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Author: ppippi-dev

_posts/2025-04-14-ai-agent.md

@@ -3,7 +3,7 @@ layout: post
 title: "AI Agent 구현해보기"
 subtitle: "ChatGPT를 이용한 간단한 주식 agent 봇 만들기"
 feature-img: "assets/img/2025-04-14/0.webp"
-tags: [머신러닝]
+tags: [LLMOps]
 ---
 
 # AI Agent 구현해보기
@@ -213,7 +213,7 @@ def answer_paraphrase(answer_data: dict, user_input: str) -> str | None:
 
 이렇게 구축된 코드를 실행하면 아래와 같은 간단한 AI Agen가 생성한다.
 
-실행 결과물 예시
+실행 결과물 예시:
 
 ```txt
 유저의 질문: 기아 자동차 주가가 얼마지?

_posts/2025-05-03-gke-iam-role.md

@@ -3,7 +3,7 @@ layout: post
 title: "GKE에서 AWS IAM Role 연동: Access Key 없이 안전하게 AWS 리소스 접근하기"
 subtitle: "GKE Workload Identity와 AWS IAM Role을 활용한 안전한 멀티클라우드 접근 방식"
 feature-img: "assets/img/2025-05-03/0.webp"
-tags: [머신러닝]
+tags: [Infra]
 ---
 
 ## 들어가며: 멀티클라우드 환경에서의 보안

_posts/2025-05-07-llm-function-calling.md

@@ -1,9 +1,9 @@
 ---
 layout: post
-title: "[LLM] Function Calling 구현하기"
-subtitle: ""
-feature-img: "assets/img/2025-05-03/0.webp"
-tags: [머신러닝]
+title: "AI 에이전트의 핵심, LLM Function Calling 파헤치기"
+subtitle: "Python과 OpenAI API를 활용해 LLM이 외부 함수를 호출하고 실제 작업을 수행하는 방법을 알아봅니다."
+feature-img: "assets/img/2025-05-07/0.webp"
+tags: [LLMOps]
 ---
 
 최근 대규모 언어 모델(LLM) 분야에서 **Function Calling**이라는 흥미로운 개념이 등장했습니다.
@@ -43,7 +43,7 @@ ReAct 에이전트와 Function Calling 에이전트의 차이점에 대해 더 
 
 먼저 yahoo finance 라이브러리를 통해 가격을 조회하는 코드는 다음과 같습니다. (실제 실행을 위해서는 yfinance 라이브러리가 설치되어 있어야 합니다: pip install yfinance)
 
-```
+```python
 def get_stock_price(stock_code: str) -> dict:
     """주식 가격 정보를 가져오기 위한 함수"""
     try:
@@ -85,7 +85,7 @@ def get_stock_price(stock_code: str) -> dict:
 
 이제 이런 함수가 있다는 것을 LLM에게 알려주기 위한 형태로 만들어야 합니다.
 
-```
+```python
     tools = [
         {
             "type": "function",
@@ -109,7 +109,7 @@ def get_stock_price(stock_code: str) -> dict:
 
 이제 이 tools 정보를 포함하여 GPT를 호출합니다. (실제 API 호출을 위해서는 openai 라이브러리가 필요하며, API 키 설정이 필요합니다.)
 
-```
+```python
     messages: list[ChatCompletionMessageParam] = [
         {
             "role": "user",
@@ -130,7 +130,7 @@ def get_stock_price(stock_code: str) -> dict:
 
 위 코드를 실행하면 (실제 API 호출 시), LLM은 사용자의 질문을 이해하고 get\_stock\_price 함수를 호출해야 한다고 판단할 경우 다음과 유사한 응답을 반환합니다.
 
-```
+```shell
 ChatCompletion(id='chatcmpl-BUYpFct1sxYpeNUb7IBeegqOb6XJS', 
 choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, 
 message=ChatCompletionMessage(content=None, refusal=None, role='assistant', 
@@ -149,7 +149,7 @@ prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))
 
 이제 개발자는 이 응답을 바탕으로 실제로 위에서 정의한 get\_stock\_price 함수를 실행합니다.
 
-```
+```python
     if response.choices and response.choices[0].message.tool_calls:
         tool_call = response.choices[0].message.tool_calls[0]
         if tool_call.function.name == "get_stock_price":
@@ -170,7 +170,7 @@ prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))
 
 마지막으로, 이 answer\_data (함수 실행 결과)를 다시 LLM에게 전달하여 사용자에게 친절한 형태로 답변을 생성하도록 요청합니다.
 
-```
+```python
 messages = [
         {
             "role": "system",

_posts/2025-05-16-nvidia-triton-inference-server.md

@@ -0,0 +1,229 @@
+---
+layout: post
+title: "Implementing an AI Agent"
+subtitle: "Creating a simple stock agent bot using ChatGPT"
+feature-img: "assets/img/2025-04-14/0.webp"
+tags: [LLMOps]
+---
+
+# Implementing an AI Agent
+
+Since ChatGPT was released to the world on November 30, 2022, we have been experiencing a wave of rapid change.
+
+Among these changes, the concept of the AI Agent is particularly noteworthy. Although a bit late, to keep up with the times, I want to take a closer look at this concept and try to implement a simple AI Agent myself.
+
+## AI Agent??
+
+When I first encountered the term AI Agent, I thought it was just another name for the existing AI Assistant.
+
+However, there is a significant difference between the two, which I want to clarify first.
+
+While searching for related information, I found useful information in the [GCP docs](https://cloud.google.com/discover/what-are-ai-agents). The link is below.
+
