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Ollama Environment Configuration

Overview

When using Ollama as your LLM provider (instead of OpenAI), you need to configure system-wide environment variables before starting the Ollama service. These settings optimize performance, enable parallel processing, and help manage resource constraints.

Environment Variables

Configure these environment variables on your system (not in the Flexible GraphRAG .env file):

Context Length

OLLAMA_CONTEXT_LENGTH=8192

Configuration Options: - 4096: Minimum for limited resources - 8192: Recommended default - 16384: For improved speed and extraction quality

Important Notes: - The full 128k possible context window for llama3.2:3b requires 16.4GB of RAM for the key-value (KV) cache alone, plus ~3GB for model weights - The 128K token context window allows processing ~96,240 words of text in a single interaction - By default, inference engines (llama.cpp, transformers, Ollama) store both model weights and KV cache in GPU VRAM when available (fastest) - If GPU VRAM is insufficient, the KV cache falls back to system RAM with potential speed penalty

Debug Logging

OLLAMA_DEBUG=1

Values: - 1: Enable debug logging - 0: Disable debug logging

Log Locations: - Windows: C:\Users\<username>\AppData\Local\Ollama\server.log - Linux/macOS: Check Ollama documentation for your platform

Use Cases: - Checking GPU memory availability - Identifying CPU fallback behavior - Troubleshooting performance issues

Model Persistence

OLLAMA_KEEP_ALIVE=30m

Keeps models loaded in memory for faster subsequent requests. Adjust time based on your usage patterns and available memory.

Maximum Loaded Models

OLLAMA_MAX_LOADED_MODELS=4

Values: - 0: No limit (loads as many as needed) - 4: Recommended for most systems - Adjust based on your available memory

Model Storage Directory

# Windows example
OLLAMA_MODELS=C:\Users\<username>\.ollama\models

# Linux/macOS example
OLLAMA_MODELS=/home/<username>/.ollama/models

Usually set automatically by Ollama, but can be customized for specific storage locations.

Parallel Request Handling

OLLAMA_NUM_PARALLEL=4

⚠️ CRITICAL SETTING: - Required for Flexible GraphRAG parallel file processing - Prevents processing errors during parallel document ingestion - Allows Ollama to handle multiple concurrent requests - Must match or exceed the number of worker threads used by the system

Installation Steps

Windows

  1. Open System Properties → Advanced → Environment Variables
  2. Under System variables (not User variables), click New
  3. Add each variable name and value
  4. Click OK to save
  5. Restart the Ollama service: 
    net stop Ollama
net start Ollama

Linux/macOS

  1. Add to your shell profile (~/.bashrc, ~/.zshrc, etc.): 

    export OLLAMA_CONTEXT_LENGTH=8192
export OLLAMA_DEBUG=1
export OLLAMA_KEEP_ALIVE=30m
export OLLAMA_MAX_LOADED_MODELS=4
export OLLAMA_NUM_PARALLEL=4

  2. Reload your shell configuration: 

    source ~/.bashrc  # or ~/.zshrc

  3. Restart Ollama service: 

    systemctl restart ollama  # On Linux with systemd
# or
brew services restart ollama  # On macOS with Homebrew

Verification

After configuration, verify the settings are active:

  1. Check Ollama is running: 

    ollama list

  2. Test with a simple request: 

    ollama run llama3.2:3b "Hello"

  3. Check debug logs (if OLLAMA_DEBUG=1):

  4. Windows: C:\Users\<username>\AppData\Local\Ollama\server.log
  5. Look for configuration values and GPU/CPU usage information

Troubleshooting

Issue: Processing Errors with Multiple Files

Symptom: Errors when processing multiple documents simultaneously

Solution: Ensure OLLAMA_NUM_PARALLEL=4 is set system-wide and Ollama service has been restarted

Issue: Slow Performance

Symptoms: - Document processing takes much longer than expected - High CPU usage but low GPU usage

Possible Causes: 1. GPU VRAM exhausted: Context window too large for available VRAM 2. CPU fallback: Model running on CPU instead of GPU

Solutions: 1. Reduce OLLAMA_CONTEXT_LENGTH to 4096 2. Check debug logs for GPU memory issues 3. Close other GPU-intensive applications 4. Consider using a smaller model (e.g., llama3.2:3b instead of gpt-oss:20b)

Issue: "Out of Memory" Errors

Solution: 1. Reduce OLLAMA_CONTEXT_LENGTH 2. Reduce OLLAMA_MAX_LOADED_MODELS 3. Ensure adequate system RAM (16GB+ recommended)

Performance Considerations

Model Selection

  • llama3.2:3b: Lightweight, fast, good for testing
  • llama3.1:8b: Balanced performance and quality
  • gpt-oss:20b: Higher quality, requires more resources

Resource Requirements

Component Minimum Recommended Optimal
System RAM 8GB 16GB 32GB+
GPU VRAM 4GB 8GB 12GB+
Context Length 4096 8192 16384

Parallel Processing

  • OLLAMA_NUM_PARALLEL=4 enables 4 concurrent requests
  • Higher values require more memory but improve throughput
  • Match this value to your available resources

Additional Resources

Summary

Key Points: 1. ✓ Set environment variables system-wide (not in Flexible GraphRAG .env) 2. ✓ OLLAMA_NUM_PARALLEL=4 is critical for parallel processing 3. ✓ Always restart Ollama service after changing environment variables 4. ✓ Use OLLAMA_DEBUG=1 to troubleshoot performance issues 5. ✓ Adjust OLLAMA_CONTEXT_LENGTH based on available resources

These settings ensure optimal Ollama performance with Flexible GraphRAG's parallel document processing capabilities.