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from typing import TYPE_CHECKING, Any, Optional, TypedDict, cast
import numpy as np
from scipy.spatial.distance import cosine
from .base_agent import BaseAgent
from autoresttest.graph import OperationGraph
from autoresttest.models import ParameterKey, is_parameter_key
from autoresttest.utils import get_body_params
class DependentInfo(TypedDict):
"""Type for dependency information returned by get_best_action and get_random_action."""
dependent_val: ParameterKey | str | None
dependent_operation: str | None
value: float
in_value: str | None
if TYPE_CHECKING:
from autoresttest.marl import QLearning
# Type alias for the inner Q-table structure: dependent_param -> Q-value
# dependent_param can be either str (body property or response) or ParameterKey (parameter)
DepParamDict = dict[ParameterKey | str, float]
# Type alias for location bucket: location -> {dependent_param -> Q-value}
LocBucketDict = dict[str, DepParamDict]
# Type alias for dependent operation: operation_id -> {location -> {dependent_param -> Q-value}}
DepOpDict = dict[str, LocBucketDict]
# Type alias for parameter level: ParameterKey|str -> {operation_id -> ...}
ParamLevelDict = dict[ParameterKey | str, DepOpDict]
class DependencyAgent(BaseAgent):
"""
Agent that learns inter-operation parameter dependencies.
Q-table structure: operation_id -> {"params": {...}, "body": {...}}
- "params" dict uses ParameterKey tuples as keys (for operation parameters)
- "body" dict uses str keys (for request body property names)
ParameterKey | str is intentional to support both key types in the same structure.
"""
def __init__(
self,
operation_graph: OperationGraph,
alpha: float = 0.1,
gamma: float = 0.9,
epsilon: float = 0.1,
) -> None:
self.q_table: dict[str, dict[str, ParamLevelDict]] = {}
self.operation_graph = operation_graph
self.alpha = alpha
self.gamma = gamma
self.epsilon = epsilon
self.dependencies_discovered: int = 0
def _notify(self, message: str) -> None:
"""Increment the dependencies discovered counter."""
self.dependencies_discovered += 1
@staticmethod
def _bucket_location(location: str, is_source: bool = False) -> str:
"""
Normalize various parameter locations into the q_table buckets.
Source locations default to 'params' unless explicitly 'body'.
Destination locations can be 'params', 'body', or 'response'.
"""
if location == "body":
return "body"
if not is_source and location == "response":
return "response"
return "params"
@staticmethod
def _param_label(param_key: ParameterKey | str) -> str:
return param_key[0] if isinstance(param_key, tuple) else param_key
def initialize_q_table(self) -> None:
# NOTE: Could flip dependent operation id and curr parameter name around to group by operation if need-be
for (
operation_id,
operation_node,
) in self.operation_graph.operation_nodes.items():
if operation_id not in self.q_table:
params_dict: ParamLevelDict = {}
body_dict: ParamLevelDict = {}
self.q_table[operation_id] = {"params": params_dict, "body": body_dict}
for edge in operation_node.outgoing_edges:
for parameter, similarities in edge.similar_parameters.items():
# parameter can be either "str" (body property) or "ParameterKey" (parameter)
for similarity in similarities:
processed_in_val = similarity.in_value.split(" to ")
src_loc = processed_in_val[0] if processed_in_val else "params"
dest_loc = (
processed_in_val[1]
if len(processed_in_val) > 1
else "params"
)
dependent_parameter = similarity.dependent_val
# dependent_parameter can either be a "str" (body property or response) or "ParameterKey" (parameter)
destination = edge.destination.operation_id
# Note: Body should be nested
source_bucket = self._bucket_location(src_loc, is_source=True)
dest_bucket = self._bucket_location(dest_loc)
if (
source_bucket == "params"
and parameter not in self.q_table[operation_id]["params"]
):
dep_op: DepOpDict = {}
self.q_table[operation_id]["params"][parameter] = dep_op
elif (
source_bucket == "body"
and parameter not in self.q_table[operation_id]["body"]
):
dep_op_body: DepOpDict = {}
self.q_table[operation_id]["body"][parameter] = dep_op_body
if (
source_bucket == "params"
and destination
not in self.q_table[operation_id]["params"][parameter]
):
loc_bucket: LocBucketDict = {
"params": {},
"body": {},
"response": {},
}
self.q_table[operation_id]["params"][parameter][
destination
] = loc_bucket
elif (
source_bucket == "body"
and destination
not in self.q_table[operation_id]["body"][parameter]
):
loc_bucket_body: LocBucketDict = {
"params": {},
"body": {},
"response": {},
}
self.q_table[operation_id]["body"][parameter][
destination
] = loc_bucket_body
if source_bucket == "params":
self.q_table[operation_id]["params"][parameter][
destination
][dest_bucket][dependent_parameter] = 0
elif source_bucket == "body":
self.q_table[operation_id]["body"][parameter][destination][
dest_bucket
][dependent_parameter] = 0
def get_action(
self, operation_id: str, qlearning: "QLearning"
) -> tuple[str, dict[ParameterKey, Any], dict[str, Any]]:
if operation_id not in self.q_table:
raise ValueError(
f"Operation '{operation_id}' not found in the Q-table for DependencyAgent."
