สถาปัตยกรรม LangChain: โมเดล พรอมต์ และสายงาน

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

openai_model = ChatOpenAI(model='gpt-4o-mini', temperature=0.2)
claude_model = ChatAnthropic(model='claude-sonnet-4-5')

resp = openai_model.invoke('Hello!')
print(resp.content)

from langchain.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ('system', 'You are a {role} assistant.'),
    ('user', '{question}')
])

messages = prompt.invoke({'role': 'SQL', 'question': 'How do I delete rows?'})
print(messages.to_messages())

from langchain.schema.output_parser import StrOutputParser

chain = prompt | openai_model | StrOutputParser()
result = chain.invoke({'role': 'SQL', 'question': 'How do I delete rows?'})
print(result)

from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel

class Plan(BaseModel):
    steps: list[str]

parser = PydanticOutputParser(pydantic_object=Plan)
chain = prompt | model | parser

for chunk in chain.stream({'role': 'helpful', 'question': 'Tell a story.'}):
    print(chunk, end='', flush=True)

results = chain.batch([
    {'role': 'SQL', 'question': 'Q1'},
    {'role': 'SQL', 'question': 'Q2'}
])
# Runs in parallel.

result = chain.invoke(
    {'role': 'helpful', 'question': '...'},
    config={'configurable': {'temperature': 0.0}}
)

from langchain import hub
rag_prompt = hub.pull('rlm/rag-prompt')