lightrag 0.0.0a16

The 'PyTorch' library for LLM applications. RAG=Retriever-Agent-Generator.

⚡ The PyTorch Library for Large Language Model Applications ⚡

LightRAG helps developers with both building and optimizing Retriever-Agent-Generator (RAG) pipelines. It is light, modular, and robust.

PyTorch

import torch
import torch.nn as nn

class Net(nn.Module):
   def __init__(self):
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(1, 32, 3, 1)
      self.conv2 = nn.Conv2d(32, 64, 3, 1)
      self.dropout1 = nn.Dropout2d(0.25)
      self.dropout2 = nn.Dropout2d(0.5)
      self.fc1 = nn.Linear(9216, 128)
      self.fc2 = nn.Linear(128, 10)

   def forward(self, x):
      x = self.conv1(x)
      x = self.conv2(x)
      x = self.dropout1(x)
      x = self.dropout2(x)
      x = self.fc1(x)
      return self.fc2(x)

LightRAG

from lightrag.core import Component, Generator
from lightrag.components.model_client import GroqAPIClient
from lightrag.utils import setup_env #noqa

class SimpleQA(Component):
   def __init__(self):
      super().__init__()
      template = r"""<SYS>
      You are a helpful assistant.
      </SYS>
      User: {{input_str}}
      You:
      """
      self.generator = Generator(
            model_client=GroqAPIClient(),
            model_kwargs={"model": "llama3-8b-8192"},
            template=template,
      )

   def call(self, query):
      return self.generator({"input_str": query})

   async def acall(self, query):
      return await self.generator.acall({"input_str": query})

Quick Install

Install LightRAG with pip:

pip install lightrag

Please refer to the full installation guide for more details.

Documentation

LightRAG full documentation available at lightrag.sylph.ai:

• Introduction
• Full installation guide
• Design philosophy: Design based on three principles: Simplicity over complexity, Quality over quantity, and Optimizing over building.
• Class hierarchy: We have no more than two levels of subclasses. The bare minimum abstraction provides developers with maximum customizability and simplicity.
• Tutorials: Learn the why and how-to (customize and integrate) behind each core part within the LightRAG library.
• API reference

Contributors

Citation

@software{Yin2024LightRAG,
  author = {Li Yin},
  title = {{LightRAG: The PyTorch Library for Large Language Model (LLM) Applications}},
  month = {7},
  year = {2024},
  doi = {10.5281/zenodo.12639531},
  url = {https://github.com/SylphAI-Inc/LightRAG}
}