Tutorial: Building Your First LLM-Powered Application with LangChain

Unlock LLM Potential: Your First LangChain Application

LangChain has emerged as a powerful open-source framework for building applications powered by Large Language Models (LLMs). This tutorial will guide you through the fundamental concepts and help you build your first simple LangChain application.

What is LangChain?

LangChain provides a standard interface for Chains, lots of integrations with other tools, and end-to-end Chains for common applications. It simplifies the process of connecting LLMs to other sources of data and allowing LLMs to interact with their environment.

Core Concepts:

1. Models (LLMs & Chat Models):

   • Interface with various LLM providers (OpenAI, Hugging Face, Cohere, etc.).
   • Example: from langchain_openai import ChatOpenAI

2. Prompts:

   • Manage and optimize prompts sent to LLMs.
   • Includes prompt templates, example selectors, and output parsers.
   • Example: PromptTemplate.from_template("Tell me a joke about {topic}")

3. Chains:

   • Sequences of calls, either to an LLM or a different utility.
   • The most basic chain is LLMChain, which takes a prompt template, formats it with user input, and returns the LLM's response.
   • Example: chain = LLMChain(llm=my_llm, prompt=my_prompt_template)

4. Indexes (and Retrievers):

   • Structure your documents for LLMs to use, often for question-answering over your data (Retrieval Augmented Generation - RAG).
   • Involves document loaders, text splitters, vector stores, and retrievers.
   • Example: Using a Chroma vector store with OpenAI embeddings.

5. Memory:

   • Allow chains and agents to remember previous interactions, crucial for chatbots.
   • Example: ConversationBufferMemory

6. Agents:

   • LLMs that can make decisions about which actions to take, take that action, see an observation, and repeat until done.
   • Use tools (e.g., search, calculator, API calls) to perform tasks.
   • Example: An agent that can search the web to answer questions.

Building a Simple Q&A Bot:

Let's create a basic application that answers questions based on a small piece of text.

Prerequisites:

• Python installed
• LangChain and OpenAI Python libraries: pip install langchain langchain-openai python-dotenv
• An OpenAI API key (set as an environment variable OPENAI_API_KEY)

Code Example:

from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv

load_dotenv() # Load environment variables (for API key)

# 1. Initialize the LLM
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.7)

# 2. Create a Prompt Template
prompt_template = """
Answer the following question based on the context provided.
If the answer is not in the context, say "I don't know".

Context: The quick brown fox jumps over the lazy dog. This sentence is famous
for containing all letters of the English alphabet.

Question: {user_question}

Answer:
"""
prompt = PromptTemplate(template=prompt_template, input_variables=["user_question"])

# 3. Create an LLMChain
chain = LLMChain(llm=llm, prompt=prompt)

# 4. Run the chain
question1 = "What does the quick brown fox do?"
response1 = chain.invoke({"user_question": question1})
print(f"Q: {question1}\nA: {response1['text']}\n")

question2 = "What is the capital of France?"
response2 = chain.invoke({"user_question": question2})
print(f"Q: {question2}\nA: {response2['text']}")

Next Steps:

• Explore different types of chains (e.g., SequentialChain).
• Integrate document loaders and vector stores for RAG.
• Build a simple agent with tools.
• Dive into the LangChain Expression Language (LCEL) for more complex chain construction.

LangChain opens up a world of possibilities for LLM application development. This tutorial is just the beginning!

Share your LangChain project ideas or questions in the comments!