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LSTM Chatbot Sequence-to-Sequence Attention Open Source

LSTM-based sequence-to-sequence chatbot system for conversational AI using encoder-decoder architecture with attention mechanism. The system uses LSTM networks to process input sequences and generate contextually relevant responses, making it excellent for natural conversations and multi-turn dialogue. Complete implementation with TensorFlow, Keras, PyTorch, including beam search decoding, conversation history, temperature sampling, evaluation metrics, REST API, and comprehensive training tools.

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LSTM Chatbot Project - RSK World
LSTM Chatbot Project - RSK World
Sequence-to-Sequence Conversational AI Python TensorFlow Keras LSTM

This project implements an LSTM-based sequence-to-sequence chatbot system for conversational AI. The encoder-decoder architecture with attention mechanism processes input sequences and generates contextually relevant responses, making it excellent for multi-turn conversations and natural dialogue. Perfect for conversational AI applications, featuring TensorFlow and Keras implementation, beam search decoding, conversation history management, temperature sampling, attention visualization, REST API, and comprehensive evaluation tools.

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LSTM Encoder-Decoder Architecture

LSTM-based sequence-to-sequence architecture with encoder-decoder design for conversational AI. Uses LSTM networks to process input sequences and generate responses with long-term dependency handling.

  • LSTM encoder-decoder architecture
  • Sequence-to-sequence modeling
  • Long-term dependency handling
  • Context-aware response generation

Attention Mechanism

Bahdanau attention mechanism that helps the model focus on relevant parts of the input when generating each word, improving conversation quality and context understanding.

  • Bahdanau attention mechanism
  • Focus on relevant input parts
  • Improved context understanding
  • Attention weight visualization

Beam Search Decoding

Better response generation with beam search algorithm that explores multiple candidate sequences to find the best response.

  • Beam search decoding
  • Multiple candidate exploration
  • Configurable beam width
  • Improved response quality

Conversation History

Multi-turn conversation support with context management, automatic history tracking, and configurable history length for natural dialogue flow.

  • Multi-turn conversation support
  • Automatic context management
  • Save/load conversation history
  • Configurable history length

Evaluation Metrics

Comprehensive evaluation metrics including BLEU score, Word Error Rate (WER), and Perplexity calculation for response quality assessment.

  • BLEU score calculation
  • Word Error Rate (WER)
  • Perplexity calculation
  • Sample-based evaluation

Jupyter Notebook

Interactive Jupyter Notebooks for data preprocessing, model training, and chatbot inference demonstrations with step-by-step tutorials.

  • Data preprocessing notebook
  • Model training notebook
  • Chatbot inference notebook
  • Interactive tutorials

Attention Visualization

Visualize attention weights to understand which input words the model focuses on when generating each output word for model interpretability.

  • Attention weight visualization
  • Model interpretability
  • Input-output alignment
  • Visual attention maps

Temperature Sampling

Control response randomness and creativity with temperature sampling. Lower values produce focused responses, higher values produce diverse outputs.

  • Temperature-controlled sampling
  • Configurable randomness
  • Balanced vs creative responses
  • Fine-tuned output control

REST API Server

Full REST API with Flask-based web interface with multiple endpoints for chat, conversation history, export, and health checks.

  • Flask-based REST API
  • Chat endpoint
  • History management
  • CORS enabled

Data Augmentation

Enhance training data with various augmentation techniques including random insertion, deletion, word swapping, and synonym replacement.

  • Random insertion
  • Random deletion
  • Word swapping
  • Synonym replacement

Chat Log Export

Export conversation history to multiple formats including JSON, plain text, and CSV for analysis and backup purposes.

  • JSON export
  • Plain text export
  • CSV export
  • History backup

Model Training

Complete training pipeline with data preprocessing, vocabulary management, model checkpointing, and training history visualization.

  • Data preprocessing
  • Vocabulary management
  • Model checkpointing
  • Training visualization

Training Visualization

Improved training history visualization with multiple metrics including loss curves, accuracy tracking, and overfitting detection.

  • Loss curve visualization
  • Accuracy tracking
  • Learning rate monitoring
  • Overfitting detection

Utility Functions

Helper functions for logging, file management, text preprocessing, model utilities, and common development tasks.

  • Logging setup and management
  • Text preprocessing utilities
  • Model utility functions
  • Common development helpers

Requirements

The following are the technical requirements for this project:

  • Python 3.8+
  • TensorFlow 2.13+
  • PyTorch 2.0+
  • Keras
  • Flask 2.3+
  • Jupyter Notebook 1.0.0+
  • NumPy 1.24+
  • Pandas 2.0+

Credits & Acknowledgments

This project is developed for educational purposes and utilizes the following resources:

  • Python - PSF License
  • PyTorch - BSD License
  • TensorFlow - Apache 2.0 License
  • Keras - Apache 2.0 License
  • RSK World - Project Inspiration
  • GitHub Repository - Source code and documentation

Support & Contact

For paid applications, please contact us for integration help or feedback.

  • Support Email: help@rskworld.in
  • Contact Number: +91 9330539277
  • Website: RSKWORLD.in
  • GitHub Project
  • Join Our Discord
  • Slack Support Channel
  • LSTM Chatbot Documentation
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Categories

Sequence-to-Sequence Conversational AI Python TensorFlow Keras LSTM

Technologies

Python 3.8+
TensorFlow
Keras
LSTM
Seq2Seq

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Designer & Tester: Rima Khatun

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