Chat with your email history using Telegram, Mistral and Pgvector for RAG

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Who is this for? Everyone! Did you dream of asking an AI " what hotel did I stay in for holidays last summer? " or " what were my marks last semester like? ". Dream no more, as [vector similarity s...

Best for

Personal Productivity automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.telegramtrigger, n8n-nodes-base.splitinbatches, n8n-nodes-base.if, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.vectorstorepgvector, @n8n/n8n-nodes-langchain.toolworkflow, @n8n/n8n-nodes-langchain.embeddingsollama, n8n-nodes-base.set

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Alfonso Corretti.

Original n8n.io source

1.1 Workflow description

Title: Chat with your email history using Telegram, Mistral and Pgvector for RAG
Workflow name: Chat with your email history using Telegram, Mistral and Pgvector for RAG

Who is this for?

Everyone! Did you dream of asking an AI "what hotel did I stay in for holidays last summer?" or "what were my marks last semester like?".

Dream no more, as vector similarity searches and this workflow are the foundations to make it possible (as long as the information appears in your e-mails 😅).

100% Local and Open Source!

This workflow is designed to use locally-hosted open source. Ollama as LLM provider, nomic-embed-text as the embeddings model, and pgvector as the vector database engine, on top of Postgres.

Structured AND Vectorized

This workflow combines structured and semantic search on your e-mail.

No need for enterprise setups! Leverage the convenience of n8n and open source to get a bleeding edge solution.

Setup

You will need a PGVector database with embeddings for all your email. Use my other template Gmail to Vector Embeddings with PGVector and Ollama to set it up in a breeze!
Make a copy of my Email Assistant: Convert Natural Language to SQL Queries with Phi4-mini and PostgreSQL, you will need it for structured searches.
Install this template and modify the Call the SQL composer Workflow step, to point at your copy of the SQL workflow.
Adjust the rest of necessary steps: Telegram Trigger, AI Chat model, AI Embeddings...

Activate the workflow and chat around!

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Telegram Trigger

Type / Role: n8n-nodes-base.telegramTrigger - telegramTrigger
Config choices: Version 1.2

Block 2 - Loop Over Items

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 3 - Came from Telegram?

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 4 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 5 - Postgres PGVector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStorePGVector - vectorStorePGVector
Config choices: Version 1.1

Block 6 - Call the SQL composer Workflow

Type / Role: @n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices: Version 2.1

Block 7 - Embeddings Ollama

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOllama - embeddingsOllama
Config choices: Version 1

Block 8 - Beautify chat response

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 9 - Split text into chunks

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 10 - Respond on Telegram in batches

Type / Role: n8n-nodes-base.telegram - telegram
Config choices: Version 1.2

Block 11 - Escape Markdown

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 12 - No Operation, do nothing

Type / Role: n8n-nodes-base.noOp - noOp
Config choices: Version 1

Block 13 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 14 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 15 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 16 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 17 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 18 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.8

Block 19 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 20 - Generate session id

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

3. Summary Table

Workflow	Chat with your email history using Telegram, Mistral and Pgvector for RAG
Complexity	advanced
Nodes	20
Categories	Personal Productivity, AI RAG
Author	Alfonso Corretti
Published	27 Apr 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/3763/3763.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Personal Productivity workflows AI RAG workflows

Frequently asked questions

What does Chat with your email history using Telegram, Mistral and Pgvector for RAG do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Personal Productivity, AI RAG use case.

Alfonso Corretti

3 workflows

Nodes

n8n-nodes-base.telegramtrigger n8n-nodes-base.splitinbatches n8n-nodes-base.if @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.vectorstorepgvector @n8n/n8n-nodes-langchain.toolworkflow @n8n/n8n-nodes-langchain.embeddingsollama n8n-nodes-base.set

Complexity

advanced

Published 27 Apr 2025

Likes 0

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Install path: /data/workflows/3763/3763.json

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Chat with your email history using Telegram, Mistral and Pgvector for RAG

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Who is this for?

100% Local and Open Source!

Structured AND Vectorized

Setup

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Telegram Trigger

Block 2 - Loop Over Items

Block 3 - Came from Telegram?

Block 4 - When chat message received

Block 5 - Postgres PGVector Store

Block 6 - Call the SQL composer Workflow

Block 7 - Embeddings Ollama

Block 8 - Beautify chat response

Block 9 - Split text into chunks

Block 10 - Respond on Telegram in batches

Block 11 - Escape Markdown

Block 12 - No Operation, do nothing

Block 13 - Sticky Note

Block 14 - Sticky Note1

Block 15 - Sticky Note2

Block 16 - Sticky Note3

Block 17 - Simple Memory

Block 18 - AI Agent

Block 19 - OpenAI Chat Model

Block 20 - Generate session id

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions