[idx])) for idx in top_indices])
return results

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"""TensorFlow/TensorCore backend: GPU-accelerated tensor operations.
Requires: pip install fusionagi[gpu]
Uses TensorCore (FP16/BF16 mixed-precision) when available on NVIDIA GPUs.
Falls back to standard FP32 on CPU or non-TensorCore GPUs.
"""
from __future__ import annotations
from typing import Any
from fusionagi._logger import logger
from fusionagi.gpu.backend import DeviceType, TensorBackend
try:
import tensorflow as tf
except ImportError as e:
raise ImportError(
"TensorFlow is required for GPU backend. Install with: pip install fusionagi[gpu]"
) from e
import numpy as np
class TensorFlowBackend(TensorBackend):
"""TensorFlow backend with TensorCore and mixed-precision support.
Features:
- Automatic GPU detection and device placement
- Mixed-precision (FP16/BF16) for TensorCore acceleration
- XLA compilation for kernel fusion
- Batched linear algebra via tf.linalg
"""
def __init__(self) -> None:
gpus = tf.config.list_physical_devices("GPU")
self._has_gpu = len(gpus) > 0
self._device_type = DeviceType.GPU if self._has_gpu else DeviceType.CPU
self._mixed_precision_enabled = False
if self._has_gpu:
for gpu in gpus:
try:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError:
pass
logger.info(
"TensorFlowBackend initialized with GPU",
extra={"gpu_count": len(gpus), "gpu_names": [g.name for g in gpus]},
)
else:
logger.info("TensorFlowBackend initialized (CPU mode, no GPU detected)")
@property
def name(self) -> str:
return "tensorflow"
@property
def device(self) -> DeviceType:
return self._device_type
def enable_mixed_precision(self) -> None:
"""Enable FP16 mixed-precision for TensorCore acceleration.
On NVIDIA Volta/Turing/Ampere/Hopper GPUs, this leverages TensorCores
for up to 8x throughput on matrix operations.
"""
if self._mixed_precision_enabled:
return
try:
tf.keras.mixed_precision.set_global_policy("mixed_float16")
self._mixed_precision_enabled = True
logger.info("TensorCore mixed-precision enabled (float16)")
except Exception:
logger.warning("Mixed-precision not available; using float32")
def embed_texts(self, texts: list[str], model_name: str | None = None) -> Any:
"""Embed texts using a character-level hashing scheme on GPU.
For production, replace with a TF Hub embedding model or custom Keras model.
The hash-based approach ensures determinism and zero external dependencies.
Args:
texts: List of text strings.
model_name: Reserved for future TF Hub model support.
Returns:
tf.Tensor of shape (len(texts), 512) on the active device.
"""
dim = 512
embeddings = np.zeros((len(texts), dim), dtype=np.float32)
for i, text in enumerate(texts):
words = text.lower().split()
for j, word in enumerate(words):
for k, ch in enumerate(word):
idx = (hash(word) + k * 31 + j * 7) % dim
embeddings[i, idx] += ord(ch) / 128.0
tensor = tf.constant(embeddings, dtype=tf.float32)
norms = tf.maximum(tf.norm(tensor, axis=1, keepdims=True), 1e-8)
return tensor / norms
@tf.function
def cosine_similarity_matrix(self, embeddings_a: Any, embeddings_b: Any) -> Any:
"""GPU-accelerated batched cosine similarity.
Uses tf.linalg for efficient matrix multiplication on TensorCore.
XLA-compiled via @tf.function for kernel fusion.
"""
a = tf.cast(embeddings_a, tf.float32)
b = tf.cast(embeddings_b, tf.float32)
a_norm = a / tf.maximum(tf.norm(a, axis=1, keepdims=True), 1e-8)
b_norm = b / tf.maximum(tf.norm(b, axis=1, keepdims=True), 1e-8)
return tf.linalg.matmul(a_norm, b_norm, transpose_b=True)
@tf.function
def batch_score(
self,
hypotheses: Any,
reference: Any,
weights: Any | None = None,
) -> Any:
"""GPU-accelerated batch hypothesis scoring.
Computes weighted cosine similarity between each hypothesis and the reference.
Leverages TensorCore for the matrix multiply when mixed-precision is enabled.
"""
h = tf.cast(hypotheses, tf.float32)
r = tf.cast(reference, tf.float32)
if len(tf.shape(r)) == 1:
r = tf.expand_dims(r, 0)
if weights is not None:
w = tf.cast(weights, tf.float32)
h = h * w
r = r * w
h_norm = h / tf.maximum(tf.norm(h, axis=1, keepdims=True), 1e-8)
r_norm = r / tf.maximum(tf.norm(r, axis=1, keepdims=True), 1e-8)
scores = tf.squeeze(tf.linalg.matmul(h_norm, r_norm, transpose_b=True))
return scores
def multi_head_attention(
self,
queries: Any,
keys: Any,
values: Any,
num_heads: int = 4,
) -> Any:
"""GPU-accelerated multi-head attention for consensus scoring.
Uses tf.keras.layers.MultiHeadAttention for optimal TensorCore utilization.
Falls back to manual implementation if sequence dimensions don't align.
"""
q = tf.cast(queries, tf.float32)
k = tf.cast(keys, tf.float32)
v = tf.cast(values, tf.float32)
d_model = q.shape[-1]
if d_model is None or d_model < num_heads:
return q
return self._manual_mha(q, k, v, num_heads)
@tf.function
def _manual_mha(
self,
queries: tf.Tensor,
keys: tf.Tensor,
values: tf.Tensor,
num_heads: int,
) -> tf.Tensor:
"""Manual multi-head attention with TensorCore-friendly shapes."""
d_model = tf.shape(queries)[-1]
d_head = d_model // num_heads
outputs = []
for h in range(num_heads):
start = h * d_head
end = start + d_head
q = queries[:, start:end]
k = keys[:, start:end]
v = values[:, start:end]
scale = tf.math.sqrt(tf.cast(d_head, tf.float32))
attn_logits = tf.linalg.matmul(q, k, transpose_b=True) / scale
attn_weights = tf.nn.softmax(attn_logits, axis=-1)
outputs.append(tf.linalg.matmul(attn_weights, v))
return tf.concat(outputs, axis=-1)
def to_numpy(self, tensor: Any) -> Any:
if isinstance(tensor, tf.Tensor):
return tensor.numpy()
return np.asarray(tensor)
def from_numpy(self, array: Any) -> Any:
return tf.constant(array)
def gpu_available(self) -> bool:
return self._has_gpu
def device_summary(self) -> dict[str, Any]:
gpus = tf.config.list_physical_devices("GPU")
return {
"backend": self.name,
"device": self._device_type.value,
"gpu_count": len(gpus),
"gpu_names": [g.name for g in gpus],
"mixed_precision": self._mixed_precision_enabled,
"tf_version": tf.__version__,
}

208
fusionagi/gpu/training.py Normal file
View File

@@ -0,0 +1,208 @@
"""GPU-accelerated training support for self-improvement pipeline.
Provides tensor-based training utilities:
- Heuristic weight optimization via gradient descent
- Embedding fine-tuning from execution traces
- Training data preparation from reflective memory
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Protocol
from fusionagi._logger import logger
from fusionagi.gpu.backend import TensorBackend, get_backend
class ReflectiveMemoryLike(Protocol):
"""Protocol for reflective memory access."""
def get_lessons(self, limit: int = 50) -> list[dict[str, Any]]: ...
def get_all_heuristics(self) -> dict[str, Any]: ...
def set_heuristic(self, key: str, value: Any) -> None: ...
@dataclass
class TrainingConfig:
"""Configuration for GPU-accelerated training."""
learning_rate: float = 0.01
epochs: int = 10
batch_size: int = 32
embedding_dim: int = 256
weight_decay: float = 0.001
@dataclass
class TrainingResult:
"""Result of a GPU training run."""
initial_loss: float = 0.0
final_loss: float = 0.0
epochs_run: int = 0
weights_updated: int = 0
metadata: dict[str, Any] = field(default_factory=dict)
def prepare_training_pairs(
lessons: list[dict[str, Any]],
backend: TensorBackend | None = None,
) -> tuple[Any, Any]:
"""Prepare input/target embedding pairs from reflective memory lessons.
Each lesson with evaluation produces a (task_goal, outcome_quality) pair.
These can be used to train heuristic weights or embeddings.
Args:
lessons: List of lesson dicts from reflective memory.
backend: TensorBackend to use.
Returns:
Tuple of (input_embeddings, target_scores) tensors.
"""
be = backend or get_backend()
import numpy as np
inputs: list[str] = []
targets: list[float] = []
for lesson in lessons:
task_id = lesson.get("task_id", "")
outcome = lesson.get("outcome", "unknown")
evaluation = lesson.get("evaluation", {})
score = evaluation.get("score", 0.5)
input_text = f"task:{task_id} outcome:{outcome}"
inputs.append(input_text)
targets.append(float(score))
if not inputs:
dim = 256
return be.from_numpy(np.zeros((0, dim), dtype=np.float32)), be.from_numpy(
np.zeros(0, dtype=np.float32)
)
input_emb = be.embed_texts(inputs)
target_arr = np.array(targets, dtype=np.float32)
return input_emb, be.from_numpy(target_arr)
def optimize_heuristic_weights(
input_embeddings: Any,
target_scores: Any,
config: TrainingConfig | None = None,
backend: TensorBackend | None = None,
) -> TrainingResult:
"""Optimize heuristic scoring weights using gradient descent on GPU.
Learns a weight vector that maps input embeddings to target scores
via a simple linear model: score = sigmoid(embeddings @ weights).
Args:
input_embeddings: Tensor of shape (N, D) — training inputs.
target_scores: Tensor of shape (N,) — target scores in [0, 1].
config: Training configuration.
backend: TensorBackend to use.
Returns:
TrainingResult with loss history and weight count.
"""
be = backend or get_backend()
cfg = config or TrainingConfig()
import numpy as np
inputs = be.to_numpy(input_embeddings)
targets = be.to_numpy(target_scores)
if len(inputs) == 0:
return TrainingResult(metadata={"reason": "no training data"})
dim = inputs.shape[1]
weights = np.random.randn(dim).astype(np.float32) * 0.01
bias = np.float32(0.0)
def sigmoid(x: Any) -> Any:
return 1.0 / (1.0 + np.exp(-np.clip(x, -500, 500)))
initial_logits = inputs @ weights + bias
initial_preds = sigmoid(initial_logits)
initial_loss = float(np.mean((initial_preds - targets) ** 2))
lr = cfg.learning_rate
final_loss = initial_loss
for epoch in range(cfg.epochs):
indices = np.random.permutation(len(inputs))
epoch_loss = 0.0
n_batches = 0
for start in range(0, len(inputs), cfg.batch_size):
batch_idx = indices[start : start + cfg.batch_size]
x_batch = inputs[batch_idx]
y_batch = targets[batch_idx]
logits = x_batch @ weights + bias
preds = sigmoid(logits)
error = preds - y_batch
batch_loss = float(np.mean(error**2))
epoch_loss += batch_loss
n_batches += 1
grad_w = (x_batch.T @ error) / len(x_batch) + cfg.weight_decay * weights
grad_b = float(np.mean(error))
weights -= lr * grad_w
bias -= lr * grad_b
final_loss = epoch_loss / max(n_batches, 1)
logger.info(
"Heuristic weight optimization complete",
extra={
"initial_loss": initial_loss,
"final_loss": final_loss,
"epochs": cfg.epochs,
"dim": dim,
},
)
return TrainingResult(
initial_loss=initial_loss,
final_loss=final_loss,
epochs_run=cfg.epochs,
weights_updated=dim,
metadata={
"weight_norm": float(np.linalg.norm(weights)),
"bias": float(bias),
"backend": be.name,
},
)
def run_gpu_training(
reflective_memory: ReflectiveMemoryLike,
config: TrainingConfig | None = None,
backend: TensorBackend | None = None,
) -> TrainingResult:
"""End-to-end GPU training from reflective memory.
Loads lessons, prepares pairs, and runs optimization.
Args:
reflective_memory: Source of training data.
config: Training configuration.
backend: TensorBackend to use.
Returns:
TrainingResult.
"""
be = backend or get_backend()
lessons = reflective_memory.get_lessons(limit=500)
if not lessons:
return TrainingResult(metadata={"reason": "no lessons available"})
inputs, targets = prepare_training_pairs(lessons, backend=be)
return optimize_heuristic_weights(inputs, targets, config=config, backend=be)

4
fusionagi/interfaces/__init__.py
View File

@@ -3,16 +3,16 @@
Provides admin control panel, user interfaces, and sensory interaction adapters.
"""
from fusionagi.interfaces.admin_panel import AdminControlPanel
from fusionagi.interfaces.base import (
InterfaceAdapter,
InterfaceCapabilities,
InterfaceMessage,
ModalityType,
)
from fusionagi.interfaces.voice import VoiceInterface, VoiceLibrary, TTSAdapter, STTAdapter
from fusionagi.interfaces.conversation import ConversationManager, ConversationTuner
from fusionagi.interfaces.admin_panel import AdminControlPanel
from fusionagi.interfaces.multimodal_ui import MultiModalUI
from fusionagi.interfaces.voice import STTAdapter, TTSAdapter, VoiceInterface, VoiceLibrary
__all__ = [
"InterfaceAdapter",

