Chapter 6: Customizing Your Chatbot

Customizing your chatbot to meet the specific needs of your business involves training it with custom data. This chapter will guide you through the process of training your AI Mentor and AI Coach, ensuring they provide relevant and valuable support to your employees and programmers.

Training Your AI Mentor

An AI Mentor helps employees with career development, personal growth, and navigating complex business challenges. To train your AI Mentor, you need to collect and prepare relevant data, and then train the model on this custom data.

Data Collection and Preparation

Identify Relevant Data Sources:
- Collect data from performance reviews, employee feedback, career development plans, and other relevant sources.
- Ensure the data is representative of various roles and career paths within your organization.
Data Cleaning and Formatting:
- Clean the data by removing duplicates, correcting errors, and standardizing formats.
- Ensure the data is anonymized to protect employee privacy.
Labeling and Categorizing:
- Label the data according to different categories such as skills, career goals, performance metrics, and feedback.
- Categorize the data into structured formats suitable for training (e.g., CSV files, JSON files).

Example: Preparing Employee Feedback Data

import pandas as pd

# Load and clean data
data = pd.read_csv('employee_feedback.csv')
data.drop_duplicates(inplace=True)
data.fillna("", inplace=True)

# Label and categorize data
data['category'] = data['feedback'].apply(lambda x: 'positive' if 'good' in x else 'negative')
data.to_csv('cleaned_employee_feedback.csv', index=False)

Training the Model on Custom Data

Load Pre-trained Model:

Use a pre-trained NLP model from Hugging Face’s Transformers library:

from transformers import BertTokenizer, BertForSequenceClassification

model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

Prepare Data for Training:

Tokenize the data and create training and validation datasets:

from transformers import Trainer, TrainingArguments
from sklearn.model_selection import train_test_split
import torch

# Load data
data = pd.read_csv('cleaned_employee_feedback.csv')
train_texts, val_texts, train_labels, val_labels = train_test_split(
    data['feedback'].tolist(), data['category'].tolist(), test_size=0.2
)

# Tokenize data
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)

class Dataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.labels)

train_dataset = Dataset(train_encodings, train_labels)
val_dataset = Dataset(val_encodings, val_labels)

Train the Model:

Set up training arguments and train the model:

training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs',
    logging_steps=10,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

trainer. Train()

Training Your AI Coach

An AI Coach provides real-time feedback and assistance with programming tasks. To train your AI Coach, you need to collect specific data related to programming support and fine-tune the model for code assistance.

Specific Data Requirements for Programming Support

Collect Programming Data:
- Gather data from code repositories, coding forums, Q&A sites (like Stack Overflow), and internal code reviews.
- Include examples of well-written code, common errors, and best practices.
Format the Data:
- Structure the data in a way that highlights code snippets, problem descriptions, and solutions.
- Annotate the data with relevant tags and labels to facilitate training.

Example: Preparing Programming Data

import json

# Load and clean data
with open('code_reviews.json') as f:
    data = json.load(f)

# Annotate data
for entry in data:
    entry['label'] = 'good' if 'approved' in entry['review'] else 'bad'

# Save cleaned and annotated data
with open('cleaned_code_reviews.json', 'w') as f:
    json.dump(data, f)

Fine-Tuning the Model for Code Assistance

Load Pre-trained Model:

Use a pre-trained model like GPT-3 or Codex from OpenAI:

from transformers import GPT2Tokenizer, GPT2LMHeadModel

model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

Prepare Data for Training:

Tokenize the programming data and create training and validation datasets:

from transformers import Trainer, TrainingArguments

# Load data
with open('cleaned_code_reviews.json') as f:
    data = json.load(f)

train_texts, val_texts, train_labels, val_labels = train_test_split(
    [entry['code'] for entry in data], [entry['label'] for entry in data], test_size=0.2
)

# Tokenize data
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)

class CodeDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.labels)

train_dataset = CodeDataset(train_encodings, train_labels)
val_dataset = CodeDataset(val_encodings, val_labels)

Fine-Tune the Model:

Set up training arguments and fine-tune the model on the programming data:

training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs',
    logging_steps=10,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

trainer. Train()

Conclusion

By customizing your chatbot through training on specific data, you can ensure that your AI Mentor and AI Coach provide valuable support tailored to your business needs. In the next chapter, we will explore how to implement the chatbot by building the framework and integrating it with communication platforms.