132 lines
4.0 KiB
Markdown
132 lines
4.0 KiB
Markdown
---
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layout: default
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title: "Design Guidance"
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parent: "Apps"
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nav_order: 1
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---
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# LLM System Design Guidance
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## Example LLM Project File Structure
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```
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my_project/
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├── main.py
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├── flow.py
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├── utils/
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│ ├── __init__.py
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│ ├── call_llm.py
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│ └── search_web.py
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├── tests/
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│ ├── __init__.py
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│ ├── test_flow.py
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│ └── test_nodes.py
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├── requirements.txt
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└── docs/
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└── design.md
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```
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### `docs/`
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Store the documentation of the project.
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It should include a `design.md` file, which describes
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- Project requirements
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- Required utility functions
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- High-level flow with a mermaid diagram
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- Shared memory data structure
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- For each node, discuss
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- Node purpose and design (e.g., should it be a batch or async node?)
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- How the data shall be read (for `prep`) and written (for `post`)
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- How the data shall be processed (for `exec`)
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### `utils/`
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Houses functions for external API calls (e.g., LLMs, web searches, etc.).
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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:
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- The function to call the API
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- A main function to run that API call
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For instance, here’s a simplified `call_llm.py` example:
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```python
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from openai import OpenAI
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def call_llm(prompt):
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client = OpenAI(api_key="YOUR_API_KEY_HERE")
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response = client.chat.completions.create(
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model="gpt-4o",
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messages=[{"role": "user", "content": prompt}]
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)
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return response.choices[0].message.content
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def main():
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prompt = "Hello, how are you?"
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print(call_llm(prompt))
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if __name__ == "__main__":
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main()
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```
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### `main.py`
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Serves as the project’s entry point.
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### `flow.py`
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Implements the application’s flow, starting with node followed by the flow structure.
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### `tests/`
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Optionally contains all tests. Use `pytest` for testing flows, nodes, and utility functions.
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For example, `test_call_llm.py` might look like:
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```python
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from utils.call_llm import call_llm
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def test_call_llm():
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prompt = "Hello, how are you?"
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assert call_llm(prompt) is not None
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```
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## System Design Steps
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1. **Project Requirements**
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- Identify the project's core entities.
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- Define each functional requirement and map out how these entities interact step by step.
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2. **Utility Functions**
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- Determine the low-level utility functions you’ll need (e.g., for LLM calls, web searches, file handling).
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- Implement these functions and write basic tests to confirm they work correctly.
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3. **Flow Design**
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- Develop a high-level process flow that meets the project’s requirements.
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- Specify which utility functions are used at each step.
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- Identify possible decision points for *Node Actions* and data-intensive operations for *Batch* tasks.
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- Illustrate the flow with a Mermaid diagram.
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4. **Data Structure**
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- Decide how to store and update state, whether in memory (for smaller applications) or a database (for larger or persistent needs).
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- Define data schemas or models that detail how information is stored, accessed, and updated.
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5. **Implementation**
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- Start coding with a simple, direct approach (avoid over-engineering at first).
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- For each node in your flow:
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- **prep**: Determine how data is accessed or retrieved.
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- **exec**: Outline the actual processing or logic needed.
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- **post**: Handle any final updates or data persistence tasks.
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6. **Optimization**
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- **Prompt Engineering**: Use clear and specific instructions with illustrative examples to reduce ambiguity.
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- **Task Decomposition**: Break large, complex tasks into manageable, logical steps.
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7. **Reliability**
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- **Structured Output**: Verify outputs conform to the required format. Consider increasing `max_retries` if needed.
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- **Test Cases**: Develop clear, reproducible tests for each part of the flow.
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- **Self-Evaluation**: Introduce an additional Node (powered by LLMs) to review outputs when the results are uncertain.
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