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hub / github.com/FastMAS/KVCOMM / _process_inputs

Method _process_inputs

KVCOMM/agents/final_decision.py:95–268  ·  view source on GitHub ↗

To be overriden by the descendant class

(
        self,
        raw_inputs:Dict[str,str],
        spatial_info:Dict[str,Any],
        temporal_info:Dict[str,Any],
        mode: str = "default",
        **kwargs,
    )

Source from the content-addressed store, hash-verified

93 return "".join(paragraphs)
94
95 async def _process_inputs(
96 self,
97 raw_inputs:Dict[str,str],
98 spatial_info:Dict[str,Any],
99 temporal_info:Dict[str,Any],
100 mode: str = "default",
101 **kwargs,
102 )->Dict[str, Any]:
103 """ To be overriden by the descendant class """
104 """ Process the raw_inputs(most of the time is a List[Dict]) """
105
106 if mode == "allow_kv_reuse":
107 request_uid = raw_inputs.get("_request_uid") or kwargs.get("request_uid")
108 if request_uid is None:
109 raise ValueError("request_uid is required for request-scoped anchor updates.")
110
111 preferred_mode = "kv_reuse"
112 agent_memory = self.llm._ensure_agent_memory(self.id)
113 prefix_text = kwargs.get("prefix", "")
114
115 has_shared_prefix = (
116 self.llm.has_prefix_initialized(self.id)
117 and "placeholder_info" in agent_memory
118 )
119 early_response: str | None = None
120
121 if has_shared_prefix:
122 task = raw_inputs["task"]
123 for agent_id, info in spatial_info.items():
124 cond_text: str | None = None
125 cond_prefix: str | None = None
126
127 if self.domain == "gsm8k" and info["role"] == "Programming Expert":
128 answer = execute_code_get_return(
129 info["output"].split("```python\n")[-1].split("\n```")[0]
130 )
131 if answer is None:
132 answer = "No variable is named answer."
133 cond_text = f"the answer is {answer}"
134 cond_prefix = "the answer is "
135 elif (
136 self.domain == "humaneval"
137 and self.role not in {"Normal Programmer", "Stupid Programmer"}
138 and info["role"] != "Algorithm Designer"
139 ):
140 code = info["output"].split("```python\n")[-1].split("\n```")[0]
141 is_solved, feedback, _ = PyExecutor().execute(
142 code, getattr(self, "internal_tests", []), timeout=10
143 )
144 cond_text = (
145 "Whether it passes internal testing?\n"
146 f"{is_solved}.\n\nThe feedback is:\n\n {feedback}."
147 )
148 cond_prefix = "Whether it passes internal testing?\n"
149
150 if cond_text and cond_prefix:
151 self.llm.update_condition_anchor(
152 request_uid=request_uid,

Callers 2

_executeMethod · 0.95
_async_executeMethod · 0.95

Calls 14

execute_code_get_returnFunction · 0.90
PyExecutorClass · 0.90
_ensure_agent_memoryMethod · 0.80
itemsMethod · 0.80
splitMethod · 0.80
update_input_anchorMethod · 0.80
getMethod · 0.45
executeMethod · 0.45
get_decision_roleMethod · 0.45

Tested by

no test coverage detected