Have you ever wondered how to use OpenAI APIs to create custom chatbots? With advancements in large language models (LLMs), anyone can develop intelligent, customized chatbots tailored to specific needs. In this blog, we’ll explore how LangChain and OpenAI LLMs work together to help you build your own AI-driven chatbot from scratch.
Prerequisites
Before getting started, ensure you have Python (version 3.8 or later) installed and the required libraries. You can install the necessary packages using the following command:
pip install langchain-core langchain_openai
Setting Up OpenAI API Key
To use OpenAI’s services, you need an API key, which you can obtain by signing up at OpenAI’s website (OpenAI) and generating a key from the API settings in your account. Set up the key in your environment using:
import os
os.environ["OPENAI_API_KEY"] = 'sk-proj-keyyyyy'
Creating a Prompt Template
LangChain allows us to create flexible prompt templates, which are structured text formats that help shape the model’s responses. These templates define how input queries are framed, ensuring consistency and relevance in chatbot interactions.
For example, modifying the template to include an encouraging tone could lead to responses that are more engaging, while adding constraints like word limits or response formats can make the chatbot more structured and precise. Below, we define a prompt that ensures responses are concise and relevant to a given expertise:
from langchain_core.prompts import ChatPromptTemplate
prompt_template = ChatPromptTemplate.from_messages(
[
(
"system",
"Act as a {expertise} expert. Answer all questions in 50 words in a precise manner without adding any fluff."
),
(
"user",
"{query}"
)
]
)
Creating a Processing Chain
A chain helps structure our chatbot’s interactions by linking different components together. These components include prompts, language models, and processing logic, creating a seamless conversational flow.
chain = prompt_template | llm
Invoking the Model
Now, we can generate responses using our chatbot by providing the required inputs:
input = {"expertise": "Machine Learning", "query": "What is random forest?"}
response = chain.invoke(input)
print(response)
Conclusion
With these simple steps, you can set up an AI-powered chatbot using LangChain and OpenAI. This chatbot framework can be applied across industries such as customer support, healthcare, education, and finance, providing automated yet personalized responses.
To further enhance your chatbot, explore LangChain’s documentation (LangChain Docs), experiment with different LLMs, and integrate additional tools like vector databases for better contextual understanding. You can also check out the LangChain GitHub repository (LangChain GitHub) and OpenAI’s API guides (OpenAI Docs) for more insights.
This chatbot can be modified to support different expertise areas and prompt styles, making it a versatile tool for various applications. Try experimenting with different prompts to customize responses further!
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
- Building an OpenAI Chatbot with LangChain - January 30, 2025
- Building a RAG Application with LangChain: Example Code - January 29, 2025
- How Indexing Works in LLM-Based RAG Applications - January 26, 2025
I found it very helpful. However the differences are not too understandable for me