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Chat assistant (OpenAI assistant) with Postgres memory and API calling capabalities

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Chat assistant (OpenAI assistant) with Postgres memory and API calling capabalities preview
Open on n8n.io

Important notice

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

1. Workflow Overview

Workflow Description Your workflow is an intelligent chatbot, using ++OpenAI assistant++, integrated with a backend that supports WhatsApp Business, designed to handle various use cases such as sal...

Best for

  • Support Chatbot automation workflows
  • AI Chatbot automation workflows
  • intermediate n8n builders looking for reusable templates

Tools used

n8n-nodes-base.if, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.openai, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.memorypostgreschat, n8n-nodes-base.mysqltool, @n8n/n8n-nodes-langchain.toolhttprequest, n8n-nodes-base.stickynote

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title
Chat assistant (OpenAI assistant) with Postgres memory and API calling capabalities
Workflow name
Chat assistant (OpenAI assistant) with Postgres memory and API calling capabalities

Workflow Description

Your workflow is an intelligent chatbot, using ++OpenAI assistant++, integrated with a backend that supports WhatsApp Business, designed to handle various use cases such as sales and customer support. Below is a breakdown of its functionality and key components:


Workflow Structure and Functionality

Chat Input (Chat Trigger)

  • The flow starts by receiving messages from customers via WhatsApp Business.
  • Collects basic information, such as session_id, to organize interactions.

Condition Check (If Node)

  • Checks if additional customer data (e.g., name, age, dependents) is sent along with the message.
  • If additional data is present, a customized prompt is generated, which includes this information. The prompt specifies that this data is for the assistant's awareness and doesn’t require a response.

Data Preparation (Edit Fields Nodes)

  • Formats customer data and the interaction details to be processed by the AI assistant.
  • Compiles the customer data and their query into a single text block.

AI Responses (OpenAI Nodes)

  • The assistant’s prompt is carefully designed to guide the AI in providing accurate and relevant responses based on the customer’s query and data provided.
  • Prompts describe the available functionalities, including which APIs to call and their specific purposes, helping to prevent “hallucinated” or irrelevant responses.

Memory and Context (Postgres Chat Memory)

  • Stores context and messages in continuous sessions using a database, ensuring the chatbot maintains conversation history.

API Calls

  • The workflow allows the use of APIs with any endpoints you choose, depending on your specific use case. This flexibility enables integration with various services tailored to your needs.
  • The OpenAI assistant understands JSON structures, and you can define in the prompt how the responses should be formatted. This allows you to structure responses neatly for the client, ensuring clarity and professionalism.
  • Make sure to describe the purpose of each endpoint in the assistant’s prompt to help guide the AI and prevent misinterpretation.

Customer Response Delivery

  • After processing and querying APIs, the generated response is sent to the backend and ultimately delivered to the customer through WhatsApp Business.

Best Practices Implemented

  • Preventing Hallucinations
    Every API has a clear description in its prompt, ensuring the AI understands its intended use case.

  • Versatile Functionality
    The chatbot is modular and flexible, capable of handling both sales and general customer inquiries.

  • Context Persistence
    By utilizing persistent memory, the flow maintains continuous interaction context, which is crucial for longer conversations or follow-up queries.


Additional Recommendations

  • Include practical examples in the assistant’s prompt, such as frequently asked questions or decision-making flows based on API calls.
  • Ensure all responses align with the customer’s objectives (e.g., making a purchase or resolving technical queries).
  • Log interactions in detail for future analysis and workflow optimization.

This workflow provides a solid foundation for a robust and multifunctional virtual assistant 🚀

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 - If

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

Block 2 - Edit Fields1

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

Block 3 - Edit Fields2

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

Block 4 - OpenAI

Type / Role
@n8n/n8n-nodes-langchain.openAi - openAi
Config choices
Version 1.4

Block 5 - Chat Trigger

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

Block 6 - Postgres Chat Memory

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

Block 7 - Postgres Chat Memory1

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

Block 8 - Products in Daatabase

Type / Role
n8n-nodes-base.mySqlTool - mySqlTool
Config choices
Version 2.4

Block 9 - Knowledge Base

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

Block 10 - External API

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

Block 11 - Sticky Note

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

Block 12 - OpenAI2

Type / Role
@n8n/n8n-nodes-langchain.openAi - openAi
Config choices
Version 1.4

3. Summary Table

Workflow Chat assistant (OpenAI assistant) with Postgres memory and API calling capabalities
Complexity intermediate
Nodes 12
Categories Support Chatbot, AI Chatbot
Author Fernanda Silva
Published 13 Dec 2024

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/2637/2637.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

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

  4. 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. 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.

Frequently asked questions

What does Chat assistant (OpenAI assistant) with Postgres memory and API calling capabalities do?

Workflow Description Your workflow is an intelligent chatbot, using ++OpenAI assistant++, integrated with a backend that supports WhatsApp Business, designed to handle various use cases such as sal...

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 Support Chatbot, AI Chatbot use case.