+This document defines an AI Agent as "a software system that uses AI to pursue goals and complete tasks on behalf of a user."
+
+On the other hand, the AI Assistant, which I was confused about, is described as "an AI agent designed to collaborate directly with users, understand and respond to natural language and input, and perform tasks."
+
+For example, an AI Assistant, like Apple's Siri or Amazon's Alexa, answers user questions and performs tasks by interacting with the user.
+
+In contrast, an AI Agent has a higher level of autonomy and makes independent decisions to achieve specific goals, much like an autonomous driving system.
+
+Of course, it is difficult to draw a clear line between an AI Assistant and an AI Agent.
+
+<img src="/assets/img/2025-04-14/1.png">
+
+The speaker uses Cursor as an IDE. (I've become someone who can't develop without Cursor...)
+
+Cursor has already introduced an AI Agent.
+
+<img src="/assets/img/2025-04-14/2.png">
+
+As shown in the picture above, when you give the Agent a goal, it performs the goal in various ways, such as checking my folder structure, using the knowledge that the LLM has, and searching the web if necessary.
+
+If an error occurs during a task, it finds the cause on its own and solves the problem in a different way.
+
+## AI Agent Practice: Implementing it myself
+
+The easiest way to understand is to practice it myself, so I'm going to implement a simple AI Agent.
+
+The AI Agent to be implemented is a simple bot that checks stock prices.
+
+The environment used is as follows.
+
+### Environment Used
+
+-   python 3.12
+-   openai - gpt4o-mini
+-   yfinance
+
+The complete code can be found in the [GitHub repository](https://github.com/ppippi-dev/ai-agent-test) below.
+
+### Simple Flow Introduction
+
+The bot to be implemented operates in the following order.
+
+1.  The user enters a question.
+2.  GPT extracts the stock name from the question and returns the corresponding stock code.
+3.  The yfinance library retrieves the stock information for the stock code.
+4.  GPT organizes the data and prints it in a user-friendly way.
+
+Now let's implement the above flow with code.
+
+First, I will implement the necessary functions for each step and call them from the main function.
+
+The functions that make up the Agent are as follows.
+
+-   `act()`: A function that controls the main flow
+-   `get_stock_code_from_gpt()`: A function that extracts the stock code
+-   `get_stock_price()`: A function that retrieves stock price information
+-   `answer_paraphrase()`: A function that generates the final answer
+
+Let's look at the implementation of each function.
+
+First, let's look at the `act()` function for the flow.
+
+```python
+def act(user_input):
+    """Determine action based on user input"""
+
+    stock_code = get_stock_code_from_gpt(user_input)
+    add_ks_mark = f"{stock_code.stock_code}.KS"
+    stock_info = get_stock_price(add_ks_mark)
+    answer = answer_paraphrase(stock_info, user_input)
+    return answer
+```
+
+This function manages the entire flow of receiving user input, querying stock information, and generating a response.
+
+It's easier to think of it as a simple LLM pipeline.
+
+Next is the main function that processes the user's question.
+
+```python
+def main():
+    """main function"""
+    agent = StockAgent()
+
+    while True:
+        user_input = input("User's question: ")
+        if user_input == "exit":
+            break
+        response = agent.act(user_input)
+        print(response)
+```
+
+This function processes the user's question through a simple while loop.
+
+Now, this is the part where we actually implement the AI Agent.
+
+It extracts the stock included in the user's question through GPT and returns the code for that stock.
+
+```python
+class StockCode(BaseModel):
+    """Model to return stock code"""
+
+    stock_code: str
+
+def get_stock_code_from_gpt(user_input: str) -> StockCode | None:
+    """Function to return stock code"""
