compact doc

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)
-```
+```