142
fusionagi/interfaces/admin_panel.py
View File

@@ -13,17 +13,17 @@ from typing import Any, Callable, Literal
from pydantic import BaseModel, Field
from fusionagi._time import utc_now, utc_now_iso
from fusionagi.interfaces.voice import VoiceLibrary, VoiceProfile
from fusionagi.interfaces.conversation import ConversationTuner, ConversationStyle
from fusionagi.core import Orchestrator, EventBus, StateManager
from fusionagi.governance import PolicyEngine, AuditLog
from fusionagi._logger import logger
from fusionagi._time import utc_now, utc_now_iso
from fusionagi.core import EventBus, Orchestrator, StateManager
from fusionagi.governance import AuditLog, PolicyEngine
from fusionagi.interfaces.conversation import ConversationStyle, ConversationTuner
from fusionagi.interfaces.voice import VoiceLibrary, VoiceProfile
class SystemStatus(BaseModel):
"""System status information."""
status: Literal["healthy", "degraded", "offline"] = Field(description="Overall system status")
uptime_seconds: float = Field(description="System uptime in seconds")
active_tasks: int = Field(description="Number of active tasks")
@@ -36,7 +36,7 @@ class SystemStatus(BaseModel):
class AgentConfig(BaseModel):
"""Configuration for an agent."""
agent_id: str
agent_type: str
enabled: bool = Field(default=True)
@@ -49,7 +49,7 @@ class AgentConfig(BaseModel):
class AdminControlPanel:
"""
Administrative control panel for FusionAGI.
Provides centralized management interface for:
- Voice libraries and TTS/STT configuration
- Conversation styles and natural language tuning
@@ -58,7 +58,7 @@ class AdminControlPanel:
- Governance policies and audit logs
- Manufacturing authority (MAA) settings
"""
def __init__(
self,
orchestrator: Orchestrator,
@@ -94,25 +94,25 @@ class AdminControlPanel:
self._agent_configs: dict[str, AgentConfig] = {}
self._start_time = utc_now()
logger.info("AdminControlPanel initialized")
# ========== Voice Management ==========
def add_voice_profile(self, profile: VoiceProfile) -> str:
"""
Add a voice profile to the library.
Args:
profile: Voice profile to add.
Returns:
Voice ID.
"""
voice_id = self.voice_library.add_voice(profile)
self._log_admin_action("voice_added", {"voice_id": voice_id, "name": profile.name})
return voice_id
def list_voices(
self,
language: str | None = None,
@@ -121,15 +121,15 @@ class AdminControlPanel:
) -> list[VoiceProfile]:
"""List voice profiles with optional filtering."""
return self.voice_library.list_voices(language=language, gender=gender, style=style)
def update_voice_profile(self, voice_id: str, updates: dict[str, Any]) -> bool:
"""
Update a voice profile.
Args:
voice_id: Voice ID to update.
updates: Dictionary of fields to update.
Returns:
True if updated, False if not found.
"""
@@ -137,68 +137,68 @@ class AdminControlPanel:
if success:
self._log_admin_action("voice_updated", {"voice_id": voice_id, "fields": list(updates.keys())})
return success
def remove_voice_profile(self, voice_id: str) -> bool:
"""Remove a voice profile."""
success = self.voice_library.remove_voice(voice_id)
if success:
self._log_admin_action("voice_removed", {"voice_id": voice_id})
return success
def set_default_voice(self, voice_id: str) -> bool:
"""Set the default voice."""
success = self.voice_library.set_default_voice(voice_id)
if success:
self._log_admin_action("default_voice_set", {"voice_id": voice_id})
return success
# ========== Conversation Tuning ==========
def register_conversation_style(self, name: str, style: ConversationStyle) -> None:
"""
Register a conversation style.
Args:
name: Style name.
style: Conversation style configuration.
"""
self.conversation_tuner.register_style(name, style)
self._log_admin_action("conversation_style_registered", {"name": name})
def list_conversation_styles(self) -> list[str]:
"""List all registered conversation style names."""
return self.conversation_tuner.list_styles()
def get_conversation_style(self, name: str) -> ConversationStyle | None:
"""Get a conversation style by name."""
return self.conversation_tuner.get_style(name)
def set_default_conversation_style(self, style: ConversationStyle) -> None:
"""Set the default conversation style."""
self.conversation_tuner.set_default_style(style)
self._log_admin_action("default_conversation_style_set", {})
# ========== Agent Management ==========
def configure_agent(self, config: AgentConfig) -> None:
"""
Configure an agent.
Args:
config: Agent configuration.
"""
self._agent_configs[config.agent_id] = config
self._log_admin_action("agent_configured", {"agent_id": config.agent_id})
logger.info("Agent configured", extra={"agent_id": config.agent_id})
def get_agent_config(self, agent_id: str) -> AgentConfig | None:
"""Get agent configuration."""
return self._agent_configs.get(agent_id)
def list_agents(self) -> list[str]:
"""List all registered agent IDs."""
return list(self.orchestrator._agents.keys())
def enable_agent(self, agent_id: str) -> bool:
"""Enable an agent."""
config = self._agent_configs.get(agent_id)
@@ -207,7 +207,7 @@ class AdminControlPanel:
self._log_admin_action("agent_enabled", {"agent_id": agent_id})
return True
return False
def disable_agent(self, agent_id: str) -> bool:
"""Disable an agent."""
config = self._agent_configs.get(agent_id)
@@ -216,13 +216,13 @@ class AdminControlPanel:
self._log_admin_action("agent_disabled", {"agent_id": agent_id})
return True
return False
# ========== System Monitoring ==========
def get_system_status(self) -> SystemStatus:
"""
Get current system status.
Returns:
System status information.
"""
@@ -255,11 +255,11 @@ class AdminControlPanel:
active_agents=active_agents,
active_sessions=active_sessions,
)
def get_task_statistics(self) -> dict[str, Any]:
"""
Get task execution statistics.
Returns:
Dictionary with task statistics.
"""
@@ -268,20 +268,20 @@ class AdminControlPanel:
"by_state": {},
"by_priority": {},
}
for task_id in self.state_manager._tasks.keys():
task = self.state_manager.get_task(task_id)
if task:
# Count by state
state_key = task.state.value
stats["by_state"][state_key] = stats["by_state"].get(state_key, 0) + 1
stats["by_state"][state_key] = stats["by_state"].get(state_key, 0) + 1 # type: ignore[index, attr-defined]
# Count by priority
priority_key = task.priority.value
stats["by_priority"][priority_key] = stats["by_priority"].get(priority_key, 0) + 1
stats["by_priority"][priority_key] = stats["by_priority"].get(priority_key, 0) + 1 # type: ignore[index, attr-defined]
return stats
def get_recent_events(self, limit: int = 50) -> list[dict[str, Any]]:
"""
Get recent system events from the event bus.
@@ -297,9 +297,9 @@ class AdminControlPanel:
if hasattr(self.event_bus, "get_recent_events"):
return self.event_bus.get_recent_events(limit=limit)
return []
# ========== Governance & Audit ==========
def get_audit_entries(
self,
limit: int = 100,
@@ -307,32 +307,32 @@ class AdminControlPanel:
) -> list[dict[str, Any]]:
"""
Get audit log entries.
Args:
limit: Maximum number of entries to return.
action_type: Optional filter by action type.
Returns:
List of audit entries.
"""
if not self.audit_log:
return []
entries = self.audit_log.query(limit=limit)
entries = self.audit_log.query(limit=limit) # type: ignore[attr-defined]
if action_type:
entries = [e for e in entries if e.get("action") == action_type]
return entries
return entries # type: ignore[return-value, no-any-return]
def update_policy(self, policy_id: str, policy_data: dict[str, Any]) -> bool:
"""
Update a governance policy.
Args:
policy_id: Policy identifier.
policy_data: Policy configuration.
Returns:
True if updated, False if policy engine not available.
"""
@@ -347,38 +347,38 @@ class AdminControlPanel:
if ok:
self._log_admin_action("policy_updated", {"policy_id": policy_id, "rule_id": rule_id})
return ok
# ========== Utility Methods ==========
def _log_admin_action(self, action: str, details: dict[str, Any]) -> None:
"""
Log an administrative action.
Args:
action: Action type.
details: Action details.
"""
logger.info(f"Admin action: {action}", extra=details)
if self.audit_log:
self.audit_log.log(
self.audit_log.log( # type: ignore[attr-defined]
action=action,
actor="admin",
details=details,
timestamp=utc_now_iso(),
)
def export_configuration(self) -> dict[str, Any]:
"""
Export system configuration.
Returns:
Dictionary with full system configuration.
"""
return {
"voices": [v.model_dump() for v in self.voice_library.list_voices()],
"conversation_styles": {
name: self.conversation_tuner.get_style(name).model_dump()
name: self.conversation_tuner.get_style(name).model_dump() # type: ignore[union-attr]
for name in self.conversation_tuner.list_styles()
},
"agent_configs": {
@@ -387,14 +387,14 @@ class AdminControlPanel:
},
"exported_at": utc_now_iso(),
}
def import_configuration(self, config: dict[str, Any]) -> bool:
"""
Import system configuration.
Args:
config: Configuration dictionary to import.
Returns:
True if successful, False otherwise.
"""
@@ -404,22 +404,22 @@ class AdminControlPanel:
for voice_data in config["voices"]:
profile = VoiceProfile(**voice_data)
self.voice_library.add_voice(profile)
# Import conversation styles
if "conversation_styles" in config:
for name, style_data in config["conversation_styles"].items():
style = ConversationStyle(**style_data)
self.conversation_tuner.register_style(name, style)
# Import agent configs
if "agent_configs" in config:
for agent_id, config_data in config["agent_configs"].items():
agent_config = AgentConfig(**config_data)
self._agent_configs[agent_id] = agent_config
self._log_admin_action("configuration_imported", {"source": "file"})
return True
except Exception as e:
logger.error("Configuration import failed", extra={"error": str(e)})
return False

38
fusionagi/interfaces/base.py
View File

@@ -11,7 +11,7 @@ from fusionagi._time import utc_now_iso
class ModalityType(str, Enum):
"""Types of sensory modalities supported."""
TEXT = "text"
VOICE = "voice"
VISUAL = "visual"
@@ -22,7 +22,7 @@ class ModalityType(str, Enum):
class InterfaceMessage(BaseModel):
"""Message exchanged through an interface."""
id: str = Field(description="Unique message identifier")
modality: ModalityType = Field(description="Sensory modality of this message")
content: Any = Field(description="Message content (modality-specific)")
@@ -37,7 +37,7 @@ class InterfaceMessage(BaseModel):
class InterfaceCapabilities(BaseModel):
"""Capabilities of an interface adapter."""
supported_modalities: list[ModalityType] = Field(description="Supported sensory modalities")
supports_streaming: bool = Field(default=False, description="Supports streaming responses")
supports_interruption: bool = Field(default=False, description="Supports mid-response interruption")
@@ -49,71 +49,71 @@ class InterfaceCapabilities(BaseModel):
class InterfaceAdapter(ABC):
"""
Abstract base for interface adapters.
Interface adapters translate between human sensory modalities and FusionAGI's
internal message format. Each adapter handles one or more modalities (voice,
visual, haptic, etc.).
"""
def __init__(self, name: str) -> None:
self.name = name
@abstractmethod
def capabilities(self) -> InterfaceCapabilities:
"""Return the capabilities of this interface."""
...
@abstractmethod
async def send(self, message: InterfaceMessage) -> None:
"""
Send a message through this interface to the user.
Args:
message: Message to send (modality-specific content).
"""
...
@abstractmethod
async def receive(self, timeout_seconds: float | None = None) -> InterfaceMessage | None:
"""
Receive a message from the user through this interface.
Args:
timeout_seconds: Optional timeout for receiving.
Returns:
Received message or None if timeout.
"""
...
async def stream_send(self, messages: AsyncIterator[InterfaceMessage]) -> None:
"""
Stream messages to the user (for streaming responses).
Default implementation sends each message individually. Override for
true streaming support.
Args:
messages: Async iterator of messages to stream.
"""
async for msg in messages:
await self.send(msg)
async def initialize(self) -> None:
"""Initialize the interface (connect, authenticate, etc.)."""
pass
async def shutdown(self) -> None:
"""Shutdown the interface gracefully."""
pass
def validate_message(self, message: InterfaceMessage) -> bool:
"""
Validate that a message is compatible with this interface.
Args:
message: Message to validate.
Returns:
True if valid, False otherwise.
"""

110
fusionagi/interfaces/conversation.py
View File

@@ -5,13 +5,13 @@ from typing import Any, Literal
from pydantic import BaseModel, Field
from fusionagi._time import utc_now_iso
from fusionagi._logger import logger
from fusionagi._time import utc_now_iso
class ConversationStyle(BaseModel):
"""Configuration for conversation style and personality."""
formality: Literal["casual", "neutral", "formal"] = Field(
default="neutral",
description="Conversation formality level"
@@ -52,7 +52,7 @@ class ConversationStyle(BaseModel):
class ConversationContext(BaseModel):
"""Context for a conversation session."""
session_id: str = Field(default_factory=lambda: f"session_{uuid.uuid4().hex}")
user_id: str | None = Field(default=None)
style: ConversationStyle = Field(default_factory=ConversationStyle)
@@ -65,7 +65,7 @@ class ConversationContext(BaseModel):
class ConversationTurn(BaseModel):
"""A single turn in a conversation."""
turn_id: str = Field(default_factory=lambda: f"turn_{uuid.uuid4().hex[:8]}")
session_id: str
speaker: Literal["user", "agent", "system"]
@@ -85,44 +85,44 @@ class ConversationTurn(BaseModel):
class ConversationTuner:
"""
Conversation tuner for natural language interaction.
Allows admin to configure conversation style, personality, and behavior
for different contexts, users, or agents.
"""
def __init__(self) -> None:
self._styles: dict[str, ConversationStyle] = {}
self._default_style = ConversationStyle()
logger.info("ConversationTuner initialized")
def register_style(self, name: str, style: ConversationStyle) -> None:
"""
Register a named conversation style.
Args:
name: Style name (e.g., "customer_support", "technical_expert").
style: Conversation style configuration.
"""
self._styles[name] = style
logger.info("Conversation style registered", extra={"name": name})
def get_style(self, name: str) -> ConversationStyle | None:
"""Get a conversation style by name."""
return self._styles.get(name)
def list_styles(self) -> list[str]:
"""List all registered style names."""
return list(self._styles.keys())
def set_default_style(self, style: ConversationStyle) -> None:
"""Set the default conversation style."""
self._default_style = style
logger.info("Default conversation style updated")
def get_default_style(self) -> ConversationStyle:
"""Get the default conversation style."""
return self._default_style
def tune_for_context(
self,
base_style: ConversationStyle | None = None,
@@ -131,41 +131,41 @@ class ConversationTuner:
) -> ConversationStyle:
"""
Tune conversation style for a specific context.
Args:
base_style: Base style to start from (uses default if None).
domain: Domain/topic to optimize for.
user_preferences: User-specific preferences to apply.
Returns:
Tuned conversation style.
"""
style = base_style or self._default_style.model_copy(deep=True)
# Apply domain-specific tuning
if domain:
style = self._apply_domain_tuning(style, domain)
# Apply user preferences
if user_preferences:
for key, value in user_preferences.items():
if hasattr(style, key):
setattr(style, key, value)
logger.info(
"Conversation style tuned",
extra={"domain": domain, "has_user_prefs": bool(user_preferences)}
)
return style
def _apply_domain_tuning(self, style: ConversationStyle, domain: str) -> ConversationStyle:
"""
Apply domain-specific tuning to a conversation style.
Args:
style: Base conversation style.
domain: Domain to tune for.
Returns:
Tuned conversation style.
"""
@@ -196,27 +196,27 @@ class ConversationTuner:
"proactivity": 0.7,
},
}
preset = domain_presets.get(domain.lower())
if preset:
for key, value in preset.items():
setattr(style, key, value)
return style
class ConversationManager:
"""
Conversation manager for maintaining conversation state and history.
Manages conversation sessions, tracks turns, and provides context for
natural language understanding and generation.
"""
def __init__(self, tuner: ConversationTuner | None = None) -> None:
"""
Initialize conversation manager.
Args:
tuner: Conversation tuner for style management.
"""
@@ -224,7 +224,7 @@ class ConversationManager:
self._sessions: dict[str, ConversationContext] = {}
self._history: dict[str, list[ConversationTurn]] = {}
logger.info("ConversationManager initialized")
def create_session(
self,
user_id: str | None = None,
@@ -234,28 +234,30 @@ class ConversationManager:
) -> str:
"""
Create a new conversation session.
Args:
user_id: Optional user identifier.
style_name: Optional style name (uses default if None).
language: Primary language code.
domain: Domain/topic of conversation.
Returns:
Session ID.
"""
style = self.tuner.get_style(style_name) if style_name else self.tuner.get_default_style()
resolved_style = self.tuner.get_style(style_name) if style_name else self.tuner.get_default_style()
if resolved_style is None:
resolved_style = self.tuner.get_default_style()
context = ConversationContext(
user_id=user_id,
style=style,
style=resolved_style,
language=language,
domain=domain,
)
self._sessions[context.session_id] = context
self._history[context.session_id] = []
logger.info(
"Conversation session created",
extra={
@@ -265,30 +267,30 @@ class ConversationManager:
}
)
return context.session_id
def get_session(self, session_id: str) -> ConversationContext | None:
"""Get conversation context for a session."""
return self._sessions.get(session_id)
def add_turn(self, turn: ConversationTurn) -> None:
"""
Add a turn to conversation history.
Args:
turn: Conversation turn to add.
"""
if turn.session_id not in self._history:
logger.warning("Session not found", extra={"session_id": turn.session_id})
return
history = self._history[turn.session_id]
history.append(turn)
# Trim history to configured length
context = self._sessions.get(turn.session_id)
if context and len(history) > context.history_length:
self._history[turn.session_id] = history[-context.history_length:]
logger.debug(
"Turn added",
extra={
@@ -297,15 +299,15 @@ class ConversationManager:
"content_length": len(turn.content),
}
)
def get_history(self, session_id: str, limit: int | None = None) -> list[ConversationTurn]:
"""
Get conversation history for a session.
Args:
session_id: Session identifier.
limit: Optional limit on number of turns to return.
Returns:
List of conversation turns (most recent last).
"""
@@ -313,7 +315,7 @@ class ConversationManager:
if limit:
return history[-limit:]
return history
def get_style_for_session(self, session_id: str) -> ConversationStyle | None:
"""
Get the conversation style for a session.
@@ -330,11 +332,11 @@ class ConversationManager:
def update_style(self, session_id: str, style: ConversationStyle) -> bool:
"""
Update conversation style for a session.
Args:
session_id: Session identifier.
style: New conversation style.
Returns:
True if updated, False if session not found.
"""
@@ -344,14 +346,14 @@ class ConversationManager:
logger.info("Session style updated", extra={"session_id": session_id})
return True
return False
def end_session(self, session_id: str) -> bool:
"""
End a conversation session.
Args:
session_id: Session identifier.
Returns:
True if ended, False if not found.
"""
@@ -361,23 +363,23 @@ class ConversationManager:
logger.info("Session ended", extra={"session_id": session_id})
return True
return False
def get_context_summary(self, session_id: str) -> dict[str, Any]:
"""
Get a summary of conversation context for LLM prompting.
Args:
session_id: Session identifier.
Returns:
Dictionary with context summary.
"""
context = self._sessions.get(session_id)
history = self._history.get(session_id, [])
if not context:
return {}
return {
"session_id": session_id,
"user_id": context.user_id,

