Talk to your SQLite database with a LangChain AI Agent 🧠💬

Open on n8n.io

$20/month : Unlimited workflows

2500 executions/month

Try free

THE #1 IN WEB SCRAPING

Scrape any website without limits

Try free

HOSTINGER

Early Deal
DISCOUNT 20%

Self-hosted n8n

Unlimited workflows - from $4.99/mo

Try free

#1 hub for scraping, AI & automation

6000+ actors - $5 credits/mo

Try free

Important notice

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

1. Workflow Overview

This n8n workflow demonstrates how to create an agent using LangChain and SQLite. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. ...

Best for

Internal Wiki automation workflows
AI RAG automation workflows
intermediate n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.manualtrigger, n8n-nodes-base.httprequest, n8n-nodes-base.compression, n8n-nodes-base.readwritefile, n8n-nodes-base.stickynote, 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 Yulia.

Original n8n.io source

1.1 Workflow description

Title: Talk to your SQLite database with a LangChain AI Agent 🧠💬
Workflow name: Talk to your SQLite database with a LangChain AI Agent 🧠💬

🚀 Setup

Run the top part of the workflow once.
It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook.db).

🗣️ Chatting with Your Data

Send a message in a chat window.
Locally saved SQLite database loads automatically.
User's chat input is combined with the binary data.
The LangChain Agend node gets both data and begins to work.

The AI Agent will process the user's message, perform necessary SQL queries, and generate a response based on the database information. 🗄️

🌟 Example Queries

Try these sample queries to see the AI Agent in action:

"Please describe the database" - Get a high-level overview of the database structure, only one or two queries are needed.
"What are the revenues by genre?" - Retrieve revenue information grouped by genre, LangChain agent iterates several time before producing the answer.

The AI Agent will store the final answer in its memory, allowing for context-aware conversations. 💬

Read the full article: 👉 https://blog.n8n.io/ai-agents/

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 - Window Buffer Memory

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

Block 2 - OpenAI Chat Model

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

Block 3 - When clicking "Test workflow"

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

Block 4 - Get chinook.zip example

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 5 - Extract zip file

Type / Role: n8n-nodes-base.compression - compression
Config choices: Version 1.1

Block 6 - Save chinook.db locally

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

Block 7 - Load local chinook.db

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

Block 8 - Sticky Note

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

Block 9 - Sticky Note1

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

Block 10 - Combine chat input with the binary

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.3

Block 11 - Sticky Note2

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

Block 12 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.6

Block 13 - Chat Trigger

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

3. Summary Table

Workflow	Talk to your SQLite database with a LangChain AI Agent 🧠💬
Complexity	intermediate
Nodes	13
Categories	Internal Wiki, AI RAG
Author	Yulia
Published	14 Jun 2024

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2292/2292.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 Internal Wiki workflows AI RAG workflows

Frequently asked questions

What does Talk to your SQLite database with a LangChain AI Agent 🧠💬 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 Internal Wiki, AI RAG use case.

Yulia

13 workflows

Nodes

@n8n/n8n-nodes-langchain.memorybufferwindow @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.manualtrigger n8n-nodes-base.httprequest n8n-nodes-base.compression n8n-nodes-base.readwritefile n8n-nodes-base.stickynote n8n-nodes-base.set

Complexity

intermediate

Published 14 Jun 2024

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/2292/2292.json

Share Your Workflow

Have a useful automation to share? Publish it and help the community.

Submit Your Template How to Submit

Related Workflows

Build a RAG knowledge base from PDFs with Gemini, Supabase and Google Sheets

## Quick Overview This workflow ingests educational PDF URLs from Google Sheets, extracts and chunks their text, generates embeddings with Google Gemini, and stores them in a Supabase pgvector table for retrieval, while also exposing a public chat webhook that answers questions using Gemini and the same Supabase knowledge base. ## How it works 1. Runs every hour on a schedule and reads rows from a Google Sheets document, keeping only entries where the status is empty. 2. Processes each queued URL one at a time and routes it based on whether it is a seraj-uae.com page or a direct Google Drive file link. 3. For seraj-uae.com URLs, fetches the HTML page to extract the PDF download link (or embedded Google Drive file ID) and the document title. 4. Downloads the PDF from either the source website via HTTP or from Google Drive, waits briefly, and extracts text from the PDF binary. 5. Cleans and validates the extracted text, then splits it into overlapping chunks and generates embeddings using Google Gemini. 6. Inserts the embedded chunks into a Supabase pgvector table with file/title/source metadata and updates the Google Sheets row to “Embedded” or “Not Text Based PDF” if no text is found. 7. Separately, exposes a public n8n Chat webhook that receives student questions and uses Gemini Flash with Supabase vector retrieval to return answers. ## Setup 1. Create a Supabase project with pgvector enabled, create the seraj_documents table, and add the match_seraj_documents function used for vector search. 2. Add Supabase API credentials in n8n and ensure the table name (seraj_documents) and query function name (match_seraj_documents) match your Supabase setup. 3. Add Google Sheets OAuth2 credentials and update the Google Sheet document ID/sheet reference, ensuring columns include source_url, status, and row_number. 4. Add Google Drive OAuth2 credentials for downloading PDFs hosted in Drive. 5. Add a Google Gemini (PaLM) API credential for embeddings and Gemini Flash, then activate the workflow and copy the public chat webhook URL for your website chat widget.

