chapter15.md

Chapter 15: LangChain Best Practices and Optimization

We will introduce best practices for using LangChain effectively and techniques for optimizing performance.

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.cache import InMemoryCache
import langchain
import time

# 
langchain.llm_cache = InMemoryCache()

# LLM
llm = OpenAI(temperature=0.1)

#
template = """
You are an AI assistant specialized in {topic}.
Please provide a concise answer to the following question:
Question: {question}
Answer:
"""

prompt = PromptTemplate(
    input_variables=["topic", "question"],
    template=template
)

# 
chain = LLMChain(llm=llm, prompt=prompt)

# 
start_time = time.time()

# 
for _ in range(5):
    response = chain.run(topic="Python programming", question="What is a list comprehension?")
    print(response)

end_time = time.time()
print(f"Execution time: {end_time - start_time} seconds")

# 
try:
    invalid_response = chain.run(topic="Invalid topic", question="This will cause an error")
except Exception as e:
    print(f"An error occurred: {e}")
    

# 
del llm
del chain