# Conchylicultor/DeepQA

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

Repository: https://github.com/Conchylicultor/DeepQA
Canonical: https://ross.abutalabs.com/products/deepqa
Language: Python
License: Apache-2.0
License Family: permissive
Topics: chatbot, deep-learning, tensorflow, seq2seq
Last push: 2022-12-30T14:42:37+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3709, "days_push": 1342, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2910, forks 1154 (observed 2026-08-28T04:07:29.209919+00:00)

## What it is
DeepQA is a TensorFlow implementation of Google's 'A Neural Conversational Model', a seq2seq RNN-based deep learning chatbot. It supports training on several dialog corpora (Cornell Movie Dialogs, OpenSubtitles, Ubuntu, Supreme Court) and includes a Django web chat interface.

## Use cases
- train a seq2seq neural chatbot
- reproduce the Google neural conversational model paper
- build a chatbot from movie dialog data
- experiment with RNN conversation models in TensorFlow
- run a web-based chatbot demo
- train a chatbot on custom conversation data

## When to choose
- you want a classic seq2seq chatbot implementation for learning or research
- you need to reproduce the 'A Neural Conversational Model' paper results
- you want to train a conversational model on Cornell Movie Dialogs or OpenSubtitles

## When to avoid
- you need a production-grade modern LLM chatbot
- you require up-to-date TensorFlow 2.x or PyTorch support
- you need actively maintained code with recent fixes

## Facets
- artifact type: library
- maturity: maintenance
- function: chatbot, machine-learning, deep-learning, nlp, llm-training
- domain: artificial-intelligence, deep-learning, chatbots
- platform: python
- tags: seq2seq, tensorflow, rnn, conversation-model, cornell-movie-dialogs, research-reproduction, natural-language-processing, linux, docker, gpu

## Member repositories
- Conchylicultor/DeepQA (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:29.209919+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:34:17.454289+00:00, confidence not recorded.
  - readme: https://github.com/Conchylicultor/DeepQA (fetched 2026-08-28T04:07:29.209919+00:00, sha d22cb44fbe42)
- Data as of 2026-08-30T08:39:29.467469+00:00.