⚡ 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.

Design Philosophy

LightRAG follows three fundamental principles from day one: simplicity over complexity, quality over quantity, and optimizing over building. This design philosophy results in a library with bare minimum abstraction, providing developers with maximum customizability. View Class hierarchy here.

LightRAG Task Pipeline

We will ask the model to respond with explanation and example of a concept. And we will build a pipeline to get the structured output as QAOutput.

from dataclasses import dataclass, field

from lightrag.core import Component, Generator, DataClass
from lightrag.components.model_client import GroqAPIClient
from lightrag.components.output_parsers import JsonOutputParser

@dataclass
class QAOutput(DataClass):
    explaination: str = field(
        metadata={"desc": "A brief explaination of the concept in one sentence."}
    )
    example: str = field(metadata={"desc": "An example of the concept in a sentence."})



qa_template = r"""<SYS>
You are a helpful assistant.
<OUTPUT_FORMAT>
{{output_format_str}}
</OUTPUT_FORMAT>
</SYS>
User: {{input_str}}
You:"""

class QA(Component):
    def __init__(self):
        super().__init__()

        parser = JsonOutputParser(data_class=QAOutput, return_data_class=True)
        self.generator = Generator(
            model_client=GroqAPIClient(),
            model_kwargs={"model": "llama3-8b-8192"},
            template=qa_template,
            prompt_kwargs={"output_format_str": parser.format_instructions()},
            output_processors=parser,
        )

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

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

Run the following code for visualization and calling the model.

qa = QA()
print(qa)

# call
output = qa("What is LLM?")
print(output)

Structure of the pipeline

Here is what we get from print(qa):

QA(
  (generator): Generator(
    model_kwargs={'model': 'llama3-8b-8192'},
    (prompt): Prompt(
      template: <SYS>
      You are a helpful assistant.
      <OUTPUT_FORMAT>
      {{output_format_str}}
      </OUTPUT_FORMAT>
      </SYS>
      User: {{input_str}}
      You:, prompt_kwargs: {'output_format_str': 'Your output should be formatted as a standard JSON instance with the following schema:\n```\n{\n    "explaination": "A brief explaination of the concept in one sentence. (str) (required)",\n    "example": "An example of the concept in a sentence. (str) (required)"\n}\n```\n-Make sure to always enclose the JSON output in triple backticks (```). Please do not add anything other than valid JSON output!\n-Use double quotes for the keys and string values.\n-Follow the JSON formatting conventions.'}, prompt_variables: ['output_format_str', 'input_str']
    )
    (model_client): GroqAPIClient()
    (output_processors): JsonOutputParser(
      data_class=QAOutput, examples=None, exclude_fields=None, return_data_class=True
      (json_output_format_prompt): Prompt(
        template: Your output should be formatted as a standard JSON instance with the following schema:
        ```
        {{schema}}
        ```
        {% if example %}
        Examples:
        ```
        {{example}}
        ```
        {% endif %}
        -Make sure to always enclose the JSON output in triple backticks (```). Please do not add anything other than valid JSON output!
        -Use double quotes for the keys and string values.
        -Follow the JSON formatting conventions., prompt_variables: ['schema', 'example']
      )
      (output_processors): JsonParser()
    )
  )
)

The output

Here is what we get from print(output):

GeneratorOutput(data=QAOutput(explaination='LLM stands for Large Language Model, which refers to a type of artificial intelligence designed to process and generate human-like language.', example='For instance, LLMs are used in chatbots and virtual assistants, such as Siri and Alexa, to understand and respond to natural language input.'), error=None, usage=None, raw_response='```\n{\n  "explaination": "LLM stands for Large Language Model, which refers to a type of artificial intelligence designed to process and generate human-like language.",\n  "example": "For instance, LLMs are used in chatbots and virtual assistants, such as Siri and Alexa, to understand and respond to natural language input."\n}', metadata=None)

See the prompt

Use the following code:

qa2.generator.print_prompt(
        output_format_str=qa2.generator.output_processors.format_instructions(),
        input_str="What is LLM?",
)

The output will be:

<SYS>
You are a helpful assistant.
<OUTPUT_FORMAT>
Your output should be formatted as a standard JSON instance with the following schema:
```
{
    "explaination": "A brief explaination of the concept in one sentence. (str) (required)",
    "example": "An example of the concept in a sentence. (str) (required)"
}
```
-Make sure to always enclose the JSON output in triple backticks (```). Please do not add anything other than valid JSON output!
-Use double quotes for the keys and string values.
-Follow the JSON formatting conventions.
</OUTPUT_FORMAT>
</SYS>
User: What is LLM?
You:

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:

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}
}