update doc

docs/design_pattern/mapreduce.md

@@ -12,27 +12,59 @@ MapReduce is a design pattern suitable when you have either:
 - Large output data (e.g., multiple forms to fill)
 
 and there is a logical way to break the task into smaller, ideally independent parts. 
+
+<div align="center">
+  <img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/mapreduce.png?raw=true" width="400"/>
+</div>
+
 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
-class MapSummaries(BatchNode):
-    def prep(self, shared): return [shared["text"][i:i+10000] for i in range(0, len(shared["text"]), 10000)]
-    def exec(self, chunk): return call_llm(f"Summarize this chunk: {chunk}")
-    def post(self, shared, prep_res, exec_res_list): shared["summaries"] = exec_res_list
+class SummarizeAllFiles(BatchNode):
+    def prep(self, shared):
+        files_dict = shared["files"]  # e.g. 10 files
+        return list(files_dict.items())  # [("file1.txt", "aaa..."), ("file2.txt", "bbb..."), ...]
 
-class ReduceSummaries(Node):
-    def prep(self, shared): return shared["summaries"]
-    def exec(self, summaries): return call_llm(f"Combine these summaries: {summaries}")
-    def post(self, shared, prep_res, exec_res): shared["final_summary"] = exec_res
+    def exec(self, one_file):
+        filename, file_content = one_file
+        summary_text = call_llm(f"Summarize the following file:\n{file_content}")
+        return (filename, summary_text)
 
-# Connect nodes
-map_node = MapSummaries()
-reduce_node = ReduceSummaries()
-map_node >> reduce_node
+    def post(self, shared, prep_res, exec_res_list):
+        shared["file_summaries"] = dict(exec_res_list)
 
-# Create flow 
-summarize_flow = Flow(start=map_node)
-summarize_flow.run(shared)
+class CombineSummaries(Node):
+    def prep(self, shared):
+        return shared["file_summaries"]
+
+    def exec(self, file_summaries):
+        # format as: "File1: summary\nFile2: summary...\n"
+        text_list = []
+        for fname, summ in file_summaries.items():
+            text_list.append(f"{fname} summary:\n{summ}\n")
+        big_text = "\n---\n".join(text_list)
+
+        return call_llm(f"Combine these file summaries into one final summary:\n{big_text}")
+
+    def post(self, shared, prep_res, final_summary):
+        shared["all_files_summary"] = final_summary
+
+batch_node = SummarizeAllFiles()
+combine_node = CombineSummaries()
+batch_node >> combine_node
+
+flow = Flow(start=batch_node)
+
+shared = {
+    "files": {
+        "file1.txt": "Alice was beginning to get very tired of sitting by her sister...",
+        "file2.txt": "Some other interesting text ...",
+        # ...
+    }
+}
+flow.run(shared)
+print("Individual Summaries:", shared["file_summaries"])
+print("\nFinal Summary:\n", shared["all_files_summary"])
 ```

docs/design_pattern/workflow.md

@@ -9,6 +9,10 @@ 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.
 
+<div align="center">
+  <img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/workflow.png?raw=true" width="400"/>
+</div>
+
 > - 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*.
 >