I am learning to use Role-playing with Auto RAG, but the second function call goes wrong. Could you please advise how to solve this issue?

[Ming-jiayou]

code:

from camel.societies import RolePlaying
from camel.models import ModelFactory
from camel.types import ModelPlatformType, StorageType
from camel.toolkits import FunctionTool
from camel.embeddings import OpenAICompatibleEmbedding
from camel.types.agents import ToolCallingRecord
from camel.retrievers import AutoRetriever
from camel.utils import Constants
from typing import List, Union
from camel.utils import print_text_animated
from colorama import Fore
import pathlib
import os
from dotenv import load_dotenv

sys_msg = 'You are a curious stone wondering about the universe.'

base_dir = pathlib.Path(__file__).parent.parent
env_path = base_dir / ".env"
load_dotenv(dotenv_path=str(env_path))

modeltype = os.getenv("Silicon_Model_ID")
modeltype2= os.getenv("ZHIPU_Model_ID")
embedding_modeltype = os.getenv("Embedding_Model_ID")
api_key = os.getenv("SiliconCloud_API_KEY")
base_url = os.getenv("SiliconCloud_Base_URL")
siliconcloud_model = ModelFactory.create(
     model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
            model_type=modeltype,
            api_key=api_key,
            url=base_url,
            model_config_dict={"temperature": 0.4, "max_tokens": 4096},
)

siliconcloud_model2 = ModelFactory.create(
     model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
            model_type=modeltype2,
            api_key=api_key,
            url=base_url,
            model_config_dict={"temperature": 0.4, "max_tokens": 4096},
)

embedding_instance = OpenAICompatibleEmbedding(model_type=embedding_modeltype, api_key=api_key, url=base_url)
embedding_instance.output_dim=1024

auto_retriever = AutoRetriever(
        vector_storage_local_path="local_data2/",
        storage_type=StorageType.QDRANT,
        embedding_model=embedding_instance)

def information_retrieval(
        query: str,
        contents: Union[str, List[str]],
        top_k: int = Constants.DEFAULT_TOP_K_RESULTS,
        similarity_threshold: float = Constants.DEFAULT_SIMILARITY_THRESHOLD,
    ) -> str:
        r"""Retrieves information from a local vector storage based on the
        specified query. This function connects to a local vector storage
        system and retrieves relevant information by processing the input
        query. It is essential to use this function when the answer to a
        question requires external knowledge sources.

        Args:
            query (str): The question or query for which an answer is required.
            contents (Union[str, List[str]]): Local file paths, remote URLs or
                string contents.
            top_k (int, optional): The number of top results to return during
                retrieve. Must be a positive integer. Defaults to
                `DEFAULT_TOP_K_RESULTS`.
            similarity_threshold (float, optional): The similarity threshold
                for filtering results. Defaults to
                `DEFAULT_SIMILARITY_THRESHOLD`.

        Returns:
            str: The information retrieved in response to the query, aggregated
                and formatted as a string.

        Example:
            # Retrieve information about CAMEL AI.
            information_retrieval(query = "How to contribute to CAMEL AI?",
                                contents="https://github.com/camel-ai/camel/blob/master/CONTRIBUTING.md")
        """
        retrieved_info = auto_retriever.run_vector_retriever(
            query=query,
            contents=contents,
            top_k=top_k,
            similarity_threshold=similarity_threshold,
        )
        return str(retrieved_info)

RETRUEVE_FUNCS: list[FunctionTool] = [
    FunctionTool(func) for func in [information_retrieval]
]

def role_playing_with_rag(
    task_prompt,
    chat_turn_limit=5,
) -> None:
    task_prompt = task_prompt

    role_play_session = RolePlaying(
        assistant_role_name="Searcher",
        assistant_agent_kwargs=dict(
            model=siliconcloud_model,
            tools=[
                *RETRUEVE_FUNCS,
            ],
        ),
        user_role_name="Professor",
        user_agent_kwargs=dict(
            model=siliconcloud_model2,
        ),
        task_prompt=task_prompt,
        with_task_specify=False,
    )

    print(
        Fore.GREEN
        + f"AI Assistant sys message:\n{role_play_session.assistant_sys_msg}\n"
    )
    print(
        Fore.BLUE + f"AI User sys message:\n{role_play_session.user_sys_msg}\n"
    )

    print(Fore.YELLOW + f"Original task prompt:\n{task_prompt}\n")
    print(
        Fore.CYAN
        + "Specified task prompt:"
        + f"\n{role_play_session.specified_task_prompt}\n"
    )
    print(Fore.RED + f"Final task prompt:\n{role_play_session.task_prompt}\n")

    n = 0
    input_msg = role_play_session.init_chat()
    while n < chat_turn_limit:
        n += 1
        assistant_response, user_response = role_play_session.step(input_msg)

        if assistant_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI Assistant terminated. Reason: "
                    f"{assistant_response.info['termination_reasons']}."
                )
            )
            break
        if user_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI User terminated. "
                    f"Reason: {user_response.info['termination_reasons']}."
                )
            )
            break

        # Print output from the user
        print_text_animated(
            Fore.BLUE + f"AI User:\n\n{user_response.msg.content}\n"
        )

        # Print output from the assistant, including any function
        # execution information
        print_text_animated(Fore.GREEN + "AI Assistant:")
        tool_calls: List[ToolCallingRecord] = [
            ToolCallingRecord(**call.as_dict())
            for call in assistant_response.info['tool_calls']
        ]
        for func_record in tool_calls:
            print_text_animated(f"{func_record}")
        print_text_animated(f"{assistant_response.msg.content}\n")

        if "CAMEL_TASK_DONE" in user_response.msg.content:
            break

if __name__ == "__main__":
   role_playing_with_rag(
        task_prompt="4月17日凌晨，OpenAI正式宣布推出了什么？请参考https://www.thepaper.cn/newsDetail_forward_30670507",
        chat_turn_limit=5,
    )

The first function call was successful:

But due to the similarity_threshold being set too high, the relevant information was not successfully retrieved:

{'Original Query': '4月17日凌晨，OpenAI正式宣布推出了什么？', 'Retrieved Context': ['No suitable information retrieved from https://www.thepaper.cn/newsDetail_forward_30670507 with similarity_threshold = 0.75.']}

The second call lowered the similarity_threshold:

Then, an error would be encountered:

Could you please tell me how to resolve this issue?