)
has_success = any(
status_code // 100 == 2
for status_codes in qlearning.operation_response_counter.values()
for status_code in status_codes
)
if random.random() < self.epsilon:
if has_success and random.random() < 0.3:
return self.assign_random_dependency_from_successful(
operation_id, qlearning
)
return self.get_random_action(operation_id, qlearning)
return self.get_best_action(operation_id, qlearning)
def get_best_action(
self, operation_id: str, qlearning: "QLearning"
) -> tuple[str, dict[ParameterKey, Any], dict[str, Any]]:
""" "Returns 'BEST', the parameter mapping, and body mapping."""
successful_responses = qlearning.successful_responses
successful_params = qlearning.successful_parameters
successful_body = qlearning.successful_bodies
best_params: dict[ParameterKey, DependentInfo] = {}
for param, dependent_ops in self.q_table[operation_id]["params"].items():
best_dependent: DependentInfo = {
"dependent_val": None,
"dependent_operation": None,
"value": float(-np.inf),
"in_value": None,
}
for dependent_op, value_dict in dependent_ops.items():
for location, loc_params in value_dict.items():
for dependent_param, value in loc_params.items():
if (
value > best_dependent["value"]
and location == "response"
and dependent_op in successful_responses
and dependent_param in successful_responses[dependent_op]
and successful_responses[dependent_op][dependent_param]
):
best_dependent = {
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
elif (
value > best_dependent["value"]
and location == "params"
and dependent_op in successful_params
and dependent_param in successful_params[dependent_op]
and successful_params[dependent_op][dependent_param]
):
best_dependent = {
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
elif (
value > best_dependent["value"]
and location == "body"
and dependent_op in successful_body
and dependent_param in successful_body[dependent_op]
and successful_body[dependent_op][dependent_param]
):
best_dependent = {
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
# Param for "params" should always be ParameterKey type
best_params[cast(ParameterKey, param)] = best_dependent
best_body: dict[str, DependentInfo] = {}
for param, dependent_ops in self.q_table[operation_id]["body"].items():
best_dependent_body: DependentInfo = {
"dependent_val": None,
"dependent_operation": None,
"value": float(-np.inf),
"in_value": None,
}
for dependent_op, value_dict in dependent_ops.items():
for location, loc_params in value_dict.items():
for dependent_param, value in loc_params.items():
if (
value > best_dependent_body["value"]
and location == "response"
and dependent_op in successful_responses
and dependent_param in successful_responses[dependent_op]
and successful_responses[dependent_op][dependent_param]
):
best_dependent_body = {
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
elif (
value > best_dependent_body["value"]
and location == "params"
and dependent_op in successful_params
and dependent_param in successful_params[dependent_op]
and successful_params[dependent_op][dependent_param]
):
best_dependent_body = {
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
elif (
value > best_dependent_body["value"]
and location == "body"
and dependent_op in successful_body
and dependent_param in successful_body[dependent_op]
and successful_body[dependent_op][dependent_param]
):
best_dependent_body = {
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
# param for body should always be string
best_body[cast(str, param)] = best_dependent_body
return "BEST", best_params, best_body
def get_random_action(
self, operation_id: str, qlearning: "QLearning"
) -> tuple[str, dict[ParameterKey, Any], dict[str, Any]]:
""" "Returns 'EXPLORE', the parameter mapping, and body mapping."""
