update docs

docs/index.md

@@ -8,11 +8,6 @@ nav_order: 1
 
 A [100-line](https://github.com/zachary62/miniLLMFlow/blob/main/minillmflow/__init__.py) minimalist LLM framework for *Agents, Task Decomposition, RAG, etc*.
 
-<div align="center">
-  <img src="https://github.com/zachary62/miniLLMFlow/blob/main/assets/minillmflow.jpg?raw=true" width="400"/>
-</div>
-
-## Core Abstraction
 
 We model the LLM workflow as a **Nested Directed Graph**:
 - **Nodes** handle simple (LLM) tasks.
@@ -22,7 +17,12 @@ We model the LLM workflow as a **Nested Directed Graph**:
 - **Batch** Nodes/Flows for data-intensive tasks.
 - **Async** Nodes/Flows allow waits or **Parallel** execution
 
-To learn more:
+<div align="center">
+  <img src="https://github.com/zachary62/miniLLMFlow/blob/main/assets/minillmflow.jpg?raw=true" width="400"/>
+</div>
+
+## Core Abstraction
+
 - [Node](./node.md)
 - [Flow](./flow.md)
 - [Communication](./communication.md)
@@ -30,22 +30,12 @@ To learn more:
 - [(Advanced) Async](./async.md)
 - [(Advanced) Parallel](./parallel.md)
 
-## LLM Wrapper & Tools
+## Low-Level Details (We Do Not Provide)
 
-**We DO NOT provide built-in LLM wrappers and tools!**
-
-I believe it is a *bad practice* to provide low-level implementations in a general framework:
-- **APIs change frequently.** Hardcoding them makes maintenance a nightmare.
-- You may need **flexibility.** E.g., using fine-tunined LLMs or deploying local ones.
-- You may need **optimizations.** E.g., prompt caching, request batching, response streaming...
-
-We provide some simple example implementations:
 - [LLM Wrapper](./llm.md)
 - [Tool](./tool.md)
 
-## Paradigm
-
-Based on the core abstraction, we implement common high-level paradigms:
+## High-Level Paradigm
 
 - [Structured Output](./structure.md)
 - Task Decomposition

docs/llm.md

@@ -62,3 +62,8 @@ def call_llm(prompt):
     return response
 ```
 
+## Why Not Provide Built-in LLM Wrappers?
+I believe it is a **bad practice** to provide LLM-specific implementations in a general framework:
+- **LLM APIs change frequently**. Hardcoding them makes maintenance a nighmare.
+- You may need **flexibility** to switch vendors, use fine-tuned models, or deploy local LLMs.
+- You may need **optimizations** like prompt caching, request batching, or response streaming.