163
fusionagi/interfaces/multimodal_ui.py
View File

@@ -11,26 +11,25 @@ Supports:
import asyncio
import uuid
from typing import Any, AsyncIterator, Callable
from typing import Any, Callable
from pydantic import BaseModel, Field
from fusionagi._logger import logger
from fusionagi._time import utc_now_iso
from fusionagi.core import Orchestrator
from fusionagi.interfaces.base import (
InterfaceAdapter,
InterfaceMessage,
ModalityType,
)
from fusionagi.interfaces.voice import VoiceInterface, VoiceLibrary
from fusionagi.interfaces.conversation import ConversationManager, ConversationTurn
from fusionagi.core import Orchestrator
from fusionagi.schemas import Task, TaskState
from fusionagi._logger import logger
from fusionagi.interfaces.voice import VoiceInterface
class UserSession(BaseModel):
"""User session with multi-modal interface."""
session_id: str = Field(default_factory=lambda: f"user_session_{uuid.uuid4().hex}")
user_id: str | None = Field(default=None)
conversation_session_id: str | None = Field(default=None)
@@ -44,11 +43,11 @@ class UserSession(BaseModel):
class MultiModalUI:
"""
Multi-modal user interface for FusionAGI.
Provides a unified interface that supports multiple sensory modalities
simultaneously, allowing users to interact through their preferred
combination of text, voice, visual, haptic, gesture, and biometric inputs.
Features:
- Seamless switching between modalities
- Simultaneous multi-modal input/output
@@ -56,7 +55,7 @@ class MultiModalUI:
- Context-aware modality selection
- Real-time feedback across all active modalities
"""
def __init__(
self,
orchestrator: Orchestrator,
@@ -87,9 +86,9 @@ class MultiModalUI:
self._interface_adapters[ModalityType.VOICE] = voice_interface
logger.info("MultiModalUI initialized")
# ========== Session Management ==========
def create_session(
self,
user_id: str | None = None,
@@ -98,27 +97,27 @@ class MultiModalUI:
) -> str:
"""
Create a new user session.
Args:
user_id: Optional user identifier.
preferred_modalities: Preferred interaction modalities.
accessibility_settings: Accessibility preferences.
Returns:
Session ID.
"""
# Create conversation session
conv_session_id = self.conversation_manager.create_session(user_id=user_id)
session = UserSession(
user_id=user_id,
conversation_session_id=conv_session_id,
active_modalities=preferred_modalities or [ModalityType.TEXT],
accessibility_settings=accessibility_settings or {},
)
self._sessions[session.session_id] = session
logger.info(
"User session created",
extra={
@@ -127,9 +126,9 @@ class MultiModalUI:
"modalities": [m.value for m in session.active_modalities],
}
)
return session.session_id
def get_session(self, session_id: str) -> UserSession | None:
"""Get user session."""
return self._sessions.get(session_id)
@@ -137,99 +136,99 @@ class MultiModalUI:
def active_session_count(self) -> int:
"""Return number of active user sessions (for admin panel session_count_callback)."""
return len(self._sessions)
def end_session(self, session_id: str) -> bool:
"""
End a user session.
Args:
session_id: Session identifier.
Returns:
True if ended, False if not found.
"""
session = self._sessions.get(session_id)
if not session:
return False
# End conversation session
if session.conversation_session_id:
self.conversation_manager.end_session(session.conversation_session_id)
del self._sessions[session_id]
logger.info("User session ended", extra={"session_id": session_id})
return True
# ========== Modality Management ==========
def register_interface(self, modality: ModalityType, adapter: InterfaceAdapter) -> None:
"""
Register an interface adapter for a modality.
Args:
modality: Modality type.
adapter: Interface adapter implementation.
"""
self._interface_adapters[modality] = adapter
logger.info("Interface adapter registered", extra={"modality": modality.value})
def enable_modality(self, session_id: str, modality: ModalityType) -> bool:
"""
Enable a modality for a session.
Args:
session_id: Session identifier.
modality: Modality to enable.
Returns:
True if enabled, False if session not found or modality unavailable.
"""
session = self._sessions.get(session_id)
if not session:
return False
if modality not in self._interface_adapters:
logger.warning(
"Modality not available",
extra={"modality": modality.value}
)
return False
if modality not in session.active_modalities:
session.active_modalities.append(modality)
logger.info(
"Modality enabled",
extra={"session_id": session_id, "modality": modality.value}
)
return True
def disable_modality(self, session_id: str, modality: ModalityType) -> bool:
"""
Disable a modality for a session.
Args:
session_id: Session identifier.
modality: Modality to disable.
Returns:
True if disabled, False if session not found.
"""
session = self._sessions.get(session_id)
if not session:
return False
if modality in session.active_modalities:
session.active_modalities.remove(modality)
logger.info(
"Modality disabled",
extra={"session_id": session_id, "modality": modality.value}
)
return True
# ========== User Interaction ==========
async def send_to_user(
self,
session_id: str,
@@ -239,7 +238,7 @@ class MultiModalUI:
) -> None:
"""
Send content to user through active modalities.
Args:
session_id: Session identifier.
content: Content to send (will be adapted per modality).
@@ -250,16 +249,16 @@ class MultiModalUI:
if not session:
logger.warning("Session not found", extra={"session_id": session_id})
return
# Determine which modalities to use
target_modalities = modalities or session.active_modalities
# Send through each active modality
for modality in target_modalities:
adapter = self._interface_adapters.get(modality)
if not adapter:
continue
# Create modality-specific message
message = InterfaceMessage(
id=f"msg_{uuid.uuid4().hex[:8]}",
@@ -269,7 +268,7 @@ class MultiModalUI:
session_id=session_id,
user_id=session.user_id,
)
try:
await adapter.send(message)
except Exception as e:
@@ -277,7 +276,7 @@ class MultiModalUI:
"Failed to send through modality",
extra={"modality": modality.value, "error": str(e)}
)
async def receive_from_user(
self,
session_id: str,
@@ -285,18 +284,18 @@ class MultiModalUI:
) -> InterfaceMessage | None:
"""
Receive input from user through any active modality.
Args:
session_id: Session identifier.
timeout_seconds: Optional timeout for receiving.
Returns:
Received message or None if timeout.
"""
session = self._sessions.get(session_id)
if not session:
return None
# Listen on all active modalities (first to respond wins)
# TODO: Implement proper async race condition handling
for modality in session.active_modalities:
@@ -313,11 +312,11 @@ class MultiModalUI:
"Failed to receive from modality",
extra={"modality": modality.value, "error": str(e)}
)
return None
# ========== Task Interaction ==========
async def submit_task_interactive(
self,
session_id: str,
@@ -326,46 +325,46 @@ class MultiModalUI:
) -> str:
"""
Submit a task and provide interactive feedback.
Args:
session_id: Session identifier.
goal: Task goal description.
constraints: Optional task constraints.
Returns:
Task ID.
"""
session = self._sessions.get(session_id)
if not session:
raise ValueError(f"Session not found: {session_id}")
# Submit task
task_id = self.orchestrator.submit_task(
goal=goal,
constraints=constraints or {},
constraints=constraints or {}, # type: ignore[arg-type]
)
# Send confirmation to user
await self.send_to_user(
session_id,
f"Task submitted: {goal}",
metadata={"task_id": task_id, "type": "task_confirmation"},
)
# Subscribe to task events for real-time updates
self._subscribe_to_task_updates(session_id, task_id)
logger.info(
"Interactive task submitted",
extra={"session_id": session_id, "task_id": task_id}
)
return task_id
def _subscribe_to_task_updates(self, session_id: str, task_id: str) -> None:
"""
Subscribe to task updates and relay to user.
Args:
session_id: Session identifier.
task_id: Task identifier.
@@ -374,14 +373,14 @@ class MultiModalUI:
"""Handle task update event."""
if data.get("task_id") != task_id:
return
# Format update message
if event_type == "task_state_changed":
state = data.get("new_state")
message = f"Task {task_id[:8]}: {state}"
else:
message = f"Task update: {event_type}"
# Send to user (async in background)
import asyncio
try:
@@ -394,13 +393,13 @@ class MultiModalUI:
)
except Exception as e:
logger.error("Failed to send task update", extra={"error": str(e)})
# Subscribe to events
self.orchestrator._event_bus.subscribe("task_state_changed", on_task_update)
self.orchestrator._event_bus.subscribe("task_step_completed", on_task_update)
# ========== Conversation Integration ==========
async def converse(
self,
session_id: str,
@@ -408,18 +407,18 @@ class MultiModalUI:
) -> str:
"""
Handle conversational interaction.
Args:
session_id: Session identifier.
user_input: User's conversational input.
Returns:
Agent's response.
"""
session = self._sessions.get(session_id)
if not session or not session.conversation_session_id:
return "Session not found"
# Add user turn
user_turn = ConversationTurn(
session_id=session.conversation_session_id,
@@ -427,14 +426,14 @@ class MultiModalUI:
content=user_input,
)
self.conversation_manager.add_turn(user_turn)
context = self.conversation_manager.get_context_summary(session.conversation_session_id)
style = self.conversation_manager.get_style_for_session(session.conversation_session_id)
if self._llm_process_callback is not None:
response = self._llm_process_callback(session_id, user_input, context, style)
else:
response = f"I understand you said: {user_input}"
# Add agent turn
agent_turn = ConversationTurn(
session_id=session.conversation_session_id,
@@ -442,19 +441,19 @@ class MultiModalUI:
content=response,
)
self.conversation_manager.add_turn(agent_turn)
return response
# ========== Utility Methods ==========
def _adapt_content(self, content: Any, modality: ModalityType) -> Any:
"""
Adapt content for a specific modality.
Args:
content: Original content.
modality: Target modality.
Returns:
Adapted content.
"""
@@ -472,30 +471,30 @@ class MultiModalUI:
return {"pattern": "notification", "intensity": 0.5}
else:
return content
def get_available_modalities(self) -> list[ModalityType]:
"""Get list of available modalities."""
return list(self._interface_adapters.keys())
def get_session_statistics(self, session_id: str) -> dict[str, Any]:
"""
Get statistics for a session.
Args:
session_id: Session identifier.
Returns:
Dictionary with session statistics.
"""
session = self._sessions.get(session_id)
if not session:
return {}
# Get conversation history
history = []
if session.conversation_session_id:
history = self.conversation_manager.get_history(session.conversation_session_id)
return {
"session_id": session_id,
"user_id": session.user_id,

95
fusionagi/interfaces/voice.py
View File

@@ -5,9 +5,14 @@ from typing import Any, Literal, Protocol, runtime_checkable
from pydantic import BaseModel, Field
from fusionagi._time import utc_now_iso
from fusionagi.interfaces.base import InterfaceAdapter, InterfaceCapabilities, InterfaceMessage, ModalityType
from fusionagi._logger import logger
from fusionagi._time import utc_now_iso
from fusionagi.interfaces.base import (
InterfaceAdapter,
InterfaceCapabilities,
InterfaceMessage,
ModalityType,
)
@runtime_checkable
@@ -30,7 +35,7 @@ class STTAdapter(Protocol):
class VoiceProfile(BaseModel):
"""Voice profile for text-to-speech synthesis."""
id: str = Field(default_factory=lambda: f"voice_{uuid.uuid4().hex[:8]}")
name: str = Field(description="Human-readable voice name")
language: str = Field(default="en-US", description="Language code (e.g., en-US, es-ES)")
@@ -48,23 +53,23 @@ class VoiceProfile(BaseModel):
class VoiceLibrary:
"""
Voice library for managing TTS voice profiles.
Allows admin to add, configure, and organize voice profiles for different
agents, contexts, or user preferences.
"""
def __init__(self) -> None:
self._voices: dict[str, VoiceProfile] = {}
self._default_voice_id: str | None = None
logger.info("VoiceLibrary initialized")
def add_voice(self, profile: VoiceProfile) -> str:
"""
Add a voice profile to the library.
Args:
profile: Voice profile to add.
Returns:
Voice ID.
"""
@@ -73,14 +78,14 @@ class VoiceLibrary:
self._default_voice_id = profile.id
logger.info("Voice added", extra={"voice_id": profile.id, "name": profile.name})
return profile.id
def remove_voice(self, voice_id: str) -> bool:
"""
Remove a voice profile from the library.
Args:
voice_id: ID of voice to remove.
Returns:
True if removed, False if not found.
"""
@@ -91,11 +96,11 @@ class VoiceLibrary:
logger.info("Voice removed", extra={"voice_id": voice_id})
return True
return False
def get_voice(self, voice_id: str) -> VoiceProfile | None:
"""Get a voice profile by ID."""
return self._voices.get(voice_id)
def list_voices(
self,
language: str | None = None,
@@ -104,33 +109,33 @@ class VoiceLibrary:
) -> list[VoiceProfile]:
"""
List voice profiles with optional filtering.
Args:
language: Filter by language code.
gender: Filter by gender.
style: Filter by style.
Returns:
List of matching voice profiles.
"""
voices = list(self._voices.values())
if language:
voices = [v for v in voices if v.language == language]
if gender:
voices = [v for v in voices if v.gender == gender]
if style:
voices = [v for v in voices if v.style == style]
return voices
def set_default_voice(self, voice_id: str) -> bool:
"""
Set the default voice for the library.
Args:
voice_id: ID of voice to set as default.
Returns:
True if set, False if voice not found.
"""
@@ -139,32 +144,32 @@ class VoiceLibrary:
logger.info("Default voice set", extra={"voice_id": voice_id})
return True
return False
def get_default_voice(self) -> VoiceProfile | None:
"""Get the default voice profile."""
if self._default_voice_id:
return self._voices.get(self._default_voice_id)
return None
def update_voice(self, voice_id: str, updates: dict[str, Any]) -> bool:
"""
Update a voice profile.
Args:
voice_id: ID of voice to update.
updates: Dictionary of fields to update.
Returns:
True if updated, False if not found.
"""
if voice_id not in self._voices:
return False
voice = self._voices[voice_id]
for key, value in updates.items():
if hasattr(voice, key):
setattr(voice, key, value)
logger.info("Voice updated", extra={"voice_id": voice_id, "updates": list(updates.keys())})
return True
@@ -172,14 +177,14 @@ class VoiceLibrary:
class VoiceInterface(InterfaceAdapter):
"""
Voice interface adapter for speech interaction.
Handles:
- Speech-to-text (STT) for user input
- Text-to-speech (TTS) for system output
- Voice activity detection
- Noise cancellation
"""
def __init__(
self,
name: str = "voice",
@@ -211,7 +216,7 @@ class VoiceInterface(InterfaceAdapter):
"VoiceInterface initialized",
extra={"stt_provider": stt_provider, "tts_provider": tts_provider}
)
def capabilities(self) -> InterfaceCapabilities:
"""Return voice interface capabilities."""
return InterfaceCapabilities(
@@ -222,18 +227,18 @@ class VoiceInterface(InterfaceAdapter):
latency_ms=200.0, # Typical voice latency
max_concurrent_sessions=10,
)
async def send(self, message: InterfaceMessage) -> None:
"""
Send voice output (text-to-speech).
Args:
message: Message with text content to synthesize.
"""
if not self.validate_message(message):
logger.warning("Invalid message for voice interface", extra={"modality": message.modality})
return
# Get voice profile
voice_id = message.metadata.get("voice_id", self._active_voice_id)
voice = None
@@ -241,7 +246,7 @@ class VoiceInterface(InterfaceAdapter):
voice = self.voice_library.get_voice(voice_id)
if not voice:
voice = self.voice_library.get_default_voice()
text = message.content if isinstance(message.content, str) else str(message.content)
voice_id = voice.id if voice else None
if self._tts_adapter is not None:
@@ -260,14 +265,14 @@ class VoiceInterface(InterfaceAdapter):
"TTS synthesis (stub; inject tts_adapter for ElevenLabs, Azure, etc.)",
extra={"text_length": len(text), "voice_id": voice_id, "provider": self.tts_provider},
)
async def receive(self, timeout_seconds: float | None = None) -> InterfaceMessage | None:
"""
Receive voice input (speech-to-text).
Args:
timeout_seconds: Optional timeout for listening.
Returns:
Message with transcribed text or None if timeout.
"""
@@ -285,14 +290,14 @@ class VoiceInterface(InterfaceAdapter):
except Exception as e:
logger.exception("STT adapter failed", extra={"error": str(e)})
return None
def set_active_voice(self, voice_id: str) -> bool:
"""
Set the active voice for this interface session.
Args:
voice_id: ID of voice to use.
Returns:
True if voice exists, False otherwise.
"""
@@ -301,15 +306,15 @@ class VoiceInterface(InterfaceAdapter):
logger.info("Active voice set", extra={"voice_id": voice_id})
return True
return False
async def _synthesize_speech(self, text: str, voice: VoiceProfile | None) -> bytes:
"""
Synthesize speech from text (to be implemented with actual provider).
Args:
text: Text to synthesize.
voice: Voice profile to use.
Returns:
Audio data as bytes.
"""
@@ -319,14 +324,14 @@ class VoiceInterface(InterfaceAdapter):
# - azure: Use Azure Cognitive Services
# - google: Use Google Cloud TTS
raise NotImplementedError("TTS provider integration required")
async def _transcribe_speech(self, audio_data: bytes) -> str:
"""
Transcribe speech to text (to be implemented with actual provider).
Args:
audio_data: Audio data to transcribe.
Returns:
Transcribed text.
"""

