From fad4ed171764af8a1ee850b0d108c189f55c3ea3 Mon Sep 17 00:00:00 2001
From: zachary62 <zhuang333@wisc.edu>
Date: Sun, 3 Aug 2025 16:53:38 -0700
Subject: [PATCH] update rule file

---
 .cursorrules                 | 128 +++++++++++++++++++++++++++++++----
 docs/guide.md                | 103 ++++++++++++++++++++++++++++
 docs/utility_function/llm.md |   5 +-
 3 files changed, 221 insertions(+), 15 deletions(-)

diff --git a/.cursorrules b/.cursorrules
index c042f3c..4d7e2f4 100644
--- a/.cursorrules
+++ b/.cursorrules
@@ -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)
diff --git a/docs/guide.md b/docs/guide.md
index 1b45bdc..0a9eda8 100644
--- a/docs/guide.md
+++ b/docs/guide.md
@@ -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
diff --git a/docs/utility_function/llm.md b/docs/utility_function/llm.md
index a849f79..60a6a9d 100644
--- a/docs/utility_function/llm.md
+++ b/docs/utility_function/llm.md
@@ -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