update the llm setup

README.md

@@ -70,7 +70,12 @@ This is a tutorial project of [Pocket Flow](https://github.com/The-Pocket/Pocket
    pip install -r requirements.txt
    ```
 
-3. Generate a complete codebase tutorial by running the main script:
+3. Set up LLM in [`utils/call_llm.py`](./utils/call_llm.py) by providing credentials (API key or project name). We highly recommend the latest models with thinking capabilities (Gemini Pro 2.5, Claude 3.7 with thinking, O1). You can verify if it is correctly set up by running:
+   ```bash
+   python utils/call_llm.py
+   ```
+
+4. Generate a complete codebase tutorial by running the main script:
     ```bash
     python main.py https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*" --max-size 50000
     ```

utils/call_llm.py

@@ -20,6 +20,7 @@ logger.addHandler(file_handler)
 # Simple cache configuration
 cache_file = "llm_cache.json"
 
+# By default, we Google Gemini 2.5 pro, as it shows great performance for code understanding
 def call_llm(prompt: str, use_cache: bool = True) -> str:
     # Log the prompt
     logger.info(f"PROMPT: {prompt}")
@@ -43,6 +44,7 @@ def call_llm(prompt: str, use_cache: bool = True) -> str:
     # Call the LLM if not in cache or cache disabled
     client = genai.Client(
         vertexai=True, 
+        # TODO: change to your own project id and location
         project=os.getenv("GEMINI_PROJECT_ID", "your-project-id"),
         location=os.getenv("GEMINI_LOCATION", "us-central1")
     )
@@ -77,6 +79,38 @@ def call_llm(prompt: str, use_cache: bool = True) -> str:
     
     return response_text
 
+# # Use Anthropic Claude 3.7 Sonnet Extended Thinking
+# def call_llm(prompt, use_cache: bool = True):
+#     from anthropic import Anthropic
+#     client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY", "your-api-key"))
+#     response = client.messages.create(
+#         model="claude-3-7-sonnet-20250219",
+#         max_tokens=21000,
+#         thinking={
+#             "type": "enabled",
+#             "budget_tokens": 20000
+#         },
+#         messages=[
+#             {"role": "user", "content": prompt}
+#         ]
+#     )
+#     return response.content[1].text
+
+# # Use OpenAI o1
+# def call_llm(prompt, use_cache: bool = True):    
+#     from openai import OpenAI
+#     client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "your-api-key"))
+#     r = client.chat.completions.create(
+#         model="o1",
+#         messages=[{"role": "user", "content": prompt}],
+#         response_format={
+#             "type": "text"
+#         },
+#         reasoning_effort="medium",
+#         store=False
+#     )
+#     return r.choices[0].message.content
+
 if __name__ == "__main__":
     test_prompt = "Hello, how are you?"