make data
make run
Head over to localhost:5000
/dataprovider/dataprovider.py - After core query is parsed, control passes to get_short_answer() where the processing logic lies. There, MongoDB lookup occurs. The priority of search is MongoDB -> wolfram -> wikipedia.
/nlp/rasa.py - Parsing logic - What to do with intents + all the hard-coded replies are defined here. Based on intent, if falls into hard-coded case, randomly one of the replies in the reply array is returned.
initDb.py - General documentation of how to push/pull to/from MongoDB - also includes the data which has already been inserted to MongoDB. This is the file needed to insert data to MongoDB (manually).
/data/training_data.json - Training dataset containing all the intents as well as entities. Use rasa-nlu-trainer
instead of manual filling.
- The bot learns to speak
- Database integration
- Flask API
- Front-End to interact
- The bot remembers
- The bot gives price, COD details, discounts
- The bot can bargain (randomly bring down product price upto given limit)
- The bot can accept an order and redirect you to the payment page
- The bot is mature
- The bot doesn't faint at all
Tip: Make use of Studio 3T on any platform to view MongoDB using an UI.