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fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
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- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available)
- Integrate GPU deduplication into multi_agent/consensus_engine.py
- Add semantic_search() method to memory/semantic_graph.py with GPU acceleration
- Integrate GPU training into self_improvement/training.py AutoTrainer
- Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names)
- Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.)
- Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint])
- Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds
- Add type hints and docstrings to verification/outcome.py
- Fix E402 import ordering in witness_agent.py
- Fix F821 undefined names in vector_pgvector.py and native.py
- Fix E741 ambiguous variable names in reflective.py and recommender.py

All 276 tests pass. 0 ruff errors. 0 mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
This commit is contained in:
Devin AI
2026-04-28 05:48:37 +00:00
parent fa71f973a6
commit 445865e429
112 changed files with 1160 additions and 955 deletions
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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