Split View: LLM 프롬프트 엔지니어링 고급 기법: Chain-of-Thought·ReAct·Tree of Thoughts 실전 적용

from openai import OpenAI

client = OpenAI()

def few_shot_sentiment_analysis(text: str) -> str:
    """Sentiment analysis using few-shot prompting"""
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": "Analyze the sentiment of the given text and classify it as positive, negative, or neutral."
            },
            # Few-shot example 1
            {"role": "user", "content": "This product is amazing! Fast shipping and great quality."},
            {"role": "assistant", "content": "Sentiment: Positive\nReason: Contains positive expressions like 'amazing', 'fast', 'great quality'"},
            # Few-shot example 2
            {"role": "user", "content": "The delivery was extremely late and the product had scratches."},
            {"role": "assistant", "content": "Sentiment: Negative\nReason: Describes negative experiences with 'extremely late', 'had scratches'"},
            # Few-shot example 3
            {"role": "user", "content": "It's an average product. Fair for the price."},
            {"role": "assistant", "content": "Sentiment: Neutral\nReason: Balanced evaluation with 'average', 'fair'"},
            # Actual analysis target
            {"role": "user", "content": text}
        ],
        temperature=0.0
    )
    return response.choices[0].message.content

def chain_of_thought_math(problem: str) -> str:
    """Math problem solving using Chain-of-Thought"""
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are a math tutor who solves problems step by step. "
                    "Explain each step clearly and present the final answer."
                )
            },
            # CoT example with reasoning process
            {
                "role": "user",
                "content": "A cafe has 23 customers. 12 more arrived in the morning, and 8 left in the afternoon. How many customers are in the cafe now?"
            },
            {
                "role": "assistant",
                "content": (
                    "Let me solve this step by step.\n\n"
                    "Step 1: Identify the initial count\n"
                    "- Customers initially in the cafe: 23\n\n"
                    "Step 2: Calculate morning change\n"
                    "- 12 more arrived: 23 + 12 = 35\n\n"
                    "Step 3: Calculate afternoon change\n"
                    "- 8 customers left: 35 - 8 = 27\n\n"
                    "Final Answer: There are 27 customers in the cafe now."
                )
            },
            {"role": "user", "content": problem}
        ],
        temperature=0.0
    )
    return response.choices[0].message.content

import json
import requests
from openai import OpenAI

client = OpenAI()

# Tool definitions
def search_wikipedia(query: str) -> str:
    """Wikipedia search tool"""
    url = "https://en.wikipedia.org/w/api.php"
    params = {
        "action": "query",
        "list": "search",
        "srsearch": query,
        "format": "json",
        "srlimit": 3
    }
    resp = requests.get(url, params=params)
    results = resp.json().get("query", {}).get("search", [])
    if not results:
        return "No search results found."
    return "\n".join([r["title"] + ": " + r["snippet"] for r in results])


def calculator(expression: str) -> str:
    """Safe arithmetic calculator"""
    allowed_chars = set("0123456789+-*/.(). ")
    if all(c in allowed_chars for c in expression):
        try:
            result = eval(expression)
            return str(result)
        except Exception as e:
            return f"Calculation error: {e}"
    return "Contains disallowed characters."


TOOLS = {
    "search_wikipedia": search_wikipedia,
    "calculator": calculator,
}


def react_agent(question: str, max_steps: int = 5) -> str:
    """Agent implementing the ReAct pattern"""
    system_prompt = """You are a ReAct agent. Respond in the following format:

Thought: Analyze the current situation and reason about what to do next.
Action: Provide the tool and input in JSON format.
Observation: Review the tool execution result.
... (repeat as needed)
Final Answer: Present the final answer.

Available tools:
- search_wikipedia: Search Wikipedia (input: search query)
- calculator: Arithmetic calculation (input: math expression)"""

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": question}
    ]

    for step in range(max_steps):
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            temperature=0.0
        )
        assistant_msg = response.choices[0].message.content

        # Exit if Final Answer is present
        if "Final Answer:" in assistant_msg:
            return assistant_msg.split("Final Answer:")[-1].strip()

