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344 | class ToolRegistry:
"""
A central, governed registry for tools that includes RBAC, automatic retries,
circuit breakers, cost/latency tracking, caching, async support, output sinks,
and dynamic schemas.
"""
def __init__(
self,
governance_manager: GovernanceManager,
access_manager: Optional[AccessManager] = None,
embedding_config: Optional[Dict] = None,
similarity_metric: SimilarityMetric = SimilarityMetric.L2,
embedding_dimension: int = 768
):
self._tools: Dict[str, Callable] = {}
self._tool_metadata: Dict[str, Dict] = {}
self.gov = governance_manager
self.access_manager = access_manager or AccessManager()
self.embedding_config = embedding_config or {}
self.similarity_metric = similarity_metric
self.embedding_dimension = embedding_dimension
self._circuit_breaker_state: Dict[str, Dict] = {}
self._cache: Dict[str, Dict] = {} # In-memory cache
self._tool_index = None
self._index_to_tool_name: Dict[int, str] = {}
if _EMBEDDINGS_ENABLED:
self._initialize_faiss_index()
def _initialize_faiss_index(self):
"""Initializes the correct FAISS index based on the chosen similarity metric."""
if self.similarity_metric == SimilarityMetric.L2:
self._tool_index = faiss.IndexFlatL2(self.embedding_dimension)
elif self.similarity_metric in (SimilarityMetric.COSINE, SimilarityMetric.DOT_PRODUCT):
self._tool_index = faiss.IndexFlatIP(self.embedding_dimension)
else:
raise ValueError("Unsupported similarity metric: {}".format(self.similarity_metric))
def _index_tool(self, tool_name: str):
"""Embeds and indexes a tool's description for semantic search."""
if not _EMBEDDINGS_ENABLED or self._tool_index is None: return
metadata = self._tool_metadata.get(tool_name, {})
description = "Tool: {}. Description: {}".format(tool_name, metadata.get("docstring", ""))
api_key = self.embedding_config.get("api_key", "")
vector = gemini_embed(text=description, api_key=api_key)
if vector:
vector_np = np.array([vector], dtype=np.float32)
if self.similarity_metric == SimilarityMetric.COSINE:
faiss.normalize_L2(vector_np)
new_index_id = self._tool_index.ntotal
self._tool_index.add(vector_np)
self._index_to_tool_name[new_index_id] = tool_name
def register(
self,
required_role: Optional[str] = None,
retry_attempts: int = 0,
retry_delay: float = 1.0,
circuit_breaker_threshold: int = 0,
cache_ttl_seconds: int = 0,
cost_per_call: Optional[float] = None,
cost_calculator: Optional[Callable[[Any], float]] = None,
output_sinks: Optional[List[BaseOutputSink]] = None
) -> Callable:
"""A decorator to register a tool with advanced, governed execution policies."""
def decorator(func: Callable) -> Callable:
tool_name = func.__name__
self._tools[tool_name] = func
self._tool_metadata[tool_name] = {
"docstring": inspect.getdoc(func),
"signature": inspect.signature(func),
"is_async": inspect.iscoroutinefunction(func),
"policies": {
"role": required_role, "retry_attempts": retry_attempts,
"retry_delay": retry_delay, "circuit_breaker_threshold": circuit_breaker_threshold,
"cache_ttl_seconds": cache_ttl_seconds, "cost_per_call": cost_per_call,
"cost_calculator": cost_calculator, "output_sinks": output_sinks or []
}
}
self._circuit_breaker_state[tool_name] = {'failure_count': 0, 'is_open': False, 'opened_at': 0}
self._index_tool(tool_name)
return func
return decorator
def _create_cache_key(self, tool_name: str, **kwargs) -> str:
"""Creates a stable cache key from the tool name and arguments."""
hasher = hashlib.md5()
encoded = json.dumps(kwargs, sort_keys=True).encode('utf-8')
hasher.update(encoded)
return "{}:{}".format(tool_name, hasher.hexdigest())
def _check_pre_execution_policies(self, name: str, user_id: str, policies: Dict, **kwargs) -> Optional[Any]:
"""Handles caching, circuit breaker, and RBAC checks. Returns cached result if hit."""
# Caching
if policies["cache_ttl_seconds"] > 0:
cache_key = self._create_cache_key(name, **kwargs)
if cache_key in self._cache:
cached_item = self._cache[cache_key]
if time.time() - cached_item["timestamp"] < policies["cache_ttl_seconds"]:
self.gov.audit(user_id, "tool_cache_hit", name, {"args": kwargs})
return cached_item["result"]
# Circuit Breaker
cb_state = self._circuit_breaker_state[name]
if cb_state['is_open']:
if time.time() - cb_state['opened_at'] > 60: # 1-minute cooldown
cb_state['is_open'] = False
else:
msg = "Circuit breaker for tool '{}' is open.".format(name)
self.gov.audit(user_id, "tool_circuit_breaker_open", name, {"error": msg})
raise ToolExecutionError(msg)
# RBAC
if policies["role"] and not self.access_manager.check_access(user_id, policies["role"]):
msg = "User '{}' lacks required role '{}' for tool '{}'.".format(user_id, policies["role"], name)
self.gov.audit(user_id, "tool_access_denied", name, {"required_role": policies["role"]})
raise RBACError(msg)
return None
def _handle_post_execution(self, name: str, user_id: str, policies: Dict, result: Any, latency_ms: float, **kwargs):
"""Handles auditing, cost calculation, caching, and output sinks after successful execution."""
