# LightRAG
**Repository Path**: rwwang/LightRAG
## Basic Information
- **Project Name**: LightRAG
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-06
- **Last Updated**: 2024-07-06
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://opensource.org/license/MIT)
[](https://pypi.org/project/lightRAG/)
[](https://pypistats.org/packages/lightRAG)
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### ⚡ 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*.
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](https://lightrag.sylph.ai/developer_notes/class_hierarchy.html).
## 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``.
```python
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"""
You are a helpful assistant.
{{output_format_str}}
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.
```python
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:
You are a helpful assistant.
{{output_format_str}}
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:
```python
qa2.generator.print_prompt(
output_format_str=qa2.generator.output_processors.format_instructions(),
input_str="What is LLM?",
)
```
The output will be:
````markdown
You are a helpful assistant.
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.
User: What is LLM?
You:
````
## Quick Install
Install LightRAG with pip:
```bash
pip install lightrag
```
Please refer to the [full installation guide](https://lightrag.sylph.ai/get_started/installation.html) for more details.
# Documentation
LightRAG full documentation available at [lightrag.sylph.ai](https://lightrag.sylph.ai/):
- [Introduction](https://lightrag.sylph.ai/)
- [Full installation guide](https://lightrag.sylph.ai/get_started/installation.html)
- [Design philosophy](https://lightrag.sylph.ai/developer_notes/lightrag_design_philosophy.html)
- [Class hierarchy](https://lightrag.sylph.ai/developer_notes/class_hierarchy.html)
- [Tutorials](https://lightrag.sylph.ai/developer_notes/index.html)
- [API reference](https://lightrag.sylph.ai/apis/index.html)
## Contributors
[](https://github.com/SylphAI-Inc/LightRAG/graphs/contributors)
# Citation
```bibtex
@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}
}
```