Hello Rasa Community,
I am currently working on a project that involves integrating a massive dataset into Rasa. Specifically, I have a CSV file with 730,000 question-answer pairs, structured with a “question” column and an “answer” column. I’m looking for guidance on how to effectively import and use this extensive dataset within the Rasa framework while still leveraging Rasa’s natural language understanding (NLU) capabilities.
Here are my key questions:
- Data Import and Management: What’s the best approach for importing such a large-scale Q&A dataset into Rasa? Is there a recommended way to structure or pre-process this data so it’s compatible with the framework?
- NLU and Domain Configuration: How should I set up the NLU and domain files for such a large dataset? Are there specific practices or tools within Rasa that facilitate handling thousands of Q&A pairs while maintaining performance?
- Search and Response Mechanisms: Should I rely solely on Rasa actions with a custom fuzzy search for handling these question-answer pairs, or is there a way to incorporate this data more directly into the training data for the NLU model?
- Maintaining Conversational Flow: How can I ensure that the responses are contextually relevant and not just a simple retrieval from the Q&A database? I want to make full use of Rasa’s dialogue management rather than creating a pure lookup-based system.
I’m keen to develop a system that integrates seamlessly with Rasa’s conversational AI capabilities while handling a large dataset efficiently.
Any insights, best practices, or examples from similar implementations would be greatly appreciated!
Thank you in advance for your help!