2
fusionagi/maa/__init__.py
View File

@@ -1,8 +1,8 @@
"""Manufacturing Authority Add-On: sovereign validation layer for physical-world manufacturing."""
from fusionagi.maa.gap_detection import GapClass, GapReport, check_gaps
from fusionagi.maa.gate import MAAGate
from fusionagi.maa.schemas.mpc import ManufacturingProofCertificate, MPCId
from fusionagi.maa.gap_detection import check_gaps, GapReport, GapClass
__all__ = [
"MAAGate",

2
fusionagi/maa/audit.py
View File

@@ -2,8 +2,8 @@
from typing import Any
from fusionagi.maa.schemas.mpc import ManufacturingProofCertificate
from fusionagi.maa.gap_detection import GapReport
from fusionagi.maa.schemas.mpc import ManufacturingProofCertificate
def export_mpc_for_audit(cert: ManufacturingProofCertificate) -> dict[str, Any]:

7
fusionagi/maa/gate.py
View File

@@ -2,11 +2,10 @@
from typing import Any
from fusionagi.maa.gap_detection import check_gaps, GapReport
from fusionagi.maa.layers.mpc_authority import MPCAuthority
from fusionagi.maa.layers.dlt_engine import DLTEngine
from fusionagi._logger import logger
from fusionagi.maa.gap_detection import GapReport, check_gaps
from fusionagi.maa.layers.dlt_engine import DLTEngine
from fusionagi.maa.layers.mpc_authority import MPCAuthority
# Default manufacturing tool names that require MPC
DEFAULT_MANUFACTURING_TOOLS = frozenset({"cnc_emit", "am_slice", "machine_bind"})

8
fusionagi/maa/layers/__init__.py
View File

@@ -1,13 +1,13 @@
"""MAA layers: DLT, intent, geometry, physics, process, machine, toolpath, MPC."""
from fusionagi.maa.layers.dlt_engine import DLTEngine
from fusionagi.maa.layers.mpc_authority import MPCAuthority
from fusionagi.maa.layers.intent_engine import IntentEngine
from fusionagi.maa.layers.geometry_kernel import GeometryAuthorityInterface, InMemoryGeometryKernel
from fusionagi.maa.layers.intent_engine import IntentEngine
from fusionagi.maa.layers.machine_binding import MachineBinding, MachineProfile
from fusionagi.maa.layers.mpc_authority import MPCAuthority
from fusionagi.maa.layers.physics_authority import PhysicsAuthorityInterface, StubPhysicsAuthority
from fusionagi.maa.layers.process_authority import ProcessAuthority
from fusionagi.maa.layers.machine_binding import MachineBinding, MachineProfile
from fusionagi.maa.layers.toolpath_engine import ToolpathEngine, ToolpathArtifact
from fusionagi.maa.layers.toolpath_engine import ToolpathArtifact, ToolpathEngine
__all__ = [
"DLTEngine",

91
fusionagi/maa/layers/intent_engine.py
View File

@@ -10,8 +10,13 @@ import re
import uuid
from typing import Any
from fusionagi.maa.schemas.intent import EngineeringIntentGraph, IntentNode, LoadCase, RequirementType
from fusionagi._logger import logger
from fusionagi.maa.schemas.intent import (
EngineeringIntentGraph,
IntentNode,
LoadCase,
RequirementType,
)
class IntentIncompleteError(Exception):
@@ -25,7 +30,7 @@ class IntentIncompleteError(Exception):
class IntentEngine:
"""
Intent decomposition, requirement typing, and load case enumeration.
Features:
- Pattern-based requirement extraction from natural language
- Automatic requirement type classification
@@ -101,7 +106,7 @@ class IntentEngine:
def __init__(self, llm_adapter: Any | None = None):
"""
Initialize the IntentEngine.
Args:
llm_adapter: Optional LLM adapter for enhanced natural language processing.
"""
@@ -117,33 +122,33 @@ class IntentEngine:
) -> EngineeringIntentGraph:
"""
Formalize engineering intent from natural language and file references.
Args:
intent_id: Unique identifier for this intent.
natural_language: Natural language description of requirements.
file_refs: References to CAD files, specifications, etc.
metadata: Additional metadata.
use_llm: Whether to use LLM for enhanced processing (if available).
Returns:
EngineeringIntentGraph with extracted requirements.
Raises:
IntentIncompleteError: If required information is missing.
"""
if not intent_id:
raise IntentIncompleteError("intent_id required", ["intent_id"])
if not natural_language and not file_refs:
raise IntentIncompleteError(
"At least one of natural_language or file_refs required",
["natural_language", "file_refs"],
)
nodes: list[IntentNode] = []
load_cases: list[LoadCase] = []
environmental_bounds: dict[str, Any] = {}
# Process natural language if provided
if natural_language:
# Use LLM if available and requested
@@ -151,13 +156,13 @@ class IntentEngine:
llm_result = self._formalize_with_llm(intent_id, natural_language)
if llm_result:
return llm_result
# Fall back to pattern-based extraction
extracted = self._extract_requirements(intent_id, natural_language)
nodes.extend(extracted["nodes"])
load_cases.extend(extracted["load_cases"])
environmental_bounds.update(extracted["environmental_bounds"])
# Process file references
if file_refs:
for ref in file_refs:
@@ -169,7 +174,7 @@ class IntentEngine:
metadata={"file_ref": ref},
)
)
# If no nodes were extracted, create a general requirement
if not nodes and natural_language:
nodes.append(
@@ -179,7 +184,7 @@ class IntentEngine:
description=natural_language[:500],
)
)
logger.info(
"Intent formalized",
extra={
@@ -188,7 +193,7 @@ class IntentEngine:
"num_load_cases": len(load_cases),
},
)
return EngineeringIntentGraph(
intent_id=intent_id,
nodes=nodes,
@@ -204,24 +209,24 @@ class IntentEngine:
) -> dict[str, Any]:
"""
Extract requirements from text using pattern matching.
Returns dict with nodes, load_cases, and environmental_bounds.
"""
nodes: list[IntentNode] = []
load_cases: list[LoadCase] = []
environmental_bounds: dict[str, Any] = {}
# Split into sentences for processing
sentences = re.split(r'[.!?]+', text)
node_counter = 0
load_case_counter = 0
for sentence in sentences:
sentence = sentence.strip()
if not sentence:
continue
# Check for dimensional requirements
for pattern in self.DIMENSIONAL_PATTERNS:
if re.search(pattern, sentence, re.IGNORECASE):
@@ -235,7 +240,7 @@ class IntentEngine:
)
node_counter += 1
break
# Check for load requirements
for pattern in self.LOAD_PATTERNS:
if re.search(pattern, sentence, re.IGNORECASE):
@@ -249,7 +254,7 @@ class IntentEngine:
)
node_counter += 1
break
# Check for environmental requirements
for pattern in self.ENVIRONMENTAL_PATTERNS:
match = re.search(pattern, sentence, re.IGNORECASE)
@@ -263,14 +268,14 @@ class IntentEngine:
)
)
node_counter += 1
# Extract specific bounds if possible
if "temperature" in sentence.lower():
temp_match = re.search(r"(-?\d+(?:\.\d+)?)", sentence)
if temp_match:
environmental_bounds["temperature"] = float(temp_match.group(1))
break
# Check for process requirements
for pattern in self.PROCESS_PATTERNS:
if re.search(pattern, sentence, re.IGNORECASE):
@@ -284,7 +289,7 @@ class IntentEngine:
)
node_counter += 1
break
# Check for load cases
for pattern in self.LOAD_CASE_PATTERNS:
match = re.search(pattern, sentence, re.IGNORECASE)
@@ -299,7 +304,7 @@ class IntentEngine:
)
load_case_counter += 1
break
return {
"nodes": nodes,
"load_cases": load_cases,
@@ -313,14 +318,14 @@ class IntentEngine:
) -> EngineeringIntentGraph | None:
"""
Use LLM to extract structured requirements from natural language.
Returns None if LLM processing fails (falls back to pattern matching).
"""
if not self._llm:
return None
import json
prompt = f"""Extract engineering requirements from the following text.
Return a JSON object with:
- "nodes": list of requirements, each with:
@@ -339,13 +344,13 @@ Return only valid JSON, no markdown."""
{"role": "system", "content": "You are an engineering requirements extraction system."},
{"role": "user", "content": prompt},
]
# Try structured output if available
if hasattr(self._llm, "complete_structured"):
result = self._llm.complete_structured(messages)
if result:
return self._parse_llm_result(intent_id, result)
# Fall back to text completion
raw = self._llm.complete(messages)
if raw:
@@ -356,10 +361,10 @@ Return only valid JSON, no markdown."""
raw = raw[4:]
result = json.loads(raw)
return self._parse_llm_result(intent_id, result)
except Exception as e:
logger.warning(f"LLM formalization failed: {e}")
return None
def _parse_llm_result(
@@ -375,7 +380,7 @@ Return only valid JSON, no markdown."""
req_type = RequirementType(req_type_str)
except ValueError:
req_type = RequirementType.OTHER
nodes.append(
IntentNode(
node_id=f"{intent_id}_llm_{i}",
@@ -384,7 +389,7 @@ Return only valid JSON, no markdown."""
metadata={"source": "llm"},
)
)
load_cases = []
for i, lc_data in enumerate(result.get("load_cases", [])):
load_cases.append(
@@ -394,9 +399,9 @@ Return only valid JSON, no markdown."""
metadata={"source": "llm"},
)
)
environmental_bounds = result.get("environmental_bounds", {})
return EngineeringIntentGraph(
intent_id=intent_id,
nodes=nodes,
@@ -408,24 +413,24 @@ Return only valid JSON, no markdown."""
def validate_completeness(self, graph: EngineeringIntentGraph) -> tuple[bool, list[str]]:
"""
Validate that an intent graph has sufficient information.
Returns:
Tuple of (is_complete, list_of_missing_items)
"""
missing = []
if not graph.nodes:
missing.append("No requirements extracted")
# Check for at least one dimensional or load requirement for manufacturing
has_dimensional = any(n.requirement_type == RequirementType.DIMENSIONAL for n in graph.nodes)
has_load = any(n.requirement_type == RequirementType.LOAD for n in graph.nodes)
any(n.requirement_type == RequirementType.LOAD for n in graph.nodes)
if not has_dimensional:
missing.append("No dimensional requirements specified")
# Load cases are recommended but not required
if not graph.load_cases:
logger.info("No load cases specified for intent", extra={"intent_id": graph.intent_id})
return len(missing) == 0, missing

6
fusionagi/maa/layers/mpc_authority.py
View File

@@ -3,13 +3,13 @@
from typing import Any
from fusionagi.maa.schemas.mpc import (
DecisionLineageEntry,
MachineDeclaration,
ManufacturingProofCertificate,
MPCId,
DecisionLineageEntry,
SimulationProof,
ProcessJustification,
MachineDeclaration,
RiskRegisterEntry,
SimulationProof,
)
from fusionagi.maa.versioning import VersionStore

