compact doc

docs/_config.yml

@@ -31,5 +31,4 @@ callouts:
     color: blue
   best-practice:
     title: Best Practice
-    color: green
-
+    color: green

docs/core_abstraction/async.md

@@ -10,12 +10,9 @@ nav_order: 5
 **Async** Nodes implement `prep_async()`, `exec_async()`, `exec_fallback_async()`, and/or `post_async()`. This is useful for:
 
 1. **prep_async()**: For *fetching/reading data (files, APIs, DB)* in an I/O-friendly way.
-
 2. **exec_async()**: Typically used for async LLM calls.
-
 3. **post_async()**: For *awaiting user feedback*, *coordinating across multi-agents* or any additional async steps after `exec_async()`.
 
-
 **Note**: `AsyncNode` must be wrapped in `AsyncFlow`. `AsyncFlow` can also include regular (sync) nodes.
 
 ### Example

docs/core_abstraction/communication.md

@@ -15,7 +15,6 @@ Nodes and Flows **communicate** in two ways:
    - Great for data results, large content, or anything multiple nodes need.
    - You shall design the data structure and populate it ahead.
 
-
 2. **Params (only for [Batch](./batch.md))** 
    - Each node has a local, ephemeral `params` dict passed in by the **parent Flow**, used as an identifier for tasks. Parameter keys and values shall be **immutable**.
    - Good for identifiers like filenames or numeric IDs, in Batch mode.
@@ -85,7 +84,6 @@ Here:
 - **Set** via `set_params()`.
 - **Cleared** and updated each time a parent Flow calls it.
 
-
 > Only set the uppermost Flow params because others will be overwritten by the parent Flow. 
 > 
 > If you need to set child node params, see [Batch](./batch.md).
@@ -125,6 +123,4 @@ flow = Flow(start=node)
 # 5) Set Flow params (overwrites node params)
 flow.set_params({"filename": "doc2.txt"})
 flow.run(shared)  # The node summarizes doc2, not doc1
-```
-
----
+```

docs/core_abstraction/flow.md

@@ -86,7 +86,6 @@ flowchart TD
 - `node.run(shared)`: Just runs that node alone (calls `prep->exec->post()`), returns an Action. 
 - `flow.run(shared)`: Executes from the start node, follows Actions to the next node, and so on until the flow can't continue.
 
-
 > `node.run(shared)` **does not** proceed to the successor.
 > This is mainly for debugging or testing a single node.
 > 
@@ -108,7 +107,6 @@ A **Flow** is also a **Node**, so it will run `prep()` and `post()`. However:
 - It **won't** run `exec()`, as its main logic is to orchestrate its nodes.
 - `post()` always receives `None` for `exec_res` and should instead get the flow execution results from the shared store.
 
-
 ### Basic Flow Nesting
 
 Here's how to connect a flow to another node:
@@ -177,5 +175,4 @@ flowchart LR
         paymentFlow --> inventoryFlow
         inventoryFlow --> shippingFlow
     end
-```
-
+```

docs/core_abstraction/node.md

@@ -13,7 +13,6 @@ A **Node** is the smallest building block. Each Node has 3 steps `prep->exec->po
   <img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/node.png?raw=true" width="400"/>
 </div>
 
-
 1. `prep(shared)`
    - **Read and preprocess data** from `shared` store. 
    - Examples: *query DB, read files, or serialize data into a string*.
@@ -31,14 +30,11 @@ A **Node** is the smallest building block. Each Node has 3 steps `prep->exec->po
    - Examples: *update DB, change states, log results*.
    - **Decide the next action** by returning a *string* (`action = "default"` if *None*).
 
-
-
 > **Why 3 steps?** To enforce the principle of *separation of concerns*. The data storage and data processing are operated separately.
 >
 > All steps are *optional*. E.g., you can only implement `prep` and `post` if you just need to process data.
 {: .note }
 
-
 ### Fault Tolerance & Retries
 
 You can **retry** `exec()` if it raises an exception via two parameters when define the Node:
@@ -106,5 +102,4 @@ action_result = summarize_node.run(shared)
 
 print("Action returned:", action_result)  # "default"
 print("Summary stored:", shared["summary"])
-```  
-
+```

docs/core_abstraction/parallel.md

@@ -19,7 +19,6 @@ nav_order: 6
 > - **Consider Single-Node Batch APIs**: Some LLMs offer a **batch inference** API where you can send multiple prompts in a single call. This is more complex to implement but can be more efficient than launching many parallel requests and mitigates rate limits.
 {: .best-practice }
 
