promptfoo/site/docs/providers/ollama.md

sidebar_label	description
Ollama	Run open-source LLMs locally using Ollama's streamlined interface for rapid prototyping and offline model evaluation

Ollama

The ollama provider is compatible with Ollama, which enables access to Llama, Mixtral, Mistral, and more.

You can use its /api/generate endpoint by specifying any of the following providers from the Ollama library:

• ollama:completion:llama3.2
• ollama:completion:llama3.3
• ollama:completion:phi4
• ollama:completion:qwen2.5
• ollama:completion:granite3.2
• ollama:completion:deepcoder
• ollama:completion:codellama
• ollama:completion:llama2-uncensored
• ...

Or, use the /api/chat endpoint for chat-formatted prompts:

• ollama:chat:llama3.2
• ollama:chat:llama3.2:1b
• ollama:chat:llama3.2:3b
• ollama:chat:llama3.3
• ollama:chat:llama3.3:70b
• ollama:chat:phi4
• ollama:chat:phi4-mini
• ollama:chat:qwen2.5
• ollama:chat:qwen2.5:14b
• ollama:chat:qwen2.5:72b
• ollama:chat:qwq:32b
• ollama:chat:granite3.2
• ollama:chat:granite3.2:2b
• ollama:chat:granite3.2:8b
• ollama:chat:deepcoder
• ollama:chat:deepcoder:1.5b
• ollama:chat:deepcoder:14b
• ollama:chat:mixtral:8x7b
• ollama:chat:mixtral:8x22b
• ...

We also support the /api/embeddings endpoint via ollama:embeddings:<model name> for model-graded assertions such as similarity.

Supported environment variables:

• OLLAMA_BASE_URL - protocol, host name, and port (defaults to http://localhost:11434)
• OLLAMA_API_KEY - (optional) api key that is passed as the Bearer token in the Authorization Header when calling the API
• REQUEST_TIMEOUT_MS - request timeout in milliseconds

To pass configuration options to Ollama, use the config key like so:

providers:
  - id: ollama:chat:llama3.3
    config:
      num_predict: 1024
      temperature: 0.7
      top_p: 0.9
      think: true # Enable thinking/reasoning mode (top-level API parameter)

You can also pass arbitrary fields directly to the Ollama API using the passthrough option:

providers:
  - id: ollama:chat:llama3.3
    config:
      passthrough:
        keep_alive: '5m'
        format: 'json'
        # Any other Ollama API fields

Using Ollama as a Local Grading Provider

Using Ollama for Model-Graded Assertions

Ollama can be used as a local grading provider for assertions that require language model evaluation. When you have tests that use both text-based assertions (like llm-rubric, answer-relevance) and embedding-based assertions (like similar), you can configure different Ollama models for each type:

defaultTest:
  options:
    provider:
      # Text provider for llm-rubric, answer-relevance, factuality, etc.
      text:
        id: ollama:chat:gemma3:27b
        config:
          temperature: 0.1

      # Embedding provider for similarity assertions
      embedding:
        id: ollama:embeddings:nomic-embed-text
        config:
          # embedding-specific config if needed

providers:
  - ollama:chat:llama3.3
  - ollama:chat:qwen2.5:14b

tests:
  - vars:
      question: 'What is the capital of France?'
    assert:
      # Uses the text provider (gemma3:27b)
      - type: llm-rubric
        value: 'The answer correctly identifies Paris as the capital'

      # Uses the embedding provider (nomic-embed-text)
      - type: similar
        value: 'Paris is the capital city of France'
        threshold: 0.85

Using Ollama Embedding Models for Similarity Assertions

Ollama's embedding models can be used with the similar assertion to check semantic similarity between outputs and expected values:

providers:
  - ollama:chat:llama3.2

defaultTest:
  assert:
    - type: similar
      value: 'The expected response should explain the concept clearly'
      threshold: 0.8
      # Override the default embedding provider to use Ollama
      provider: ollama:embeddings:nomic-embed-text

tests:
  - vars:
      question: 'What is photosynthesis?'
    assert:
      - type: similar
        value: 'Photosynthesis is the process by which plants convert light energy into chemical energy'
        threshold: 0.85

You can also set the embedding provider globally for all similarity assertions:

defaultTest:
  options:
    provider:
      embedding:
        id: ollama:embeddings:nomic-embed-text
  assert:
    - type: similar
      value: 'Expected semantic content'
      threshold: 0.75

providers:
  - ollama:chat:llama3.2

tests:
  # Your test cases here

Popular Ollama embedding models include:

• ollama:embeddings:nomic-embed-text - General purpose embeddings
• ollama:embeddings:mxbai-embed-large - High-quality embeddings
• ollama:embeddings:all-minilm - Lightweight, fast embeddings

localhost and IPv4 vs IPv6

If locally developing with localhost (promptfoo's default), and Ollama API calls are failing with ECONNREFUSED, then there may be an IPv4 vs IPv6 issue going on with localhost. Ollama's default host uses 127.0.0.1, which is an IPv4 address. The possible issue here arises from localhost being bound to an IPv6 address, as configured by the operating system's hosts file. To investigate and fix this issue, there's a few possible solutions:

1. Change Ollama server to use IPv6 addressing by running export OLLAMA_HOST=":11434" before starting the Ollama server. Note this IPv6 support requires Ollama version 0.0.20 or newer.
2. Change promptfoo to directly use an IPv4 address by configuring export OLLAMA_BASE_URL="http://127.0.0.1:11434".
3. Update your OS's hosts file to bind localhost to IPv4.

Evaluating models serially

By default, promptfoo evaluates all providers concurrently for each prompt. However, you can run evaluations serially using the -j 1 option:

promptfoo eval -j 1

This sets concurrency to 1, which means:

1. Evaluations happen one provider at a time, then one prompt at a time.
2. Only one model is loaded into memory, conserving system resources.
3. You can easily swap models between evaluations without conflicts.

This approach is particularly useful for:

• Local setups with limited RAM
• Testing multiple resource-intensive models
• Debugging provider-specific issues