Spaces:
Sleeping
Sleeping
File size: 5,147 Bytes
4d1746c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
import json
import os
from base_handler import BaseHandler
from constant import DEFAULT_SYSTEM_PROMPT, GORILLA_TO_OPENAPI
from model_style import ModelStyle
from utils import (
convert_to_function_call,
convert_to_tool,
default_decode_ast_prompting,
default_decode_execute_prompting,
format_execution_results_prompting,
func_doc_language_specific_pre_processing,
system_prompt_pre_processing_chat_model,
convert_system_prompt_into_user_prompt,
combine_consecutive_user_prompts,
)
from openai import OpenAI
class OpenAIHandler(BaseHandler):
def __init__(self, model_name, temperature) -> None:
super().__init__(model_name, temperature)
self.model_style = ModelStyle.OpenAI
self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
def decode_ast(self, result, language="Python"):
if "FC" not in self.model_name:
return default_decode_ast_prompting(result, language)
else:
decoded_output = []
for invoked_function in result:
name = list(invoked_function.keys())[0]
params = json.loads(invoked_function[name])
decoded_output.append({name: params})
return decoded_output
def decode_execute(self, result):
if "FC" not in self.model_name:
return default_decode_execute_prompting(result)
else:
function_call = convert_to_function_call(result)
return function_call
#### FC methods ####
def _query_FC(self, inference_data: dict):
message: list[dict] = inference_data["message"]
tools = inference_data["tools"]
inference_data["inference_input_log"] = {"message": repr(message), "tools": tools}
if len(tools) > 0:
api_response = self.client.chat.completions.create(
messages=message,
model=self.model_name.replace("-FC", ""),
temperature=self.temperature,
tools=tools,
)
else:
api_response = self.client.chat.completions.create(
messages=message,
model=self.model_name.replace("-FC", ""),
temperature=self.temperature,
)
return api_response
def _pre_query_processing_FC(self, inference_data: dict, test_entry: dict) -> dict:
inference_data["message"] = []
return inference_data
def _compile_tools(self, inference_data: dict, test_entry: dict) -> dict:
functions: list = test_entry["function"]
test_category: str = test_entry["id"].rsplit("_", 1)[0]
functions = func_doc_language_specific_pre_processing(functions, test_category)
tools = convert_to_tool(functions, GORILLA_TO_OPENAPI, self.model_style)
inference_data["tools"] = tools
return inference_data
def _parse_query_response_FC(self, api_response: any) -> dict:
try:
model_responses = [
{func_call.function.name: func_call.function.arguments}
for func_call in api_response.choices[0].message.tool_calls
]
tool_call_ids = [
func_call.id for func_call in api_response.choices[0].message.tool_calls
]
except:
model_responses = api_response.choices[0].message.content
tool_call_ids = []
model_responses_message_for_chat_history = api_response.choices[0].message
return {
"model_responses": model_responses,
"model_responses_message_for_chat_history": model_responses_message_for_chat_history,
"tool_call_ids": tool_call_ids,
"input_token": api_response.usage.prompt_tokens,
"output_token": api_response.usage.completion_tokens,
}
def add_first_turn_message_FC(
self, inference_data: dict, first_turn_message: list[dict]
) -> dict:
inference_data["message"].extend(first_turn_message)
return inference_data
def _add_next_turn_user_message_FC(
self, inference_data: dict, user_message: list[dict]
) -> dict:
inference_data["message"].extend(user_message)
return inference_data
def _add_assistant_message_FC(
self, inference_data: dict, model_response_data: dict
) -> dict:
inference_data["message"].append(
model_response_data["model_responses_message_for_chat_history"]
)
return inference_data
def _add_execution_results_FC(
self,
inference_data: dict,
execution_results: list[str],
model_response_data: dict,
) -> dict:
# Add the execution results to the current round result, one at a time
for execution_result, tool_call_id in zip(
execution_results, model_response_data["tool_call_ids"]
):
tool_message = {
"role": "tool",
"content": execution_result,
"tool_call_id": tool_call_id,
}
inference_data["message"].append(tool_message)
return inference_data
|