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working hallucination integration
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commit
c12d63a109
3 changed files with 28 additions and 19 deletions
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@ -394,7 +394,8 @@ class ArchFunctionHandler(ArchBaseHandler):
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return is_valid, invalid_tool_call, error_message
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# Verify the data type of each parameter in the tool calls
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for param_name, param_value in func_args:
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for param_name in func_args:
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param_value = func_args[param_name]
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data_type = functions[func_name]["properties"][param_name]["type"]
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if data_type in self.support_data_types:
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@ -469,6 +470,8 @@ class ArchFunctionHandler(ArchBaseHandler):
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stream=True,
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extra_body=self.generation_params,
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)
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# initialize the hallucination handler, which is an iterator
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hallu_handler = HallucinationStateHandler(
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response_iterator=response, function=req.tools
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)
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@ -476,8 +479,9 @@ class ArchFunctionHandler(ArchBaseHandler):
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model_response, has_tool_call = "", None
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for token in hallu_handler:
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# check if the first token is <tool_call>
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if len(hallu_handler.tokens) > 0 and has_tool_call == False:
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if hallu_handler.tokens[-0] == "<tool_call>":
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if hallu_handler.tokens[0] == "<tool_call>":
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has_tool_call = True
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else:
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has_tool_call = False
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@ -27,10 +27,10 @@ class MaskToken(Enum):
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HALLUCINATION_THRESHOLD_DICT = {
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MaskToken.TOOL_CALL.value: {"entropy": 0.05, "varentropy": 0.25},
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MaskToken.TOOL_CALL.value: {"entropy": 0.001, "varentropy": 0.005},
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MaskToken.PARAMETER_VALUE.value: {
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"entropy": 0.05,
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"varentropy": 0.25,
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"entropy": 0.001,
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"varentropy": 0.005,
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},
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}
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@ -105,7 +105,6 @@ class HallucinationStateHandler:
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hallucination (bool): Flag indicating if a hallucination is detected.
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hallucination_message (str): Message describing the hallucination.
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parameter_name (list): List of extracted parameter names.
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function_description (dict): Description of functions and their parameters.
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token_probs_map (list): List mapping tokens to their entropy and variance of entropy.
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"""
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@ -130,13 +129,9 @@ class HallucinationStateHandler:
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self.function = function
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if self.function is None:
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raise ValueError("API descriptions not set.")
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parameter_names = {}
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for func in self.function:
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func_name = func["name"]
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parameters = func["parameters"]["properties"]
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parameter_names[func_name] = list(parameters.keys())
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self.function_description = parameter_names
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self.function_properties = {x["name"]: x["parameters"] for x in self.function}
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self.function_properties = {
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x["function"]["name"]: x["function"]["parameters"] for x in self.function
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}
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def append_and_check_token_hallucination(self, token, logprob):
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"""
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