Practical overview
When it fits
Use Deepqa when you want to study or experiment with a seq2seq chatbot implementation and can manage its Python dependencies, data and model training. Use its web interface when a trained checkpoint is available and you want a local browser-based way to interact with it.
Who it is for
- Developers learning or experimenting with neural conversational models.
- Researchers or practitioners comparing chatbot training data and model outputs.
- Python users who want to inspect an implementation with a documented training and testing workflow.
Documented uses
- Train a conversational model on the included Cornell corpus or another supported dialogue dataset.
- Test generated replies interactively or save sample predictions to a file.
- Explore training cost and the computational graph with TensorBoard.
- Provide a local browser interface for chatting with a trained checkpoint.
Requirements & limits
The implementation is public on GitHub and licensed under Apache-2.0. The repository documents local setup and training rather than a hosted chatbot service.
- Core model dependencies are documented as Python 3.5 and TensorFlow tested with 1.0, alongside NumPy, NLTK and tqdm.
- GPU use requires CUDA; the Cornell Movie Dialogs corpus is included, while other corpora have separate instructions.
- The web interface has additional Django, Channels, Redis and asgi_redis requirements and expects a trained checkpoint at a particular path.
- The README's sample conversations illustrate unreliable and sometimes incoherent outputs.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Train and interactively test a chatbot
Train the documented conversational model and open an interactive test session.
Before you start
- Python 3.5, TensorFlow (the README says version 1.0 was tested), NumPy, NLTK and tqdm.
- A dialogue corpus; Cornell Movie Dialogs is included by default. CUDA is listed for GPU use.
- The README notes that additional NLTK data may be needed.
Steps
Install the listed dependencies using the repository's requirements file.
If needed, obtain the NLTK punkt data using the README's documented downloader command.
Run main.py to train the model.
Run main.py --test interactive to test it interactively.
Expected outcome
A trained model can be explored through interactive text exchanges; the README also documents a non-interactive test mode that writes sample predictions.
Guide limitations
- The README's results section demonstrates inconsistent and sometimes nonsensical replies; it does not establish answer reliability.
- The documented dependency versions are specific to the README and may require environment matching.
Configure and launch the local web interface
Set up the documented Django interface to chat with a trained model locally.
Before you start
- A trained model checkpoint copied to save/model-server/model.ckpt.
- Django, Channels, Redis and asgi_redis; the README says Django 1.10 was tested and asgi_redis 1.0 or later is required.
- The chatbot secret key environment variable.
Steps
Set CHATBOT_SECRET_KEY to a secret value.
From chatbot_website/, run the documented Django migration-generation and migration commands.
Start Redis in the background, then launch the Django development server.
Open the local address http://localhost:8000/.
Expected outcome
The README describes a locally available browser interface connected to the trained model.
Guide limitations
- The interface setup assumes the model checkpoint is already present at the stated path.
- The documented runserver command is for local use; the README points to Django deployment guidance for server deployment.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- Conchylicultor/DeepQA · Official homepage title · retained in its source language