In the digital age, language-based applications play a vital role in our lives, powering various tools like chatbots and virtual assistants. Learn to master prompt engineering for LLM applications with LangChain, an open-source Python framework that has revolutionized the creation of cutting-edge LLM-powered applications. This guide aims to equip readers with the knowledge and tools to craft dynamic and context-aware language applications using LangChain. We will explore prompt management, leveraging additional LLMs and external data, and mastering chaining for sophisticated language applications. Whether you are a developer or an AI enthusiast, this guide will help you unleash the power of language and turn your LLM application ideas into reality with LangChain.
This article was published as a part of the Data Science Blogathon.
Large Language Models are robust AI systems built on deep learning architectures trained on massive amounts of data. These models can understand complex language patterns, nuances, and context, making them proficient in language translation, text generation, summarization, and more. A prominent example of an LLM is OpenAI’s GPT (Generative Pre-trained Transformer) model.
LangChain is a comprehensive open-source platform that offers a suite of tools, components, and interfaces to simplify the process of building applications powered by large language models. The platform’s primary goal is to enable developers to seamlessly integrate language processing capabilities into their applications without starting from scratch. LangChain provides a user-friendly and efficient approach to managing interactions with LLMs, seamlessly linking different components and incorporating resources like APIs and databases.
LangChain, an open-source framework designed to facilitate the development of applications powered by large language models (LLMs), opens up many potential applications in natural language processing (NLP) and beyond. Here are some of the critical applications of LangChain:
pip install langchain
pip install openaiconda install langchain -c conda-forge
conda install -c conda-forge openaiThis will set up the necessities of LangChain. However, the true power and versatility of LangChain are realized when it is seamlessly integrated with diverse model providers, data stores, and other essential components.
LangChain provides an LLM class for interfacing with various language model providers, such as OpenAI, Cohere, and Hugging Face. The most basic functionality of an LLM is generating text.
import os
os.environ["OPENAI_API_KEY"] = ""The OpenAI module provides a class that can be used to access the OpenAI API. The LLMChain module provides a class that can chain together multiple language models.
The code then creates an instance of the OpenAI class and sets the temperature parameter to 0.7. The temperature parameter controls the creativity of the text generated by the OpenAI API. A higher temperature will produce more creative text, while a lower temperature will produce more predictable text.
from langchain.llms import OpenAI
from langchain.chains import LLMChain
llm = OpenAI(temperature=0.7)A PromptTemplate in LangChain allows you to use templating to generate a prompt. This is useful when using the same prompt outline in multiple places but with certain values changed.
from langchain import PromptTemplateWe have set up an LLMChain that acts as a financial advisor capable of explaining the basics of income tax or any other financial concept specified by the user. When executed, the chain will explain the financial concept easily.
template1 = '''I want you to act as a acting financial advisor for people.
In an easy way, explain the basics of {financial_concept}.'''
prompt1 = PromptTemplate(
input_variables = ['financial_concept'],
template = template1
)
prompt1.format(financial_concept='income tax')
chain1 = LLMChain(llm=llm,prompt=prompt1)
chain1.run('income tax')
chain1.run('GDP')
We have set up LLMChain capable of translating a sentence from English to Hindi and French. When executed, the chain will take the sentence “How are you?” and the target language ‘Hindi’ and ‘French’ as inputs, and the language model will generate the translated output in Hindi and French as a response.
template2='''In an easy way translate the following sentence '{sentence}' into {target_language}'''
language_prompt = PromptTemplate(
input_variables = ["sentence","target_language"],
template=template2
)
language_prompt.format(sentence="How are you",target_language='hindi')
chain2 = LLMChain(llm=llm,prompt=language_prompt)data = chain2({
'sentence':"What is your name?",
'target_language':'hindi'
})
print("English Sentence:", data['sentence'])
print("Target Language:", data['target_language'])
print("Translated Text:")
print(data['text'])
data = chain2({
'sentence':"Hello How are you?",
'target_language':'french'
})
print("English Sentence:", data['sentence'])
print("Target Language:", data['target_language'])
print("Translated Text:")
print(data['text'])
We have created a language model-powered application that provides travel recommendations for India. The language model will respond with three bullet points of specific things to do while traveling to India based on the input provided in the prompt template.
template3 = """ I am travelling to {location}. What are the top 3 things I can do while I am there.