successful_responses = qlearning.successful_responses
successful_params = qlearning.successful_parameters
successful_body = qlearning.successful_bodies
random_params: dict[ParameterKey, DependentInfo] = {}
for param, dependent_ops in self.q_table[operation_id]["params"].items():
random_dependencies: list[DependentInfo] = []
for dependent_op, value_dict in dependent_ops.items():
for location, loc_params in value_dict.items():
for dependent_param, value in loc_params.items():
if (
location == "response"
and dependent_op in successful_responses
and dependent_param in successful_responses[dependent_op]
and successful_responses[dependent_op][dependent_param]
):
random_dependencies.append(
{
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
)
elif (
location == "params"
and dependent_op in successful_params
and dependent_param in successful_params[dependent_op]
and successful_params[dependent_op][dependent_param]
):
random_dependencies.append(
{
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
)
elif (
location == "body"
and dependent_op in successful_body
and dependent_param in successful_body[dependent_op]
and successful_body[dependent_op][dependent_param]
):
random_dependencies.append(
{
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
)
default_dep: DependentInfo = {
"dependent_val": None,
"dependent_operation": None,
"value": 0.0,
"in_value": None,
}
random_params[cast(ParameterKey, param)] = (
random.choice(random_dependencies)
if random_dependencies
else default_dep
)
random_body: dict[str, DependentInfo] = {}
for param, dependent_ops in self.q_table[operation_id]["body"].items():
random_dependencies_body: list[DependentInfo] = []
for dependent_op, value_dict in dependent_ops.items():
for location, loc_params in value_dict.items():
for dependent_param, value in loc_params.items():
if (
location == "response"
and dependent_op in successful_responses
and dependent_param in successful_responses[dependent_op]
and successful_responses[dependent_op][dependent_param]
):
random_dependencies_body.append(
{
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
)
elif (
location == "params"
and dependent_op in successful_params
and dependent_param in successful_params[dependent_op]
and successful_params[dependent_op][dependent_param]
):
random_dependencies_body.append(
{
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
)
elif (
location == "body"
and dependent_op in successful_body
and dependent_param in successful_body[dependent_op]
and successful_body[dependent_op][dependent_param]
):
random_dependencies_body.append(
{
"dependent_val": dependent_param,
"dependent_operation": dependent_op,
"value": value,
"in_value": location,
}
)
default_dep_body: DependentInfo = {
"dependent_val": None,
"dependent_operation": None,
"value": 0.0,
"in_value": None,
}
random_body[cast(str, param)] = (
random.choice(random_dependencies_body)
if random_dependencies_body
else default_dep_body
)
return "EXPLORE", random_params, random_body
def update_q_table(
self,
operation_id: str,
dependent_params: dict[ParameterKey | str, dict[str, Any]] | None,
dependent_body: dict[str, dict[str, Any]] | None,
reward: float,
) -> None:
if operation_id not in self.q_table:
return
if dependent_params:
for param, dependent in dependent_params.items():
current_q: float = 0
best_next_q: float = -np.inf
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("params", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["params"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["params"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
current_q = value
best_next_q = max(best_next_q, value)
new_q = current_q + self.alpha * (
reward + self.gamma * best_next_q - current_q
)
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
self.q_table[operation_id]["params"][param][
dependent["dependent_operation"]
][location][dependent_param] = new_q
if dependent_body:
for param, dependent in dependent_body.items():
current_q = 0.0
best_next_q = float(-np.inf)
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("body", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["body"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["body"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
current_q = value
best_next_q = max(best_next_q, value)
new_q = current_q + self.alpha * (
reward + self.gamma * best_next_q - current_q
)
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
self.q_table[operation_id]["body"][param][
dependent["dependent_operation"]
][location][dependent_param] = new_q
def get_Q_next(
self,
operation_id: str,
dependent_params: dict[ParameterKey | str, dict[str, Any]] | None,
dependent_body: dict[str, dict[str, Any]] | None,
) -> tuple[list[float], list[float]]:
best_next_q_params: list[float] = []
best_next_q_body: list[float] = []
if operation_id not in self.q_table:
return best_next_q_params, best_next_q_body
if dependent_params:
for param, dependent in dependent_params.items():
best_next_q: float = -np.inf
