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update rule file

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zachary62 2025-08-03 16:53:38 -07:00
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128
.cursorrules
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@ -157,9 +157,112 @@ my_project/
```
- **`docs/design.md`**: Contains project documentation for each step above. This should be *high-level* and *no-code*.
~~~
# 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 Store
> Notes for AI: Try to minimize data redundancy
The shared store 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
...
~~~
- **`utils/`**: Contains all utility functions.
- It's recommended to dedicate one Python file to each API call, for example `call_llm.py` or `search_web.py`.
- Each file should also include a `main()` function to try that API call
```python
from google import genai
import os
def call_llm(prompt: str) -> str:
client = genai.Client(
api_key=os.getenv("GEMINI_API_KEY", ""),
)
model = os.getenv("GEMINI_MODEL", "gemini-2.5-flash")
response = client.models.generate_content(model=model, contents=[prompt])
return response.text
if __name__ == "__main__":
test_prompt = "Hello, how are you?"
# First call - should hit the API
print("Making call...")
response1 = call_llm(test_prompt, use_cache=False)
print(f"Response: {response1}")
```
- **`nodes.py`**: Contains all the node definitions.
```python
# nodes.py
@ -1559,24 +1662,25 @@ Here, we provide some minimal example implementations:
def call_llm(prompt):
from anthropic import Anthropic
client = Anthropic(api_key="YOUR_API_KEY_HERE")
response = client.messages.create(
model="claude-2",
messages=[{"role": "user", "content": prompt}],
max_tokens=100
r = client.messages.create(
model="claude-sonnet-4-0",
messages=[
{"role": "user", "content": prompt}
]
)
return response.content
return r.content[0].text
```
3. Google (Generative AI Studio / PaLM API)
```python
def call_llm(prompt):
import google.generativeai as genai
genai.configure(api_key="YOUR_API_KEY_HERE")
response = genai.generate_text(
model="models/text-bison-001",
prompt=prompt
)
return response.result
from google import genai
client = genai.Client(api_key='GEMINI_API_KEY')
response = client.models.generate_content(
model='gemini-2.5-pro',
contents=prompt
)
return response.text
```
4. Azure (Azure OpenAI)

103
docs/guide.md
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@ -157,9 +157,112 @@ my_project/
```
- **`docs/design.md`**: Contains project documentation for each step above. This should be *high-level* and *no-code*.
~~~
# 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 Store
> Notes for AI: Try to minimize data redundancy
The shared store 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
...
~~~
- **`utils/`**: Contains all utility functions.
- It's recommended to dedicate one Python file to each API call, for example `call_llm.py` or `search_web.py`.
- Each file should also include a `main()` function to try that API call
```python
from google import genai
import os
def call_llm(prompt: str) -> str:
client = genai.Client(
api_key=os.getenv("GEMINI_API_KEY", ""),
)
model = os.getenv("GEMINI_MODEL", "gemini-2.5-flash")
response = client.models.generate_content(model=model, contents=[prompt])
return response.text
if __name__ == "__main__":
test_prompt = "Hello, how are you?"
# First call - should hit the API
print("Making call...")
response1 = call_llm(test_prompt, use_cache=False)
print(f"Response: {response1}")
```
- **`nodes.py`**: Contains all the node definitions.
```python
# nodes.py

5
docs/utility_function/llm.md
View File

@ -33,8 +33,7 @@ Here, we provide some minimal example implementations:
from anthropic import Anthropic
client = Anthropic(api_key="YOUR_API_KEY_HERE")
r = client.messages.create(
model="claude-3-7-sonnet-20250219",
max_tokens=3000,
model="claude-sonnet-4-0",
messages=[
{"role": "user", "content": prompt}
]
@ -48,7 +47,7 @@ Here, we provide some minimal example implementations:
from google import genai
client = genai.Client(api_key='GEMINI_API_KEY')
response = client.models.generate_content(
model='gemini-2.0-flash-001',
model='gemini-2.5-pro',
contents=prompt
)
return response.text