update the readme and remove star counts from the file

SylphAI-Inc · Jul 14, 2024 · 71ed1fe · 71ed1fe
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diff --git a/README.md b/README.md
@@ -2,10 +2,10 @@
 
 <!-- [![release](https://img.shields.io/github/v/release/SylphAI-Inc/LightRAG?sort=semver)](https://github.com/SylphAI-Inc/LightRAG/releases) -->
 <!-- [![Dependency Status](https://img.shields.io/librariesio/github/SylphAI-Inc/LightRAG?style=flat-square)](https://libraries.io/github/SylphAI-Inc/LightRAG) -->
+<!-- [![GitHub star chart](https://img.shields.io/github/stars/SylphAI-Inc/LightRAG?style=flat-square)](https://star-history.com/#SylphAI-Inc/LightRAG) -->
 [![License](https://img.shields.io/github/license/SylphAI-Inc/LightRAG)](https://opensource.org/license/MIT)
 [![PyPI](https://img.shields.io/pypi/v/lightRAG?style=flat-square)](https://pypi.org/project/lightRAG/)
 [![PyPI - Downloads](https://img.shields.io/pypi/dm/lightRAG?style=flat-square)](https://pypistats.org/packages/lightRAG)
-[![GitHub star chart](https://img.shields.io/github/stars/SylphAI-Inc/LightRAG?style=flat-square)](https://star-history.com/#SylphAI-Inc/LightRAG)
 [![Open Issues](https://img.shields.io/github/issues-raw/SylphAI-Inc/LightRAG?style=flat-square)](https://github.com/SylphAI-Inc/LightRAG/issues)
 [![](https://dcbadge.vercel.app/api/server/zt2mTPcu?compact=true&style=flat)](https://discord.gg/zt2mTPcu)
 
@@ -58,8 +58,11 @@ class Net(nn.Module):
 ``` -->
 # LightRAG Task Pipeline
 
+We will ask the model to respond with ``explanation`` and ``example`` of a concept. To achieve this, we will build a simple pipeline to get the structured output as ``QAOutput``.
 
-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``.
+## Well-designed Base Classes
+
+This leverages our two and only powerful base classes: `Component` as building blocks for the pipeline and `DataClass` to ease the data interaction with LLMs.
 
 ```python
 
@@ -120,9 +123,9 @@ output = qa("What is LLM?")
 print(output)
 ```
 
-**Structure of the pipeline**
+## Clear Pipeline Structure
 
-Here is what we get from ``print(qa)``:
+Simply by using `print(qa)`, you can see the pipeline structure, which helps users understand any LLM workflow quickly.
 
 ```
 QA(
@@ -162,16 +165,17 @@ QA(
 )
 ```
 
-**The output**
+**The Output**
 
+We structure the output to both track the data and potential errors if any part of the Generator component fails.
 Here is what we get from ``print(output)``:
 
 ```
 GeneratorOutput(data=QAOutput(explanation='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  "explanation": "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**
+**Focus on the Prompt**
 
-Use the following code:
+Use the following code will let us see the prompt after it is formatted:
 
 ```python
 
@@ -204,6 +208,28 @@ User: What is LLM?
 You:
 ````
 
+## Model-agnostic
+
+
+You can switch to any model simply by using a different model_client (provider) and model_kwargs.
+Let's use OpenAI's gpt-3.5-turbo model on the same pipeline.
+
+
+You can switch to any model simply by using a different `model_client` (provider) and `model_kwargs`.
+Let's use OpenAI's `gpt-3.5-turbo` model.
+
+```python
+from lightrag.components.model_client import OpenAIClient
+
+self.generator = Generator(
+    model_client=OpenAIClient(),
+    model_kwargs={"model": "gpt-3.5-turbo"},
+    template=qa_template,
+    prompt_kwargs={"output_format_str": parser.format_instructions()},
+    output_processors=parser,
+)
+```
+
 
 # Quick Install
 

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -9,14 +9,14 @@
 .. raw:: html
 