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a program that finds the stock code of the stock item included in the user's question. Find the stock code of the stock item included in the user's question and return the stock code.",
+        },
+        {
+            "role": "user",
+            "content": f"Please tell me the stock code through the question below. The stock code consists of 6 digits, like '000000'. \n User's question: {user_input}",
+        },
+    ]
+    response = client.beta.chat.completions.parse(
+        model="gpt-4o-mini",
+        messages=messages,
+        max_tokens=150,
+        temperature=0.7,
+        response_format=StockCode,
+    )
+    return response.choices[0].message.parsed
+```
+
+I configured the prompt to extract the stock code if there is a stock item in the user's question.
+
+Then, it extracts stock information data using the extracted stock code and the yfinance library.
+
+```python
+def get_stock_price(ticker_symbol: str) -> dict:
+    """Function to get stock price information"""
+    try:
+        # Get stock information using the yfinance library
+        ticker = yf.Ticker(ticker_symbol)
+        info = ticker.info
+
+        # Extract necessary information
+        current_price = info.get("currentPrice", "No information")
+        previous_close = info.get("previousClose", "No information")
+        company_name = info.get("longName", ticker_symbol)
+
+        # Calculate price change
+        if current_price != "No information" and previous_close != "No information":
+            change = current_price - previous_close
+            change_percent = (change / previous_close) * 100
+            change_str = f"{change:.2f} ({change_percent:.2f}%)"
+        else:
+            change_str = "No information"
+
+        return {
+            "success": True,
+            "company_name": company_name,
+            "ticker": ticker_symbol,
+            "current_price": current_price,
+            "previous_close": previous_close,
+            "change": change_str,
+        }
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+```
+
+yfinance is a library that retrieves data for stock codes through Yahoo Finance, but it doesn't seem to have all domestic stock items.
+
+Finally, it generates an answer through GPT with the obtained data.
+
+```python
+def answer_paraphrase(answer_data: dict, user_input: str) -> str | None:
+    """Paraphrasing function for answering with stock information data"""
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a program that creates answers to user questions. Please create an answer to the user's question.",
+        },
+        {
+            "role": "user",
+            "content": f"User's question: {user_input}, Data: {answer_data}",
+        },
+    ]
+    response = client.beta.chat.completions.parse(
+        model="gpt-4o-mini",
+        messages=messages,
+        temperature=0.7,
+    )
+    return response.choices[0].message.content
+```
+
+Running the code built this way creates a simple AI Agent as shown below.
+
+Example of execution result:
+
+```txt
+User's question: What is the stock price of Kia Motors?
+The current stock price of Kia Motors is 84,300 won. The previous closing price was 84,600 won, and the current price has fallen by 300 won (-0.35%).
+User's question: How much is Samsung Electronics?
+The current stock price of Samsung Electronics is 53,500 won. The previous closing price was 53,200 won, and today it has risen by 300 won (0.56%).
+```
+
+## Afterword
+
+I have implemented a simple AI Agent as above. In fact, most LLMs already perform agent functions through web searches with internal logic without the need for the above method.
+
+In particular, looking at the process by which inference models (Chain of Thought) obtain answers, it is presumed that the following process is included.
+
+For reference, OpenAI has already created an [API to easily build agents](https://openai.com/index/new-tools-for-building-agents/).
+