pocketflow/docs/guide.md

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LLM System Design Guidance

Example LLM Project File Structure

my_project/
├── main.py
├── flow.py
├── utils/
│   ├── __init__.py
│   ├── call_llm.py
│   └── search_web.py
├── tests/
│   ├── __init__.py
│   ├── test_flow.py
│   └── test_nodes.py
├── requirements.txt
└── docs/
    └── design.md

docs/

Store the documentation of the project.

It should include a design.md file, which describes

• Project requirements
• Required utility functions
• High-level flow with a mermaid diagram
• Shared memory data structure
• For each node, discuss
  • Node purpose and design (e.g., should it be a batch or async node?)
  • How the data shall be read (for prep) and written (for post)
  • How the data shall be processed (for exec)

utils/

Houses functions for external API calls (e.g., LLMs, web searches, etc.).

It’s recommended to dedicate one Python file per API call, with names like call_llm.py or search_web.py. Each file should include:

• The function to call the API
• A main function to run that API call

For instance, here’s a simplified call_llm.py example:

from openai import OpenAI

def call_llm(prompt):
    client = OpenAI(api_key="YOUR_API_KEY_HERE")
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

def main():
    prompt = "Hello, how are you?"
    print(call_llm(prompt))

if __name__ == "__main__":
    main()

main.py

Serves as the project’s entry point.

flow.py

Implements the application’s flow, starting with node followed by the flow structure.

tests/

Optionally contains all tests. Use pytest for testing flows, nodes, and utility functions. For example, test_call_llm.py might look like:

from utils.call_llm import call_llm

def test_call_llm():
    prompt = "Hello, how are you?"
    assert call_llm(prompt) is not None

System Design Steps:

1. Understand Requirements

   • Clarify application objectives.
   • Determine and implement the necessary utility functions (e.g., for LLMs, web searches, file handling).

2. High-Level Flow Design

   • Design the flow on how to use the utility functions to achieve the objectives.
   • Identify possible branching for Node Action and data-heavy steps for Batch.

3. Shared Memory Structure

   • For small apps, in-memory data is sufficient.
   • For larger or persistent needs, use a database.
   • Define data schemas or structures and how states are stored or updated.

4. Implementation

   • Start with minimal, straightforward code (e.g., avoid heavy type checking initially).
   • For each node, specify data access (for prep and post) and data processing (for exec).

5. Optimization

   • Prompt Engineering: Provide clear instructions and examples to reduce ambiguity.
   • Task Decomposition: Break complex tasks into manageable steps.

6. Reliability

   • Structured Output: Verify outputs match the desired format, and increase max_retries.
   • Test Cases: Create tests for parts of the flow with clear inputs/outputs.
   • Self-Evaluation: For unclear areas, add another Node for LLMs to review the results.