🚀 Quick Start#

We provide several manners to use MemEngine. We take local deployment as examples.

Using Stand-alone memory#

You can just run our sample run_memory_samples.py for the quick start.

python run_memory_samples.py

Using memory in LLM-based agents#

We provide two example usage of applying MemEngine inside agents.

I. LLM-based Agents for HotPotQA#

You need to install some dependencies as follows:

pip install libzim beautifulsoup4

Then, download the wiki dump wikipedia_en_all_nopic_2024-06.zim and the data hotpot_dev_fullwiki_v1.json in your own path. After that, change the path and API keys in run_agent_samples/run_hotpotqa.py. And you can run the program with the command:

cd run_agent_samples
python run_hotpotqa.py

II. LLM-based Agents for Dialogue#

You need to change the API keys in run_agent_samples/run_dialogue.py. And you can run the program with the command:

cd run_agent_samples
python run_dialogue.py

Using memory with automatic selection#

Developers can select the appropriate memory models, hyper-parameters, and prompts from the provided ranges, based on a specific task’s criteria.

First of all, define a reward function as the ceriteria, whose input is a memory object and output is a float. An example of the dialogue task is shown as follows:

def sample_reward_func(memory):
    """Given a memory, utilize it and obtain a reward score to reflect how good it is.

    Args:
        memory (BaseMemory): the memory in MemEngine.

    Returns:
        float: the reward score to reflect how good the memory is.

    """
    dialogue_record = []

    user = DialogueAgent('User', 'Assistant', FUMemory(MemoryConfig(DEFAULT_FUMEMORY)))
    assistant = DialogueAgent('Assistant', 'User', memory)
    assistant_response = assistant.response('Please start the dialogue between User and Assistant.')

    for current_step in range(MAX_STEP):
        user_response = user.response(assistant_response)
        assistant_response = assistant.response(user_response)
        dialogue_record.append('User: %s' % user_response)
        dialogue_record.append('Assistant: %s' % assistant_response)

    score = eval_assistant(dialogue_record)
    return score

Then, prepare the range of model or config selection. An example is shown as follows:

# Option 1: Direct Assign
ModelCandidate = [{
    'model': 'FUMemory',
    'config': DEFAULT_FUMEMORY
},  {
    'model': 'LTMemory',
    'config': DEFAULT_LTMEMORY
},  {
    'model': 'STMemory',
    'config': DEFAULT_STMEMORY
}]

# Option 2: Generate with Combination (Recommended for Hyper-parameter Tuning)
ModelCandidate += generate_candidate({
    'model': 'LTMemory',
    'base_config': DEFAULT_LTMEMORY,
    'adjust_name': 'recall.text_retrieval.topk',
    'adjust_range': [1, 3, 5, 10]
})

Finally, start automatic selection and get the result.

def sample_automode():
    selection_result = automatic_select(sample_reward_func, ModelCandidate)
    print('The full ranking of candidate is shown as follows:')
    print(selection_result)

    print('The best model/config is shown as follows:')
    print(selection_result[0])

The full example can be found in run_automode_sample.py.

🚀 Quick Start

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