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Building a Local AI Chatbot with Open Source LLMs

Authors
  • avatar
    Name
    Adil ABBADI
    Twitter

Introduction

The rise of conversational AI has led to a surge in the development of chatbots that can understand and respond to human language. While many chatbot platforms rely on cloud-based services, building a local AI chatbot can provide greater control, security, and customization. In this blog post, we will explore how to build a local AI chatbot using open source large language models (LLMs).

Chatbot with LLM

Why Open Source LLMs?

Open source LLMs offer a cost-effective and customizable alternative to proprietary models. They can be fine-tuned for specific tasks and domains, allowing for greater flexibility and control. Moreover, open source LLMs can be deployed locally, reducing reliance on cloud services and ensuring data privacy.

Choosing the Right Open Source LLM

Several open source LLMs are available, each with its strengths and weaknesses. For this example, we will use the Hugging Face Transformers library, which provides a wide range of pre-trained models.

Hugging Face Transformers

Hugging Face is a popular open source library that provides a wide range of pre-trained language models, including BERT, RoBERTa, and DistilBERT. The library is built on top of PyTorch and provides an easy-to-use interface for fine-tuning and deploying models.

pip install transformers

Model Selection

For this example, we will use the DistilBERT model, which is a smaller and more efficient version of BERT.

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

Building the Chatbot

Our chatbot will consist of the following components:

  1. Natural Language Processing (NLP) Module: responsible for processing user input and generating responses.
  2. Knowledge Graph: stores domain-specific knowledge and information.
  3. Integration Layer: integrates the NLP module and knowledge graph to generate responses.

NLP Module

The NLP module will use the DistilBERT model to process user input and generate responses.

import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

# Load pre-trained DistilBERT model and tokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')

def process_input(input_text):
    # Tokenize input text
    inputs = tokenizer.encode_plus(input_text,
                                    add_special_tokens=True,
                                    max_length=512,
                                    return_attention_mask=True,
                                    return_tensors='pt')

    # Forward pass
    output = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])

    # Get generated response
    response = output.logits.argmax(-1).item()

    return response

Knowledge Graph

The knowledge graph will store domain-specific knowledge and information.

import networkx as nx

# Create a knowledge graph using NetworkX
G = nx.Graph()

# Add nodes and edges to the graph
G.add_node('Node 1', description='This is node 1')
G.add_node('Node 2', description='This is node 2')
G.add_edge('Node 1', 'Node 2', relation='Related to')

def get_response_from_kg(input_text):
    # Query the knowledge graph using the input text
    response = nx.get_node_attributes(G, 'description')[input_text]

    return response

Integration Layer

The integration layer will integrate the NLP module and knowledge graph to generate responses.

def generate_response(input_text):
    # Process input text using the NLP module
    nlp_response = process_input(input_text)

    # Query the knowledge graph using the NLP response
    kg_response = get_response_from_kg(nlp_response)

    # Combine responses
    response = f'{nlp_response} {kg_response}'

    return response

Deploying the Chatbot

Once the chatbot is built, it can be deployed using a variety of platforms and frameworks, such as Flask, Django, or React. For this example, we will use Flask to create a simple web interface for the chatbot.

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/chat', methods=['POST'])
def chat():
    input_text = request.json['input_text']
    response = generate_response(input_text)

    return jsonify({'response': response})

if __name__ == '__main__':
    app.run(debug=True)

Conclusion

In this blog post, we explored how to build a local AI chatbot using open source large language models. By leveraging the Hugging Face Transformers library and fine-tuning the DistilBERT model, we can create a highly accurate and efficient chatbot. The chatbot can be deployed using a variety of platforms and frameworks, providing a flexible and customizable solution for conversational AI applications.

Ready to Build Your Own Chatbot?

Start building your own local AI chatbot today and explore the possibilities of open source LLMs.

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