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| <!-- # LightRAG | ||
| [1000 lines of code are all you need](https://github.com/Sylph-AI/LightRAG/blob/main/lightrag/light_rag.py). No lock-in to vendors and frameworks, only the best practices of **productionable RAG and Agent**. | ||
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| ## What is LightRAG? | ||
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| ⚡ The PyTorch Library for Large language Model (LLM) Applications ⚡ | ||
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| LightRAG comes from the best of the AI research and engineering. Fundamentally, we ask ourselves: what kind of system that combines the best of research(such as LLM), engineering (such as 'jinja') to build the best applications? | ||
| We are not a framework. We do not want you to directly install the package. We want you to carefully decide to take modules and structures from here to build your own library and applications. This is a cookbook organized uniquely for easy understanding: you can read the 1000 lines of code to see a typical RAG end-to-end without jumping between files and going through multi-level class inheritance. If we build our system expanding from `light_rag.py`, we as a community will share the same RAG languages, and share other building blocks and use cases easily without depending on a complex framework. | ||
| We help developers with both building and optimizing `Retriever`-`Agent`-`Generator` (RAG) pipelines. | ||
| It is *light*, *modular*, and *robust*. | ||
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| **Our principles:** | ||
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| - We recognize that building a RAG is a highly complex and customized process, and no framework can offer that flexibility. | ||
| - We seek to understand, not to establish. | ||
| - We seek to be flexible, not rigid. | ||
| - We seek to be organized and clear, not complex and confusing. | ||
| - We seek to be open, with open contributions from the community and research on use cases, maybe we will come up with a more established framework in the future. | ||
| - We seek to learn from code; we don't read documentation that calls very high-level functions and classes. | ||
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| Note: If we can't understand your code quickly and easily, you should not add it to the library. | ||
| **PyTorch** | ||
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| This is a new beginning, where all developers can come together, enjoy learning and building, have fun, and build the best RAGs in the world. | ||
| ```python | ||
| import torch | ||
| import torch.nn as nn | ||
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| We stay neutral to all frameworks and all vendors, and we do not allow vendors to merge code without a clear, articulate community vouch for their performance, and comparison with other vendor or open-source solutions. | ||
| 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) | ||
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| **Our opinions:** | ||
| 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) | ||
| ``` | ||
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| We are very opinionated but we ground our opinions on best practices. Here are some of our unique opinions: | ||
| - We code in a way that we can switch between model providers or between oss and proprietary models easily. | ||
| **LightRAG** | ||
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| How? LLM is a "text-in-text-out" model. `Prompts` are the new model parameters--The in-context learning. We want full-control over it. Any model provider API that manipulates our input prompts and output text, we dont use it. Three examples are OpenAI's `role`, `function call` (`tool`), `json output mode`. The problem with this: our prompt cannot be directly adapted to other LLM providers and we lose transparency, adding more uncontrollable variables to our system. | ||
| - We write the `prompts` all together like writing a document instead of separating them into multiple strings or variables. We think `jinja2` speaks the best of the prompt language. [Here show how Llamaindex addes different prompts together but we put all of them together.] Yes, we think manual prompt enginerring is just a stage, like manually label your data. The future is another LLM can take your description and will be optimized to do your prompt, or another `hypernetwork` will convert the lengthy prompt into parameters that can be plugged into the model. | ||
| ```python | ||
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| # Structure | ||
| <p align="center"> | ||
| <img src="images/lightrag_structure.png" alt="Alt text" width="800"> | ||
| <br> | ||
| <em>LightRAG structure</em> | ||
| </p> | ||
| from lightrag.core import Component, Generator | ||
| from lightrag.components.model_client import GroqAPIClient | ||
| from lightrag.utils import setup_env #noqa | ||
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| ## Foundation | ||
| - `lightrag/`: All core data structures, the core 1000 lines of code to cover the essence of a performant RAG. | ||
| ## Building blocks | ||
| - `extend/`: All modules that can be used to extend the core RAG. | ||
| 1. Mainly including functional modules: we can extend different `Embedder`, `Retriever`, `Generator`, `Agent`, `Reranker`, etc. | ||
| 2. Tracking or monitoring modules: such as `CallbackManager`, `Phoenix` integration. When necessary, please add a `README.md` to explain why this module is necessary and how to use it. | ||
| - `tests/`: All tests for the core RAG and its extensions. Includng `dummy modules` for testing new modules. | ||
| 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, | ||
| ) | ||
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| ## End-to-end applications | ||
| - `use_cases/`: All use cases that can be solved using LightRAG. For instance, we can solve `Question Answering`, `Summarization`, `Information Extraction`, etc. | ||
| def call(self, query): | ||
| return self.generator({"input_str": query}) | ||
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| To add a new use case, you can add a new folder in `use_cases/` with the name `application_name` and add the following files: | ||
| - `/prompt`: a directory containing all prompts used in the application. | ||
| - `/data`: a directory containing all data used in the application, or instructions on how to download the data. | ||
| - `/main.py`: a file containing the main code to run the application. | ||
| async def acall(self, query): | ||
| return await self.generator.acall({"input_str": query}) | ||
| ``` | ||
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| # What is not part of LightRAG? | ||
| - Data processing: For instance, llamaindex has `from llama_index.core.ingestion import IngestionPipeline` which transforms the data that are either in the `Document` or `Chunk`. We do not cover this in LightRAG. | ||
| Similarly, `from llama_index.core.postprocessor import SimilarityPostprocessor` which processes the retrieved `chunk`, sometimes with further filtering. | ||
| ## Quick Install | ||
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| # How to start? | ||
| Install LightRAG with pip: | ||
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| 1. Clone the repository. | ||
| 2. Setup API keys by make a copy of `.env.example` to `.env` and fill in the necessary API keys. | ||
| 3. Setup the Python environment using `poetry install`. And activate the environment using `poetry shell`. | ||
| 4. (For contributors only) Install pre-commit into your git hooks using `pre-commit install`, which will automatically check the code standard on every commit. | ||
| 5. Now you should run any file in the repo. --> | ||
| ```bash | ||
| pip install lightrag | ||
| ``` | ||
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| Please refer to the [full installation guide](https://lightrag.sylph.ai/get_started/installation.html) for more details. | ||
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| You can place the above code in your project's root ``__init__.py`` file. | ||
| This setup ensures that LightRAG can access all necessary configurations during runtime. | ||
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| # Documentation | ||
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| LightRAG full documentation available at [lightrag.sylph.ai](https://lightrag.sylph.ai/): | ||
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| - [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) | ||
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| ## Contributors | ||
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| [](https://github.com/SylphAI-Inc/LightRAG/graphs/contributors) | ||
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| # Citation | ||
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| ```bibtex | ||
| @software{Yin-LightRAG-2024, | ||
| author = {Yin, Li}, | ||
| title = {{LightRAG: The PyTorch Library for Large language Model (LLM) Applications}}, | ||
| month = {7}, | ||
| year = {2024}, | ||
| url = {https://github.com/SylphAI-Inc/LightRAG} | ||
| } | ||
| ``` |