Basic LangChain: วิธีใช้ LangChain เพื่อทำงานกับ LLM อย่างง่าย ใน 5 ขั้นตอน—ตัวอย่างการสร้าง AI ตอบคำถามเรื่อง Mental Model

# Import package
from langchain_google_genai import ChatGoogleGenerativeAI

# Create model instance
llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    temperature=0.5,
    api_key="YOUR_API_KEY"
)

# Import package
from langchain_core.prompts import ChatPromptTemplate

# Define system prompt
system_prompt = """
You are an expert curator of mental models across science, philosophy, and applied reasoning.

Your task is to explain mental models clearly and accurately using a fixed schema.

If the origin of a model is unclear or debated, state that explicitly.

Do not invent historical sources. Be concise and concrete.
"""

# Define user prompt
user_prompt = "Explain the following mental model: {model_query}"

# Create prompt template
prompts = ChatPromptTemplate.from_messages(
    [
        # System prompt
        ("system", system_prompt.strip()),

        # User prompt
        ("human", user_prompt)
    ]
)

# Inspect prompt template
prompts.format_messages(model_query="Pareto Principle")

[SystemMessage(content='You are an expert curator of mental models across science, philosophy, and applied reasoning.\\n\\nYour task is to explain mental models clearly and accurately using a fixed schema.\\n\\nIf the origin of a model is unclear or debated, state that explicitly.\\n\\nDo not invent historical sources. Be concise and concrete.', additional_kwargs={}, response_metadata={}),
 HumanMessage(content='Explain the following mental model: Pareto Princinple', additional_kwargs={}, response_metadata={})]

# Import packages
from pydantic import BaseModel, Field
from typing import List, Literal

# Define output structure
class MentalModel(BaseModel):

    # Mental model name
    model_name: str = Field(description="The commonly accepted name of the mental model")

    # Source/origin
    origin: str = Field(
        description="Where the model comes from (person, book, field, or cultural origin)"
    )

    # Brief description
    description: str = Field(
        description="A brief explanation of what the mental model is and why it matters"
    )

    # Example
    example: str = Field(
        description="A concrete real-world example illustrating the mental model"
    )

    # Tags
    tags: List[str] = Field(
        description="Short tags such as decision-making, systems thinking, learning, philosophy"
    )

# Add output structure to LLM
llm_with_structured_output = llm.with_structured_output(MentalModel)

# Build chain
chain = prompts | llm_with_structured_output

# Run query
result = chain.invoke({"model_query": "Compound Interest"})

# Import package
import json

# Load result
result_dict = result.model_dump()

# Print
print(json.dumps(result_dict, indent=4))

{
    "model_name": "Compound Interest",
    "origin": "Finance, Mathematics; concept dates back to ancient times, formalized in the Renaissance.",
    "description": "Compound interest is the interest on a loan or deposit calculated based on both the initial principal and the accumulated interest from previous periods. It is often called 'interest on interest' and leads to exponential growth over time, making it a powerful force in finance for both wealth creation and debt accumulation.",
    "example": "If you invest $1,000 at an annual interest rate of 5% compounded annually, after the first year you'll have $1,050. In the second year, the 5% interest is calculated on $1,050, not just the original $1,000, leading to a balance of $1,102.50. This snowball effect accelerates over decades, significantly increasing the total return compared to simple interest.",
    "tags": [
        "Finance",
        "Economics",
        "Wealth Building",
        "Decision-making",
        "Long-term thinking"
    ]
}

# Create list of mental models
mental_model_queries = [
    "First Principles Thinking",
    "Occam's Razor",
    "Confirmation Bias"
]

# Create batch inputs
batch_inputs = [{"model_query": query} for query in mental_model_queries]

# Run queries
results = chain.batch(batch_inputs)

# Instantiate collector
query_collector = [result.model_dump() for result in results]

# Instantiate counter
i = 1

# Loop through elements in collector
for result in query_collector:

    # Print result
    print(f"👉 Query {i}:")
    print(json.dumps(result, indent=4))
    print("\\n")

    # Add 1 to counter
    i += 1

👉 Query 1:
{
    "model_name": "First Principles Thinking",
    "origin": "Often attributed to Aristotle; popularized in modern business by Elon Musk.",
    "description": "First Principles Thinking involves breaking down complex problems into their most basic, fundamental truths or 'first principles,' rather than reasoning by analogy or conventional wisdom. It matters because it allows for innovative solutions by challenging assumptions and building new knowledge from the ground up.",
    "example": "Instead of accepting the high cost of batteries for electric cars, Elon Musk famously broke down a battery into its constituent raw materials (cobalt, nickel, lithium, etc.) to understand their actual cost, then sought ways to procure and assemble them more efficiently, leading to significant cost reductions and innovation.",
    "tags": [
        "Problem-solving",
        "Innovation",
        "Critical Thinking",
        "Decision-making"
    ]
}

👉 Query 2:
{
    "model_name": "Occam's Razor",
    "origin": "William of Ockham (14th-century philosopher and theologian)",
    "description": "Occam's Razor is a problem-solving principle stating that among competing hypotheses that explain an event or phenomenon equally well, the simplest solution is most likely the correct one. It advocates for parsimony, suggesting that one should not multiply entities beyond necessity, thereby favoring theories with fewer assumptions.",
    "example": "If you hear hoofbeats outside, it is more likely to be horses than zebras, assuming you are in a location where horses are common and zebras are not. The 'horse' explanation is simpler and requires fewer extraordinary assumptions.",
    "tags": [
        "Philosophy",
        "Decision-making",
        "Problem-solving",
        "Critical thinking",
        "Science"
    ]
}

👉 Query 3:
{
    "model_name": "Confirmation Bias",
    "origin": "Psychology; early concepts traced to Francis Bacon's Novum Organum (1620)",
    "description": "Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms one's pre-existing beliefs or hypotheses. It matters because it can lead to flawed reasoning, poor decision-making, and resistance to new or contradictory evidence, hindering objective analysis.",
    "example": "A person who believes a certain stock will perform well might selectively read news articles and analyst reports that support this positive outlook, while ignoring or downplaying any negative news or warnings about the company.",
    "tags": [
        "cognitive bias",
        "decision-making",
        "psychology",
        "critical thinking"
    ]
}