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integrationPostgres Chat Memory node
integrationTelegram node

Postgres Chat Memory and Telegram integration

Save yourself the work of writing custom integrations for Postgres Chat Memory and Telegram and use n8n instead. Build adaptable and scalable AI, Langchain, and Communication workflows that work with your technology stack. All within a building experience you will love.

How to connect Postgres Chat Memory and Telegram

  • Step 1: Create a new workflow
  • Step 2: Add and configure nodes
  • Step 3: Connect
  • Step 4: Customize and extend your integration
  • Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

Postgres Chat Memory and Telegram integration: Create a new workflow and add the first step

Step 2: Add and configure Postgres Chat Memory and Telegram nodes

You can find Postgres Chat Memory and Telegram in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Postgres Chat Memory and Telegram nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Postgres Chat Memory and Telegram integration: Add and configure Postgres Chat Memory and Telegram nodes

Step 3: Connect Postgres Chat Memory and Telegram

A connection establishes a link between Postgres Chat Memory and Telegram (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

Postgres Chat Memory and Telegram integration: Connect Postgres Chat Memory and Telegram

Step 4: Customize and extend your Postgres Chat Memory and Telegram integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Postgres Chat Memory and Telegram with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Postgres Chat Memory and Telegram integration: Customize and extend your Postgres Chat Memory and Telegram integration

Step 5: Test and activate your Postgres Chat Memory and Telegram workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Postgres Chat Memory to Telegram or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

Postgres Chat Memory and Telegram integration: Test and activate your Postgres Chat Memory and Telegram workflow

HR & IT Helpdesk Chatbot with Audio Transcription

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries.

How It Works
Message Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input.
Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text).
Query Processing:
The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings.
These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation.
The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response.
Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio.

Set Up Steps
Estimated Time**: 20–25 minutes
Prerequisites**:
Create an account with an AI provider (e.g., OpenAI).
Connect WhatsApp or Telegram credentials in n8n.
Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text).
Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation.
Import the workflow template into n8n and update environment variables for your credentials.

Nodes used in this workflow

Popular Postgres Chat Memory and Telegram workflows

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All-in-One Telegram/Baserow AI Assistant 🤖🧠 Voice/Photo/Save Notes/Long Term Mem

Telegram Personal Assistant with Long-Term Memory & Note-Taking This n8n workflow transforms your Telegram bot into a powerful personal assistant that handles voice, photo, and text messages. The assistant uses AI to interpret messages, save important details as long-term memories or notes in a Baserow database, and recall information for future interactions. 🌟 How It Works Message Reception & Routing Telegram Integration: The workflow is triggered by incoming messages on your Telegram bot. Dynamic Routing: A switch node inspects the message to determine whether it's voice, text, or photo (with captions) and routes it for the appropriate processing. Content Processing Voice Messages: Audio files are retrieved and sent to an AI transcription node to convert spoken words into text. Text Messages: Text is directly captured and prepared for analysis. Photos: If an image is received, the bot fetches the file (and caption, if provided) and uses an AI-powered image analysis node to extract relevant details. AI-Powered Agent & Memory Management The core AI agent (powered by GPT-4o-mini) processes the incoming message along with any previous conversation history stored in PostgreSQL memory buffers. Long-Term Memory: When a message contains personal or noteworthy information, the assistant uses a dedicated tool to save this data as a long-term memory in Baserow. Note-Taking: For specific instructions or reminders, the assistant saves concise notes in a separate Baserow table. The AI agent follows defined rules to decide which details are saved as memories and which are saved as notes. Response Generation After processing the message and updating memory/notes as needed, the AI agent crafts a contextual and personalized response. The response is sent back to the user via Telegram, ensuring smooth and natural conversation flow. 🚀 Key Features Multimodal Input:** Seamlessly handles voice, photo (with captions), and text messages. Long-Term Memory & Note-Taking:** Uses a Baserow database to store personal details and notes, enhancing conversational context over time. AI-Driven Contextual Responses:** Leverages an AI agent to generate personalized, context-aware replies based on current input and past interactions. User Security & Validation:** Incorporates validation steps to verify the user's Telegram ID before processing, ensuring secure and personalized interactions. Easy Baserow Setup:** Comes with a clear setup guide and sample configurations to quickly integrate Baserow for managing memories and notes. 🔧 Setup Guide Telegram Bot Setup: Create your bot via BotFather and obtain the Bot Token. Configure the Telegram webhook in n8n with your bot's token and URL. Baserow Database Configuration: Memory Table: Create a workspace titled "Memories and Notes". Set up a table (e.g., "Memory Table") with at least two fields: Memory (long text) Date Added (US date format with time) Notes Table: Duplicate the Memory Table and rename it to "Notes Table". Change the first field's name from "Memory" to "Notes". n8n Workflow Import & Configuration: Import the workflow JSON into your n8n instance. Update credentials for Telegram, Baserow, OpenAI, and PostgreSQL (for memory buffering) as needed. Adjust node settings if you need to customize AI agent prompts or memory management rules. Testing & Deployment: Test your bot by sending various message types (text, voice, photo) to confirm that the workflow processes them correctly, updates Baserow, and returns the appropriate response. Monitor logs to ensure that memory and note entries are correctly stored and retrieved. ✨ Example Interactions Voice Message Processing:** User sends a voice note requesting a reminder. Bot Response: "Thanks for your message! I've noted your reminder and saved it for future reference." Photo with Caption:** User sends a photo with the caption "Save this recipe for dinner ideas." Bot Response: "Got it! I've saved this recipe along with the caption for you." Text Message for Memory Saving:** User: "I love hiking on weekends." Bot Response: "Noted! I’ll remember your interest in hiking." Retrieving Information:** User asks: "What notes do I have?" Bot Response: "Here are your latest notes: [list of saved notes]." 🛠️ Resources & Next Steps Telegram Bot Configuration:** Telegram BotFather Guide n8n Documentation:** n8n Docs Community Forums:** Join discussions and share your customizations! This workflow not only streamlines message processing but also empowers users with a personal AI assistant that remembers details over time. Customize the rules and responses further to fit your unique requirements and enjoy a more engaging, intelligent conversation experience on Telegram!