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("params", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["params"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["params"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
best_next_q = max(best_next_q, value)
best_next_q_params.append(best_next_q)
if dependent_body:
for param, dependent in dependent_body.items():
best_next_q = float(-np.inf)
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("body", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["body"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["body"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
best_next_q = max(best_next_q, value)
best_next_q_body.append(best_next_q)
return best_next_q_params, best_next_q_body
def get_Q_curr(
self,
operation_id: str,
dependent_params: dict[ParameterKey | str, dict[str, Any]] | None,
dependent_body: dict[str, dict[str, Any]] | None,
) -> tuple[list[float], list[float]]:
current_Q_params: list[float] = []
current_Q_body: list[float] = []
if operation_id not in self.q_table:
return current_Q_params, current_Q_body
if dependent_params:
for param, dependent in dependent_params.items():
current_q: float = 0
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("params", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["params"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["params"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
current_q = value
current_Q_params.append(current_q)
if dependent_body:
for param, dependent in dependent_body.items():
current_q = 0.0
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("body", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["body"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["body"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
current_q = value
current_Q_body.append(current_q)
return current_Q_params, current_Q_body
def update_Q_item(
self,
operation_id: str,
dependent_params: dict[ParameterKey | str, dict[str, Any]] | None,
dependent_body: dict[str, dict[str, Any]] | None,
td_error: float,
) -> None:
if operation_id not in self.q_table:
return
if dependent_params:
for param, dependent in dependent_params.items():
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("params", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["params"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["params"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
self.q_table[operation_id]["params"][param][
dependent["dependent_operation"]
][location][dependent_param] += (self.alpha * td_error)
if dependent_body:
for param, dependent in dependent_body.items():
if not dependent["dependent_operation"]:
continue
if param not in self.q_table[operation_id].get("body", {}):
continue
if (
dependent["dependent_operation"]
not in self.q_table[operation_id]["body"][param]
):
continue
dep_op_dict = self.q_table[operation_id]["body"][param][
dependent["dependent_operation"]
]
for location, loc_params in dep_op_dict.items():
for dependent_param, value in loc_params.items():
if dependent_param == dependent["dependent_val"]:
self.q_table[operation_id]["body"][param][
dependent["dependent_operation"]
][location][dependent_param] += (self.alpha * td_error)
def add_undocumented_responses(
self, new_operation_response_id: str, new_property: str
) -> bool:
updated_tables = False
dependency_comparator = self.operation_graph.dependency_comparator
embedding_model = self.operation_graph.embedding_model
for operation_id, operation_props in self.q_table.items():
for location, param_values in operation_props.items():
for param, dependent_values in param_values.items():
processed_param = embedding_model.handle_word_cases(
self._param_label(param)
)
processed_response = embedding_model.handle_word_cases(new_property)
param_embedding = embedding_model.encode_sentence_or_word(
processed_param
)
response_embedding = embedding_model.encode_sentence_or_word(
processed_response
)
if param_embedding is not None and response_embedding is not None:
similarity = 1 - cosine(param_embedding, response_embedding)
if similarity > dependency_comparator.threshold:
if new_operation_response_id not in dependent_values:
dependent_values[new_operation_response_id] = {}
if (
"response"
not in dependent_values[new_operation_response_id]
):
dependent_values[new_operation_response_id][
"response"
] = {}
dependent_values[new_operation_response_id]["response"][
new_property
] = 0
updated_tables = True
self._notify(
f"Dependency: {operation_id} → {new_operation_response_id} ({param} → {new_property})"
)
return updated_tables
def add_new_dependency(
self,
operation_id: str,
param_location: str,
operation_param: ParameterKey | str,
dependent_operation_id: str,
dependent_location: str,
dependent_param: str,
) -> None:
# Validate type matches location to maintain Q-table invariants
if param_location == "params" and not is_parameter_key(operation_param):
print(
f"Warning: Expected ParameterKey for 'params', got {type(operation_param).__name__}. Skipping dependency."
