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Information Extractor and Vector Store Tool integration

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

How to connect Information Extractor and Vector Store Tool

  • 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 Vector Store Tool integration: Create a new workflow and add the first step

Step 2: Add and configure Information Extractor and Vector Store Tool nodes

You can find Information Extractor and Vector Store Tool 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 Vector Store Tool nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Information Extractor and Vector Store Tool integration: Add and configure Information Extractor and Vector Store Tool nodes

Step 3: Connect Information Extractor and Vector Store Tool

A connection establishes a link between Information Extractor and Vector Store Tool (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 Vector Store Tool integration: Connect Information Extractor and Vector Store Tool

Step 4: Customize and extend your Information Extractor and Vector Store Tool 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 Vector Store Tool with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Information Extractor and Vector Store Tool integration: Customize and extend your Information Extractor and Vector Store Tool integration

Step 5: Test and activate your Information Extractor and Vector Store Tool workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Information Extractor to Vector Store Tool 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 Vector Store Tool integration: Test and activate your Information Extractor and Vector Store Tool workflow

Scale Deal Flow with a Pitch Deck AI Vision, Chatbot and QDrant Vector Store

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck!

This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss!

Requires n8n v1.62.1+

How It Works

  • Airtable is used as the pitch deck database and PDF decks are downloaded from it.
  • An AI Vision model is used to transcribe each page of the pitch deck into markdown.
  • An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database.
  • The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck.

Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc

How To Use

  • This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow.
  • When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank.
  • If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow.
  • When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions.

Requirements

  • OpenAI for LLM
  • Airtable For Database and Interface
  • Qdrant for Vector Store

Customising This Workflow

  • Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.

Nodes used in this workflow

Popular Information Extractor and Vector Store Tool workflows

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HTTP Request node
Code node
+5

Scale Deal Flow with a Pitch Deck AI Vision, Chatbot and QDrant Vector Store

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! Requires n8n v1.62.1+ How It Works Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc How To Use This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow. When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. Requirements OpenAI for LLM Airtable For Database and Interface Qdrant for Vector Store Customising This Workflow Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.

Build your own Information Extractor and Vector Store Tool integration

Create custom Information Extractor and Vector Store Tool 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.

Information Extractor and Vector Store Tool integration details

FAQs

  • Can Information Extractor connect with Vector Store Tool?

  • Can I use Information Extractor’s API with n8n?

  • Can I use Vector Store Tool’s API with n8n?

  • Is n8n secure for integrating Information Extractor and Vector Store Tool?

  • How to get started with Information Extractor and Vector Store Tool integration in n8n.io?

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