llama-index-packs-mixture-of-agents 0.1.0

llama-index packs mixture_of_agents paper implementation

Mixture-Of-Agents Pack

Implementation Of Mixture-Of-Agents paper from TogetherAI as LlamaPack.

Disclaimer: While the paper named the method "Mixture of Agents", agents appear to refer to LLMs themselves, not actual agentic behaviour

Approach

The capabilities of LLMs have advanced significantly, and there is now a growing number of these models available. To maximize their potential, we need to harness the collective expertise of multiple LLMs. This is where the Mixture-of-Agents (MoA) approach comes in.

The MoA approach is a layered architecture where each layer consists of multiple LLM agents. These agents collaborate by taking the outputs of other agents in the previous layer as auxiliary information to generate their responses. This collaboration allows for the refinement and enhancement of responses, as agents build upon each other's strengths. The process can be categorized into two roles: Proposers(base LLM), who generate diverse context and perspectives, and Aggregators(reference LLMs), who synthesize these proposals into a single, high-quality output. By introducing additional aggregators and iteratively refining the responses, the MoA approach aims to maximize the collaborative potential of multiple LLMs, leading to superior outcomes.

CLI Usage

You can download llamapacks directly using llamaindex-cli, which comes installed with the llama-index python package:

llamaindex-cli download-llamapack MixtureOfAgentsPack --download-dir ./mixture_of_agents_pack

You can then inspect the files at ./mixture_of_agents_pack and use them as a template for your own project.

Code Usage

You can download the pack to a the ./mixture_of_agents_pack directory:

from llama_index.core.llama_pack import download_llama_pack

# download and install dependencies
MixtureOfAgentsPack = download_llama_pack(
    "MixtureOfAgentsPack", "./mixture_of_agents_pack"
)

from llama_index.llms.openai import OpenAI
from llama_index.llms.mistralai import MistralAI

# Add OPENAI_API_KEY and MISTRAL_API_KEY to your env variable

mixture_of_agents_pack = MixtureOfAgentsPack(
    llm=OpenAI(model="gpt-4"),  # Aggregator
    reference_llms=[
        OpenAI(model="gpt-3.5-turbo"),
        MistralAI(model="mistral-medium"),
    ],  # Proposers
    num_layers=3,
    temperature=0.1,
)

From here, you can use the pack, or inspect and modify the pack in ./mixture_of_agents_pack.

The run() function is a light wrapper around the proposed approach in the paper.

response = mixture_of_agents_pack.run("What is LlamaIndex?")