Allow Users to Send a Sequence of Messages to an AI Agent in Telegram

Use Case When creating chatbots that interface through applications such as Telegram and WhatsApp, users can often sends multiple shorter messages in quick succession, in place of a single, longer message. This workflow accounts for this behaviour. What it Does This workflow allows users to send several messages in quick succession, treating them as one coherent conversation instead of separate messages requiring individual responses. How it Works When messages arrive, they are stored in a Supabase PostgreSQL table The system waits briefly to see if additional messages arrive If no new messages arrive within the waiting period, all queued messages are: Combined and processed as a single conversation Responded to with one unified reply Deleted from the queue Setup Create a table in Supabase called message_queue. It needs to have the following columns: user_id (uint8), message (text), and message_id (uint8) Add your Telegram, Supabase, OpenAI, and PostgreSQL credentials Activate the workflow and test by sending multiple messages the Telegram bot in one go Wait ten seconds after which you will receive a single reply to all of your messages How to Modify it to Your Needs Change the value of Wait Amount in the Wait 10 Seconds node in order to to modify the buffering window Add a System Message to the AI Agent to tailor it to your specific use case Replace the OpenAI sub-node to use a different language model
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HR & IT Helpdesk Chatbot with Audio Transcription

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries. How It Works Message Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input. Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text). Query Processing: The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings. These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation. The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response. Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio. Set Up Steps Estimated Time**: 20–25 minutes Prerequisites**: Create an account with an AI provider (e.g., OpenAI). Connect WhatsApp or Telegram credentials in n8n. Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text). Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation. Import the workflow template into n8n and update environment variables for your credentials.

Build your own Postgres Chat Memory and Telegram integration

Create custom Postgres Chat Memory and Telegram workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.

Telegram supported actions

Get
Get up to date information about a chat
Get Administrators
Get the Administrators of a chat
Get Member
Get the member of a chat
Leave
Leave a group, supergroup or channel
Set Description
Set the description of a chat
Set Title
Set the title of a chat
Answer Query
Send answer to callback query sent from inline keyboard
Answer Inline Query
Send answer to callback query sent from inline bot
Get
Get a file
Delete Chat Message
Delete a chat message
Edit Message Text
Edit a text message
Pin Chat Message
Pin a chat message
Send Animation
Send an animated file
Send Audio
Send a audio file
Send Chat Action
Send a chat action
Send Document
Send a document
Send Location
Send a location
Send Media Group
Send group of photos or videos to album
Send Message
Send a text message
Send Photo
Send a photo
Send Sticker
Send a sticker
Send Video
Send a video
Unpin Chat Message
Unpin a chat message

FAQs

  • Can Postgres Chat Memory connect with Telegram?

  • Can I use Postgres Chat Memory’s API with n8n?

  • Can I use Telegram’s API with n8n?

  • Is n8n secure for integrating Postgres Chat Memory and Telegram?

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