    <div style="text-align: center; margin-bottom: 20px;">
-
       <a href="https://pypi.org/project/lightRAG/"><img src="https://img.shields.io/pypi/v/lightRAG?style=flat-square" alt="PyPI Version"></a>
-      <a href="https://star-history.com/#SylphAI-Inc/LightRAG"><img src="https://img.shields.io/github/stars/SylphAI-Inc/LightRAG?style=flat-square" alt="GitHub Stars"></a>
       <a href="https://discord.gg/ezzszrRZvT">  <img src="https://img.shields.io/discord/1065084981904429126?style=flat-square" alt="Discord"></a>
       <a href="https://opensource.org/license/MIT"><img src="https://img.shields.io/github/license/SylphAI-Inc/LightRAG" alt="License"></a>
    </div>
 
 
+.. <a href="https://star-history.com/#SylphAI-Inc/LightRAG"><img src="https://img.shields.io/github/stars/SylphAI-Inc/LightRAG?style=flat-square" alt="GitHub Stars"></a>
+
 ..  <a href="https://pypistats.org/packages/lightRAG"><img src="https://img.shields.io/pypi/dm/lightRAG?style=flat-square" alt="PyPI Downloads"></a>
 
 
@@ -245,10 +245,9 @@ We are building a library that unites the two worlds, forming a healthy LLM appl
 
 .. hide the for contributors now
 
-.. toctree::
-   :glob:
-   :maxdepth: 1
-   :caption: For Contributors
-   :hidden:
+   .. :glob:
+   .. :maxdepth: 1
+   .. :caption: For Contributors
+   .. :hidden:
 
-   contributor/index
+   .. contributor/index
diff --git a/lightrag/README.md b/lightrag/README.md
@@ -1,12 +1,36 @@
 ![LightRAG Logo](https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/LightRAG-logo-doc.jpeg)
 
+<!-- [![release](https://img.shields.io/github/v/release/SylphAI-Inc/LightRAG?sort=semver)](https://github.com/SylphAI-Inc/LightRAG/releases) -->
+<!-- [![Dependency Status](https://img.shields.io/librariesio/github/SylphAI-Inc/LightRAG?style=flat-square)](https://libraries.io/github/SylphAI-Inc/LightRAG) -->
+<!-- [![GitHub star chart](https://img.shields.io/github/stars/SylphAI-Inc/LightRAG?style=flat-square)](https://star-history.com/#SylphAI-Inc/LightRAG) -->
+[![License](https://img.shields.io/github/license/SylphAI-Inc/LightRAG)](https://opensource.org/license/MIT)
+[![PyPI](https://img.shields.io/pypi/v/lightRAG?style=flat-square)](https://pypi.org/project/lightRAG/)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/lightRAG?style=flat-square)](https://pypistats.org/packages/lightRAG)
+[![Open Issues](https://img.shields.io/github/issues-raw/SylphAI-Inc/LightRAG?style=flat-square)](https://github.com/SylphAI-Inc/LightRAG/issues)
+[![](https://dcbadge.vercel.app/api/server/zt2mTPcu?compact=true&style=flat)](https://discord.gg/zt2mTPcu)
+
 
 ### ⚡ The Lightning 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* helps developers with both building and optimizing *Retriever-Agent-Generator* pipelines.
+It is *light*, *modular*, and *robust*, with a 100% readable codebase.
+
+
+
+
+# Why LightRAG?
+
+LLMs are like water; they can be shaped into anything, from GenAI applications such as chatbots, translation, summarization, code generation, and autonomous agents to classical NLP tasks like text classification and named entity recognition. They interact with the world beyond the model’s internal knowledge via retrievers, memory, and tools (function calls). Each use case is unique in its data, business logic, and user experience.
+
+Because of this, no library can provide out-of-the-box solutions. Users must build towards their own use case. This requires the library to be modular, robust, and have a clean, readable codebase. The only code you should put into production is code you either 100% trust or are 100% clear about how to customize and iterate.
+
+This is what LightRAG is: light, modular, and robust, with a 100% readable codebase.
 
 
+Further reading: [Introduction](https://lightrag.sylph.ai/), [Design Philosophy](https://lightrag.sylph.ai/tutorials/lightrag_design_philosophy.html) and [Class hierarchy](https://lightrag.sylph.ai/tutorials/class_hierarchy.html).
+
+
+<!--
 
 **PyTorch**
 
@@ -31,39 +55,183 @@ class Net(nn.Module):
       x = self.dropout2(x)
       x = self.fc1(x)
       return self.fc2(x)
-```
+``` -->
+# LightRAG Task Pipeline
+
+We will ask the model to respond with ``explanation`` and ``example`` of a concept. To achieve this, we will build a simple pipeline to get the structured output as ``QAOutput``.
 
