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Integrate Monkey Learn with PostgreSQL

Appy Pie Connect allows you to automate multiple workflows between Monkey Learn and PostgreSQL

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About Monkey Learn

MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.

About PostgreSQL

PostgreSQL is a robust, open-source database engine with a sophisticated query optimizer and a slew of built-in capabilities, making it an excellent choice for production databases.

PostgreSQL Integrations
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Best ways to Integrate Monkey Learn + PostgreSQL

  • Monkey Learn Integration Monkey Learn Integration

    PostgreSQL + Monkey Learn

    Classify Text in monkeylearn when New Row is created in PostgreSQL Read More...
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  • Monkey Learn Integration Monkey Learn Integration

    PostgreSQL + Monkey Learn

    Extract Text in monkeylearn when New Row is created in PostgreSQL Read More...
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  • Monkey Learn Integration Monkey Learn Integration

    PostgreSQL + Monkey Learn

    Upload training Data in monkeylearn when New Row is created in PostgreSQL Read More...
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    Monkey Learn Integration Upload training Data
  • Monkey Learn Integration Monkey Learn Integration

    PostgreSQL + Monkey Learn

    Classify Text in monkeylearn when New Row (Custom Query) is created in PostgreSQL Read More...
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    When this happens...
    Monkey Learn Integration New Row (Custom Query)
     
    Then do this...
    Monkey Learn Integration Classify Text
  • Monkey Learn Integration Monkey Learn Integration

    PostgreSQL + Monkey Learn

    Extract Text in monkeylearn when New Row (Custom Query) is created in PostgreSQL Read More...
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    When this happens...
    Monkey Learn Integration New Row (Custom Query)
     
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    Monkey Learn Integration Extract Text
  • Monkey Learn Integration {{item.actionAppName}} Integration

    Monkey Learn + {{item.actionAppName}}

    {{item.message}} Read More...
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    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
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    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Monkey Learn + PostgreSQL in easier way

It's easy to connect Monkey Learn + PostgreSQL without coding knowledge. Start creating your own business flow.

    Triggers
  • New Column

    Triggered when you add a new column.

  • New Row

    Triggered when you add a new row.

  • New Row (Custom Query)

    Triggered when new rows are returned from a custom query that you provide. Advanced Users Only

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Create Row

    Adds a new row.

  • Update Row

    Updates an existing row.

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Monkey Learn & PostgreSQL Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick PostgreSQL as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Monkey Learn to PostgreSQL.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and PostgreSQL

This document contains an introduction to the concept of Monkey Learn, a Python-based machine learning framework that can be integrated with PostgreSQL, which is an open source object-relational database management system. The purpose of this document is to introduce the reader to the idea of machine learning using artificial intelligence (AI. and to outline the process of integrating Monkey Learn and PostgreSQL.

The outline for this document will comprise three sections. introduction, body, conclusion. The introduction section will define what monkey learn and PostgreSQL are, and why they are important. The body section will provide information on how to integrate the two applications. Finally, the conclusion section will provide a summary of the key points presented in the document.

This section will provide step-by-step instructions to help you enable Monkey Learn on your machine. To begin, you must install PostgreSQL on your machine using either Homebrew or apt-get. If you choose to use Homebrew, run the fplowing commands in Terminal:

brew tap homebrew/versions

brew install postgresql

Next, install PostGIS using Homebrew:

brew install postgis

If you prefer to use apt-get, run the fplowing command in your terminal:

sudo apt-get install postgresql postgis

At this point, you should have all of the necessary software installed on your machine. Next, you must create an instance of PostgreSQL on your localhost server. Use the fplowing command to create a database called monkey_learn , and move into the directory containing your database files:

createdb monkey_learn psql monkey_learn -c ‘create extension postgis;’

PostgreSQL has a special function that allows you to create a table that will automatically add new cpumns when you add new data. This function is called declarative partitioning. In order to make use of this function, we will need to add a primary key constraint to our schema. This way, we can add additional cpumns without the fear of them conflicting with existing cpumns in our table. Run the fplowing command in your terminal to add a primary key constraint:

ALTER TABLE public . session_data ADD PRIMARY KEY (id. USING BTREE; ALTER TABLE public . session_data ADD PRIMARY KEY (id. USING BTREE;

Now that we have a table with a primary key constraint, we can add cpumns with declarative partitioning. Use the fplowing command to add a cpumn called metadata :

ALTER TABLE public . session_data ADD COLUMN metadata varchar ( 255 . NOT NULL AFTER userid USING PARTITIONING BY RANGE (to_tsvector(metadata)); ALTER TABLE public . session_data ADD COLUMN metadata varchar (255. NOT NULL AFTER userid USING PARTITIONING BY RANGE (to_tsvector(metadata));

After adding the metadata cpumn, we need to add another cpumn called tags . Use the fplowing command to add this new cpumn:

ALTER TABLE public . session_data ADD COLUMN tags text [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [] FROM public . tags; ALTER TABLE public.session_data ADD COLUMN tags text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] , text[] FROM public.tags;

Now that we have added our two new cpumns, we need to load some data into our database. Download the CSV file from here and save it in your PostgreSQL directory. After saving the file, run these commands in your terminal:

psql monkey_learn i sessions_data.csv psql monkey_learn i sessions_data.csv

The process to integrate 403 Forbidden and 403 Forbidden may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.