Build a private Llama chatbot with Ollama, Groq, Slack and Google Sheets

Description

This workflow builds a fully private, self-hosted AI chatbot using Meta Llama models. Unlike cloud-based AI APIs, every conversation stays on your infrastructure — no data leaves your environment. The chatbot remembers conversation history per session, routes different query types to specialized Llama prompts, logs all interactions, and can escalate unresolved queries to a human agent via Slack.

Powered by Ollama (local) or Groq/Together AI (cloud Llama endpoints) — configurable in one node.

What's the Goal?
To give businesses a production-grade private AI chatbot that:
Runs on their own servers with zero data exposure
Handles customer support, internal helpdesk, sales FAQs, and onboarding
Remembers context across a full conversation session
Routes intelligently: support vs sales vs general vs escalation
Logs every turn for quality review, training, and compliance

Why Does It Matter?
Most businesses cannot send sensitive conversations to OpenAI or Anthropic due to:
GDPR, HIPAA, SOC2, or internal data governance policies
Confidential customer data in support queries
Proprietary internal knowledge that must stay private

Llama models run fully on-premise. This workflow gives those businesses the same quality AI chatbot experience with complete data sovereignty.

Monetization: sell this as a private AI chatbot deployment package to enterprises. Setup fee plus monthly hosting — recurring revenue.

How It Works

Stage A — Message Intake
Webhook receives incoming chat message with session ID and user message text. Set node stores Llama endpoint config and normalizes the payload.

Stage B — Session Memory
Code node loads conversation history for the session from an in-memory store. Appends the new user message to build the full context window for Llama.

Stage C — Intent Router
IF node checks the message for keywords to classify intent: support issue, sales inquiry, general question, or escalation request. Routes to the matching Llama system prompt branch.

Stage D — Llama Inference
HTTP Request calls the Llama API (Ollama local, Groq, or Together AI). Sends full conversation history plus the matched system prompt. Returns the assistant reply.

Stage E — Response Handling
Code node parses the Llama output, updates the session memory, checks if escalation is needed, and formats the final response.

Stage F — Logging and Delivery
Google Sheets logs every turn. Slack fires only when escalation is flagged. Webhook responds with the chatbot reply and session metadata.

Configuration Requirements
LLAMA_ENDPOINT — Your Ollama URL (http://localhost:11434) or Groq/Together AI base URL
LLAMA_API_KEY — API key if using Groq or Together AI (leave blank for local Ollama)
LLAMA_MODEL — Model name e.g. llama3, llama3.1:8b, llama3.1:70b, mixtral
SLACK_WEBHOOK_URL — For human escalation alerts
GOOGLE_SHEET_ID — Conversation audit log

Setup Guide
Option A (Local / Private):
Install Ollama: curl -fsSL https://ollama.ai/install.sh | sh
Pull model: ollama pull llama3.1
Set LLAMA_ENDPOINT to http://localhost:11434
Leave LLAMA_API_KEY blank

Option B (Cloud Llama via Groq — fastest):
Sign up at groq.com and copy your API key
Set LLAMA_ENDPOINT to https://api.groq.com/openai/v1
Set LLAMA_MODEL to llama-3.1-8b-instant or llama-3.1-70b-versatile
Paste your Groq API key in LLAMA_API_KEY

Option C (Together AI):
Sign up at together.ai
Set endpoint to https://api.together.xyz/v1
Set model to meta-llama/Llama-3.1-8B-Instruct-Turbo

Steps for all options:
Open Set Llama Config node — fill in all values
Set SLACK_WEBHOOK_URL and GOOGLE_SHEET_ID
Activate and POST to /webhook/llama-chat

Sample Payload
{
sessionId: user-abc-123,
message: My order arrived damaged and I need a refund,
userId: user_123,
botPersona: support,
userName: Sarah
}


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