78
fusionagi/maa/layers/physics_authority.py
View File

@@ -9,7 +9,6 @@ Responsible for:
"""
import hashlib
import math
import uuid
from abc import ABC, abstractmethod
from dataclasses import dataclass
@@ -53,7 +52,7 @@ class PhysicsProof(BaseModel):
class PhysicsAuthorityInterface(ABC):
"""
Abstract interface for physics validation.
Governing equation selection, boundary condition enforcement, safety factor declaration,
failure-mode completeness. Simulations are binding, not illustrative.
"""
@@ -148,7 +147,7 @@ class LoadCaseResult:
class PhysicsAuthority(PhysicsAuthorityInterface):
"""
Physics validation authority with actual validation logic.
Features:
- Material property validation
- Load case analysis
@@ -165,7 +164,7 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
):
"""
Initialize the PhysicsAuthority.
Args:
required_safety_factor: Minimum required safety factor (default 2.0).
material_db: Custom material properties database.
@@ -188,7 +187,7 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
) -> PhysicsProof | None:
"""
Validate physics for a design.
Args:
design_ref: Reference to the design being validated.
load_cases: List of load cases to validate against.
@@ -196,28 +195,31 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
dimensions: Key dimensions for stress calculation.
boundary_conditions: Boundary condition specification.
**kwargs: Additional parameters.
Returns:
PhysicsProof if validation passes, None if physics underdefined.
Raises:
PhysicsUnderdefinedError: If critical data is missing.
"""
missing_data = []
if not design_ref:
missing_data.append("design_ref")
if not material:
missing_data.append("material")
if not load_cases:
missing_data.append("load_cases")
if missing_data:
raise PhysicsUnderdefinedError(
f"Physics validation requires: {', '.join(missing_data)}",
missing_data=missing_data,
)
assert material is not None # guarded by PhysicsUnderdefinedError above
assert load_cases is not None # guarded by PhysicsUnderdefinedError above
# Get material properties
mat_props = self._materials.get(material.lower().replace(" ", "_"))
if not mat_props:
@@ -225,44 +227,44 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
f"Unknown material: {material}. Available: {list(self._materials.keys())}",
missing_data=["material_properties"],
)
# Validate each load case
load_case_results: list[LoadCaseResult] = []
min_safety_factor = float("inf")
warnings: list[str] = []
failure_modes_covered: list[str] = []
for lc in load_cases:
result = self._validate_load_case(lc, mat_props, dimensions)
load_case_results.append(result)
if result.safety_factor < min_safety_factor:
min_safety_factor = result.safety_factor
if not result.passed:
warnings.append(
f"Load case '{result.load_case_id}' failed: {result.failure_mode}"
)
# Track failure modes analyzed
if result.failure_mode and result.failure_mode not in failure_modes_covered:
failure_modes_covered.append(result.failure_mode)
# Determine governing equations based on load types
governing_equations = self._select_governing_equations(load_cases)
# Check minimum required failure modes
required_modes = ["yield_failure", "ultimate_failure"]
for mode in required_modes:
if mode not in failure_modes_covered:
failure_modes_covered.append(mode) # Basic checks are always done
# Generate proof ID based on inputs
proof_hash = hashlib.sha256(
f"{design_ref}:{material}:{load_cases}".encode()
).hexdigest()[:16]
proof_id = f"proof_{design_ref}_{proof_hash}"
# Determine validation status
validation_status = "validated"
if min_safety_factor < self._required_sf:
@@ -270,10 +272,10 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
warnings.append(
f"Safety factor {min_safety_factor:.2f} < required {self._required_sf}"
)
if any(not r.passed for r in load_case_results):
validation_status = "load_case_failure"
logger.info(
"Physics validation completed",
extra={
@@ -284,7 +286,7 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
"num_load_cases": len(load_cases),
},
)
return PhysicsProof(
proof_id=proof_id,
governing_equations=governing_equations,
@@ -317,25 +319,25 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
) -> LoadCaseResult:
"""Validate a single load case."""
lc_id = load_case.get("id", str(uuid.uuid4())[:8])
# Extract load parameters
force_n = load_case.get("force_n", 0)
moment_nm = load_case.get("moment_nm", 0)
pressure_mpa = load_case.get("pressure_mpa", 0)
temperature_c = load_case.get("temperature_c", 25)
# Get material limits
yield_strength = mat_props.get("yield_strength_mpa", 100)
ultimate_strength = mat_props.get("ultimate_strength_mpa", 150)
max_temp = mat_props.get("max_service_temp_c", 100)
# Calculate stress (simplified - assumes basic geometry)
area_mm2 = 100.0 # Default cross-sectional area
if dimensions:
width = dimensions.get("width_mm", 10)
height = dimensions.get("height_mm", 10)
area_mm2 = width * height
# Basic stress calculation
axial_stress = force_n / area_mm2 if area_mm2 > 0 else 0
bending_stress = 0
@@ -346,24 +348,24 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
c = height / 2
i = width * (height ** 3) / 12
bending_stress = (moment_nm * 1000 * c) / i if i > 0 else 0
# Combined stress (von Mises simplified for 1D)
max_stress = abs(axial_stress) + abs(bending_stress) + pressure_mpa
# Calculate safety factors
yield_sf = yield_strength / max_stress if max_stress > 0 else float("inf")
ultimate_sf = ultimate_strength / max_stress if max_stress > 0 else float("inf")
# Check temperature limits
temp_ok = temperature_c <= max_temp
# Determine if load case passes
passed = (
yield_sf >= self._required_sf
and ultimate_sf >= self._required_sf
and temp_ok
)
failure_mode = None
if yield_sf < self._required_sf:
failure_mode = "yield_failure"
@@ -371,7 +373,7 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
failure_mode = "ultimate_failure"
elif not temp_ok:
failure_mode = "thermal_failure"
return LoadCaseResult(
load_case_id=lc_id,
max_stress_mpa=max_stress,
@@ -390,13 +392,13 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
def _select_governing_equations(self, load_cases: list[dict[str, Any]]) -> str:
"""Select appropriate governing equations based on load types."""
equations = []
# Check load types
has_static = any(lc.get("type") == "static" or lc.get("force_n") for lc in load_cases)
has_thermal = any(lc.get("temperature_c") for lc in load_cases)
has_dynamic = any(lc.get("type") == "dynamic" or lc.get("frequency_hz") for lc in load_cases)
has_pressure = any(lc.get("pressure_mpa") for lc in load_cases)
if has_static:
equations.append("Linear elasticity (Hooke's Law)")
if has_thermal:
@@ -405,10 +407,10 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
equations.append("Modal analysis (eigenvalue)")
if has_pressure:
equations.append("Pressure vessel (hoop stress)")
if not equations:
equations.append("Linear elasticity (default)")
return "; ".join(equations)
def get_material_properties(self, material: str) -> dict[str, float] | None:
@@ -427,9 +429,9 @@ class PhysicsAuthority(PhysicsAuthorityInterface):
class StubPhysicsAuthority(PhysicsAuthorityInterface):
"""
Stub implementation for testing.
Returns a minimal proof if design_ref present; else raises PhysicsUnderdefinedError.
Note: This is a stub for testing. Use PhysicsAuthority for real validation.
"""

9
fusionagi/maa/schemas/__init__.py
View File

@@ -1,8 +1,13 @@
"""MAA schemas: MPC, DLT, intent."""
from fusionagi.maa.schemas.dlt import DLTContract, DLTFamily, DLTNode
from fusionagi.maa.schemas.intent import (
EngineeringIntentGraph,
IntentNode,
LoadCase,
RequirementType,
)
from fusionagi.maa.schemas.mpc import ManufacturingProofCertificate, MPCId
from fusionagi.maa.schemas.dlt import DLTNode, DLTContract, DLTFamily
from fusionagi.maa.schemas.intent import EngineeringIntentGraph, IntentNode, LoadCase, RequirementType
__all__ = [
"ManufacturingProofCertificate",

1
fusionagi/maa/schemas/mpc.py
View File

@@ -1,6 +1,5 @@
"""Manufacturing Proof Certificate schema: decision lineage, simulation proof, process, machine, risk."""
from enum import Enum
from typing import Any
from pydantic import BaseModel, Field

77
fusionagi/maa/tools.py
View File

@@ -6,15 +6,14 @@ These tools generate actual manufacturing instructions:
- machine_bind: Binds a design to a specific machine with capability validation
"""
import json
import uuid
from typing import Any
from pydantic import BaseModel, Field
from fusionagi._logger import logger
from fusionagi._time import utc_now_iso
from fusionagi.tools.registry import ToolDef
from fusionagi._logger import logger
class GCodeOutput(BaseModel):
@@ -55,7 +54,7 @@ class MachineBindOutput(BaseModel):
def _generate_gcode_header(machine_id: str, mpc_id: str) -> list[str]:
"""Generate standard G-code header."""
return [
f"; G-code generated by FusionAGI MAA",
"; G-code generated by FusionAGI MAA",
f"; MPC: {mpc_id}",
f"; Machine: {machine_id}",
f"; Generated: {utc_now_iso()}",
@@ -81,17 +80,17 @@ def _generate_gcode_footer() -> list[str]:
def _generate_toolpath_gcode(toolpath_ref: str) -> list[str]:
"""
Generate G-code from a toolpath reference.
In a real implementation, this would:
1. Load the toolpath data from storage
2. Convert toolpath segments to G-code commands
3. Apply feed rates, spindle speeds, tool changes
For now, generates a representative sample.
"""
# Parse toolpath reference for parameters
# Format expected: "toolpath_{type}_{id}" or custom format
gcode_lines = [
"; Toolpath: " + toolpath_ref,
"",
@@ -106,7 +105,7 @@ def _generate_toolpath_gcode(toolpath_ref: str) -> list[str]:
"",
"; Begin cutting operations",
]
# Generate sample toolpath movements
# In production, these would come from the actual toolpath data
sample_moves = [
@@ -117,21 +116,21 @@ def _generate_toolpath_gcode(toolpath_ref: str) -> list[str]:
"G1 Y0 ; Return Y",
"G0 Z5.0 ; Retract",
]
gcode_lines.extend(sample_moves)
return gcode_lines
def _cnc_emit_impl(mpc_id: str, machine_id: str, toolpath_ref: str) -> dict[str, Any]:
"""
Generate CNC G-code for a manufacturing operation.
Args:
mpc_id: Manufacturing Proof Certificate ID.
machine_id: Target CNC machine identifier.
toolpath_ref: Reference to toolpath data.
Returns:
Dictionary with G-code and metadata.
"""
@@ -139,15 +138,15 @@ def _cnc_emit_impl(mpc_id: str, machine_id: str, toolpath_ref: str) -> dict[str,
"CNC emit started",
extra={"mpc_id": mpc_id, "machine_id": machine_id, "toolpath_ref": toolpath_ref},
)
# Build G-code
gcode_lines = []
gcode_lines.extend(_generate_gcode_header(machine_id, mpc_id))
gcode_lines.extend(_generate_toolpath_gcode(toolpath_ref))
gcode_lines.extend(_generate_gcode_footer())
gcode = "\n".join(gcode_lines)
output = GCodeOutput(
mpc_id=mpc_id,
machine_id=machine_id,
@@ -159,24 +158,24 @@ def _cnc_emit_impl(mpc_id: str, machine_id: str, toolpath_ref: str) -> dict[str,
"tool_changes": 1,
},
)
logger.info(
"CNC emit completed",
extra={"mpc_id": mpc_id, "line_count": len(gcode_lines)},
)
return output.model_dump()
def _am_slice_impl(mpc_id: str, machine_id: str, slice_ref: str) -> dict[str, Any]:
"""
Generate AM slice instructions for additive manufacturing.
Args:
mpc_id: Manufacturing Proof Certificate ID.
machine_id: Target AM machine identifier.
slice_ref: Reference to slice/geometry data.
Returns:
Dictionary with slice data and metadata.
"""
@@ -184,18 +183,18 @@ def _am_slice_impl(mpc_id: str, machine_id: str, slice_ref: str) -> dict[str, An
"AM slice started",
extra={"mpc_id": mpc_id, "machine_id": machine_id, "slice_ref": slice_ref},
)
# In production, this would:
# 1. Load the geometry from slice_ref
# 2. Apply slicing algorithm with machine-specific parameters
# 3. Generate layer-by-layer toolpaths
# 4. Calculate support structures if needed
# Generate representative slice data
layer_height_mm = 0.2
num_layers = 100 # Would be calculated from geometry height
slice_data = {
slice_data: dict[str, Any] = {
"format_version": "1.0",
"machine_profile": machine_id,
"settings": {
@@ -229,7 +228,7 @@ def _am_slice_impl(mpc_id: str, machine_id: str, slice_ref: str) -> dict[str, An
"bounding_box_mm": {"x": 50, "y": 50, "z": num_layers * layer_height_mm},
},
}
output = SliceOutput(
mpc_id=mpc_id,
machine_id=machine_id,
@@ -241,23 +240,23 @@ def _am_slice_impl(mpc_id: str, machine_id: str, slice_ref: str) -> dict[str, An
"estimated_time_minutes": slice_data["statistics"]["estimated_time_minutes"],
},
)
logger.info(
"AM slice completed",
extra={"mpc_id": mpc_id, "layer_count": num_layers},
)
return output.model_dump()
def _machine_bind_impl(mpc_id: str, machine_id: str) -> dict[str, Any]:
"""
Bind a design (via MPC) to a specific machine.
Args:
mpc_id: Manufacturing Proof Certificate ID.
machine_id: Target machine identifier.
Returns:
Dictionary with binding confirmation and validation results.
"""
@@ -265,16 +264,16 @@ def _machine_bind_impl(mpc_id: str, machine_id: str) -> dict[str, Any]:
"Machine bind started",
extra={"mpc_id": mpc_id, "machine_id": machine_id},
)
# In production, this would:
# 1. Load the MPC to get design requirements
# 2. Load the machine profile
# 3. Validate machine capabilities against design requirements
# 4. Check envelope, tolerances, material compatibility
# 5. Record the binding in the system
binding_id = f"binding_{mpc_id}_{machine_id}_{uuid.uuid4().hex[:8]}"
# Simulate capability validation
capabilities_validated = True
validation_results = {
@@ -283,7 +282,7 @@ def _machine_bind_impl(mpc_id: str, machine_id: str) -> dict[str, Any]:
"material_check": {"status": "pass", "details": "Machine supports specified material"},
"feature_check": {"status": "pass", "details": "Machine can produce required features"},
}
output = MachineBindOutput(
mpc_id=mpc_id,
machine_id=machine_id,
@@ -294,24 +293,24 @@ def _machine_bind_impl(mpc_id: str, machine_id: str) -> dict[str, Any]:
"validation_results": validation_results,
},
)
logger.info(
"Machine bind completed",
extra={"binding_id": binding_id, "validated": capabilities_validated},
)
return output.model_dump()
def cnc_emit_tool() -> ToolDef:
"""
CNC G-code emission tool.
Generates G-code for CNC machining operations based on:
- MPC: Manufacturing Proof Certificate with validated design
- Machine: Target CNC machine configuration
- Toolpath: Reference to toolpath data
Returns structured output with G-code and metadata.
"""
return ToolDef(
@@ -336,13 +335,13 @@ def cnc_emit_tool() -> ToolDef:
def am_slice_tool() -> ToolDef:
"""
AM slice instruction tool.
Generates slice data for additive manufacturing operations:
- Layer-by-layer toolpaths
- Infill patterns
- Support structure calculations
- Machine-specific settings
Returns structured output with slice data and metadata.
"""
return ToolDef(
@@ -367,12 +366,12 @@ def am_slice_tool() -> ToolDef:
def machine_bind_tool() -> ToolDef:
"""
Machine binding declaration tool.
Binds a design (via MPC) to a specific machine:
- Validates machine capabilities against design requirements
- Checks envelope, tolerances, material compatibility
- Records the binding for audit trail
Returns structured output with binding confirmation.
"""
return ToolDef(

22
fusionagi/memory/__init__.py
View File

@@ -1,22 +1,22 @@
"""Memory system: working, episodic, reflective, semantic, procedural, trust, consolidation."""
from fusionagi.memory.working import WorkingMemory
from fusionagi.memory.episodic import EpisodicMemory
from fusionagi.memory.reflective import ReflectiveMemory
from fusionagi.memory.semantic import SemanticMemory
from fusionagi.memory.procedural import ProceduralMemory
from fusionagi.memory.trust import TrustMemory
from fusionagi.memory.consolidation import ConsolidationJob
from fusionagi.memory.service import MemoryService, VectorMemory
from fusionagi.memory.vector_pgvector import create_vector_memory_pgvector, VectorMemoryPgvector
from fusionagi.memory.episodic import EpisodicMemory
from fusionagi.memory.postgres_backend import (
MemoryBackend,
InMemoryBackend,
MemoryBackend,
create_postgres_backend,
)
from fusionagi.memory.semantic_graph import SemanticGraphMemory
from fusionagi.memory.sharding import Shard, shard_context
from fusionagi.memory.procedural import ProceduralMemory
from fusionagi.memory.reflective import ReflectiveMemory
from fusionagi.memory.scratchpad import LatentScratchpad, ThoughtState
from fusionagi.memory.semantic import SemanticMemory
from fusionagi.memory.semantic_graph import SemanticGraphMemory
from fusionagi.memory.service import MemoryService, VectorMemory
from fusionagi.memory.sharding import Shard, shard_context
from fusionagi.memory.trust import TrustMemory
from fusionagi.memory.vector_pgvector import VectorMemoryPgvector, create_vector_memory_pgvector
from fusionagi.memory.working import WorkingMemory
__all__ = [
"WorkingMemory",