        # Parse and execute Action
        if "Action:" in assistant_msg:
            action_line = assistant_msg.split("Action:")[-1].strip()
            try:
                action_data = json.loads(action_line.split("\n")[0])
                tool_name = action_data.get("tool", "")
                tool_input = action_data.get("input", "")

                if tool_name in TOOLS:
                    observation = TOOLS[tool_name](tool_input)
                else:
                    observation = f"Unknown tool: {tool_name}"
            except json.JSONDecodeError:
                observation = "Failed to parse Action"

            messages.append({"role": "assistant", "content": assistant_msg})
            messages.append({
                "role": "user",
                "content": f"Observation: {observation}"
            })

    return "Reached maximum steps without generating an answer."

from collections import Counter
from openai import OpenAI

client = OpenAI()


def self_consistency_solve(
    question: str,
    num_samples: int = 5,
    temperature: float = 0.7
) -> dict:
    """Improved reasoning accuracy through Self-Consistency sampling"""
    system_prompt = (
        "Solve the math problem step by step. "
        "On the last line, write the answer in the format 'Answer: [number]'."
    )
    answers = []
    reasoning_paths = []

    for i in range(num_samples):
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": question}
            ],
            temperature=temperature  # Generate diverse reasoning paths
        )
        content = response.choices[0].message.content
        reasoning_paths.append(content)

        # Extract answer
        for line in content.split("\n"):
            if line.strip().startswith("Answer:"):
                answer = line.split("Answer:")[-1].strip()
                answers.append(answer)
                break

    # Majority voting
    if not answers:
        return {"final_answer": "Failed to extract answer", "confidence": 0.0}

    counter = Counter(answers)
    most_common = counter.most_common(1)[0]
    confidence = most_common[1] / len(answers)

    return {
        "final_answer": most_common[0],
        "confidence": confidence,
        "vote_distribution": dict(counter),
        "num_samples": num_samples,
        "reasoning_paths": reasoning_paths
    }


# Usage example
result = self_consistency_solve(
    "A school has 450 students. 60% are boys, and 25% of boys wear glasses. "
    "How many boys wear glasses?",
    num_samples=7
)
print(f"Final answer: {result['final_answer']}")
print(f"Confidence: {result['confidence']:.1%}")
print(f"Vote distribution: {result['vote_distribution']}")

from openai import OpenAI
from typing import List

client = OpenAI()


def generate_thoughts(problem: str, current_state: str, n: int = 3) -> List[str]:
    """Generate possible next thoughts from the current state"""
    prompt = f"""Problem: {problem}
Reasoning so far: {current_state}

Propose {n} possible next reasoning steps from this state.
Each step should represent a different approach.
Format:
1. [First approach]
2. [Second approach]
3. [Third approach]"""

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.8
    )
    content = response.choices[0].message.content
    thoughts = []
    for line in content.split("\n"):
        line = line.strip()
        if line and line[0].isdigit() and "." in line:
            thoughts.append(line.split(".", 1)[1].strip())
    return thoughts[:n]


def evaluate_thought(problem: str, thought_path: str) -> float:
    """Evaluate the promise of a reasoning path on a 0-1 scale"""
    prompt = f"""Problem: {problem}
Reasoning path: {thought_path}

Evaluate how likely this reasoning path is to reach the correct answer.
Provide a score between 0.0 (not promising at all) and 1.0 (very promising).
Respond with only the numeric score."""

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.1
    )
    try:
        score = float(response.choices[0].message.content.strip())
        return min(max(score, 0.0), 1.0)
    except ValueError:
        return 0.5


def tree_of_thoughts_bfs(
    problem: str,
    max_depth: int = 3,
    beam_width: int = 2
) -> str:
    """BFS-based Tree of Thoughts implementation"""
    # Initial state
    current_states = [("", 1.0)]  # (reasoning path, score)

    for depth in range(max_depth):
        all_candidates = []

        for state, _ in current_states:
            # Generate candidate thoughts from each state
            thoughts = generate_thoughts(problem, state)

            for thought in thoughts:
                new_path = f"{state}\nStep {depth+1}: {thought}" if state else f"Step 1: {thought}"
                score = evaluate_thought(problem, new_path)
                all_candidates.append((new_path, score))

        # Select top candidates by beam_width
        all_candidates.sort(key=lambda x: x[1], reverse=True)
        current_states = all_candidates[:beam_width]

        print(f"Depth {depth+1}: Best score = {current_states[0][1]:.2f}")

    # Generate final answer
    best_path = current_states[0][0]
    final_prompt = f"""Problem: {problem}
Reasoning path: {best_path}

Based on the reasoning above, write the final answer."""

