Token prediction and compression techniques

[shoutsid]

Issue 1: Token prediction and compression techniques

Title: Implement Token Prediction and Compression Techniques
Description:
Develop and implement specific techniques for token prediction and data compression to improve the efficiency and responsiveness of the Townhall chatbot.

Context

The Townhall chatbot is growing in complexity and is handling larger datasets. Efficient token prediction and data compression are critical for improving computational performance and user experience.

Expected Outcomes

• Implement Markov Chain and N-gram models for token prediction.
• Implement Huffman coding for data compression.
• Achieve at least a 25% reduction in processing time for token-related operations.

Challenges

• Balancing the trade-off between computational complexity and prediction/compression effectiveness.
• Ensuring backward compatibility and that new features do not introduce bugs.

Recommended Libraries and Algorithms

• Token Prediction
  • Use nltk for implementing N-gram models.
  • Use numpy or pandas for Markov Chain calculations.
• Compression
  • Use Python's zlib library for implementing Huffman coding.

Resources

• NLTK documentation for N-gram models
• Research papers on Huffman coding and its applications
• Markov Chain tutorials to understand the algorithm's behavior in text prediction