Uploaded April 2025 | Updated September 2026, 3 weeks ago
🚀 Access ALL video resources & get personalized help in my community:
skool.com/agentic-labs/about?ref=3fd61190e13d426dbf4f3b38adc7de69
🚀 Start building with n8n (use my link to support my channel ❤️): n8n.partnerlinks.io/f7f19w3vrhin
💬 My AI voice-to-text software (Wispr Flow): wisprflow.ai/r?LEON114
☕ Buy me a coffee: buymeacoffee.com/leonvanzyl
💵 Donate using PayPal: paypal.com/ncp/payment/EKRQ8QSGV6CWW
Discover how to build a powerful AI agent that runs completely on your local machine in this step-by-step tutorial. Learn to combine N8N's workflow automation with Ollama's LLM capabilities and PostgreSQL's vector database features to create a responsive assistant that can access your documents, maintain conversation history, and retrieve real-time information from the web.
This video demonstrates a cost-effective solution requiring no paid API services or cloud subscriptions. You'll see how to properly configure Ollama with two essential models, set up Docker with PostgreSQL for vector search, and orchestrate everything through N8N's intuitive interface.
Perfect for developers, AI enthusiasts, and business users looking to implement private AI solutions, this tutorial covers common troubleshooting issues and provides simplified alternatives to more complex setups. Watch as your agent retrieves information from uploaded documents, maintains context across conversations, and delivers accurate responses using locally-run large language models.
If you're interested in self-hosted AI tools that respect your privacy while delivering powerful functionality, this comprehensive guide walks you through building an extensible foundation you can customize for your specific needs.
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POSTGRES DOCKER COMMAND:
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docker run --name pgvector-container -e POSTGRES_USER=myuser -e POSTGRES_PASSWORD=mypassword -e POSTGRES_DB=mydatabase -p 5432:5432 -d ankane/pgvector
🕒 TIMESTAMPS:
00:00 - Intro
00:33 - Demo
02:29 - Note on Starter Kit
02:55 - Ollama Setup
04:31 - Postgres Setup
06:20 - n8n Setup
07:01 - Create AI Agent
08:49 - Assign LLM
10:01 - Chat Memory
11:44 - Postgres Chat Memory
13:13 - Adding Tools
13:42 - Adding RAG
15:13 - Using OpenAI Node instead
16:24 - Housekeeping
17:02 - Add Web Search
17:59 - Add Docs to Vector DB
#n8n #aiagents #ollama
🚀 Access ALL video resources & get personalized help in my community:
skool.com/agentic-labs/about?ref=3fd61190e13d426dbf4f3b38adc7de69
🚀 Start building with n8n (use my link to support my channel ❤️): n8n.partnerlinks.io/f7f19w3vrhin
💬 My AI voice-to-text software (Wispr Flow): wisprflow.ai/r?LEON114
☕ Buy me a coffee: buymeacoffee.com/leonvanzyl
💵 Donate using PayPal: paypal.com/ncp/payment/EKRQ8QSGV6CWW
Discover how to build a powerful AI agent that runs completely on your local machine in this step-by-step tutorial. Learn to combine N8N's workflow automation with Ollama's LLM capabilities and PostgreSQL's vector database features to create a responsive assistant that can access your documents, maintain conversation history, and retrieve real-time information from the web.
This video demonstrates a cost-effective solution requiring no paid API services or cloud subscriptions. You'll see how to properly configure Ollama with two essential models, set up Docker with PostgreSQL for vector search, and orchestrate everything through N8N's intuitive interface.
Perfect for developers, AI enthusiasts, and business users looking to implement private AI solutions, this tutorial covers common troubleshooting issues and provides simplified alternatives to more complex setups. Watch as your agent retrieves information from uploaded documents, maintains context across conversations, and delivers accurate responses using locally-run large language models.
If you're interested in self-hosted AI tools that respect your privacy while delivering powerful functionality, this comprehensive guide walks you through building an extensible foundation you can customize for your specific needs.
--------------------------------------------------------
POSTGRES DOCKER COMMAND:
--------------------------------------------------------
docker run --name pgvector-container -e POSTGRES_USER=myuser -e POSTGRES_PASSWORD=mypassword -e POSTGRES_DB=mydatabase -p 5432:5432 -d ankane/pgvector
🕒 TIMESTAMPS:
00:00 - Intro
00:33 - Demo
02:29 - Note on Starter Kit
02:55 - Ollama Setup
04:31 - Postgres Setup
06:20 - n8n Setup
07:01 - Create AI Agent
08:49 - Assign LLM
10:01 - Chat Memory
11:44 - Postgres Chat Memory
13:13 - Adding Tools
13:42 - Adding RAG
15:13 - Using OpenAI Node instead
16:24 - Housekeeping
17:02 - Add Web Search
17:59 - Add Docs to Vector DB
#n8n #aiagents #ollama
