Hello LLM: Building a Local Chatbot with LangChain and Llama2

This article is part of the series Hello LLM.

Image generated by the author with DALL-E

Hello LLM beginners! Ever wondered how to build your own interactive AI chatbot, right on your local machine? Well, grab your coding hat and step into the exciting world of open-source libraries and models, because this post is your hands-on hello world guide to crafting a local chatbot with LangChain and LLAMA2.

Forget reliance on external services or cloud-based APIs. This adventure dives into two powerful open-source tools: LangChain, your LLM orchestrator, and LLAMA2, a state-of-the-art LLM powerhouse. Together, they’ll empower you to create a basic chatbot right on your own computer, unleashing the magic of LLMs in a local environment.

LangChain: Your LLM Conductor

Before we dive into code, let’s get acquainted with LangChain, your key tool for LLM interactions. It’s an open-source library designed specifically to make working with LLMs more approachable and efficient. Think of it as an orchestra conductor, seamlessly coordinating various model actions and interactions. Here’s how it shines:

• Prompt Engineering: LangChain provides a structured way to craft prompts, the instructions that guide LLMs to generate specific responses. This means you can carefully tailor prompts to achieve your desired outcomes.
• Chaining Interactions: It allows you to chain together multiple model interactions, creating more complex and interactive dialogues. This opens up possibilities for multi-step conversations and building context within interactions.
• Integrations Galore: LangChain supports integrations with external tools and data sources, expanding the possibilities for LLM-powered applications. Imagine tapping into databases, code interpreters, or even APIs within your chatbot experiences.

LLAMA2: The Open-Source LLM Model

Now, let’s introduce the star of our chatbot show: LLAMA2. Developed by Meta AI, this model family boasts impressive capabilities and a commitment to open-source access. Here’s what makes it a standout:

• Open-Source Power: LLAMA2 is available in various model sizes, with the 7B, 13B, and 70B parameter versions being open-sourced. This means you have flexibility and control over your chatbot’s capabilities.
• Pre-trained for Chat: The models are specifically pre-trained for chat-oriented tasks, making them a natural fit for building conversational AI applications. Expect more fluent and engaging responses.
• Performance Excellence: LLAMA2 scores high on benchmarks for reasoning, coding, proficiency, and knowledge tests, demonstrating its versatility and ability to handle diverse tasks.

Let’s Build the Local Chatbot

Here’s a hands-on demonstration of how to create a local chatbot using LangChain and LLAMA2:

1. Initialize a Python virtualenv, install required packages.

# Create a project dir
$ mkdir llm_chatbot
$ cd llm_chatbot
$ mkdir models

# Initialize a virtualenv
$ python3 -m venv venv
$ source venv/bin/activate

# Install required packages
$ pip install langchain llama-cpp-python

2. Download a LLAMA2 model file into the models directory. For instance, consider TheBloke’s Llama-2–7B-Chat-GGUF model, which is a relatively compact 7-billion-parameter model suitable for execution on a modern CPU/GPU.

3. Build a local chatbot with LangChain and LLAMA2.

llm_chatbot.py:

from langchain_community.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Load the LlamaCpp language model, adjust GPU usage based on your hardware
llm = LlamaCpp(
    model_path="models/llama-2-7b-chat.Q4_0.gguf",
    n_gpu_layers=40,
    n_batch=512,  # Batch size for model processing
    verbose=False,  # Enable detailed logging for debugging
)

# Define the prompt template with a placeholder for the question
template = """
Question: {question}

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

# Create an LLMChain to manage interactions with the prompt and model
llm_chain = LLMChain(prompt=prompt, llm=llm)

print("Chatbot initialized, ready to chat...")
while True:
    question = input("> ")
    answer = llm_chain.run(question)
    print(answer, '\n')

4. Interact with the chatbot

$ python3 llm_chatbot.py

Chatbot initialized, ready to chat...
> Hello
Hello there! It's nice to meet you. How are you today? 

> Tell me something about Elon Musk.
Elon Musk is a South African-born entrepreneur, inventor, and business magnate who is best known for his ambitious goals in revolutionizing transportation, energy, and space exploration through his companies SpaceX and Tesla. He was born on June 28, 1971, in Pretoria, South Africa, and moved to Canada in 1992 to attend college. Musk co-founded PayPal in 1998 and sold it to eBay for $1.5 billion in 2002. He then went on to found SpaceX in 2002 and Tesla in 2004, with the goal of reducing the world's reliance on fossil fuels and creating a sustainable future for humanity. Musk has been a pioneer in the electric vehicle industry and has played a significant role in popularizing the idea of renewable energy. He is also known for his innovative ideas, such as the development of reusable rockets and the Hyperloop, a high-speed transportation system that could revolutionize the way people travel. Musk has received numerous awards and honors for his contributions 

> How old is he?
He is 35 years old.

Conclusion

Did you have fun? I hope you did! LangChain and LLAMA2 empower you to explore the potential of LLMs without relying on external services. Dive into this exciting realm and unlock the possibilities of local language model applications! This is just the beginning of the exciting journey! Keep learning and exploring!