remove essay example

docs/apps.md

@@ -1,6 +0,0 @@
----
-layout: default
-title: "Apps"
-nav_order: 5
-has_children: true
----

docs/essay.md

@@ -1,150 +0,0 @@
----
-layout: default
-title: "Essay"
-parent: "Apps"
-nav_order: 2
----
-
-# Summarization + QA agent for Paul Graham Essay
-
-```python
-from pocketflow import *
-import openai, os, yaml
-
-# Minimal LLM wrapper
-def call_llm(prompt):
-    openai.api_key = "YOUR_API_KEY_HERE"
-    r = openai.ChatCompletion.create(
-        model="gpt-4o",
-        messages=[{"role": "user", "content": prompt}]
-    )
-    return r.choices[0].message.content
-
-shared = {"data": {}, "summary": {}}
-
-# Load data into shared['data']
-class LoadData(Node):
-    def prep(self, shared):
-        path = "./PocketFlow/data/PaulGrahamEssaysLarge"
-        for fn in os.listdir(path):
-            with open(os.path.join(path, fn), 'r') as f:
-                shared['data'][fn] = f.read()
-    def exec(self, res): pass
-    def post(self, s, pr, er): pass
-
-LoadData().run(shared)
-
-# Summarize one file
-class SummarizeFile(Node):
-    def prep(self, s): return s['data'][self.params['filename']]
-    def exec(self, content): return call_llm(f"{content} Summarize in 10 words.")
-    def post(self, s, pr, sr): s["summary"][self.params['filename']] = sr
-
-node_summ = SummarizeFile()
-node_summ.set_params({"filename":"addiction.txt"})
-node_summ.run(shared)
-
-# Map-Reduce summarization
-class MapSummaries(BatchNode):
-    def prep(self, s):
-        text = s['data'][self.params['filename']]
-        return [text[i:i+10000] for i in range(0, len(text), 10000)]
-    def exec(self, chunk):
-        return call_llm(f"{chunk} Summarize in 10 words.")
-    def post(self, s, pr, er):
-        s["summary"][self.params['filename']] = [f"{i}. {r}" for i,r in enumerate(er)]
-
-class ReduceSummaries(Node):
-    def prep(self, s): return s["summary"][self.params['filename']]
-    def exec(self, chunks): return call_llm(f"{chunks} Combine into 10 words summary.")
-    def post(self, s, pr, sr): s["summary"][self.params['filename']] = sr
-
-map_summ = MapSummaries()
-reduce_summ = ReduceSummaries()
-map_summ >> reduce_summ
-
-flow = Flow(start=map_summ)
-flow.set_params({"filename":"before.txt"})
-flow.run(shared)
-
-# Summarize all files
-class SummarizeAllFiles(BatchFlow):
-    def prep(self, s): return [{"filename":fn} for fn in s['data']]
-
-SummarizeAllFiles(start=flow).run(shared)
-
-# QA agent
-class FindRelevantFile(Node):
-    def prep(self, s):
-        q = input("Enter a question: ")
-        filenames = list(s['summary'].keys())
-        file_summaries = [f"- '{fn}': {s['summary'][fn]}" for fn in filenames]
-        return q, filenames, file_summaries
-
-    def exec(self, p):
-        q, filenames, file_summaries = p
-        if not q:
-            return {"think":"no question", "has_relevant":False}
-        
-        resp = call_llm(f"""
-Question: {q} 
-Find the most relevant file from: {file_summaries}
-If none, explain why
-
-Output in code fence:
-```yaml
-think: >
-    reasoning about relevance
-has_relevant: true/false
-most_relevant: filename if relevant
-```""")
-        yaml_str = resp.split("```yaml")[1].split("```")[0].strip()
-        result = yaml.safe_load(yaml_str)
-        
-        # Validate response
-        assert isinstance(result, dict)
-        assert "think" in result
-        assert "has_relevant" in result
-        assert isinstance(result["has_relevant"], bool)
-        
-        if result["has_relevant"]:
-            assert "most_relevant" in result
-            assert result["most_relevant"] in filenames
-            
-        return result
-
-    def exec_fallback(self, p, exc): return {"think":"error","has_relevant":False}
-    def post(self, s, pr, res):
-        q, _ = pr
-        if not q:
-            print("No question asked"); return "end"
-        if res["has_relevant"]:
-            s["question"], s["relevant_file"] = q, res["most_relevant"]
-            print("Relevant file:", res["most_relevant"])
-            return "answer"
-        else:
-            print("No relevant file:", res["think"])
-            return "retry"
-
-class AnswerQuestion(Node):
-    def prep(self, s):
-        return s['question'], s['data'][s['relevant_file']]
-    def exec(self, p):
-        q, txt = p
-        return call_llm(f"Question: {q}\nText: {txt}\nAnswer in 50 words.")
-    def post(self, s, pr, ex):
-        print("Answer:", ex)
-
-class NoOp(Node): pass
-
-frf = FindRelevantFile(max_retries=3)
-aq = AnswerQuestion()
-noop = NoOp()
-
-frf - "answer" >> aq >> frf
-frf - "retry"  >> frf
-frf - "end"    >> noop
-
-qa = Flow(start=frf)
-qa.run(shared)
-```

docs/guide.md

@@ -1,8 +1,6 @@
 ---
 layout: default
 title: "Development Playbook"
-parent: "Apps"
-nav_order: 1
 ---
 
 # LLM Application Development Playbook
@@ -38,7 +36,6 @@ These system designs should be a collaboration between humans and AI assistants:
    - Writing outputs (e.g., generating reports, sending emails)
    - External tool usage (e.g., calling LLMs, searching the web)
    - **LLM-based tasks** (e.g., summarizing text, analyzing sentiment) are **not** utility functions. Instead, they are *internal core functions* within the AI system—designed in step 3—and are built on top of the utility functions.
-
    -  > **Start small!** Only include the most important ones to begin with!
       {: .best-practice }