Prompt Title: Python Chatbot for Copyleaks.

To create a chatbot in Python that ingests your company's API, SDK documentation, and FAQ from your website, you'll need to perform a few tasks: data retrieval, data preprocessing, training, and implementation of the chatbot.

Here's a high-level outline of the process:

1. Data retrieval: First, you'll need to fetch the text content from your API, SDK documentation, and FAQ page. You can use the requests library for fetching the content and BeautifulSoup for parsing the HTML.

2. Data preprocessing: Once you have the text content, you need to preprocess it. This might include tokenization, removing stop words, and lemmatization to make the text ready for training.

3. Training: Train your chatbot on the preprocessed text data. You can use the Rasa or ChatterBot libraries, or you could use more advanced machine learning techniques like transformers (e.g., GPT-2 or GPT-3) for better performance.

4. Implementation: Implement your chatbot in your desired platform (e.g., a website, Slack, or other messaging platforms). You can use Flask or Django for the webserver, and integrate with the platform using APIs.

Here's a simple example using ChatterBot:

pythonCopy code
import requests
from bs4 import BeautifulSoup
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer

# Step 1: Data Retrieval
url = "https://www.copyleaks.com/faq"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

# Extract relevant content (adjust the code as per the website's HTML structure)
faq_sections = soup.find_all("div", class_="faq-section")

qa_pairs = []
for section in faq_sections:
    questions = section.find_all("div", class_="question")
    answers = section.find_all("div", class_="answer")

    for question, answer in zip(questions, answers):
        qa_pairs.append((question.text.strip(), answer.text.strip()))

# Step 2: Data Preprocessing (skipped, as ChatterBot handles it internally)

# Step 3: Training
chatbot = ChatBot("CopyleaksBot")
trainer = ChatterBotCorpusTrainer(chatbot)

# Train on the QA pairs
for question, answer in qa_pairs:
    trainer.train(question, answer)

# Step 4: Implementation
# Here's an example of how you would use the chatbot in a simple CLI interaction:
while True:
    user_input = input("User: ")
    if user_input.lower() == "exit":
        break

    response = chatbot.get_response(user_input)
    print("CopyleaksBot:", response)

This code provides a basic structure for creating a chatbot that ingests your company's documentation and FAQ. You'll need to modify the data retrieval and preprocessing steps based on your specific website's structure and content. Additionally, consider using more advanced models or libraries for better performance and integration with your desired platform.