test new workflow that will try to get the domain name right

.github/workflows/documentation_li.yml

@@ -51,6 +51,10 @@ jobs:
           touch .nojekyll
         working-directory: ${{ github.workspace }}/docs/build
 
+      - name: Copy CNAME file
+        run: |
+          cp ${{ github.workspace }}/CNAME ${{ github.workspace }}/docs/build/CNAME
+
       - name: Deploy to GitHub Pages
         uses: peaceiris/actions-gh-pages@v3
         with:

CNAME

@@ -0,0 +1 @@
+lightrag.sylph.ai

README.md

@@ -1,8 +1,8 @@
 ![LightRAG Logo](docs/source/_static/images/LightRAG-logo-doc.jpeg)
 
-⚡ The PyTorch Library for Large language Model (LLM) Applications ⚡
+## ⚡⚡⚡ The PyTorch Library for Large language Model (LLM) Applications ⚡⚡⚡
 
-We help developers with both building and optimizing `Retriever`-`Agent`-`Generator` (RAG) pipelines.
+*LightRAG* helps developers with both building and optimizing *Retriever-Agent-Generator (RAG)* pipelines.
 It is *light*, *modular*, and *robust*.
 
 
@@ -38,7 +38,6 @@ class Net(nn.Module):
 
 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):
@@ -74,22 +73,21 @@ Please refer to the [full installation guide](https://lightrag.sylph.ai/get_star
 
 
 
-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.
 
 # 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)
+- [Design philosophy](https://lightrag.sylph.ai/developer_notes/lightrag_design_philosophy.html): Design based on three principles: Simplicity over complexity, Quality over quantity, and Optimizing over building.
+- [Class hierarchy](https://lightrag.sylph.ai/developer_notes/class_hierarchy.html): We have no more than two levels of subclasses. The bare minimum abstraction will developers with maximum customizability and simplicity.
+- [Tutorials](https://lightrag.sylph.ai/developer_notes/index.html): Learn the `why` and `how-to` (customize and integrate) behind each core part within the `LightRAG` library.
 - [API reference](https://lightrag.sylph.ai/apis/index.html)
 
 
 
+
 ## Contributors
 
 [![contributors](https://contrib.rocks/image?repo=SylphAI-Inc/LightRAG&max=2000)](https://github.com/SylphAI-Inc/LightRAG/graphs/contributors)

docs/source/developer_notes/class_hierarchy.rst

@@ -1,6 +1,6 @@
 Class Hierarchy
 =============================
-From the plot of the `LightRAG` library's class hierarchy, we can see the library is well-centered around two base classes: `Component` and `DataClass`, and it has no more than two levels of subclasses.
+From the plot of the `LightRAG` library's class hierarchy, we can see the library is well-centered around two base classes: `Component` and `DataClass`, and it maintains a class inheritance hierarchy with no more than two levels.
 This design philosophy results in a library with bare minimum abstraction, providing developers with maximum customizability.
 
 .. raw:: html

docs/source/developer_notes/lightrag_design_philosophy.rst

@@ -4,7 +4,17 @@ Design Philosophy
 Right from the begining, `LightRAG` follows three fundamental principles.
 
 
-Principle 1: Quality over Quantity
+Principle 1:  Simplicity over Complexity
+-----------------------------------------------------------------------
+We put these three hard rules while designing LightRAG:
+
+- Every layer of abstraction needs to be adjusted and overall we do not allow more than 3 layers of abstraction.
+- We minimize the lines of code instead of maximizing the lines of code.
+- Go *deep* and *wide* in order to *simplify*.  The clarity we achieve is not the result of being easy, but the result of being deep.
+
+
+
+Principle 2: Quality over Quantity
 -----------------------------------------------------------------------
 
  The Quality of core building blocks over the Quantity of integrations.
@@ -14,7 +24,7 @@ This goes for the prompt, the model client, the retriever, the optimizer, and th
 
 
 
-Principle 2: Optimizing over Building
+Principle 3: Optimizing over Building
 -----------------------------------------------------------------------
 
  We help users build the task pipeline, but we want to help with optimizing even more so.
@@ -28,20 +38,14 @@ to ease the existing frustrations of optimizing the task pipeline.
 
 
 
-Principle 3: Practicality over Showmanship
------------------------------------------------------------------------
-We put these three hard rules while designing LightRAG:
-
-- Every layer of abstraction needs to be adjusted and overall we do not allow more than 3 layers of abstraction.
-- We minimize the lines of code instead of maximizing the lines of code.
-- Go `deep` and `wide` in order to `simplify`.  The clarity we achieve is not the result of being easy, but the result of being deep.
 
 
 
-Our deep understanding of LLM workflow
+Our understanding of LLM workflow
 -----------------------------------------------------------------------
 
-The above principles are distilled from our deep understanding of the LLM workflow.
+The above principles are distilled from our experiences and continuous learning about the LLM workflow.
+
 
 
 **Developers are the ultimate heroes**

lightrag/README.md

@@ -1,9 +1,9 @@
 ![LightRAG Logo](../docs/source/_static/images/LightRAG-logo-doc.jpeg)
 
 
-⚡ The PyTorch Library for Large language Model (LLM) Applications ⚡
+## ⚡⚡⚡ The PyTorch Library for Large language Model (LLM) Applications ⚡⚡⚡
 
-We help developers with both building and optimizing `Retriever`-`Agent`-`Generator` (RAG) pipelines.
+*LightRAG* helps developers with both building and optimizing *Retriever-Agent-Generator (RAG)* pipelines.
 It is *light*, *modular*, and *robust*.
 
 
@@ -75,18 +75,15 @@ Please refer to the [full installation guide](https://lightrag.sylph.ai/get_star
 
 
 
-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.
-
 # 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)
+- [Design philosophy](https://lightrag.sylph.ai/developer_notes/lightrag_design_philosophy.html): Design based on three principles: Simplicity over complexity, Quality over quantity, and Optimizing over building.
+- [Class hierarchy](https://lightrag.sylph.ai/developer_notes/class_hierarchy.html): We have no more than two levels of subclasses. The bare minimum abstraction will developers with maximum customizability and simplicity.
+- [Tutorials](https://lightrag.sylph.ai/developer_notes/index.html): Learn the `why` and `how-to` (customize and integrate) behind each core part within the `LightRAG` library.
 - [API reference](https://lightrag.sylph.ai/apis/index.html)
 
 

lightrag/lightrag/__init__.py

@@ -1,3 +0,0 @@
-from lightrag.utils import setup_env
-
-setup_env()