Be very specific and respond as three bullet points """
travel_prompt = PromptTemplate(
input_variables=["location"],
template=template3,
)
travel_prompt = travel_prompt.format(location='Paris')
print(f"LLM Output: {llm(travel_prompt)}")
Users can input a celebrity’s name, and the application will provide detailed information about the celebrity, including their date of birth and significant events around that day.
# Chain 1: Tell me about celebrity
first_input_prompt = PromptTemplate(
input_variables = ['name'],
template = "Tell me about celebrity {name}"
)
chain1 = LLMChain(
llm=llm,
prompt=first_input_prompt,
output_key='person'
)
# Chain 2: celebrity DOB
second_input_prompt = PromptTemplate(
input_variables = ['person'],
template = "when was {person} born"
)
chain2 = LLMChain(
llm=llm,
prompt=second_input_prompt,
output_key='dob'
)
# Chain 3: 5 major events on that day
third_input_prompt = PromptTemplate(
input_variables = ['dob'],
template = "Mention 5 major events happened around {dob} in the world"
)
chain3 = LLMChain(
llm=llm,
prompt=third_input_prompt,
output_key='description'
)
#combining chains
from langchain.chains import SequentialChain
celebrity_chain = SequentialChain(
chains=[chain1,chain2,chain3],
input_variables=['name'],
output_variables=['person','dob','description']
)data = celebrity_chain({'name':"MS Dhoni"})
print("Name:", data['name'])
print("Date of Birth:", data['dob'])
print("Description:")
print(data['person'])
print("Historical Events:")
print(data['description'])
Users can input a cuisine type, and the application will respond with a suggested restaurant name for that cuisine and a list of menu items for the suggested restaurant.
# Chain 1: Restaurant Name
prompt_template_name = PromptTemplate(
input_variables=['cuisine'],
template="I want to open a restaurant for {cuisine} food. Suggest a fancy name for this."
)
name_chain = LLMChain(llm=llm, prompt=prompt_template_name, output_key="restaurant_name")
# Chain 2: Menu Items
prompt_template_items = PromptTemplate(
input_variables=['restaurant_name'],
template="""Suggest some menu items for {restaurant_name}. Return it as a comma separated string"""
)
food_items_chain = LLMChain(llm=llm, prompt=prompt_template_items, output_key="menu_items")
#combining chains
from langchain.chains import SequentialChain
restaurant_chain = SequentialChain(
chains=[name_chain, food_items_chain],
input_variables=['cuisine'],
output_variables=['restaurant_name', "menu_items"]
)data = restaurant_chain({'cuisine':'Indian'})
print("Cuisine:", data['cuisine'])
print("Restaurant Name:", data['restaurant_name'])
print("Menu Items:")
print(data['menu_items'])
In conclusion, LangChain has revolutionized the world of Language Models, providing developers with an open-source Python framework to effortlessly build cutting-edge applications powered by Large Language Models (LLMs). Its seamless integration with foundational models and external data sources, along with support for prompt management and templates, simplifies the development process and nurtures creativity. From chatbots to virtual assistants and language translation utilities, LangChain offers a robust platform that expedites project development and drives innovation in the realm of natural language processing.
Ready to transform your development skills? Enroll in our “LangChain Mastery” course today and learn how to leverage this open-source Python framework to build innovative applications powered by Large Language Models. Simplify your workflow and unleash your creativity—join us now!
The Code and Implementation are Uploaded to Github at Langchain Repository.
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A. By default, LangChain creates the chat model with a temperature value of 0.7. The temperature parameter adjusts the randomness of the output. Higher values like 0.7 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
A. A prompt template is a structured text containing placeholders for input variables, serving as a flexible way to generate dynamic prompts for language models and other natural language processing systems. Input variables act as placeholders, replacing their values with actual user-provided inputs or data during runtime.
A. LangChain provides an LLM class for interfacing with various language models providers, such as OpenAI, Cohere, and Hugging Face. The most basic functionality of an LLM is generating text. Building an application with LangChain that takes a string prompt and returns the output is very straightforward.
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Myself Mitta Sai Nitish, and I am a dedicated student at VIT Vellore with a genuine passion for exploring new technologies and transforming them into practical applications. I have an unwavering enthusiasm for the fields of machine learning and data sciences, constantly seeking opportunities to tackle real-world challenges in these domains. With an insatiable curiosity, I am driven to delve deeper into the world of data sciences, constantly expanding my knowledge and honing my skills.
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