Using NLU Only
These docs are for version 1.x of Rasa Open Source. Docs for the new version 2.0 can be found here.
User Guide
- Installation
- Tutorial: Rasa Basics
- Tutorial: Building Assistants
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Evaluating Models
- Validate Data
- Running the Server
- Running Rasa with Docker
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Choosing a Pipeline
- Language Support
- Entity Extraction
- Components
Core
- About
- Stories
- Domains
- Responses
- Actions
- Policies
- Slots
- Forms
- Retrieval Actions
- Interactive Learning
- Fallback Actions
- Knowledge Base Actions
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Rasa SDK
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Lock Stores
- Training Data Importers
- Featurization of Conversations
- Migration Guide
- Rasa OSS Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.7.0
Using NLU Only
If you want to use Rasa only as an NLU component, you can!
Training NLU-only models
To train an NLU model only, run:
rasa train nlu
This will look for NLU training data files in the data/ directory and saves a trained model in the models/ directory. The name of the model will start with nlu-.
Testing your NLU model on the command line
To try out your NLU model on the command line, use the rasa shell nlu command:
rasa shell nlu
This will start the rasa shell and ask you to type in a message to test. You can keep typing in as many messages as you like.
Alternatively, you can leave out the nlu argument and pass in an nlu-only model directly:
rasa shell -m models/nlu-20190515-144445.tar.gz
Running an NLU server
To start a server with your NLU model, pass in the model name at runtime:
rasa run --enable-api -m models/nlu-20190515-144445.tar.gz
You can then request predictions from your model using the /model/parse endpoint. To do this, run:
curl localhost:5005/model/parse -d '{"text":"hello"}'
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