Build an intelligent agent that learns from documents and answers user inquiries
Workflow Description
A comprehensive automation that loads documents, segments them, stores content in a vector database, then deploys an AI-powered agent with conversation memory to answer questions based on loaded documents and automatically post responses to X.
How it works
- 1.Load documents and extract content using the default data loader for multiple file formats
- 2.Split text into smaller chunks and convert them to embedding vectors using Mistral embeddings
- 3.Store vectors in Qdrant vector database for fast retrieval and semantic search capabilities
- 4.Receive user questions through chat interface and route them to the intelligent agent
- 5.Use OpenAI-powered agent with memory to generate accurate answers based on indexed documents
- 6.Post generated responses and content to X account automatically for audience engagement
Use cases
- Create a customer support assistant that answers questions based on your documented knowledge base
- Automate publishing of insights and summaries to social media directly from your documents
- Build a technical support system that learns from internal documentation and provides instant guidance
Requirements
- Documents in supported formats (PDF, Word, text files) ready for loading and analysis
- Active OpenAI API account with appropriate credits for agent processing and language understanding
- Configured Qdrant instance with a vector database ready to store embedded content
- Twitter/X account with API access credentials enabled for automated posting
- Relevant technical or business documents to train the agent on your domain knowledge
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (38)
When clicking ‘Test workflow’
Manual Trigger
Embeddings Mistral Cloud
Embeddings Mistral Cloud
Default Data Loader
Document Default Data Loader
Recursive Character Text Splitter
Text Splitter Recursive Character Text Splitter
Get Tax Code Zip File
HTTP Request
Extract Zip Files
Compression
Files as Items
Split Out
Extract PDF Contents
Extract From File
Extract From Chapter
Set
Map To Sections
Set
Execute Workflow Trigger
Execute Workflow Trigger
Get Mistral Embeddings
HTTP Request
Content Chunking @ 50k Chars
Set
Split Out Chunks
Split Out
For Each Section...
Split In Batches
Sections To List
Split Out
Only Valid Sections
Filter
Use Qdrant Search API1
HTTP Request
Use Qdrant Scroll API
HTTP Request
Get Search Response
Set
Sticky Note
Sticky Note
Sticky Note1
Sticky Note
Sticky Note2
Sticky Note
Qdrant Vector Store
Vector Store Qdrant
Sticky Note3
Sticky Note
Sticky Note4
Sticky Note
AI Agent
Agent
Window Buffer Memory
Memory Buffer Window
When chat message received
Chat Trigger
Window Buffer Memory1
Memory Buffer Window
OpenAI Chat Model
OpenAI
1sec
Wait
Ask Tool
Tool Workflow
Search Tool
Tool Workflow
Switch
Switch
Get Ask Response
Set
Sticky Note5
Sticky Note
Sticky Note6
Sticky Note