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": final_prompt}],
        temperature=0.0
    )
    return response.choices[0].message.content

from openai import OpenAI
from pydantic import BaseModel, Field
from typing import List, Optional

client = OpenAI()


class CodeReview(BaseModel):
    """Code review result schema"""
    file_path: str = Field(description="File path under review")
    severity: str = Field(description="Severity: critical, warning, info")
    category: str = Field(description="Category: security, performance, style, logic")
    line_number: Optional[int] = Field(description="Line number")
    message: str = Field(description="Review comment")
    suggestion: str = Field(description="Suggested improvement code")


class CodeReviewResult(BaseModel):
    """Overall code review result"""
    reviews: List[CodeReview]
    summary: str = Field(description="Review summary")
    overall_score: int = Field(description="Overall score (1-10)")


def structured_code_review(code: str, language: str = "python") -> CodeReviewResult:
    """Code review with structured output"""
    response = client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": (
                    f"You are a senior {language} developer. "
                    "Review the given code and provide feedback in a structured format."
                )
            },
            {"role": "user", "content": f"Please review the following code:\n\n{code}"}
        ],
        response_format=CodeReviewResult
    )
    return response.choices[0].message.parsed

import json
from openai import OpenAI

client = OpenAI()

# Tool schema definition
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_stock_price",
            "description": "Retrieves the current price of a stock",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {
                        "type": "string",
                        "description": "Stock symbol (e.g., AAPL, GOOGL)"
                    },
                    "currency": {
                        "type": "string",
                        "enum": ["USD", "KRW", "JPY"],
                        "description": "Display currency"
                    }
                },
                "required": ["symbol"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "create_alert",
            "description": "Sets up a stock price alert",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {"type": "string"},
                    "target_price": {"type": "number"},
                    "direction": {
                        "type": "string",
                        "enum": ["above", "below"]
                    }
                },
                "required": ["symbol", "target_price", "direction"]
            }
        }
    }
]


def function_calling_agent(user_message: str) -> str:
    """Agent using Function Calling"""
    messages = [
        {
            "role": "system",
            "content": "You are a financial assistant that provides stock information."
        },
        {"role": "user", "content": user_message}
    ]

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=tools,
        tool_choice="auto"
    )

    msg = response.choices[0].message

    # Handle Function Calls
    if msg.tool_calls:
        for tool_call in msg.tool_calls:
            func_name = tool_call.function.name
            func_args = json.loads(tool_call.function.arguments)

            # Execute function (mock data here)
            if func_name == "get_stock_price":
                result = json.dumps({
                    "symbol": func_args["symbol"],
                    "price": 185.50,
                    "change": "+2.3%"
                })
            elif func_name == "create_alert":
                result = json.dumps({
                    "status": "success",
                    "alert_id": "alert_12345"
                })
            else:
                result = json.dumps({"error": "Unknown function"})

            messages.append(msg)
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": result
            })

        # Generate final response
        final_response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages
        )
        return final_response.choices[0].message.content

    return msg.content

from dataclasses import dataclass, field
from typing import Dict, Any
from datetime import datetime


@dataclass
class PromptTemplate:
    """Prompt template management class"""
    name: str
    version: str
    template: str
    metadata: Dict[str, Any] = field(default_factory=dict)
    created_at: str = field(default_factory=lambda: datetime.now().isoformat())

    def render(self, **kwargs) -> str:
        """Render final prompt with variable substitution"""
        result = self.template
        for key, value in kwargs.items():
            placeholder = f"__{key.upper()}__"
            result = result.replace(placeholder, str(value))
        return result


# Prompt registry
class PromptRegistry:
    def __init__(self):
        self._templates: Dict[str, PromptTemplate] = {}

    def register(self, template: PromptTemplate):
        key = f"{template.name}:{template.version}"
        self._templates[key] = template

    def get(self, name: str, version: str = "latest") -> PromptTemplate:
        if version == "latest":
            candidates = [
                v for k, v in self._templates.items()
                if k.startswith(f"{name}:")
            ]
            return max(candidates, key=lambda t: t.version)
        return self._templates[f"{name}:{version}"]


# Usage example
registry = PromptRegistry()
registry.register(PromptTemplate(
    name="code_review",
    version="2.1",
    template=(
        "You are a senior __LANGUAGE__ developer.\n"
        "Review the following code.\n\n"
        "Review criteria:\n"
        "- Security vulnerabilities\n"
        "- Performance issues\n"
        "- Code style\n\n"
        "Code:\n__CODE__"
    ),
    metadata={"model": "gpt-4o", "temperature": 0.0}
))