Watson SDKs abstract much of the complexity associated with application development. By providing programming interfaces in languages that you already know, they can help you get up and running quickly with IBM Watson services.
The following Watson SDKs are supported by IBM. You can find all SDKs here
The Watson SDKs support our 11 Watson cognitive services across nine different programming languages including Node, Java, Python and Go. They enable developers to quickly and easily connect their applications to services like Speech to Text, Discovery and Assistant.
These services can be found in the API & SDK reference library Many AI/ Machine learning services can be created for free using the lite account on IBM Cloud.
In todays workshop we will walk through creating natural language understanding service service on IBM Cloud. Once we have created this services we will use the Watson SDK to get these services working with Node.js and Python.
Please create an IBM Cloud account
Once you have created an IBM Cloud Account you can search the cloud catalog for Lite and find a service you would like to create.
In our case we will be creating a Natural Language Understanding and a Language Translator Service
Once you have these services created we can get started with using the SDKs
This SDK is tested with Node versions 10 and up. All official documentation on the Node SDK is found here
Make a directory and install the ibm-watson node package
npm install ibm-watson@^5.7.0
Let's get started by creating a simple nlu_example1.js file
In this file you will be extracting metadata such as concepts, keywords, categories, sentiment, and emotion from the url www.nytimes.com
For more documentation on NLU data extraction please see Natural Language API DOCs
const NaturalLanguageUnderstandingV1 = require('ibm-watson/natural-language-understanding/v1');
const { IamAuthenticator } = require('ibm-watson/auth');
const naturalLanguageUnderstanding = new NaturalLanguageUnderstandingV1({
version: '2019-07-12',
authenticator: new IamAuthenticator({
apikey: "<apikey>",
}),
url: "<url>",
});
const analyzeParams = {
'url': 'www.nytimes.com',
'features': {
'categories': {
'limit': 3,
'explanation' : true
},
'concepts' : {
'limit' : 3
},
'entities': {
'sentiment': true,
'limit': 1,
'emotion': true
},
'keywords': {
'sentiment': true,
'emotion': true,
'limit': 3
}
}
};
naturalLanguageUnderstanding.analyze(analyzeParams)
.then(analysisResults => {
console.log(JSON.stringify(analysisResults, null, 2));
})
.catch(err => {
console.log('error:', err);
});
You will have to include the API Key and service URL which you can find in the credentials section of the service you just created :
Once you add your service credentials you should be able to analyze with NLU.
Seenlu_example1 for more details
You can also authenticate to a service by automatically allowing credentials to be read from an environment file.
With a credentials file, you just need to put the file in the right place and the SDK will do the work of parsing it and authenticating. You can get this file by clicking the Download button for the credentials in the Manage tab of your service instance.
The file downloaded will be called ibm-credentials.env. This is the name the SDK will search for and must be preserved unless you want to configure the file path (see docs). The SDK will look for your ibm-credentials.env file in the following places (in order):
- Directory provided by the environment variable IBM_CREDENTIALS_FILE
- Your system's home directory
- Your current working directory (the directory Node is executed from)
As long as you set that up correctly, you don't have to worry about setting any authentication options in your code.
Example of using .env file
'use strict';
require('dotenv').config({
silent: true,
});
const NaturalLanguageUnderstandingV1 = require('ibm-watson/natural-language-understanding/v1');
const naturalLanguageUnderstanding = new NaturalLanguageUnderstandingV1({
version: '2019-07-12'
});
const analyzeParams = {
'url': 'www.ibm.com',
'features': {
'categories': {
'limit': 3
}
}
};
naturalLanguageUnderstanding.analyze(analyzeParams)
.then(analysisResults => {
console.log(JSON.stringify(analysisResults, null, 2));
})
.catch(err => {
console.log('error:', err);
});
Seenlu_example2 for more details
nlu_example3 also has an example that authenticates using the ibm-credentials.env file with text file data .
Feel free to play around with more NLU API and configurations found in the Natural Language API Docs as well as any other services that are supported by the SDK. Other services can be found in the API & SDK reference library
Now that you are familiar with using the Node SDK lets create a NLU Node Demo project.
Clone this repo into your directory : https://github.com/IBM/natural-language-understanding-code-pattern
npm install
npm run build
npm run dev
Your browser will point to localhost:5000
You can follow these steps on this repo to deploy application to the cloud
This SDK is tested with Python versions 3.5 and up. All official documentation on the Python SDK is found here
Make a directory and install using pip or easy_install:
pip install --upgrade ibm-watson
or
easy_install --upgrade ibm-watson
Let's get started by creating a simple nlu_example1.py file
from ibm_watson import NaturalLanguageUnderstandingV1
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from ibm_watson.natural_language_understanding_v1 import Features, CategoriesOptions
authenticator = IAMAuthenticator('<apiKey>')
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2019-07-12',
authenticator=authenticator
)
natural_language_understanding.set_service_url('<URL>')
response = natural_language_understanding.analyze(
url='www.ibm.com',
features=Features(categories=CategoriesOptions(limit=3))).get_result()
print(json.dumps(response, indent=2))
Add your API Key and URL from NLU Credentials
You can also authenticate to a service by automatically allowing credentials to be read from an environment file. Download the ibm-credentials.env file from the credentials page and try running this python file
from ibm_watson import NaturalLanguageUnderstandingV1
from ibm_watson.natural_language_understanding_v1 import Features, EntitiesOptions, KeywordsOptions
service = NaturalLanguageUnderstandingV1(
version='2018-03-16')
service.set_service_url('https://gateway.watsonplatform.net/natural-language-understanding/api')
response = service.analyze(
text='Bruce Banner is the Hulk and Bruce Wayne is BATMAN! '
'Superman fears not Banner, but Wayne.',
features=Features(entities=EntitiesOptions(),
keywords=KeywordsOptions())).get_result()
print(json.dumps(response, indent=2))
This jupyter notebook will parse wikipedia and analyze text using NLU
To run note book , download wiki-nlu parse.ipynb
We will be running jupyter notebooks locally so make sure you have Jupyter installed: https://jupyter.org/install
Run
jupyter notebook
Find or upload wiki-nlu parse.ipynb
Add credentials from NLU service credentials and run each cell
You should be able to see the output of the analyzed wikipedia. Feel free to change the query.