+3

Chat Assistant (OpenAI assistant) with Postgres Memory And API Calling Capabalities

Published 1 month ago

Categories

Template description

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 🚀

Share Template

More AI workflow templates

OpenAI Chat Model node
SerpApi (Google Search) node

AI agent chat

This workflow employs OpenAI's language models and SerpAPI to create a responsive, intelligent conversational agent. It comes equipped with manual chat triggers and memory buffer capabilities to ensure seamless interactions. To use this template, you need to be on n8n version 1.50.0 or later.
n8n-team
n8n Team
HTTP Request node
Merge node
+7

Scrape and summarize webpages with AI

This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.
n8n-team
n8n Team
HTTP Request node
Markdown node
+5

AI agent that can scrape webpages

⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: url** – an address of the page to fetch method** = full / simplified maxlimit** - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: Extract HTML BODY; content Remove all unnecessary tags to recude the page size Further eliminate external URLs and IMG scr values (based on the method query parameter) Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.
eduard
Eduard
Merge node
Telegram node
Telegram Trigger node
+2

Telegram AI Chatbot

The workflow starts by listening for messages from Telegram users. The message is then processed, and based on its content, different actions are taken. If it's a regular chat message, the workflow generates a response using the OpenAI API and sends it back to the user. If it's a command to create an image, the workflow generates an image using the OpenAI API and sends the image to the user. If the command is unsupported, an error message is sent. Throughout the workflow, there are additional nodes for displaying notes and simulating typing actions.
eduard
Eduard
Google Sheets node
HTTP Request node
Merge node
+4

OpenAI GPT-3: Company Enrichment from website content

Enrich your company lists with OpenAI GPT-3 ↓ You’ll get valuable information such as: Market (B2B or B2C) Industry Target Audience Value Proposition This will help you to: add more personalization to your outreach make informed decisions about which accounts to target I've made the process easy with an n8n workflow. Here is what it does: Retrieve website URLs from Google Sheets Extract the content for each website Analyze it with GPT-3 Update Google Sheets with GPT-3 data
lempire
Lucas Perret
Google Drive node
Binary Input Loader node
Embeddings OpenAI node
OpenAI Chat Model node
+5

Ask questions about a PDF using AI

The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to the vectors in Pinecone. The most similar vectors are retrieved and passed to OpenAI for generating a chat response. Note that to use this template, you need to be on n8n version 1.19.4 or later.
davidn8n
David Roberts

Implement complex processes faster with n8n

red icon yellow icon red icon yellow icon