SIM using Ollama: 100% Local AI Agents Builder! (No Code)

SIM: Open Source AI Agent Workflow Platform - Detailed Summary

Key Concepts:

• AI Agents
• Workflows
• Open Source
• Local Deployment
• Ollama
• Docker
• Postgres Database
• RAG (Retrieval Augmented Generation)
• API Deployment
• Chatbot Deployment

1. Introduction to SIM

SIM is presented as an open-source platform designed for building and deploying AI agent workflows. The platform allows users to create workflows where multiple agents collaborate to produce a final output. The key selling points are its ease of use, local deployment capabilities, and the ability to integrate knowledge for RAG (Retrieval Augmented Generation).

2. Core Features and UI Overview

The UI showcases a workflow builder where agents can be connected. Users can add various components like:

• Agents: Individual AI agents with configurable models and prompts.
• Knowledge: Integration of external knowledge sources for RAG.
• Memory: Adding memory capabilities to agents.
• Router: Routing logic for directing workflow execution.
• Workflow: Defining the overall flow of execution.
• Loop: Implementing iterative processes.
• Tools: Integration of external tools and functions.

The platform supports local execution using Ollama and can be deployed as a chatbot.

3. Local Installation and Setup (Step-by-Step)

The video provides a detailed walkthrough of installing SIM locally:

Step 1: Cloning the Repository

• Clone the SIM repository from GitHub using git clone <repository_url>. The URL is available in the video description.

Step 2: Configuring Environment Variables

• Navigate to the apps/sim directory within the cloned repository.
• Copy example.env to .env.
• Uncomment the DATABASE_URL line.
• Set the database password and name in the .env file:
  • DATABASE_URL=postgresql://postgres:<your_password>@localhost:5432/<sim_studio>
  • Example: DATABASE_URL=postgresql://postgres:yourpassword@localhost:5432/simstudio
• The password and database name are used later in the Docker setup.

Step 3: Installing and Running Ollama

• Download and install Ollama from ollama.com.
• Pull the llama3:2-latest model using the command ollama pull llama3:2-latest. This model will be used for the agents.

Step 4: Setting up the Postgres Database with Docker

• Ensure Docker is installed.
• Run the following Docker command to start a Postgres container:
  • docker run --name sim-studio-db -e POSTGRES_PASSWORD=<your_password> -e POSTGRES_DB=<sim_studio> -p 5432:5432 -d ankane/pgvector:latest
  • Replace <your_password> and <sim_studio> with the values set in the .env file.
• Verify the database is running using docker ps. You should see pgvector running.

Step 5: Installing and Running SIM

• Install Bun (a JavaScript runtime) if not already installed.
• Navigate to the apps/sim directory in the terminal.
• Run bun install to install the necessary dependencies.
• Run bun run migrate to set up the database schema. This is important for a fresh database setup.
• Start the application using bun run dev.
• The application will be accessible at localhost:3000.

4. Creating a Workflow

The video demonstrates creating a simple workflow with two agents:

1. Agent 1 (Blog Writer):
   • Model: llama3:2-latest
   • Prompt: "Write based on the provided topic user prompt. The topic is AI."
2. Agent 2 (Formatting Agent):
   • Model: llama3:2-latest
   • Prompt: "Format the provided blog article and make it easy to understand. <content_from_agent_1>" (The <content_from_agent_1> placeholder is automatically populated with the output of Agent 1).

The output of Agent 2 is then connected to a "Response" node to display the final result.

5. Testing and Deployment

• The workflow can be tested by clicking the "Play" icon. The output of each agent is displayed.
• The workflow can be deployed as either an API or a chatbot.
• API Deployment: Provides an endpoint to access the workflow programmatically.
• Chatbot Deployment: Creates a chat interface for interacting with the workflow.

The video demonstrates deploying the workflow as a chatbot with the following configurations:

• Subdomain: mprazen.localhost:3000 (automatically generated)
• Title: "Merlin Prazen Blog Writer"
• Public Chat: Enabled
• Output Data: Response Output

6. Knowledge Integration (RAG)

SIM allows users to add knowledge to the agents, enabling RAG. This involves:

• Creating a new knowledge base.
• Uploading files or adding text containing the knowledge.
• The agent can then use this knowledge when processing prompts.

This feature allows customizing agents with company-specific data or private information.

7. Conclusion

SIM is presented as a user-friendly, open-source platform for building and deploying AI agent workflows. Its key advantages include local deployment, ease of use (no programming knowledge required), and the ability to integrate knowledge for RAG. The platform offers flexible deployment options as either an API or a chatbot. The video encourages viewers to try SIM and provide feedback.