)
return
if param_location == "body" and not isinstance(operation_param, str):
print(
f"Warning: Expected str for 'body', got {type(operation_param).__name__}. Skipping dependency."
)
return
if operation_param not in self.q_table[operation_id][param_location]:
self.q_table[operation_id][param_location][operation_param] = {}
if (
dependent_operation_id
not in self.q_table[operation_id][param_location][operation_param]
):
self.q_table[operation_id][param_location][operation_param][
dependent_operation_id
] = {"params": {}, "body": {}, "response": {}}
# Ensure dependent_location is valid
if dependent_location not in ["params", "body", "response"]:
print(
f"Warning: Invalid dependent_location '{dependent_location}'. Skipping dependency."
)
return
# Ensure the dependent_location key exists in the structure
if (
dependent_location
not in self.q_table[operation_id][param_location][operation_param][
dependent_operation_id
]
):
self.q_table[operation_id][param_location][operation_param][
dependent_operation_id
][dependent_location] = {}
if (
dependent_param
not in self.q_table[operation_id][param_location][operation_param][
dependent_operation_id
][dependent_location]
):
self.q_table[operation_id][param_location][operation_param][
dependent_operation_id
][dependent_location][dependent_param] = 0
self._notify(
f"Dependency: {operation_id} → {dependent_operation_id} ({operation_param} → {dependent_param})"
)
# Get a random value from the successful operations to test dependencies
def assign_random_dependency_from_successful(
self, operation_id: str, qlearning: "QLearning"
) -> tuple[str, dict[ParameterKey, Any], dict[str, Any]]:
"""Returns 'RANDOM', the parameter mapping, and body mapping"""
possible_options = []
for (
operation_idx,
operation_parameters,
) in qlearning.successful_parameters.items():
if operation_idx == operation_id:
continue
for parameter_key, parameter_values in operation_parameters.items():
for parameter_value in parameter_values:
possible_options.append(
{
"dependent_val": parameter_key,
"dependent_operation": operation_idx,
"value": parameter_value,
"in_value": "params",
}
)
for operation_idx, operation_body_parms in qlearning.successful_bodies.items():
if operation_idx == operation_id:
continue
for body_name, body_values in operation_body_parms.items():
for body_value in body_values:
possible_options.append(
{
"dependent_val": body_name,
"dependent_operation": operation_idx,
"value": body_value,
"in_value": "body",
}
)
for (
operation_idx,
operation_responses,
) in qlearning.successful_responses.items():
if operation_idx == operation_id:
continue
for response_name, response_values in operation_responses.items():
for response_value in response_values:
possible_options.append(
{
"dependent_val": response_name,
"dependent_operation": operation_idx,
"value": response_value,
"in_value": "response",
}
)
if not possible_options:
return "RANDOM", {}, {}
parameter_dependency_assignment = {}
op_props = qlearning.operation_graph.operation_nodes[
operation_id
].operation_properties
if op_props.parameters:
for (
parameter_name,
parameter_properties,
) in op_props.parameters.items():
if parameter_properties.schema:
parameter_dependency_assignment[parameter_name] = random.choice(
possible_options
)
body_dependency_assignment = {}
if op_props.request_body:
for mime, body_properties in op_props.request_body.items():
possible_body_params = get_body_params(body_properties)
for prop in possible_body_params:
body_dependency_assignment[prop] = random.choice(possible_options)
return "RANDOM", parameter_dependency_assignment, body_dependency_assignment
def number_of_zeros(self, operation_id: str) -> int:
if operation_id not in self.q_table:
return 0
zeros = 0
for location, param_values in self.q_table[operation_id].items():
for param, dependent_values in param_values.items():
for dependent_op, dependent_props in dependent_values.items():
for dependent_location, dependent_params in dependent_props.items():
for dependent_param, value in dependent_params.items():
if value == 0:
zeros += 1
return zeros
|