AI Agents

Process search queries through advanced generative model with vector storage

34 nodes 306 133 Webhook
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Workflow Description

An intelligent workflow that receives queries via webhook, loads and chunks documents, transforms them into numerical vectors, retrieves relevant information from a vector database, then sends it to a language model for generating accurate responses.

How it works

  1. 1.Receive and validate query through webhook endpoint
  2. 2.Load, split documents into chunks, and convert to vector embeddings
  3. 3.Search vector store and retrieve contextually relevant information
  4. 4.Send context and query to Gemini model to generate response
  5. 5.Publish result to Twitter/X and return response via webhook

Use cases

  • Automated Q&A system that extracts information from proprietary documents based on user questions
  • Instantly publish analytics and summaries to social media when specific queries are received

Requirements

  • Text documents available for loading into the system
  • Gemini API key and Qdrant vector database for vector storage and retrieval
  • Twitter/X account for publishing results

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.

Apps Used

Document Default Data Loader Text Splitter Twitter/X Vector Store Embeddings LLM Chain Gemini

Details

Trigger Webhook
Nodes 34
Apps 7
Views 306
Downloads 133

How to Use

  1. 1.Click "Download Template"
  2. 2.Open your n8n dashboard
  3. 3.Go to Workflows > Import from File
  4. 4.Select downloaded file and configure credentials

Nodes Used (34)

/

Bitrix24 Handler

Webhook

#1

Credentials

Set

#2

Validate Token

If

#3

Route Event

Switch

#4

Process Message

Function

#5

Process Join

Function

#6

Process Install

Function

#7

Register Bot

HTTP Request

#8

Send Message

HTTP Request

#9

Send Join Message

HTTP Request

#10

Process Delete

No Op

#11

Success Response

Webhook

#12

Error Response

Webhook

#13

Merge parameters for Subworkflow

Merge

#14

Get a list of available storages

HTTP Request

#15

Get a list of List of Files and Folders

HTTP Request

#16

Get a list of Folders files

HTTP Request

#17

Download file

HTTP Request

#18

Default Data Loader

Document Default Data Loader

#19

Recursive Character Text Splitter

Text Splitter Recursive Character Text Splitter

#20

Split Out folder files and folders

Split Out

#21

Filter for files

Filter

#22

Move files to Vector stored folder

HTTP Request

#23

Execute Workflow Trigger

Execute Workflow Trigger

#24

Sticky Note

Sticky Note

#25

Qdrant Vector Store

Vector Store Qdrant

#26

Embeddings Ollama

Embeddings Ollama

#27

Vector Store Retriever

Retriever Vector Store

#28

Question and Answer Chain

Chain Retrieval Qa

#29

Prepare output parameters

Set

#30

Embeddings Ollama1

Embeddings Ollama

#31

Google Gemini Chat Model

Gemini Model

#32

Qdrant Vector Store1

Vector Store Qdrant

#33

Execute subworkflow

Execute Workflow

#34

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