| from lagent.agents.react import ReActProtocol | |
| from mmengine.config import read_base | |
| from opencompass.lagent.actions.ipython_interpreter import IPythonInterpreter | |
| from opencompass.lagent.agents.react import CIReAct | |
| from opencompass.models.lagent import CodeAgent | |
| from opencompass.models.openai_api import OpenAI | |
| from opencompass.partitioners import SizePartitioner | |
| from opencompass.runners import LocalRunner | |
| from opencompass.tasks import OpenICLInferTask | |
| with read_base(): | |
| from .datasets.CIBench.CIBench_gen_8ab0dc import \ | |
| cibench_datasets as datasets | |
| FORCE_STOP_PROMPT_EN = """You should directly give results based on history information.""" | |
| FEWSHOT_INSTRUCTION = """\ | |
| You are an assistant who can utilize external tools. | |
| {tool_description} | |
| To use a tool, please response with the following format: | |
| ``` | |
| {thought} Think what you need to solve, do you need to use tools? | |
| {action} The tool name, should be one of [{action_names}]. | |
| {action_input} The input to the tool that you want to use. | |
| ``` | |
| The tool will give you response after your response using the following format: | |
| ``` | |
| {response} the results after call the tool. | |
| ``` | |
| Therefore DO NOT generate tool response by yourself. | |
| Also please follow the guidelines: | |
| 1. Always use code interpreter to solve the problem. | |
| 2. The generated codes should always in a markdown code block format. | |
| 3. The generated codes will be executed in an ipython manner and the results will be cached. | |
| 4. Your responded code should always be simple and only solves the problem in current step. | |
| For example: | |
| File url: `xxxx` | |
| ### Step 1. Load the dataset from the url into a pandas DataFrame named `df`. | |
| {thought} We should use `pandas` to solve this step. | |
| {action} IPythonInterpreter | |
| {action_input} ```python | |
| import pandas as pd | |
| url = "xxxx" | |
| data = pd.read_csv(url) | |
| ``` | |
| {response} The code is succeed without any outputs. | |
| Let us begin from here! | |
| """ | |
| IPYTHON_INTERPRETER_DESCRIPTION = '''\ | |
| It can run Python code in a manner as jupyter notebook. The code must be a valid code that contains only python method.''' | |
| models = [ | |
| dict( | |
| abbr='gpt-3.5-code', | |
| type=CodeAgent, | |
| agent_type=CIReAct, | |
| max_turn=3, | |
| llm=dict( | |
| type=OpenAI, | |
| path='gpt-3.5-turbo', | |
| key='ENV', | |
| query_per_second=1, | |
| max_seq_len=4096, | |
| ), | |
| actions=[ | |
| dict(type=IPythonInterpreter, | |
| description=IPYTHON_INTERPRETER_DESCRIPTION) | |
| ], | |
| protocol=dict( | |
| type=ReActProtocol, | |
| call_protocol=FEWSHOT_INSTRUCTION, | |
| force_stop=FORCE_STOP_PROMPT_EN, | |
| finish=dict(role='FINISH', begin='Final Answer:', end='\n'), | |
| ), | |
| batch_size=1, | |
| ), | |
| ] | |
| infer = dict( | |
| partitioner=dict(type=SizePartitioner, max_task_size=1000), | |
| runner=dict( | |
| type=LocalRunner, | |
| max_num_workers=16, | |
| task=dict(type=OpenICLInferTask)), | |
| ) |