cost = policies["cost_per_call"]
if policies["cost_calculator"]:
cost = policies["cost_calculator"](result)
audit_metadata = {"result_type": type(result).__name__, "latency_ms": round(latency_ms), "cost": cost}
self.gov.audit(user_id, "tool_call_end", name, audit_metadata)
if policies["cache_ttl_seconds"] > 0:
cache_key = self._create_cache_key(name, **kwargs)
self._cache[cache_key] = {"timestamp": time.time(), "result": result}
run_id = self.gov.get_current_run_id()
for sink in policies["output_sinks"]:
try:
sink_metadata = sink.handle(name, result, run_id, **kwargs)
self.gov.audit(user_id, "output_sink_success", str(sink), {"tool_name": name, **sink_metadata})
except Exception as e:
self.gov.audit(user_id, "output_sink_failure", str(sink), {"tool_name": name, "error": str(e)})
def _handle_execution_error(self, name: str, user_id: str, policies: Dict, e: Exception, attempt: int):
"""Handles failures, including retry logic and circuit breaker trips."""
self.gov.audit(user_id, "tool_call_error", name, {"error": str(e), "attempt": attempt + 1})
if attempt >= policies["retry_attempts"]:
cb_state = self._circuit_breaker_state[name]
cb_state['failure_count'] += 1
if policies["circuit_breaker_threshold"] > 0 and cb_state['failure_count'] >= policies["circuit_breaker_threshold"]:
cb_state['is_open'] = True
cb_state['opened_at'] = time.time()
self.gov.audit(user_id, "tool_circuit_breaker_tripped", name)
raise ToolExecutionError("Tool '{}' failed after all retry attempts.".format(name)) from e
def _get_governed_sync_tool(self, name: str, user_id: str, original_func: Callable, policies: Dict) -> Callable:
"""Returns the fully governed wrapper for a synchronous tool."""
def sync_wrapper(**kwargs):
cached_result = self._check_pre_execution_policies(name, user_id, policies, **kwargs)
if cached_result is not None: return cached_result
for attempt in range(policies["retry_attempts"] + 1):
start_time = time.monotonic()
try:
self.gov.audit(user_id, "tool_call_start", name, {"args": kwargs, "attempt": attempt + 1})
result = original_func(**kwargs)
latency_ms = (time.monotonic() - start_time) * 1000
self._handle_post_execution(name, user_id, policies, result, latency_ms, **kwargs)
return result
except Exception as e:
self._handle_execution_error(name, user_id, policies, e, attempt)
time.sleep(policies["retry_delay"] * (2 ** attempt))
# This line should be logically unreachable if retry_attempts >= 0
raise ToolExecutionError("Tool '{}' execution logic failed unexpectedly.".format(name))
return sync_wrapper
def _get_governed_async_tool(self, name: str, user_id: str, original_func: Callable, policies: Dict) -> Callable:
"""Returns the fully governed wrapper for an asynchronous tool."""
async def async_wrapper(**kwargs):
cached_result = self._check_pre_execution_policies(name, user_id, policies, **kwargs)
if cached_result is not None: return cached_result
for attempt in range(policies["retry_attempts"] + 1):
start_time = time.monotonic()
try:
self.gov.audit(user_id, "tool_call_start", name, {"args": kwargs, "attempt": attempt + 1})
result = await original_func(**kwargs)
latency_ms = (time.monotonic() - start_time) * 1000
self._handle_post_execution(name, user_id, policies, result, latency_ms, **kwargs)
return result
except Exception as e:
self._handle_execution_error(name, user_id, policies, e, attempt)
await asyncio.sleep(policies["retry_delay"] * (2 ** attempt))
# This line should be logically unreachable if retry_attempts >= 0
raise ToolExecutionError("Tool '{}' execution logic failed unexpectedly.".format(name))
return async_wrapper
def get_governed_tool(self, name: str, user_id: str) -> Callable:
"""
Retrieves a tool by name and wraps it in all registered governance policies.
This method correctly handles both synchronous and asynchronous tools.
"""
if name not in self._tools:
raise ToolNotFoundError("Tool '{}' not found in registry.".format(name))
metadata = self._tool_metadata[name]
original_func = self._tools[name]
policies = metadata["policies"]
if metadata["is_async"]:
return self._get_governed_async_tool(name, user_id, original_func, policies)
else:
return self._get_governed_sync_tool(name, user_id, original_func, policies)
def generate_tool_schema(self, tool_names: List[str]) -> List[Dict[str, Any]]:
"""Generates a JSON Schema-like description for a list of tools."""
schema = []
for name in tool_names:
if name in self._tool_metadata:
metadata = self._tool_metadata[name]
sig = metadata["signature"]
properties = {}
for param in sig.parameters.values():
if param.name != 'self':
type_map = {str: 'string', int: 'number', float: 'number', bool: 'boolean'}
param_type = type_map.get(param.annotation, 'string')
properties[param.name] = {'type': param_type, 'description': ''}
schema.append({
"name": name,
"description": metadata["docstring"],
"parameters": {
"type": "object",
"properties": properties,
"required": [p.name for p in sig.parameters.values() if p.default == inspect.Parameter.empty and p.name != 'self']
}
})
return schema
def get_relevant_tools(self, query: str, top_k: int = 3) -> List[str]:
"""Finds the most semantically relevant tools for a given query using a vector index."""
if not _EMBEDDINGS_ENABLED or self._tool_index is None or self._tool_index.ntotal == 0:
return []
api_key = self.embedding_config.get("api_key", "")
query_vector = gemini_embed(text=query, api_key=api_key)
if not query_vector:
return []
query_np = np.array([query_vector], dtype=np.float32)
if self.similarity_metric == SimilarityMetric.COSINE:
faiss.normalize_L2(query_np)
distances, indices = self._tool_index.search(query_np, min(top_k, self._tool_index.ntotal))
return [self._index_to_tool_name[i] for i in indices[0]]
|