Ollama Configuration

Overview

Ollama is an open-source platform for running Large Language Models (LLM) in local environments. It is configured as the default LLM provider for Fess and is suitable for use in private environments.

Using Ollama allows you to use AI search mode functionality without sending data externally.

Key Features

  • Local Execution: Data is not sent externally, ensuring privacy

  • Various Models: Supports multiple models including Llama, Mistral, Gemma, and CodeLlama

  • Cost Efficiency: No API costs (only hardware costs)

  • Customization: Can use custom fine-tuned models

Supported Models

Main models available with Ollama:

  • llama3.3:70b - Meta’s Llama 3.3 (70B parameters)

  • gemma3:4b - Google’s Gemma 3 (4B parameters, default)

  • mistral:7b - Mistral AI’s Mistral (7B parameters)

  • codellama:13b - Meta’s Code Llama (13B parameters)

  • phi3:3.8b - Microsoft’s Phi-3 (3.8B parameters)

Note

For the latest list of available models, see Ollama Library.

Prerequisites

Before using Ollama, verify the following.

  1. Ollama Installation: Download and install from https://ollama.com/

  2. Model Download: Download the model you want to use to Ollama

  3. Ollama Server Running: Verify Ollama is running

Installing Ollama

Linux/macOS

curl -fsSL https://ollama.com/install.sh | sh

Windows

Download and run the installer from the official website.

Docker

docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

Downloading Models

# Download default model (Gemma 3 4B)
ollama pull gemma3:4b

# Download Llama 3.3
ollama pull llama3.3:70b

# Verify model works
ollama run gemma3:4b "Hello, how are you?"

Basic Configuration

Add the following settings to app/WEB-INF/conf/fess_config.properties.

Minimal Configuration

# Enable AI mode functionality
rag.chat.enabled=true

# Set LLM provider to Ollama
rag.llm.type=ollama

# Ollama URL (for local environment)
rag.llm.ollama.api.url=http://localhost:11434

# Model to use
rag.llm.ollama.model=gemma3:4b

Configuration Options

All configuration options available for the Ollama client.

Property Description Default
rag.llm.ollama.api.url Ollama server base URL http://localhost:11434
rag.llm.ollama.model Model name to use (must be downloaded to Ollama) gemma3:4b
rag.llm.ollama.timeout Request timeout (in milliseconds) 60000

Network Configuration

Docker Configuration

Example configuration when running both Fess and Ollama in Docker.

docker-compose.yml:

version: '3'
services:
  fess:
    image: codelibs/fess:15.5.0
    environment:
      - RAG_CHAT_ENABLED=true
      - RAG_LLM_TYPE=ollama
      - RAG_LLM_OLLAMA_API_URL=http://ollama:11434
      - RAG_LLM_OLLAMA_MODEL=gemma3:4b
    depends_on:
      - ollama
    # ... other settings

  ollama:
    image: ollama/ollama
    volumes:
      - ollama_data:/root/.ollama
    ports:
      - "11434:11434"

volumes:
  ollama_data:

Note

In Docker Compose environments, use ollama as the hostname (not localhost).

Remote Ollama Server

When running Ollama on a separate server from Fess:

rag.llm.ollama.api.url=http://ollama-server.example.com:11434

Warning

Ollama does not have authentication by default, so when making it externally accessible, consider network-level security measures (firewall, VPN, etc.).

Model Selection Guide

Guidelines for selecting models based on intended use.

Model Size Required VRAM Use Case
phi3:3.8b Small 4GB+ Lightweight environments, simple Q&A
gemma3:4b Small-Medium 6GB+ Well-balanced general use (default)
mistral:7b Medium 8GB+ When high-quality responses are needed
llama3.3:70b Large 48GB+ Highest quality responses, complex reasoning

GPU Support

Ollama supports GPU acceleration. Using an NVIDIA GPU significantly improves inference speed.

# Check GPU support
ollama run gemma3:4b --verbose

Troubleshooting

Connection Errors

Symptom: Chat functionality shows errors, LLM displays as unavailable

Check the following:

  1. Verify Ollama is running:

    curl http://localhost:11434/api/tags

  2. Verify the model is downloaded:

    ollama list

  3. Check firewall settings

Model Not Found

Symptom: “Configured model not found in Ollama” appears in logs

Solutions:

  1. Verify the model name is correct (may need to include :latest tag):

    # Check model list
ollama list

  2. Download the required model:

    ollama pull gemma3:4b

Timeout

Symptom: Requests time out

Solutions:

  1. Extend timeout duration:

    rag.llm.ollama.timeout=120000

  2. Consider using a smaller model or GPU environment

Debug Settings

When investigating issues, adjust Fess log levels to output detailed Ollama-related logs.

app/WEB-INF/classes/log4j2.xml:

<Logger name="org.codelibs.fess.llm.ollama" level="DEBUG"/>

References