View

Build a Slack knowledge graph with Claude, Neo4j and Google Sheets

## Quick Overview This workflow ingests chat messages via a webhook or a 15-minute schedule, filters out low-signal content, uses Anthropic Claude to extract entities and relationships, and stores the resulting knowledge graph in Neo4j while appending an audit log row to Google Sheets. ## How it works 1. Receives a chat message via an n8n webhook or runs every 15 minutes to process polled Slack data. 2. Normalizes the incoming payload into consistent fields like channel, sender, timestamp, and message text. 3. Uses a Python script to tag message signals (questions, decisions, action items) and filter out noise such as greetings, bots, and very short messages. 4. Sends each relevant message to Anthropic Claude to extract entities, relationships, a summary, message type, and importance. 5. Sends the extracted graph to Anthropic Claude again to add implicit relationships, weights, decision-chain metadata, and a thread category. 6. Merges both AI passes into a single nodes-and-edges knowledge graph, validates references and edge weights, and drops invalid graphs. 7. Upserts the graph into Neo4j via its transactional HTTP endpoint, appends an audit row to Google Sheets, and returns a JSON success response to the webhook caller. ## Setup 1. Add an Anthropic API credential and select the Claude model used by the two AI steps. 2. Configure Neo4j access by replacing the Neo4j HTTP endpoint URL and setting up HTTP Basic Auth credentials for your Neo4j instance. 3. Configure Google Sheets OAuth2 credentials and replace the placeholder Google Sheet ID, sheet tab name/range (KnowledgeGraph), and any required API permissions. 4. If using the webhook ingest path, copy the production webhook URL from n8n and configure your Slack/app integration to POST message payloads to `/chat-ingest` with the expected fields (text, channel, sender, timestamps).

View

Answer voice queries from a webhook over Google Drive docs using GPT-4o-mini and Supabase

## Quick overview Placetel AI – RAG Voice Assistant with Google Drive & Supabase ## How it works 1. Runs on a daily schedule at 02:00 or via manual start to reindex documents. 2. Lists files from a specified Google Drive folder and iterates through each file. 3. Downloads each Google Drive file, loads its text content, creates embeddings with OpenAI, and stores the resulting chunks in a Supabase vector table. 4. Receives a question via a POST webhook with a JSON body containing `chatInput`. 5. Generates an answer with GPT-4o-mini by semantically retrieving relevant passages from the Supabase vector store using the same OpenAI embeddings model. 6. Returns the generated, source-cited response to the webhook caller for voice output. ## Setup 1. Add Google Drive OAuth2 credentials and replace `DEINE_ORDNER_ID` in the Drive query with the folder you want to index. 2. Add an OpenAI API credential and ensure the same embeddings model/settings are used for both indexing and querying. 3. Create/configure a Supabase project with a `documents` table and the `match_documents` RPC/query used for vector search, then add your Supabase credentials. 4. Copy the webhook URL from the webhook trigger and configure your calling system to POST `{ "chatInput": "..." }` to it.

View

Need Custom Automation?

Get help designing a custom n8n workflow that connects your stack and fits your process.

Talk to your SQLite database with a LangChain AI Agent 🧠💬

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

🚀 Setup

🗣️ Chatting with Your Data

🌟 Example Queries

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Window Buffer Memory

Block 2 - OpenAI Chat Model

Block 3 - When clicking "Test workflow"

Block 4 - Get chinook.zip example

Block 5 - Extract zip file

Block 6 - Save chinook.db locally

Block 7 - Load local chinook.db

Block 8 - Sticky Note

Block 9 - Sticky Note1

Block 10 - Combine chat input with the binary

Block 11 - Sticky Note2

Block 12 - AI Agent

Block 13 - Chat Trigger

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