autogen/test/agentchat/test_retrievechat.py

import pytest
import sys
from flaml import autogen
from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST

try:
    from flaml.autogen.agentchat.contrib.retrieve_assistant_agent import (
        RetrieveAssistantAgent,
    )
    from flaml.autogen.agentchat.contrib.retrieve_user_proxy_agent import (
        RetrieveUserProxyAgent,
    )
    from flaml.autogen.retrieve_utils import create_vector_db_from_dir, query_vector_db
    import chromadb

    skip_test = False
except ImportError:
    skip_test = True


@pytest.mark.skipif(
    sys.platform in ["darwin", "win32"] or skip_test,
    reason="do not run on MacOS or windows",
)
def test_retrievechat():
    conversations = {}
    autogen.ChatCompletion.start_logging(conversations)

    config_list = autogen.config_list_from_json(
        OAI_CONFIG_LIST,
        file_location=KEY_LOC,
        filter_dict={
            "model": ["gpt-4", "gpt4", "gpt-4-32k", "gpt-4-32k-0314"],
        },
    )

    assistant = RetrieveAssistantAgent(
        name="assistant",
        system_message="You are a helpful assistant.",
        llm_config={
            "request_timeout": 600,
            "seed": 42,
            "config_list": config_list,
        },
    )

    ragproxyagent = RetrieveUserProxyAgent(
        name="ragproxyagent",
        human_input_mode="NEVER",
        max_consecutive_auto_reply=2,
        retrieve_config={
            "docs_path": "./website/docs",
            "chunk_token_size": 2000,
            "model": config_list[0]["model"],
            "client": chromadb.PersistentClient(path="/tmp/chromadb"),
        },
    )

    assistant.reset()

    code_problem = "How can I use FLAML to perform a classification task, set use_spark=True, train 30 seconds and force cancel jobs if time limit is reached."
    ragproxyagent.initiate_chat(assistant, problem=code_problem, search_string="spark", silent=True)

    print(conversations)


@pytest.mark.skipif(
    sys.platform in ["darwin", "win32"] or skip_test,
    reason="do not run on MacOS or windows",
)
def test_retrieve_utils():
    client = chromadb.PersistentClient(path="/tmp/chromadb")
    create_vector_db_from_dir(dir_path="./website/docs", client=client, collection_name="flaml-docs")
    results = query_vector_db(
        query_texts=[
            "How can I use FLAML UserProxyAgent and AssistantAgent to do code generation?",
        ],
        n_results=4,
        client=client,
        collection_name="flaml-docs",
        search_string="FLAML",
    )
    print(results["ids"][0])
    assert len(results["ids"][0]) == 4


if __name__ == "__main__":
    test_retrievechat()
    test_retrieve_utils()