Initial commit

.gitignore

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+# Dependencies
+node_modules/
+vendor/
+.pnp/
+.pnp.js
+
+# Build outputs
+dist/
+build/
+out/
+*.pyc
+__pycache__/
+
+# Environment files
+.env
+.env.local
+.env.*.local
+.env.development
+.env.test
+.env.production
+
+# IDE - VSCode
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+
+# IDE - JetBrains
+.idea/
+*.iml
+*.iws
+*.ipr
+
+# IDE - Eclipse
+.project
+.classpath
+.settings/
+
+# Logs
+logs/
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Operating System
+.DS_Store
+Thumbs.db
+*.swp
+*.swo
+
+# Testing
+coverage/
+.nyc_output/
+
+# Temporary files
+*.tmp
+*.temp
+.cache/
+
+# Compiled files
+*.com
+*.class
+*.dll
+*.exe
+*.o
+*.so
+
+# Package files
+*.7z
+*.dmg
+*.gz
+*.iso
+*.jar
+*.rar
+*.tar
+*.zip
+
+# Database
+*.sqlite
+*.sqlite3
+*.db
+
+# Optional npm cache directory
+.npm
+
+# Optional eslint cache
+.eslintcache
+
+# Optional REPL history
+.node_repl_history 

README.md

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+<h1 align="center">Agentic Coding - Project Template</h1>
+
+<p align="center">
+  <a href="https://github.com/The-Pocket/PocketFlow" target="_blank">
+    <img 
+      src="./assets/banner.png" width="600"
+    />
+  </a>
+</p>
+
+    
+This is a project template for Agentic Coding with [Pocket Flow](https://github.com/The-Pocket/PocketFlow), a 100-line LLM framework, and Cursor.
+
+- We have included the [.cursorrules](.cursorrules) file to let Cursor AI help you build LLM projects.
+  
+- Want to learn how to build LLM projects with Agentic Coding?
+  
+  - Check out the [Agentic Coding Guidance](https://the-pocket.github.io/PocketFlow/guide.html)
+    
+  - Check out the [YouTube Tutorial](https://www.youtube.com/@ZacharyLLM?sub_confirmation=1)

docs/design.md

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+# Design Doc: Your Project Name
+
+> Please DON'T remove notes for AI
+
+## Requirements
+
+> Notes for AI: Keep it simple and clear.
+> If the requirements are abstract, write concrete user stories
+
+
+## Flow Design
+
+> Notes for AI:
+> 1. Consider the design patterns of agent, map-reduce, rag, and workflow. Apply them if they fit.
+> 2. Present a concise, high-level description of the workflow.
+
+### Applicable Design Pattern:
+
+1. Map the file summary into chunks, then reduce these chunks into a final summary.
+2. Agentic file finder
+   - *Context*: The entire summary of the file
+   - *Action*: Find the file
+
+### Flow high-level Design:
+
+1. **First Node**: This node is for ...
+2. **Second Node**: This node is for ...
+3. **Third Node**: This node is for ...
+
+```mermaid
+flowchart TD
+    firstNode[First Node] --> secondNode[Second Node]
+    secondNode --> thirdNode[Third Node]
+```
+## Utility Functions
+
+> Notes for AI:
+> 1. Understand the utility function definition thoroughly by reviewing the doc.
+> 2. Include only the necessary utility functions, based on nodes in the flow.
+
+1. **Call LLM** (`utils/call_llm.py`)
+   - *Input*: prompt (str)
+   - *Output*: response (str)
+   - Generally used by most nodes for LLM tasks
+
+2. **Embedding** (`utils/get_embedding.py`)
+   - *Input*: str
+   - *Output*: a vector of 3072 floats
+   - Used by the second node to embed text
+
+## Node Design
+
+### Shared Memory
+
+> Notes for AI: Try to minimize data redundancy
+
+The shared memory structure is organized as follows:
+
+```python
+shared = {
+    "key": "value"
+}
+```
+
+### Node Steps
+
+> Notes for AI: Carefully decide whether to use Batch/Async Node/Flow.
+
+1. First Node
+  - *Purpose*: Provide a short explanation of the node’s function
+  - *Type*: Decide between Regular, Batch, or Async
+  - *Steps*:
+    - *prep*: Read "key" from the shared store
+    - *exec*: Call the utility function
+    - *post*: Write "key" to the shared store
+
+2. Second Node
+  ...
+

flow.py

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+from pocketflow import Flow
+from nodes import GetQuestionNode, AnswerNode
+
+def create_qa_flow():
+    """Create and return a question-answering flow."""
+    # Create nodes
+    get_question_node = GetQuestionNode()
+    answer_node = AnswerNode()
+    
+    # Connect nodes in sequence
+    get_question_node >> answer_node
+    
+    # Create flow starting with input node
+    return Flow(start=get_question_node)

main.py

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+from flow import qa_flow
+
+# Example main function
+# Please replace this with your own main function
+def main():
+    shared = {
+        "question": "In one sentence, what's the end of universe?",
+        "answer": None
+    }
+
+    qa_flow.run(shared)
+    print("Question:", shared["question"])
+    print("Answer:", shared["answer"])
+
+if __name__ == "__main__":
+    main()

nodes.py

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+from pocketflow import Node
+from utils.call_llm import call_llm
+
+class GetQuestionNode(Node):
+    def exec(self, _):
+        # Get question directly from user input
+        user_question = input("Enter your question: ")
+        return user_question
+    
+    def post(self, shared, prep_res, exec_res):
+        # Store the user's question
+        shared["question"] = exec_res
+        return "default"  # Go to the next node
+
+class AnswerNode(Node):
+    def prep(self, shared):
+        # Read question from shared
+        return shared["question"]
+    
+    def exec(self, question):
+        # Call LLM to get the answer
+        return call_llm(question)
+    
+    def post(self, shared, prep_res, exec_res):
+        # Store the answer in shared
+        shared["answer"] = exec_res

requirements.txt

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+pocketflow>=0.0.1

utils/call_llm.py

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+from openai import OpenAI
+
+# Learn more about calling the LLM: https://the-pocket.github.io/PocketFlow/utility_function/llm.html
+def call_llm(prompt):    
+    client = OpenAI(api_key="YOUR_API_KEY_HERE")
+    r = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return r.choices[0].message.content
+    
+if __name__ == "__main__":
+    prompt = "What is the meaning of life?"
+    print(call_llm(prompt))