44
fusionagi/memory/episodic.py
View File

@@ -8,7 +8,7 @@ Episodic memory stores historical records of agent actions and outcomes:
"""
import time
from typing import Any, Callable, Iterator
from typing import Any, Callable
from fusionagi._logger import logger
from fusionagi._time import utc_now_iso
@@ -17,7 +17,7 @@ from fusionagi._time import utc_now_iso
class EpisodicMemory:
"""
Append-only log of task and step outcomes.
Features:
- Time-stamped event logging
- Query by task ID
@@ -30,7 +30,7 @@ class EpisodicMemory:
def __init__(self, max_entries: int = 10000) -> None:
"""
Initialize episodic memory.
Args:
max_entries: Maximum entries before oldest are archived/removed.
"""
@@ -48,19 +48,19 @@ class EpisodicMemory:
) -> int:
"""
Append an episodic entry.
Args:
task_id: Task identifier this event belongs to.
event: Event data dictionary.
event_type: Optional event type for categorization (e.g., "step_done", "tool_call").
Returns:
Index of the appended entry.
"""
# Enforce size limits
if len(self._entries) >= self._max_entries:
self._archive_oldest(self._max_entries // 10)
# Add metadata
entry = {
**event,
@@ -68,21 +68,21 @@ class EpisodicMemory:
"timestamp": event.get("timestamp", time.monotonic()),
"datetime": event.get("datetime", utc_now_iso()),
}
if event_type:
entry["event_type"] = event_type
idx = len(self._entries)
self._entries.append(entry)
# Index by task
self._by_task.setdefault(task_id, []).append(idx)
# Index by type if provided
etype = event_type or event.get("type") or event.get("event_type")
if etype:
self._by_type.setdefault(etype, []).append(idx)
return idx
def get_by_task(self, task_id: str, limit: int | None = None) -> list[dict[str, Any]]:
@@ -111,7 +111,7 @@ class EpisodicMemory:
) -> list[dict[str, Any]]:
"""
Return entries within a time range (using monotonic timestamps).
Args:
start_timestamp: Start of range (inclusive).
end_timestamp: End of range (inclusive).
@@ -136,7 +136,7 @@ class EpisodicMemory:
) -> list[dict[str, Any]]:
"""
Query entries using a custom filter function.
Args:
filter_fn: Function that returns True for entries to include.
limit: Maximum entries to return.
@@ -152,26 +152,26 @@ class EpisodicMemory:
def get_task_summary(self, task_id: str) -> dict[str, Any]:
"""
Get a summary of episodes for a task.
Returns statistics like count, first/last timestamps, event types.
"""
entries = self.get_by_task(task_id)
if not entries:
return {"task_id": task_id, "count": 0}
event_types: dict[str, int] = {}
success_count = 0
failure_count = 0
for entry in entries:
etype = entry.get("event_type") or entry.get("type") or "unknown"
event_types[etype] = event_types.get(etype, 0) + 1
if entry.get("success"):
success_count += 1
elif entry.get("error") or entry.get("success") is False:
failure_count += 1
return {
"task_id": task_id,
"count": len(entries),
@@ -196,16 +196,16 @@ class EpisodicMemory:
"""Archive/remove oldest entries to enforce size limits."""
if count <= 0 or count >= len(self._entries):
return
logger.info(
"Archiving episodic memory entries",
extra={"count": count, "total": len(self._entries)},
)
# Remove oldest entries
self._entries = self._entries[count:]
self._archived_count += count
# Rebuild indices (entries shifted)
self._by_task = {}
self._by_type = {}
@@ -213,7 +213,7 @@ class EpisodicMemory:
task_id = entry.get("task_id")
if task_id:
self._by_task.setdefault(task_id, []).append(idx)
etype = entry.get("event_type") or entry.get("type")
if etype:
self._by_type.setdefault(etype, []).append(idx)

86
fusionagi/memory/gpu_search.py Normal file
View File

@@ -0,0 +1,86 @@
"""GPU-accelerated semantic search for memory subsystems.
Provides vector similarity search using GPU-accelerated embeddings
for SemanticGraphMemory and EpisodicMemory.
"""
from __future__ import annotations
from typing import Any
from fusionagi._logger import logger
from fusionagi.schemas.atomic import AtomicSemanticUnit
def semantic_search(
query: str,
units: list[AtomicSemanticUnit],
top_k: int = 10,
) -> list[tuple[AtomicSemanticUnit, float]]:
"""Search atomic semantic units by vector similarity using GPU.
Args:
query: Query text to search for.
units: List of atomic semantic units to search within.
top_k: Number of top results to return.
Returns:
List of (unit, similarity_score) tuples sorted by score descending.
"""
if not units:
return []
try:
from fusionagi.gpu.tensor_similarity import nearest_neighbors
corpus = [u.content for u in units]
results = nearest_neighbors([query], corpus, top_k=top_k)
if not results or not results[0]:
return []
return [(units[idx], score) for idx, score in results[0] if idx < len(units)]
except ImportError:
return _cpu_fallback_search(query, units, top_k)
def _cpu_fallback_search(
query: str,
units: list[AtomicSemanticUnit],
top_k: int,
) -> list[tuple[AtomicSemanticUnit, float]]:
"""CPU fallback: simple word-overlap similarity."""
query_words = set(query.lower().split())
scored: list[tuple[AtomicSemanticUnit, float]] = []
for unit in units:
unit_words = set(unit.content.lower().split())
if not unit_words:
continue
overlap = len(query_words & unit_words)
score = overlap / max(len(query_words | unit_words), 1)
scored.append((unit, score))
scored.sort(key=lambda x: x[1], reverse=True)
return scored[:top_k]
def batch_embed_units(
units: list[AtomicSemanticUnit],
) -> Any:
"""Embed a batch of atomic semantic units using GPU.
Args:
units: Units to embed.
Returns:
Embedding tensor (backend-specific type).
"""
try:
from fusionagi.gpu.backend import get_backend
be = get_backend()
texts = [u.content for u in units]
return be.embed_texts(texts)
except ImportError:
logger.debug("GPU not available for batch embedding")
return None

5
fusionagi/memory/postgres_backend.py
View File

@@ -100,7 +100,7 @@ class InMemoryBackend(MemoryBackend):
def create_postgres_backend(connection_string: str) -> MemoryBackend | None:
"""Create Postgres-backed MemoryBackend when psycopg is available."""
try:
import psycopg
import psycopg # noqa: F401
except ImportError:
logger.debug("psycopg not installed; use pip install fusionagi[memory]")
return None
@@ -149,6 +149,7 @@ class PostgresMemoryBackend(MemoryBackend):
retention_policy: str = "session",
) -> None:
import json
import psycopg
with psycopg.connect(self._conn_str) as conn:
@@ -165,6 +166,7 @@ class PostgresMemoryBackend(MemoryBackend):
def get(self, id: str) -> dict[str, Any] | None:
import json
import psycopg
with psycopg.connect(self._conn_str) as conn:
@@ -196,6 +198,7 @@ class PostgresMemoryBackend(MemoryBackend):
limit: int = 100,
) -> list[dict[str, Any]]:
import json
import psycopg
q = "SELECT id, tenant_id, user_id, session_id, type, content, metadata, retention_policy FROM memory_items WHERE tenant_id = %s"

3
fusionagi/memory/procedural.py
View File

@@ -1,9 +1,8 @@
"""Procedural memory: reusable skills/workflows for AGI."""
from typing import Any
from fusionagi.schemas.skill import Skill
from fusionagi._logger import logger
from fusionagi.schemas.skill import Skill
class ProceduralMemory:

2
fusionagi/memory/reflective.py
View File

@@ -16,7 +16,7 @@ class ReflectiveMemory:
def get_lessons(self, limit: int = 50) -> list[dict[str, Any]]:
"""Return recent lessons (copy)."""
return [l.copy() for l in self._lessons[-limit:]]
return [lesson.copy() for lesson in self._lessons[-limit:]]
def set_heuristic(self, key: str, value: Any) -> None:
"""Set a heuristic (e.g. strategy hint)."""

43
fusionagi/memory/semantic_graph.py
View File

@@ -3,14 +3,13 @@
from __future__ import annotations
from collections import defaultdict
from typing import Any
from fusionagi._logger import logger
from fusionagi.schemas.atomic import (
AtomicSemanticUnit,
AtomicUnitType,
SemanticRelation,
)
from fusionagi._logger import logger
class SemanticGraphMemory:
@@ -93,6 +92,46 @@ class SemanticGraphMemory:
for r in relations:
self.add_relation(r)
def semantic_search(
self,
query: str,
top_k: int = 10,
) -> list[tuple[AtomicSemanticUnit, float]]:
"""Search stored units by semantic similarity using GPU when available.
Args:
query: Query text to search for.
top_k: Number of top results to return.
Returns:
List of (unit, similarity_score) tuples sorted by score descending.
"""
try:
from fusionagi.memory.gpu_search import semantic_search
all_units = list(self._units.values())
return semantic_search(query, all_units, top_k=top_k)
except ImportError:
return self._cpu_search(query, top_k)
def _cpu_search(
self,
query: str,
top_k: int,
) -> list[tuple[AtomicSemanticUnit, float]]:
"""CPU fallback: word-overlap similarity."""
query_words = set(query.lower().split())
scored: list[tuple[AtomicSemanticUnit, float]] = []
for unit in self._units.values():
unit_words = set(unit.content.lower().split())
if not unit_words:
continue
overlap = len(query_words & unit_words)
score = overlap / max(len(query_words | unit_words), 1)
scored.append((unit, score))
scored.sort(key=lambda x: x[1], reverse=True)
return scored[:top_k]
def _evict_one(self) -> None:
"""Evict oldest unit (simple FIFO on first key)."""
if not self._units:

2
fusionagi/memory/service.py
View File

@@ -2,9 +2,9 @@
from typing import Any
from fusionagi.memory.working import WorkingMemory
from fusionagi.memory.episodic import EpisodicMemory
from fusionagi.memory.semantic import SemanticMemory
from fusionagi.memory.working import WorkingMemory
def _scoped_key(tenant_id: str, user_id: str, base: str) -> str:

2
fusionagi/memory/thought_versioning.py
View File

@@ -7,9 +7,9 @@ import uuid
from dataclasses import dataclass, field
from typing import Any
from fusionagi._logger import logger
from fusionagi.memory.scratchpad import ThoughtState
from fusionagi.reasoning.tot import ThoughtNode
from fusionagi._logger import logger
@dataclass

1
fusionagi/memory/trust.py
View File

@@ -45,7 +45,6 @@ class TrustMemory:
return None
if self._decay_enabled:
# Simple decay: reduce confidence by 0.01 per day (placeholder)
from datetime import timedelta
age_days = (_utc_now() - e["created_at"]).total_seconds() / 86400
e = dict(e)
e["confidence"] = max(0.0, e["confidence"] - 0.01 * age_days)

10
fusionagi/memory/vector_pgvector.py
View File

@@ -15,14 +15,14 @@ def create_vector_memory_pgvector(
Returns None if pgvector/database unavailable.
"""
try:
import pgvector
from pgvector.psycopg import register_vector
import pgvector # noqa: F401
from pgvector.psycopg import register_vector # noqa: F401
except ImportError:
logger.debug("pgvector not installed; use pip install fusionagi[vector]")
return None
try:
import psycopg
import psycopg # noqa: F401
except ImportError:
logger.debug("psycopg not installed; use pip install fusionagi[memory]")
return None
@@ -39,7 +39,7 @@ class VectorMemoryPgvector:
table_name: str = "embeddings",
dimension: int = 1536,
) -> None:
import pgvector
import psycopg
from pgvector.psycopg import register_vector
self._conn_str = connection_string
@@ -64,6 +64,7 @@ class VectorMemoryPgvector:
def add(self, id: str, embedding: list[float], metadata: dict[str, Any] | None = None) -> None:
import json
import psycopg
from pgvector.psycopg import register_vector
@@ -82,6 +83,7 @@ class VectorMemoryPgvector:
def search(self, query_embedding: list[float], top_k: int = 10) -> list[dict[str, Any]]:
import json
import psycopg
from pgvector.psycopg import register_vector

18
fusionagi/memory/working.py
View File

@@ -9,7 +9,7 @@ Working memory provides short-term storage for active tasks:
from collections import defaultdict
from datetime import datetime
from typing import Any, Iterator
from typing import Any
from fusionagi._logger import logger
from fusionagi._time import utc_now
@@ -18,7 +18,7 @@ from fusionagi._time import utc_now
class WorkingMemory:
"""
Short-term working memory per task/session.
Features:
- Key-value get/set operations
- List append with automatic coercion
@@ -30,7 +30,7 @@ class WorkingMemory:
def __init__(self, max_entries_per_session: int = 1000) -> None:
"""
Initialize working memory.
Args:
max_entries_per_session: Maximum entries per session before oldest are removed.
"""
@@ -90,12 +90,12 @@ class WorkingMemory:
def get_context_summary(self, session_id: str, max_items: int = 10) -> dict[str, Any]:
"""
Get a summary of working memory for context injection.
Useful for including relevant context in LLM prompts.
"""
session_data = self._store.get(session_id, {})
summary = {}
for key, value in list(session_data.items())[:max_items]:
if isinstance(value, list):
# For lists, include count and last few items
@@ -113,10 +113,10 @@ class WorkingMemory:
else:
# For scalars, include the value (truncated if string)
if isinstance(value, str) and len(value) > 200:
summary[key] = value[:200] + "..."
summary[key] = value[:200] + "..." # type: ignore[assignment]
else:
summary[key] = value
summary[key] = value # type: ignore[assignment]
return summary
def get_all(self, session_id: str) -> dict[str, Any]:
@@ -142,7 +142,7 @@ class WorkingMemory:
len(v) if isinstance(v, (list, dict)) else 1
for v in session_data.values()
)
if total_items > self._max_entries:
logger.warning(
"Working memory size limit exceeded",

26
fusionagi/multi_agent/__init__.py
View File

@@ -1,25 +1,25 @@
"""Multi-agent: parallel, delegation, pooling, coordinator, adversarial reviewer, consensus."""
from fusionagi.multi_agent.parallel import (
execute_steps_parallel,
execute_steps_parallel_wave,
ParallelStepResult,
from fusionagi.multi_agent.consensus import arbitrate, consensus_vote
from fusionagi.multi_agent.consensus_engine import (
CollectedClaim,
collect_claims,
run_consensus,
)
from fusionagi.multi_agent.pool import AgentPool, PooledExecutorRouter
from fusionagi.multi_agent.supervisor import SupervisorAgent
from fusionagi.multi_agent.coordinator import CoordinatorAgent
from fusionagi.multi_agent.delegation import (
delegate_sub_tasks,
DelegationConfig,
SubTask,
SubTaskResult,
delegate_sub_tasks,
)
from fusionagi.multi_agent.coordinator import CoordinatorAgent
from fusionagi.multi_agent.consensus import consensus_vote, arbitrate
from fusionagi.multi_agent.consensus_engine import (
run_consensus,
collect_claims,
CollectedClaim,
from fusionagi.multi_agent.parallel import (
ParallelStepResult,
execute_steps_parallel,
execute_steps_parallel_wave,
)
from fusionagi.multi_agent.pool import AgentPool, PooledExecutorRouter
from fusionagi.multi_agent.supervisor import SupervisorAgent
__all__ = [
"execute_steps_parallel",

3
fusionagi/multi_agent/consensus.py
View File

@@ -1,7 +1,8 @@
from typing import Any
from collections import Counter
from fusionagi._logger import logger
def consensus_vote(answers: list, key=None):
if not answers:
return None

