- NLP
- NLP with MindsDB and OpenAI
MindsDB NLP Supported Tasks
MindsDB lets you create models that utilize features provided by OpenAI GPT-3. Currently, there are three operation modes:
- Answering Questions without Context
- Answering Questions with Context
- Prompt Completion
Currently, MindsDB’s NLP engine is powered by Hugging Face and OpenAI. But we plan to expand to other NLP options in the future, so stay tuned!
How to Bring the OpenAI Model to MindsDB
We use the CREATE MODEL statement to bring the OpenAI models to MindsDB.
Generally, it looks like this:
CREATE MODEL project_name.predictor_name -- AI TABLE TO STORE THE MODEL
PREDICT target_column -- NAME OF THE COLUMN TO STORE PREDICTED VALUES
USING
engine = 'openai', -- USING THE OPENAI ENGINE
prompt_template = 'promt/task
{{input_column}}', -- PROMPT TEMPLATE WITH PLACEHOLDERS FOR INPUT COLUMNS
model_name = 'model_name', -- OPTIONAL, DEFAULT IS THE text-davinci-002 MODEL
api_key = 'YOUR_OPENAI_API_KEY'; -- OPTIONAL, IF NOT PASSED MINDSDB FETCHES THE
-- `OPENAI_API_KEY` ENVIRONMENT VARIABLE VALUE
Example
For more examples and explanations, visit our doc page on OpenAI.
Example using SQL
Let’s go through a sentiment classification example to understand better how to bring OpenAI models to MindsDB as AI tables.
CREATE MODEL mindsdb.sentiment_classifier
PREDICT sentiment
USING
engine = 'openai',
prompt_template = 'predict the sentiment of the text:{{review}} exactly as either positive or negative or neutral';
On execution, we get:
Query successfully completed
Where:
| Expressions | Values |
|---|---|
project_name | mindsdb |
predictor_name | sentiment_classifier |
target_column | sentiment |
engine | openai |
prompt_template | predict the sentiment of the text:{{review}} exactly as either positive or negative or neutral |
In the prompt_template parameter, we use a placeholder for a text value that comes from the review column, that is, text:{{review}}.
Before querying for predictions, we should verify the status of the sentiment_classifier model.
SELECT *
FROM mindsdb.models
WHERE name = 'sentiment_classifier';
On execution, we get:
+--------------------+------+-------+-------+--------+--------+---------+-------------+---------------+------+-----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
|NAME |ENGINE|PROJECT|VERSION|STATUS |ACCURACY|PREDICT |UPDATE_STATUS|MINDSDB_VERSION|ERROR |SELECT_DATA_QUERY|TRAINING_OPTIONS |TAG |
+--------------------+------+-------+-------+--------+--------+---------+-------------+---------------+------+-----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
|sentiment_classifier|openai|mindsdb|1 |complete|[NULL] |sentiment|up_to_date |22.12.4.3 |[NULL]|[NULL] |{'target': 'sentiment', 'using': {'prompt_template': 'predict the sentiment of the text:{{review}} exactly as either positive or negative or neutral'}}|[NULL] |
+--------------------+------+-------+-------+--------+--------+---------+-------------+---------------+------+-----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
Once the status is complete, we can query for predictions.
SELECT output.sentiment, input.review
FROM example_db.demo_data.amazon_reviews AS input
JOIN mindsdb.sentiment_classifier AS output
LIMIT 3;
Don’t forget to create the example_db database before using one of its tables, like in the query above.
CREATE DATABASE example_db
WITH ENGINE = "postgres",
PARAMETERS = {
"user": "demo_user",
"password": "demo_password",
"host": "3.220.66.106",
"port": "5432",
"database": "demo"
};
On execution, we get:
+----------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| sentiment | review |
+----------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| positive | Late gift for my grandson. He is very happy with it. Easy for him (9yo ). |
| The sentiment of the text is positive. | I'm not super thrilled with the proprietary OS on this unit, but it does work okay and does what I need it to do. Appearance is very nice, price is very good and I can't complain too much - just wish it were easier (or at least more obvious) to port new apps onto it. For now, it helps me see things that are too small on my phone while I'm traveling. I'm a happy buyer.|
| positive | I purchased this Kindle Fire HD 8 was purchased for use by 5 and 8 yer old grandchildren. They basically use it to play Amazon games that you download. |
+----------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
For the full library of supported examples please go here.
Example using MQL
Now let’s go through a sentiment classification using Mongo database syntax.
We have a sample Mongo database that you can connect to your MindsDB Cloud account by running this command in Mongo Shell:
use mindsdb
Followed by:
db.databases.insertOne({
'name': 'mongo_test_db',
'engine': 'mongodb',
'connection_args': {
"host": "mongodb+srv://admin:201287aA@cluster0.myfdu.mongodb.net/admin?authSource=admin&replicaSet=atlas-5koz1i-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true",
"database": "test_data"
}
})
We use this sample database throughout the example.
The next step is to create a connection between Mongo and MindsDB. Follow the instructions to connect MindsDB via Mongo Compass or Mongo Shell.
Now, we are ready to create an OpenAI model.
db.models.insertOne({
name: 'sentiment_classifier_openai_mql',
predict: 'sentiment',
training_options: {
engine: 'openai',
prompt_template: 'predict the sentiment of the text:{{review}} exactly as either positive or negative or neutral'
}
})
On execution, we get:
{ acknowledged: true,
insertedId: ObjectId("63c19c3fe1d9855caa931df6") }
Before querying for predictions, we should verify the status of the sentiment_classifier model.
db.getCollection('models').find({'name': 'sentiment_classifier_openai_mql'})
On execution, we get:
{ NAME: 'sentiment_classifier_openai_mql',
ENGINE: 'openai',
PROJECT: 'mindsdb',
VERSION: 1,
STATUS: 'complete',
ACCURACY: null,
PREDICT: 'sentiment',
UPDATE_STATUS: 'up_to_date',
MINDSDB_VERSION: '22.12.4.3',
ERROR: null,
SELECT_DATA_QUERY: null,
TRAINING_OPTIONS: '{\'target\': \'sentiment\', \'using\': {\'prompt_template\': \'predict the sentiment of the text:{{review}} exactly as either positive or negative or neutral\'}}',
TAG: null,
_id: ObjectId("000000000000002836398080") }
Once the status is complete, we can query for a single prediction.
db.sentiment_classifier_openai_mql.find({review: 'It is ok.'})
On execution, we get:
{
sentiment: 'The sentiment of the text is neutral.',
review: 'It is ok.'
}
You can also query for batch predictions. Here we use the mongo_test_db database connected earlier in this example.
db.sentiment_classifier_openai_mql.find(
{'collection': 'mongo_test_db.amazon_reviews'},
{'sentiment_classifier_openai_mql.sentiment': 'sentiment',
'amazon_reviews.review': 'review'
}
)
On execution, we get:
{
sentiment: 'positive',
review: 'Late gift for my grandson. He is very happy with it. Easy for him (9yo ).'
}
{
sentiment: 'The sentiment of the text is positive.',
review: "I'm not super thrilled with the proprietary OS on this unit, but it does work okay and does what I need it to do. Appearance is very nice, price is very good and I can't complain too much - just wish it were easier (or at least more obvious) to port new apps onto it. For now, it helps me see things that are too small on my phone while I'm traveling. I'm a happy buyer."
}
{
sentiment: 'positive',
review: 'I purchased this Kindle Fire HD 8 was purchased for use by 5 and 8 yer old grandchildren. They basically use it to play Amazon games that you download.'
}
...
For the full library of supported examples please go here.