autogen.agentchat.contrib.retrieve_user_proxy_agent

RetrieveUserProxyAgent Objects

class RetrieveUserProxyAgent(UserProxyAgent)

__init__

def __init__(name="RetrieveChatAgent",
             is_termination_msg: Optional[Callable[
                 [Dict], bool]] = _is_termination_msg_retrievechat,
             human_input_mode: Optional[str] = "ALWAYS",
             retrieve_config: Optional[Dict] = None,
             **kwargs)

Arguments:

• name str - name of the agent.
• human_input_mode str - whether to ask for human inputs every time a message is received. Possible values are "ALWAYS", "TERMINATE", "NEVER". (1) When "ALWAYS", the agent prompts for human input every time a message is received. Under this mode, the conversation stops when the human input is "exit", or when is_termination_msg is True and there is no human input. (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or the number of auto reply reaches the max_consecutive_auto_reply. (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
• retrieve_config dict or None - config for the retrieve agent. To use default config, set to None. Otherwise, set to a dictionary with the following keys:
  • task (Optional, str): the task of the retrieve chat. Possible values are "code", "qa" and "default". System prompt will be different for different tasks. The default value is default, which supports both code and qa.
  • client (Optional, chromadb.Client): the chromadb client. If key not provided, a default client chromadb.Client() will be used.
  • docs_path (Optional, str): the path to the docs directory. It can also be the path to a single file, or the url to a single file. If key not provided, a default path ./docs will be used.
  • collection_name (Optional, str): the name of the collection. If key not provided, a default name flaml-docs will be used.
  • model (Optional, str): the model to use for the retrieve chat. If key not provided, a default model gpt-4 will be used.
  • chunk_token_size (Optional, int): the chunk token size for the retrieve chat. If key not provided, a default size max_tokens * 0.4 will be used.
  • context_max_tokens (Optional, int): the context max token size for the retrieve chat. If key not provided, a default size max_tokens * 0.8 will be used.
  • chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values are "multi_lines" and "one_line". If key not provided, a default mode multi_lines will be used.
  • must_break_at_empty_line (Optional, bool): chunk will only break at empty line if True. Default is True. If chunk_mode is "one_line", this parameter will be ignored.
  • embedding_model (Optional, str): the embedding model to use for the retrieve chat. If key not provided, a default model all-MiniLM-L6-v2 will be used. All available models can be found at https://www.sbert.net/docs/pretrained_models.html. The default model is a fast model. If you want to use a high performance model, all-mpnet-base-v2 is recommended.
  • customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.
• **kwargs dict - other kwargs in UserProxyAgent.

generate_init_message

def generate_init_message(problem: str,
                          n_results: int = 20,
                          search_string: str = "")

Generate an initial message with the given problem and prompt.

Arguments:

• problem str - the problem to be solved.
• n_results int - the number of results to be retrieved.
• search_string str - only docs containing this string will be retrieved.

Returns:

• str - the generated prompt ready to be sent to the assistant agent.