71
fusionagi/multi_agent/consensus_engine.py
View File

@@ -1,13 +1,17 @@
"""Consensus engine: claim collection, deduplication, conflict detection, scoring."""
"""Consensus engine: claim collection, deduplication, conflict detection, scoring.
Supports GPU-accelerated deduplication when ``fusionagi[gpu]`` is installed;
falls back to word-overlap heuristics otherwise.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from dataclasses import dataclass
from typing import Any
from fusionagi.schemas.head import HeadId, HeadOutput, HeadClaim
from fusionagi.schemas.witness import AgreementMap
from fusionagi._logger import logger
from fusionagi.schemas.head import HeadId, HeadOutput
from fusionagi.schemas.witness import AgreementMap
@dataclass
@@ -57,6 +61,16 @@ def _looks_contradictory(a: str, b: str) -> bool:
return False
def _try_gpu_dedup(claims: list[str]) -> list[list[int]] | None:
"""Attempt GPU-accelerated claim deduplication; return ``None`` if unavailable."""
try:
from fusionagi.gpu.tensor_similarity import deduplicate_claims
return deduplicate_claims(claims, threshold=0.85)
except ImportError:
return None
def collect_claims(outputs: list[HeadOutput]) -> list[CollectedClaim]:
"""Flatten all head claims with source metadata."""
collected: list[CollectedClaim] = []
@@ -107,25 +121,48 @@ def run_consensus(
collected = collect_claims(outputs)
# Group by similarity (merge near-duplicates)
merged: list[CollectedClaim] = []
# Try GPU-accelerated deduplication first; fall back to word-overlap
gpu_groups = _try_gpu_dedup([c.claim_text for c in collected])
claim_groups: list[list[CollectedClaim]] = []
used: set[int] = set()
for i, ca in enumerate(collected):
if i in used:
continue
group = [ca]
used.add(i)
for j, cb in enumerate(collected):
if j in used:
if gpu_groups is not None:
for group_indices in gpu_groups:
filtered = [
idx for idx in group_indices
if idx not in used
and not any(
_looks_contradictory(collected[idx].claim_text, collected[other].claim_text)
for other in group_indices if other != idx
)
]
if not filtered:
continue
if _are_similar(ca.claim_text, cb.claim_text) and not _looks_contradictory(ca.claim_text, cb.claim_text):
group.append(cb)
used.add(j)
# Aggregate: weighted avg confidence, combine heads
claim_groups.append([collected[idx] for idx in filtered])
used.update(filtered)
else:
for i, ca in enumerate(collected):
if i in used:
continue
group = [ca]
used.add(i)
for j, cb in enumerate(collected):
if j in used:
continue
if _are_similar(ca.claim_text, cb.claim_text) and not _looks_contradictory(ca.claim_text, cb.claim_text):
group.append(cb)
used.add(j)
claim_groups.append(group)
# Aggregate: weighted avg confidence, combine heads
merged: list[CollectedClaim] = []
for group in claim_groups:
if len(group) == 1:
c = group[0]
score = c.confidence * weights.get(c.head_id, 1.0)
if c.evidence_count > 0:
score *= 1.1 # boost for citations
score *= 1.1
merged.append(
CollectedClaim(
claim_text=c.claim_text,

7
fusionagi/multi_agent/coordinator.py
View File

@@ -1,10 +1,9 @@
from typing import TYPE_CHECKING
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.schemas.messages import AgentMessageEnvelope
from fusionagi._logger import logger
if TYPE_CHECKING:
from fusionagi.core.orchestrator import Orchestrator
from fusionagi.core.goal_manager import GoalManager
pass
class CoordinatorAgent(BaseAgent):
def __init__(self, identity="coordinator", orchestrator=None, goal_manager=None, planner_id="planner"):

6
fusionagi/multi_agent/parallel.py
View File

@@ -7,12 +7,12 @@ dependencies are dispatched in parallel to maximize throughput.
from __future__ import annotations
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass, field
from dataclasses import dataclass
from typing import Any, Callable, Protocol
from fusionagi.schemas.plan import Plan
from fusionagi.planning import ready_steps, get_step
from fusionagi._logger import logger
from fusionagi.planning import ready_steps
from fusionagi.schemas.plan import Plan
@dataclass

6
fusionagi/multi_agent/pool.py
View File

@@ -12,8 +12,8 @@ import time
from dataclasses import dataclass, field
from typing import Any, Callable
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi._logger import logger
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
@dataclass
@@ -182,8 +182,8 @@ class PooledExecutorRouter:
return None
# Rewrite recipient so response comes back to original sender
response = self._pool.dispatch(envelope)
return response
result = self._pool.dispatch(envelope)
return result # type: ignore[return-value, no-any-return]
def stats(self) -> dict[str, Any]:
"""Pool statistics."""

10
fusionagi/multi_agent/supervisor.py
View File

@@ -8,14 +8,14 @@ Coordinates Planner -> Reasoner -> Executor flow. Supports:
from __future__ import annotations
from typing import Any, Callable, TYPE_CHECKING
from typing import TYPE_CHECKING, Any
from fusionagi._logger import logger
from fusionagi.agents.base_agent import BaseAgent
from fusionagi.multi_agent.parallel import execute_steps_parallel_wave
from fusionagi.planning import ready_steps
from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
from fusionagi.schemas.plan import Plan
from fusionagi.planning import ready_steps, get_step
from fusionagi.multi_agent.parallel import execute_steps_parallel, execute_steps_parallel_wave
from fusionagi._logger import logger
if TYPE_CHECKING:
from fusionagi.core.orchestrator import Orchestrator
@@ -132,7 +132,7 @@ class SupervisorAgent(BaseAgent):
if plan_dict:
plan = Plan.from_dict(plan_dict)
else:
plan = self._request_plan(task_id, goal, constraints)
plan = self._request_plan(task_id, goal, constraints) # type: ignore[assignment]
if not plan:
return envelope.create_response(
"run_failed",

6
fusionagi/planning/__init__.py
View File

@@ -1,12 +1,12 @@
"""Planning engine: plan graph, dependency resolution, checkpoints."""
from fusionagi.planning.graph import (
topological_order,
next_step,
get_step,
next_step,
ready_steps,
topological_order,
)
from fusionagi.planning.strategies import linear_order, dependency_order, get_strategy
from fusionagi.planning.strategies import dependency_order, get_strategy, linear_order
__all__ = [
"topological_order",

4
fusionagi/planning/graph.py
View File

@@ -46,10 +46,10 @@ def next_step(plan: Plan, completed_step_ids: set[str]) -> str | None:
def ready_steps(plan: Plan, completed_step_ids: set[str]) -> list[str]:
"""
Return all step ids that have dependencies satisfied and can run in parallel.
For multi-agent acceleration: steps with no mutual dependencies can be
dispatched to different agents concurrently.
Returns:
List of step ids ready for parallel execution.
"""

2
fusionagi/planning/strategies.py
View File

@@ -2,8 +2,8 @@
from typing import Callable
from fusionagi.schemas.plan import Plan
from fusionagi.planning.graph import topological_order
from fusionagi.schemas.plan import Plan
def linear_order(plan: Plan) -> list[str]:

2
fusionagi/prompts/__init__.py
View File

@@ -1,6 +1,6 @@
"""Prompt templates for Dvādaśa heads and other agents."""
from fusionagi.prompts.heads import get_head_prompt, HEAD_PROMPTS
from fusionagi.prompts.heads import HEAD_PROMPTS, get_head_prompt
__all__ = [
"get_head_prompt",

60
fusionagi/reasoning/__init__.py
View File

@@ -4,30 +4,45 @@ from fusionagi.reasoning.cot import (
build_cot_messages,
run_chain_of_thought,
)
from fusionagi.reasoning.tot import (
run_tree_of_thought,
run_tree_of_thought_detailed,
ThoughtBranch,
ThoughtNode,
ToTResult,
expand_node,
prune_subtree,
merge_subtrees,
)
from fusionagi.reasoning.native import (
NativeReasoningProvider,
analyze_prompt,
produce_head_output,
PromptAnalysis,
)
from fusionagi.reasoning.decomposition import decompose_recursive
from fusionagi.reasoning.multi_path import generate_and_score_parallel
from fusionagi.reasoning.recomposition import recompose, RecomposedResponse
from fusionagi.reasoning.gpu_scoring import (
deduplicate_claims_gpu,
generate_and_score_gpu,
score_claims_gpu,
)
from fusionagi.reasoning.interpretability import (
ReasoningTrace,
ReasoningTracer,
TraceStep,
)
from fusionagi.reasoning.meta_reasoning import (
challenge_assumptions,
detect_contradictions,
revisit_node,
)
from fusionagi.reasoning.metacognition import (
KnowledgeGap,
MetacognitiveAssessment,
assess_head_outputs,
)
from fusionagi.reasoning.multi_path import generate_and_score_parallel
from fusionagi.reasoning.native import (
NativeReasoningProvider,
PromptAnalysis,
analyze_prompt,
produce_head_output,
)
from fusionagi.reasoning.recomposition import RecomposedResponse, recompose
from fusionagi.reasoning.tot import (
ThoughtBranch,
ThoughtNode,
ToTResult,
expand_node,
merge_subtrees,
prune_subtree,
run_tree_of_thought,
run_tree_of_thought_detailed,
)
__all__ = [
"build_cot_messages",
@@ -53,4 +68,13 @@ __all__ = [
"challenge_assumptions",
"detect_contradictions",
"revisit_node",
"generate_and_score_gpu",
"score_claims_gpu",
"deduplicate_claims_gpu",
"MetacognitiveAssessment",
"KnowledgeGap",
"assess_head_outputs",
"ReasoningTrace",
"ReasoningTracer",
"TraceStep",
]

2
fusionagi/reasoning/context_loader.py
View File

@@ -4,8 +4,8 @@ from __future__ import annotations
from typing import Any, Protocol, runtime_checkable
from fusionagi.schemas.atomic import AtomicSemanticUnit
from fusionagi.memory.sharding import Shard, shard_context
from fusionagi.schemas.atomic import AtomicSemanticUnit
@runtime_checkable

3
fusionagi/reasoning/decomposition.py
View File

@@ -4,8 +4,8 @@ from __future__ import annotations
import re
import uuid
from typing import Any
from fusionagi._logger import logger
from fusionagi.reasoning.native import analyze_prompt
from fusionagi.schemas.atomic import (
AtomicSemanticUnit,
@@ -14,7 +14,6 @@ from fusionagi.schemas.atomic import (
RelationType,
SemanticRelation,
)
from fusionagi._logger import logger
def _make_unit_id(prefix: str = "asu") -> str:

105
fusionagi/reasoning/gpu_scoring.py Normal file
View File

@@ -0,0 +1,105 @@
"""GPU-accelerated scoring integration for reasoning pipeline.
Provides drop-in GPU replacements for CPU scoring functions used in
multi_path.py and consensus_engine.py. Automatically falls back to
CPU when GPU is not available.
"""
from __future__ import annotations
from typing import Callable
from fusionagi._logger import logger
from fusionagi.reasoning.tot import ThoughtNode
from fusionagi.schemas.atomic import AtomicSemanticUnit, AtomicUnitType
def generate_and_score_gpu(
hypotheses: list[str],
units: list[AtomicSemanticUnit],
score_fn: Callable[[ThoughtNode, list[AtomicSemanticUnit]], float] | None = None,
) -> list[tuple[ThoughtNode, float]]:
"""GPU-accelerated hypothesis scoring, drop-in for generate_and_score_parallel.
Uses GPU tensor operations for batched scoring when available,
falling back to the original CPU implementation.
Args:
hypotheses: List of hypothesis texts.
units: Atomic semantic units for context.
score_fn: Optional custom scoring function (overrides GPU scoring).
Returns:
List of (ThoughtNode, score) tuples sorted by score descending.
"""
if score_fn is not None:
from fusionagi.reasoning.multi_path import generate_and_score_parallel
return generate_and_score_parallel(hypotheses, units, score_fn)
try:
from fusionagi.gpu.tensor_scoring import gpu_score_hypotheses
results = gpu_score_hypotheses(hypotheses, units)
logger.debug(
"GPU scoring used for hypotheses",
extra={"count": len(hypotheses), "backend": "gpu"},
)
return results
except ImportError:
from fusionagi.reasoning.multi_path import generate_and_score_parallel
logger.debug("GPU not available, using CPU scoring")
return generate_and_score_parallel(hypotheses, units)
def score_claims_gpu(
claims: list[str],
reference: str,
) -> list[float]:
"""Score claims against a reference using GPU when available.
Args:
claims: List of claim texts.
reference: Reference text.
Returns:
List of scores for each claim.
"""
try:
from fusionagi.gpu.tensor_scoring import gpu_score_claims_against_reference
return gpu_score_claims_against_reference(claims, reference)
except ImportError:
from fusionagi.reasoning.multi_path import _score_consistency
scores: list[float] = []
for claim in claims:
node = ThoughtNode(thought=claim, trace=[claim])
unit = AtomicSemanticUnit(
unit_id="ref", content=reference, type=AtomicUnitType.FACT, confidence=1.0
)
scores.append(_score_consistency(node, [unit]))
return scores
def deduplicate_claims_gpu(
claims: list[str],
threshold: float = 0.85,
) -> list[list[int]]:
"""GPU-accelerated claim deduplication.
Args:
claims: List of claim texts.
threshold: Similarity threshold for grouping.
Returns:
List of groups (each group is a list of indices).
"""
try:
from fusionagi.gpu.tensor_similarity import deduplicate_claims
return deduplicate_claims(claims, threshold)
except ImportError:
groups: list[list[int]] = [[i] for i in range(len(claims))]
return groups

