LangChain Basics 1
In this tutorial, you’ll learn how to call LLMs, providing prompts and parsing the response.
from langchain.output_parsers import StructuredOutputParser
from langchain.output_parsers import ResponseSchema
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
import datetime
import os
import openai
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai.api_key = os.environ['OPENAI_API_KEY']Let’s try how we can do the same using LangChain.
{text}’, template_format=‘f-string’, validate_template=True)\nArrr, I be fuming that me blender lid flew off and splattered me kitchen walls with smoothie! And to make matters worse, the warranty don't cover the cost of cleaning up me kitchen. I need yer help right now, matey!\n” additional_kwargs={} example=Falsecustomer_review = """\
This leaf blower is pretty amazing. It has four settings:\
candle blower, gentle breeze, windy city, and tornado. \
It arrived in two days, just in time for my wife's \
anniversary present. \
I think my wife liked it so much she was speechless. \
So far I've been the only one using it, and I've been \
using it every other morning to clear the leaves on our lawn. \
It's slightly more expensive than the other leaf blowers \
out there, but I think it's worth it for the extra features.
"""
review_template = """\
For the following text, extract the following information:
gift: Was the item purchased as a gift for someone else? \
Answer True if yes, False if not or unknown.
delivery_days: How many days did it take for the product \
to arrive? If this information is not found, output -1.
price_value: Extract any sentences about the value or price,\
and output them as a comma separated Python list.
Format the output as JSON with the following keys:
gift
delivery_days
price_value
text: {text}
"""
{
"gift": false,
"delivery_days": 2,
"price_value": ["It's slightly more expensive than the other leaf blowers out there, but I think it's worth it for the extra features."],
"text": "This leaf blower is pretty amazing. It has four settings:candle blower, gentle breeze, windy city, and tornado. It arrived in two days, just in time for my wife's anniversary present. I think my wife liked it so much she was speechless. So far I've been the only one using it, and I've been using it every other morning to clear the leaves on our lawn. It's slightly more expensive than the other leaf blowers out there, but I think it's worth it for the extra features."
}---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/Users/jankirenz/labs/lab-langchain-basics/slides/1_model_prompt_parser.qmd in line 1
----> 342 response.content.get('gift')
AttributeError: 'str' object has no attribute 'get'gift_schema = ResponseSchema(name="gift",
description="Was the item purchased\
as a gift for someone else? \
Answer True if yes,\
False if not or unknown.")
delivery_days_schema = ResponseSchema(name="delivery_days",
description="How many days\
did it take for the product\
to arrive? If this \
information is not found,\
output -1.")
price_value_schema = ResponseSchema(name="price_value",
description="Extract any\
sentences about the value or \
price, and output them as a \
comma separated Python list.")
response_schemas = [gift_schema,
delivery_days_schema,
price_value_schema]json" and "”:{
"gift": string // Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.
"delivery_days": string // How many days did it take for the product to arrive? If this information is not found, output -1.
"price_value": string // Extract any sentences about the value or price, and output them as a comma separated Python list.
}review_template_2 = """\
For the following text, extract the following information:
gift: Was the item purchased as a gift for someone else? \
Answer True if yes, False if not or unknown.
delivery_days: How many days did it take for the product\
to arrive? If this information is not found, output -1.
price_value: Extract any sentences about the value or price,\
and output them as a comma separated Python list.
text: {text}
{format_instructions}
"""
prompt = ChatPromptTemplate.from_template(template=review_template_2)
messages = prompt.format_messages(text=customer_review,
format_instructions=format_instructions)The output should be a markdown code snippet formatted in the following schema, including the leading and trailing “json" and "”:
{
"gift": string // Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.
"delivery_days": string // How many days did it take for the product to arrive? If this information is not found, output -1.
"price_value": string // Extract any sentences about the value or price, and output them as a comma separated Python list.
}This tutorial is mainly based on the excellent course “LangChain for LLM Application Development” provided by Harrison Chase and Andrew Ng
Congratulations! You have completed this tutorial 👍
Next, you may want to go back to the lab’s website
Jan Kirenz