Chat with Postgresql Database
Configured with Agent, Chat Trigger, OpenAI Chat Model, Memory Buffer Window, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- AI & models
- Data store
Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.
Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.
How it works
The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.
| Node | Category | Type | Operation |
|---|---|---|---|
| When chat message received | Trigger | Chat Trigger | See node parameters |
| AI Agent | AI & models | Agent | See node parameters |
| OpenAI Chat Model | AI & models | OpenAI Chat Model | See node parameters |
| Get Table Definition | Data store | Postgres Tool | executeQuery |
| Chat History | Data store | Memory Buffer Window | See node parameters |
| Execute SQL Query | Data store | Postgres Tool | executeQuery |
| Get DB Schema and Tables List | Data store | Postgres Tool | executeQuery |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.
Requirements
An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.
Credential types declared in the source
openAiApipostgres
Workflow notes
👨🎤 Setup
👨🎤 Setup
- Add your postgresql and OpenAI credentials.
- Click Chat button and start asking questions to your database.
- Activate the workflow and you can make the chat publicly available.
Sticky Note3
🛠️ Tools Used:
- Execute SQL Query: Used to execute any query generated by the agent.
- Get DB Schema and Tables List: It returns the list of all the tables with its schema name.
- Get Table Definition: It returns table details like column names, foreign keys and more of a particular table in a schema.
Sticky Note1
👆 You can exchange this with any other chat model of your choice.
Sticky Note2
👆 You can change how many number of messages to keep using Context Window Length option. It's 5 by default.
Chat with Postgresql Database
Chat with Postgresql Database
Purpose and structure
This folder contains the Chat with Postgresql Database workflow. Its executable nodes use agent, chatTrigger, lmChatOpenAi, memoryBufferWindow, postgresTool. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.
Setup
Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.
Entry points
When chat message received
Verification
Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.
Attribution
https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Postgrestool/1848_Postgrestool_Stickynote_Automation_Triggered.json MIT; preserve source notices.