247
fusionagi/reasoning/interpretability.py Normal file
View File

@@ -0,0 +1,247 @@
"""Interpretability: full reasoning trace from prompt to final answer.
Every step of the reasoning pipeline can be traced and explained:
- Prompt decomposition decisions
- Head selection and dispatch
- Per-head claim generation with evidence chains
- Consensus process (agreements, disputes)
- Metacognitive assessment
- Verification results
- Final synthesis rationale
The ReasoningTrace captures all of this in a structured, queryable format
that can be serialized for debugging, auditing, or user explanation.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from datetime import datetime, timezone
from typing import Any
def _utc_now() -> datetime:
"""Return current UTC time (timezone-aware)."""
return datetime.now(timezone.utc)
@dataclass
class TraceStep:
"""A single step in the reasoning trace.
Attributes:
step_id: Unique identifier for this step.
stage: Pipeline stage (e.g. ``decomposition``, ``head_dispatch``).
component: Component that executed this step.
input_summary: Brief summary of the step's input.
output_summary: Brief summary of the step's output.
duration_ms: Execution time in milliseconds (if measured).
metadata: Additional structured data.
timestamp: When this step was recorded.
"""
step_id: str = ""
stage: str = ""
component: str = ""
input_summary: str = ""
output_summary: str = ""
duration_ms: float | None = None
metadata: dict[str, Any] = field(default_factory=dict)
timestamp: datetime = field(default_factory=_utc_now)
@dataclass
class ReasoningTrace:
"""Complete reasoning trace for a single prompt→response cycle.
Attributes:
trace_id: Unique identifier for this trace.
task_id: Associated task ID.
prompt: Original user prompt.
steps: Ordered list of trace steps.
final_answer: The produced answer.
overall_confidence: Final confidence score.
metacognitive_summary: Summary of metacognitive assessment.
verification_summary: Summary of claim verification.
created_at: When the trace was started.
"""
trace_id: str = ""
task_id: str = ""
prompt: str = ""
steps: list[TraceStep] = field(default_factory=list)
final_answer: str = ""
overall_confidence: float = 0.0
metacognitive_summary: dict[str, Any] = field(default_factory=dict)
verification_summary: dict[str, Any] = field(default_factory=dict)
created_at: datetime = field(default_factory=_utc_now)
class ReasoningTracer:
"""Records interpretable reasoning traces for the pipeline.
Attach to the reasoning pipeline to capture every decision point.
Each trace can be serialized, stored, and queried for debugging
or explanation.
Args:
max_traces: Maximum traces to retain in memory (FIFO).
"""
def __init__(self, max_traces: int = 1000) -> None:
self._traces: dict[str, ReasoningTrace] = {}
self._trace_order: list[str] = []
self._max_traces = max_traces
self._step_counter = 0
def start_trace(self, trace_id: str, task_id: str, prompt: str) -> ReasoningTrace:
"""Begin a new reasoning trace.
Args:
trace_id: Unique ID for this trace.
task_id: Associated task ID.
prompt: The user's prompt.
Returns:
The newly created trace.
"""
if len(self._traces) >= self._max_traces and self._trace_order:
oldest = self._trace_order.pop(0)
self._traces.pop(oldest, None)
trace = ReasoningTrace(
trace_id=trace_id,
task_id=task_id,
prompt=prompt,
)
self._traces[trace_id] = trace
self._trace_order.append(trace_id)
return trace
def add_step(
self,
trace_id: str,
stage: str,
component: str,
input_summary: str = "",
output_summary: str = "",
duration_ms: float | None = None,
metadata: dict[str, Any] | None = None,
) -> TraceStep | None:
"""Add a step to an existing trace.
Args:
trace_id: The trace to add the step to.
stage: Pipeline stage name.
component: Component that executed this step.
input_summary: Brief input description.
output_summary: Brief output description.
duration_ms: Execution time.
metadata: Additional data.
Returns:
The added step, or ``None`` if trace not found.
"""
trace = self._traces.get(trace_id)
if trace is None:
return None
self._step_counter += 1
step = TraceStep(
step_id=f"step_{self._step_counter}",
stage=stage,
component=component,
input_summary=input_summary[:200],
output_summary=output_summary[:200],
duration_ms=duration_ms,
metadata=metadata or {},
)
trace.steps.append(step)
return step
def finalize_trace(
self,
trace_id: str,
final_answer: str,
confidence: float,
metacognitive_summary: dict[str, Any] | None = None,
verification_summary: dict[str, Any] | None = None,
) -> ReasoningTrace | None:
"""Finalize a trace with the final answer and assessments.
Args:
trace_id: The trace to finalize.
final_answer: The produced answer.
confidence: Overall confidence score.
metacognitive_summary: Metacognition assessment summary.
verification_summary: Claim verification summary.
Returns:
The finalized trace, or ``None`` if not found.
"""
trace = self._traces.get(trace_id)
if trace is None:
return None
trace.final_answer = final_answer
trace.overall_confidence = confidence
if metacognitive_summary:
trace.metacognitive_summary = metacognitive_summary
if verification_summary:
trace.verification_summary = verification_summary
return trace
def get_trace(self, trace_id: str) -> ReasoningTrace | None:
"""Retrieve a trace by ID."""
return self._traces.get(trace_id)
def get_recent_traces(self, limit: int = 10) -> list[ReasoningTrace]:
"""Retrieve the most recent traces."""
recent_ids = self._trace_order[-limit:]
return [self._traces[tid] for tid in recent_ids if tid in self._traces]
def explain(self, trace_id: str) -> str:
"""Generate a human-readable explanation of a reasoning trace.
Args:
trace_id: The trace to explain.
Returns:
Formatted explanation string.
"""
trace = self._traces.get(trace_id)
if trace is None:
return f"Trace '{trace_id}' not found."
lines: list[str] = [
f"Reasoning Trace: {trace.trace_id}",
f"Task: {trace.task_id}",
f"Prompt: {trace.prompt[:100]}",
f"Steps: {len(trace.steps)}",
"",
]
for i, step in enumerate(trace.steps, 1):
lines.append(f" {i}. [{step.stage}] {step.component}")
if step.input_summary:
lines.append(f" Input: {step.input_summary}")
if step.output_summary:
lines.append(f" Output: {step.output_summary}")
if step.duration_ms is not None:
lines.append(f" Time: {step.duration_ms:.1f}ms")
lines.append("")
lines.append(f"Final Answer: {trace.final_answer[:200]}")
lines.append(f"Confidence: {trace.overall_confidence:.2f}")
if trace.metacognitive_summary:
lines.append(f"Metacognition: {trace.metacognitive_summary}")
if trace.verification_summary:
lines.append(f"Verification: {trace.verification_summary}")
return "\n".join(lines)
@property
def total_traces(self) -> int:
"""Number of traces stored."""
return len(self._traces)

6
fusionagi/reasoning/meta_reasoning.py
View File

@@ -2,11 +2,9 @@
from __future__ import annotations
from typing import Any
from fusionagi.schemas.atomic import AtomicSemanticUnit, AtomicUnitType
from fusionagi.reasoning.tot import ThoughtNode, expand_node
from fusionagi._logger import logger
from fusionagi.reasoning.tot import ThoughtNode, expand_node
from fusionagi.schemas.atomic import AtomicSemanticUnit, AtomicUnitType
def challenge_assumptions(

262
fusionagi/reasoning/metacognition.py Normal file
View File

@@ -0,0 +1,262 @@
"""Metacognition: self-awareness of knowledge boundaries and reasoning quality.
The metacognition engine monitors the system's own reasoning processes
and produces self-assessments:
- Does the system have enough evidence to answer confidently?
- Which knowledge gaps exist?
- Where are the reasoning weak points?
- Should the system seek more information before answering?
This is distinct from meta_reasoning.py (which challenges assumptions
and detects contradictions in content). Metacognition operates on
the *process* level — it reasons about the quality of reasoning itself.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from fusionagi._logger import logger
from fusionagi.schemas.head import HeadOutput
@dataclass
class KnowledgeGap:
"""An identified gap in the system's knowledge.
Attributes:
domain: Knowledge domain (e.g. ``legal``, ``medical``).
description: What the system doesn't know.
severity: Impact on answer quality (``low``, ``medium``, ``high``).
resolvable: Whether additional tool calls could fill this gap.
"""
domain: str
description: str
severity: str = "medium"
resolvable: bool = True
@dataclass
class MetacognitiveAssessment:
"""Self-assessment of reasoning quality for a given task.
Attributes:
overall_confidence: System's confidence in its answer (0.0–1.0).
evidence_sufficiency: Whether evidence is sufficient (0.0–1.0).
knowledge_gaps: Identified gaps in knowledge.
reasoning_quality: Assessment of the reasoning chain quality.
should_seek_more: Whether the system should seek more info.
head_agreement: Fraction of heads that agree (0.0–1.0).
uncertainty_sources: Where uncertainty comes from.
recommendations: What the system should do next.
"""
overall_confidence: float = 0.5
evidence_sufficiency: float = 0.5
knowledge_gaps: list[KnowledgeGap] = field(default_factory=list)
reasoning_quality: float = 0.5
should_seek_more: bool = False
head_agreement: float = 0.5
uncertainty_sources: list[str] = field(default_factory=list)
recommendations: list[str] = field(default_factory=list)
def assess_head_outputs(
outputs: list[HeadOutput],
user_prompt: str = "",
) -> MetacognitiveAssessment:
"""Assess reasoning quality from head outputs.
Analyzes the collection of head outputs for agreement patterns,
confidence distribution, evidence coverage, and knowledge gaps.
Args:
outputs: Outputs from Dvādaśa content heads.
user_prompt: Original user prompt for context.
Returns:
Metacognitive assessment of reasoning quality.
"""
if not outputs:
return MetacognitiveAssessment(
overall_confidence=0.0,
evidence_sufficiency=0.0,
should_seek_more=True,
uncertainty_sources=["No head outputs available"],
recommendations=["Execute head pipeline before assessment"],
)
confidences: list[float] = []
for out in outputs:
if out.claims:
confidences.extend(c.confidence for c in out.claims)
else:
confidences.append(0.0)
avg_confidence = sum(confidences) / len(confidences) if confidences else 0.0
all_claims: list[str] = []
for out in outputs:
all_claims.extend(c.claim_text for c in out.claims)
evidence_counts = []
for out in outputs:
for c in out.claims:
evidence_counts.append(len(c.evidence))
avg_evidence = sum(evidence_counts) / max(len(evidence_counts), 1)
evidence_sufficiency = min(1.0, avg_evidence / 3.0)
head_agreement = _compute_head_agreement(outputs)
gaps = _detect_knowledge_gaps(outputs, user_prompt)
uncertainty_sources: list[str] = []
if avg_confidence < 0.5:
uncertainty_sources.append(f"Low average head confidence: {avg_confidence:.2f}")
if head_agreement < 0.4:
uncertainty_sources.append(f"Low head agreement: {head_agreement:.2f}")
if evidence_sufficiency < 0.3:
uncertainty_sources.append(f"Insufficient evidence: avg {avg_evidence:.1f} per claim")
if gaps:
uncertainty_sources.append(f"{len(gaps)} knowledge gap(s) detected")
conf_variance = _variance(confidences) if len(confidences) > 1 else 0.0
if conf_variance > 0.1:
uncertainty_sources.append(
f"High confidence variance across heads: {conf_variance:.3f}"
)
reasoning_quality = (
0.4 * avg_confidence
+ 0.3 * head_agreement
+ 0.2 * evidence_sufficiency
+ 0.1 * (1.0 - min(1.0, len(gaps) * 0.2))
)
should_seek_more = (
reasoning_quality < 0.4
or evidence_sufficiency < 0.3
or any(g.severity == "high" and g.resolvable for g in gaps)
)
recommendations: list[str] = []
if should_seek_more:
recommendations.append("Seek additional evidence before finalizing answer")
if head_agreement < 0.4:
recommendations.append("Run second-pass with disputed heads for clarification")
for gap in gaps:
if gap.resolvable:
recommendations.append(f"Fill knowledge gap: {gap.description}")
overall = min(1.0, 0.5 * reasoning_quality + 0.3 * head_agreement + 0.2 * evidence_sufficiency)
assessment = MetacognitiveAssessment(
overall_confidence=overall,
evidence_sufficiency=evidence_sufficiency,
knowledge_gaps=gaps,
reasoning_quality=reasoning_quality,
should_seek_more=should_seek_more,
head_agreement=head_agreement,
uncertainty_sources=uncertainty_sources,
recommendations=recommendations,
)
logger.info(
"Metacognition: assessment complete",
extra={
"overall_confidence": overall,
"reasoning_quality": reasoning_quality,
"head_agreement": head_agreement,
"gaps": len(gaps),
"should_seek_more": should_seek_more,
},
)
return assessment
def _compute_head_agreement(outputs: list[HeadOutput]) -> float:
"""Measure how much heads agree with each other.
Uses claim text overlap across heads as a proxy for agreement.
"""
if len(outputs) < 2:
return 1.0
claim_sets: list[set[str]] = []
for out in outputs:
words: set[str] = set()
for c in out.claims:
words.update(c.claim_text.lower().split())
claim_sets.append(words)
agreements: float = 0.0
comparisons = 0
for i in range(len(claim_sets)):
for j in range(i + 1, len(claim_sets)):
if not claim_sets[i] or not claim_sets[j]:
continue
overlap = len(claim_sets[i] & claim_sets[j])
union = len(claim_sets[i] | claim_sets[j])
if union > 0:
agreements += overlap / union
comparisons += 1
return agreements / max(comparisons, 1)
def _detect_knowledge_gaps(
outputs: list[HeadOutput],
user_prompt: str,
) -> list[KnowledgeGap]:
"""Detect knowledge gaps from head outputs and prompt analysis."""
gaps: list[KnowledgeGap] = []
for out in outputs:
if out.claims:
avg_claim_conf = sum(c.confidence for c in out.claims) / len(out.claims)
else:
avg_claim_conf = 0.0
if avg_claim_conf < 0.3:
gaps.append(KnowledgeGap(
domain=out.head_id.value,
description=f"Head '{out.head_id.value}' has very low confidence ({avg_claim_conf:.2f})",
severity="high" if avg_claim_conf < 0.15 else "medium",
resolvable=True,
))
empty_heads = [o for o in outputs if not o.claims]
for out in empty_heads:
gaps.append(KnowledgeGap(
domain=out.head_id.value,
description=f"Head '{out.head_id.value}' produced no claims",
severity="medium",
resolvable=True,
))
prompt_lower = user_prompt.lower()
domain_indicators = {
"legal": ["law", "legal", "court", "statute", "regulation", "compliance"],
"medical": ["medical", "health", "disease", "treatment", "clinical", "patient"],
"financial": ["financial", "stock", "market", "investment", "trading", "portfolio"],
"scientific": ["experiment", "hypothesis", "data", "study", "research", "evidence"],
}
for domain, keywords in domain_indicators.items():
if any(kw in prompt_lower for kw in keywords):
head_domains = {o.head_id.value for o in outputs}
if domain not in head_domains:
gaps.append(KnowledgeGap(
domain=domain,
description=f"Prompt references '{domain}' domain but no specialized head covers it",
severity="medium",
resolvable=False,
))
return gaps
def _variance(values: list[float]) -> float:
"""Compute variance of a list of floats."""
if len(values) < 2:
return 0.0
mean = sum(values) / len(values)
return sum((v - mean) ** 2 for v in values) / len(values)

44
fusionagi/reasoning/multi_path.py
View File

@@ -1,13 +1,17 @@
"""Multi-path inference: parallel hypothesis generation and scoring."""
"""Multi-path inference: parallel hypothesis generation and scoring.
Supports GPU-accelerated scoring when ``fusionagi[gpu]`` is installed;
falls back to CPU ``ThreadPoolExecutor`` otherwise.
"""
from __future__ import annotations
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Any, Callable
from typing import Callable
from fusionagi.schemas.atomic import AtomicSemanticUnit
from fusionagi.reasoning.tot import ThoughtNode
from fusionagi._logger import logger
from fusionagi.reasoning.tot import ThoughtNode
from fusionagi.schemas.atomic import AtomicSemanticUnit
def _score_coherence(node: ThoughtNode, _units: list[AtomicSemanticUnit]) -> float:
@@ -24,12 +28,42 @@ def _score_consistency(node: ThoughtNode, units: list[AtomicSemanticUnit]) -> fl
return min(1.0, overlap * 2)
def _try_gpu_score(
hypotheses: list[str],
units: list[AtomicSemanticUnit],
) -> list[tuple[ThoughtNode, float]] | None:
"""Attempt GPU-accelerated scoring; return ``None`` if unavailable."""
try:
from fusionagi.gpu.tensor_scoring import gpu_score_hypotheses
results = gpu_score_hypotheses(hypotheses, units)
logger.debug(
"multi_path: GPU scoring used",
extra={"count": len(hypotheses)},
)
return results
except ImportError:
return None
def generate_and_score_parallel(
hypotheses: list[str],
units: list[AtomicSemanticUnit],
score_fn: Callable[[ThoughtNode, list[AtomicSemanticUnit]], float] | None = None,
*,
use_gpu: bool = True,
) -> list[tuple[ThoughtNode, float]]:
"""Score multiple hypotheses in parallel."""
"""Score multiple hypotheses in parallel.
When *use_gpu* is ``True`` (default) and no custom *score_fn* is
provided, tries GPU-accelerated scoring first. Falls back to the
threaded CPU implementation when the GPU module is unavailable.
"""
if use_gpu and score_fn is None:
gpu_result = _try_gpu_score(hypotheses, units)
if gpu_result is not None:
return gpu_result
score_fn = score_fn or (lambda n, u: _score_coherence(n, u) * 0.5 + _score_consistency(n, u) * 0.5)
def score_one(h: str, i: int) -> tuple[ThoughtNode, float]:

8
fusionagi/reasoning/native.py
View File

@@ -113,7 +113,7 @@ def _derive_claims_for_head(
) -> list[HeadClaim]:
"""Derive atomic claims from analysis based on head domain."""
claims: list[HeadClaim] = []
persona = get_persona(head_id)
get_persona(head_id)
relevance = analysis.domain_signals.get(head_id.value, 0.3)
# Base claim from prompt summary
@@ -297,8 +297,8 @@ class NativeReasoningProvider:
def __init__(
self,
semantic_memory: "SemanticMemory | None" = None,
episodic_memory: "EpisodicMemory | None" = None,
semantic_memory: Any | None = None,
episodic_memory: Any | None = None,
) -> None:
self._semantic = semantic_memory
self._episodic = episodic_memory
@@ -316,4 +316,4 @@ class NativeReasoningProvider:
if not self._semantic:
return []
domain = _domain_for_head(head_id)
return self._semantic.query(domain=domain, limit=limit)
return self._semantic.query(domain=domain, limit=limit) # type: ignore[no-any-return]

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