DialogBot is a Python library providing a unified framework for building conversational AI agents, encompassing search-based, task-oriented, and generative dialogue models. It targets developers and researchers seeking to integrate diverse dialogue capabilities, offering solutions for question answering, task completion, and open-domain chit-chat with built-in network retrieval.

How It Works

DialogBot integrates three core dialogue paradigms: search-based (retrieving answers via local similarity or web search), task-oriented (using End-to-End Memory Networks for structured conversations), and generative (leveraging GPT-2 and Seq2Seq models for free-form responses). This multi-model approach allows for flexible and robust dialogue system development, aiming to output optimal responses by combining these strategies.

Quick Start & Requirements

• Install via pip: pip3 install torch (or conda), then pip3 install -U dialogbot.
• Requires Python 3.6+, transformers 4.4.2+, and torch 1.6.0+.
• Official Demo: https://www.mulanai.com/product/dialogbot/

Highlighted Details

• Supports local and web-based question answering using TFIDF, BM25, and search engine result summarization (Baidu, Bing).
• Implements task-oriented dialogue with End-to-End Memory Networks (memn2n) on the BABi dataset.
• Offers generative dialogue capabilities using pre-trained GPT-2 models (e.g., shibing624/gpt2-dialogbot-base-chinese) and Seq2Seq models.
• Provides access to large Chinese conversational datasets (50w, 100w samples).

Maintenance & Community

• Project maintained by shibing624. Contact via email (xuming624@qq.com) or WeChat (xuming624) for NLP group access.
• Citation details provided for academic use.

Licensing & Compatibility

• Licensed under The Apache License 2.0, permitting commercial use with attribution.

Limitations & Caveats

• The project code is described as "rough," with a call for contributions and unit tests. Fine-tuning GPT-2 requires specific data preprocessing steps.