Postman’s AI assistant

 Postbot 

While these were powerful AI features powered by Postbot, they were disconnected actions within the Postman app and it didn’t feel like you were talking to “one expert AI assistant.” As the set of capabilities grew, having multiple entry points quickly became untenable. Users needed help knowing where to find Postbot and what they could do with it. This also coincided with the rise of chat as a popular way to interact with AI-enabled systems.

Postman

 Visualize APIs <=> Visualize the Product - Postman FLOWS !

Postman Flows, is a visual tool to create API workflows. You can use Flows to chain requests, handle data and create real world workflows right in your Postman workspace.

Create a new flow

You create flows in a workspace. Use workspaces to organize your API projects and collaborate with your team.

To create a new flow, do the following:

1. Choose an existing workspace or create a new one.
2. From the Postman sidebar, select  Flows.
3. Select  Create a new Flow >  Create new Flow.
4. In the sidebar, hover over the new flow and select  View more actions > Rename.
5. Rename the flow to Hello, world.
6. Select the Return or Enter key.

Add a String block

Use the String block to define text, which you can send to another block.

To add a String block, do the following:

1. From the canvas toolbar, select  Block.
2. Select  String. You can also search for the block by entering String in search.
3. To the right of the Start block, select the canvas to place the String block in that location.
4. In the String block, enter the text Hello, world.

In the next procedure, you'll connect this block to a Display block to display your text.

Connect an Output block

When you connect two blocks, you connect one block's input to another block's output. Inputs are on the block's left side and outputs are on its right side. The Display block displays incoming data, such as the String block's string value.

To connect a Display block, do the following:

1. Hover over the String block's output port. The pointer changes to a crosshair.
2. Drag the output port to the right of the String block to place the Output block in that location.
3. Select  Output. You can also search for the block, by entering Output in search.

Run the flow

From the canvas toolbar, select  Run.

GRAPHQL Part 2

GraphQL has two parts: 

1.  A Server component, a GraphQL server to accept the incoming requests and send back data precisely. 
2. A Client component, to send a POST request (with payload) to the GraphQL server. 

GraphQL uses a type system to define the capabilities of an API. All the types declared in an API are written in a schema using the GraphQL Schema - Schema Definition Language (SDL). It serves as a contract between the client and the server to define how a client can access the data. Defining the Schema allows the back-end and front-end teams to concentrate on their piece of work without much communication. 

WORKFLOW 

     Developing a GraphQL API revolves around three main concepts. 

Firstly the Schema

Secondly, the Queries

Thirdly the Resolvers 

 • Declarative syntax to Query the data you’d need from the Client to the Server

 • GraphQL would generate the payload based on the query and it has a JSON response with data on it. 

 • Server sends the payload back to the Client and the data gets displayed onto the Client

GraphQL vs. REST API

Data fetching 

Data fetching is one of the most significant improvements in GraphQL when compared to REST.

REST API retrieves the necessary information by accessing multiple endpoints.

With REST API sometimes need to use multiple different endpoints to fetch the required data. 

GraphQL is a query language, and your query (request) when triggered it precisely responds with the expected data, no extra information gets retrieved. No more over and under fetching of data. 

GraphQL gives you precise data. GraphQL you’d send a query [nested queries] and GraphQL responds with exact data. 

 Reference https://www.howtographql.com/basics/1-graphql-is-the-better-rest/ 

 Data Operation

 In REST API, each resource is represented with an endpoint, and it’s apparent to end up having multiple endpoints. The data fetch done using the CRUD (Create, Read, Update and Delete) operation via multiple endpoints. 

Whereas, In GraphQL, everything perceived as a graph and it’s connected and represented as a single endpoint. It also has unique features like Query, Mutation and Subscription. 

Versioning

In REST API, it is prevalent to see APIs have a different version like v1, v2 and so on. 

In GraphQL, there’s no need since it is based on Schema and users can quickly mark those types as deprecation for the fields that are no longer needed. This also saves a huge time in back and forth testing on different versions of the same API. 

Benefits of a Schema & Type System

Once the GraphQL schema is defined, the teams working on frontend and backends can do their work without further communication since they both are aware of the definite structure of the data that’s sent over the network.

Frontend teams can easily test their applications by mocking the required data structures. Once the server is ready, the switch can be flipped for the client apps to load the data from the actual API.

Insightful Analytics on the Backend

GraphQL allows you to have fine-grained insights about the data that’s requested on the backend. As each client specifies exactly what information it’s interested in, it is possible to gain a deep understanding of how the available data is being used. This can for example help in evolving an API and deprecating specific fields that are not requested by any clients any more.

With GraphQL, you can also do low-level performance monitoring of the requests that are processed by your server. GraphQL uses the concept of resolver functions to collect the data that’s requested by a client. Instrumenting and measuring performance of these resolvers provides crucial insights about bottlenecks in your system.