-**LightRAG**
+## Well-designed Base Classes
+
+This leverages our two and only powerful base classes: `Component` as building blocks for the pipeline and `DataClass` to ease the data interaction with LLMs.
 
 ```python
 
-from lightrag.core import Component, Generator
+from dataclasses import dataclass, field
+
+from lightrag.core import Component, Generator, DataClass
 from lightrag.components.model_client import GroqAPIClient
-from lightrag.utils import setup_env #noqa
+from lightrag.components.output_parsers import JsonOutputParser
 
-class SimpleQA(Component):
-   def __init__(self):
-      super().__init__()
-      template = r"""<SYS>
+@dataclass
+class QAOutput(DataClass):
+    explanation: str = field(
+        metadata={"desc": "A brief explanation 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.
+
+```python
+
+qa = QA()
+print(qa)
+
+# call
+output = qa("What is LLM?")
+print(output)
+```
+
+## Clear Pipeline Structure
+
+Simply by using `print(qa)`, you can see the pipeline structure, which helps users understand any LLM workflow quickly.
+
+```
+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:
-      """
-      self.generator = Generator(
-            model_client=GroqAPIClient(),
-            model_kwargs={"model": "llama3-8b-8192"},
-            template=template,
+      You:, prompt_kwargs: {'output_format_str': 'Your output should be formatted as a standard JSON instance with the following schema:\n```\n{\n    "explanation": "A brief explanation 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**
+
+We structure the output to both track the data and potential errors if any part of the Generator component fails.
+Here is what we get from ``print(output)``:
+
+```
+GeneratorOutput(data=QAOutput(explanation='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  "explanation": "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)
+```
+**Focus on the Prompt**
+
+Use the following code will let us see the prompt after it is formatted:
+
+```python
+
+qa2.generator.print_prompt(
+        output_format_str=qa2.generator.output_processors.format_instructions(),
+        input_str="What is LLM?",
+)
+```
+
+
+The output will be:
+
+````markdown
+<SYS>
+You are a helpful assistant.
+<OUTPUT_FORMAT>
+Your output should be formatted as a standard JSON instance with the following schema:
+```
+{
+    "explanation": "A brief explanation 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:
+````
+
+## Model-agnostic
 
-   def call(self, query):
-      return self.generator({"input_str": query})
 
-   async def acall(self, query):
-      return await self.generator.acall({"input_str": query})
+You can switch to any model simply by using a different model_client (provider) and model_kwargs.
+Let's use OpenAI's gpt-3.5-turbo model on the same pipeline.
+
+
+You can switch to any model simply by using a different `model_client` (provider) and `model_kwargs`.
+Let's use OpenAI's `gpt-3.5-turbo` model.
+
+```python
+from lightrag.components.model_client import OpenAIClient
+
+self.generator = Generator(
+    model_client=OpenAIClient(),
+    model_kwargs={"model": "gpt-3.5-turbo"},
+    template=qa_template,
+    prompt_kwargs={"output_format_str": parser.format_instructions()},
+    output_processors=parser,
+)
 ```
 
-## Quick Install
+
+# Quick Install
 
 Install LightRAG with pip:
 
@@ -75,20 +243,22 @@ Please refer to the [full installation guide](https://lightrag.sylph.ai/get_star
 
 
 
+
 # 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/tutorials/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/tutorials/class_hierarchy.html): We have no more than two levels of subclasses. The bare minimum abstraction provides developers with maximum customizability and simplicity.
-- [Tutorials](https://lightrag.sylph.ai/tutorials/index.html): Learn the `why` and `how-to` (customize and integrate) behind each core part within the `LightRAG` library.
+- [Design philosophy](https://lightrag.sylph.ai/tutorials/lightrag_design_philosophy.html)
+- [Class hierarchy](https://lightrag.sylph.ai/tutorials/class_hierarchy.html)
+- [Tutorials](https://lightrag.sylph.ai/tutorials/index.html)
 - [API reference](https://lightrag.sylph.ai/apis/index.html)
 
 
 
-## Contributors
+
+# Contributors
 
 [![contributors](https://contrib.rocks/image?repo=SylphAI-Inc/LightRAG&max=2000)](https://github.com/SylphAI-Inc/LightRAG/graphs/contributors)