-
 ## AsyncParallelBatchNode
 
 Like **AsyncBatchNode**, but run `exec_async()` in **parallel**:
@@ -54,4 +53,4 @@ class SummarizeMultipleFiles(AsyncParallelBatchFlow):
 sub_flow = AsyncFlow(start=LoadAndSummarizeFile())
 parallel_flow = SummarizeMultipleFiles(start=sub_flow)
 await parallel_flow.run_async(shared)
-```
+```

docs/design_pattern/agent.md

@@ -98,5 +98,4 @@ search - "decide" >> decide  # Loop back
 
 flow = Flow(start=decide)
 flow.run({"query": "Who won the Nobel Prize in Physics 2024?"})
-```
-
+```

docs/design_pattern/mapreduce.md

@@ -14,7 +14,6 @@ MapReduce is a design pattern suitable when you have either:
 and there is a logical way to break the task into smaller, ideally independent parts. 
 You first break down the task using [BatchNode](../core_abstraction/batch.md) in the map phase, followed by aggregation in the reduce phase.
 
-
 ### Example: Document Summarization
 
 ```python
@@ -36,4 +35,4 @@ map_node >> reduce_node
 # Create flow 
 summarize_flow = Flow(start=map_node)
 summarize_flow.run(shared)
-```
+```

docs/design_pattern/memory.md

@@ -122,4 +122,4 @@ reply - "continue" >> retrieve
 flow = Flow(start=retrieve)
 shared = {}
 flow.run(shared)
-```
+```

docs/design_pattern/multi_agent.md

@@ -70,7 +70,6 @@ Agent received: Network connectivity: stable | timestamp_2
 Agent received: Processing load: optimal | timestamp_3
 ```
 
-
 ### Interactive Multi-Agent Example: Taboo Game
 
 Here's a more complex example where two agents play the word-guessing game Taboo. 

docs/design_pattern/workflow.md

@@ -9,7 +9,6 @@ nav_order: 2
 
 Many real-world tasks are too complex for one LLM call. The solution is to decompose them into a [chain](../core_abstraction/flow.md) of multiple Nodes.
 
-
 > - You don't want to make each task **too coarse**, because it may be *too complex for one LLM call*.
 > - You don't want to make each task **too granular**, because then *the LLM call doesn't have enough context* and results are *not consistent across nodes*.
 > 
@@ -47,4 +46,4 @@ shared = {"topic": "AI Safety"}
 writing_flow.run(shared)
 ```
 
-For *dynamic cases*, consider using [Agents](./agent.md).
+For *dynamic cases*, consider using [Agents](./agent.md).

docs/index.md

@@ -11,7 +11,6 @@ nav_order: 1
 
 A [100-line](https://github.com/the-pocket/PocketFlow/blob/main/pocketflow/__init__.py) minimalist LLM framework for *Agents, Task Decomposition, RAG, etc*.
 
-
 We model the LLM workflow as a **Graph + Shared Store**:
 - **Nodes** handle simple (LLM) tasks.
 - Nodes connect through **Actions** (labeled edges) for *Agents*.  
@@ -21,17 +20,13 @@ We model the LLM workflow as a **Graph + Shared Store**:
 - **Batch** Nodes/Flows for data-intensive tasks.
 - **Async** Nodes/Flows allow waits for asynchronous tasks.
 
-
 <div align="center">
   <img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/meme.jpg?raw=true" width="400"/>
 </div>
 
-
-
 > Have questions? Chat with [AI Assistant](https://chatgpt.com/g/g-677464af36588191b9eba4901946557b-mini-llm-flow-assistant)
 {: .note }
 
-
 ## Core Abstraction
 
 - [Node](./core_abstraction/node.md)

docs/utility_function/llm.md

@@ -77,7 +77,6 @@ class SummarizeNode(Node):
         return call_llm(f"Summarize: {text}", self.cur_retry==0)
 ```
 
-
 - Enable logging:
 
 ```python
@@ -93,4 +92,4 @@ def call_llm(prompt):
 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 nightmare.
 - 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.
+- You may need **optimizations** like prompt caching, request batching, or response streaming.

docs/utility_function/tool.md

@@ -68,7 +68,6 @@ def execute_sql(query):
     return result
 ```
 
-
 > ⚠️ Beware of SQL injection risk
 {: .warning }
 

docs/utility_function/viz.md

@@ -137,6 +137,4 @@ data_science_flow = DataScienceFlow(start=data_prep_node)
 data_science_flow.run({})
 ```
 
-The output would be: `Call stack: ['EvaluateModelNode', 'ModelFlow', 'DataScienceFlow']`
-
-
+The output would be: `Call stack: ['EvaluateModelNode', 'ModelFlow', 'DataScienceFlow']`