Can Information Extractor connect with Redis?

Yes, Information Extractor can connect with Redis using n8n.io. With n8n, you can create workflows that automate tasks and transfer data between Information Extractor and Redis. Configure nodes for Information Extractor and Redis in the n8n interface, specifying actions and triggers to set up their connection.

Can I use Information Extractor’s API with n8n?

Yes, with n8n, you can programmatically interact with Information Extractor’s API via pre-defined supported actions or raw HTTP requests. With the HTTP Request node, you create a REST API call. You need to understand basic API terminology and concepts.

Can I use Redis’s API with n8n?

Yes, with n8n, you can programmatically interact with Redis’s API via pre-defined supported actions or raw HTTP requests. With the HTTP Request node, you create a REST API call. You need to understand basic API terminology and concepts.

Is n8n secure for integrating Information Extractor and Redis?

Yes, it is safe to use n8n to integrate Information Extractor and Redis. n8n offers various features to ensure the safe handling of your data. These include encrypted data transfers, secure credential storage, RBAC functionality, and compliance with industry-standard security practices (SOC2 compliant). For hosted plans, data is stored within the EU on servers located in Frankfurt, Germany. You can also host it on your own infrastructure for added control. Learn more about n8n’s security practices here.

How to get started with Information Extractor and Redis integration in n8n.io?

To start integrating Information Extractor and Redis in n8n, you have different options depending on how you intend to use it: n8n Cloud: hosted solution, no need to install anything. Self-host: recommended method for full-control or customized use cases. Unlike other platforms that charge per operation, step, or task, n8n charges only for full workflow executions. This approach guarantees predictable costs and scalability, no matter the complexity or volume of your workflows.

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Information Extractor

Redis

Information Extractor and Redis integration

Save yourself the work of writing custom integrations for Information Extractor and Redis and use n8n instead. Build adaptable and scalable AI, Langchain, Development, and Data & Storage workflows that work with your technology stack. All within a building experience you will love.

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How to connect Information Extractor and Redis

Step 1: Create a new workflow
Step 2: Add and configure nodes
Step 3: Connect
Step 4: Customize and extend your integration
Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

Information Extractor and Redis integration: Create a new workflow and add the first step

Step 2: Add and configure Information Extractor and Redis nodes

You can find Information Extractor and Redis in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Information Extractor and Redis nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Information Extractor and Redis integration: Add and configure Information Extractor and Redis nodes

Step 3: Connect Information Extractor and Redis

A connection establishes a link between Information Extractor and Redis (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

Information Extractor and Redis integration: Connect Information Extractor and Redis

Step 4: Customize and extend your Information Extractor and Redis integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Information Extractor and Redis with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Information Extractor and Redis integration: Customize and extend your Information Extractor and Redis integration

Step 5: Test and activate your Information Extractor and Redis workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Information Extractor to Redis or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

Information Extractor and Redis integration: Test and activate your Information Extractor and Redis workflow

Youtube RAG search with Frontend using Apify, Qdrant and AI

I originally started to template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel.

Check out a demo here: https://jimleuk.app.n8n.cloud/webhook/n8n_videos

How it works
Stage 1 is to collect the Youtube video transcripts and push them into a vector database. For this, I've used Apify to scrape Youtube and Qdrant to store the embeddings.
Transcripts are broken down into smaller chunks and carefully tagged with metadata to assist in later search and filtering.
Stage 2 is to build a web frontend for the user to query the vectorised transcripts. I'm using a webhook to serve a simple web app and API to dynamically fetch the results.
When searching for a video, I've opted to use Qdrant's search groups API which in this use-case, performs better as it returns a wider range of videos results.
In the web frontend, when the user clicks on the results, the matching Youtube video plays in an embedded video player.

How to use
Once credentials are all set, first run steps 1 - 3 to populate your vector store.
Next, set the workflow to active to expose the web frontend. Visit the webhook URL in your browser to use it.
If only for personal use, you may want to remove the rate limiting mechanism in step 4.

Requirements
Apify for Youtube Channel and Video Scraping
Qdrant for Vector store
OpenAI for LLM and Embeddings

Customising the template
Not interested in official n8n videos? Swap to a different channel - this template will work on many as long as videos are not private or set to prevent embeds.
Technically any vector store should work but may not have the same grouping API. Use the simple vector store node and revert back to basic searching instead.

Nodes used in this workflow

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Popular Information Extractor and Redis workflows

Youtube RAG search with Frontend using Apify, Qdrant and AI

Ever wanted to build your own RAG search over Youtube videos? Well, now you can! This n8n template shows how you can build a very capable Youtube search engine powered by Apify, Qdrant and your LLM of choice to quickly and efficiently browse over many videos for research. I originally started to template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel. Check out a demo here: https://jimleuk.app.n8n.cloud/webhook/n8n_videos How it works Stage 1 is to collect the Youtube video transcripts and push them into a vector database. For this, I've used Apify to scrape Youtube and Qdrant to store the embeddings. Transcripts are broken down into smaller chunks and carefully tagged with metadata to assist in later search and filtering. Stage 2 is to build a web frontend for the user to query the vectorised transcripts. I'm using a webhook to serve a simple web app and API to dynamically fetch the results. When searching for a video, I've opted to use Qdrant's search groups API which in this use-case, performs better as it returns a wider range of videos results. In the web frontend, when the user clicks on the results, the matching Youtube video plays in an embedded video player. How to use Once credentials are all set, first run steps 1 - 3 to populate your vector store. Next, set the workflow to active to expose the web frontend. Visit the webhook URL in your browser to use it. If only for personal use, you may want to remove the rate limiting mechanism in step 4. Requirements Apify for Youtube Channel and Video Scraping Qdrant for Vector store OpenAI for LLM and Embeddings Customising the template Not interested in official n8n videos? Swap to a different channel - this template will work on many as long as videos are not private or set to prevent embeds. Technically any vector store should work but may not have the same grouping API. Use the simple vector store node and revert back to basic searching instead.

Build your own Information Extractor and Redis integration

Create custom Information Extractor and Redis workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.

Redis supported actions

Delete

Delete a key from Redis

Get

Get the value of a key from Redis

Increment

Atomically increments a key by 1. Creates the key if it does not exist.

Info

Returns generic information about the Redis instance

Keys

Returns all the keys matching a pattern

Pop

Pop data from a redis list

Publish

Publish message to redis channel

Push

Push data to a redis list

Set

Set the value of a key in redis

Information Extractor and Redis integration details

Information Extractor node docs + examples

See Information Extractor integrations

Related categories

Langchain

Redis is an open-source, advanced key-value store. It is often referred to as a data structure server. Its high performance makes it a favorite fast data store, cache, and even lightweight message broker.

Redis node docs + examples

Redis credential docs

See Redis integrations

Related categories

Development

Data & Storage

Other integrations with Information Extractor