Providers

haiku.rag supports multiple AI providers for embeddings, question answering, and reranking. This guide covers provider-specific configuration and setup.

Note

You can use a .env file in your project directory to set environment variables like OLLAMA_BASE_URL and API keys (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY). These will be automatically loaded when running haiku-rag commands.

Model Settings

Configure model behavior for the qa and analysis skills. These settings apply to any provider that supports them.

Basic Settings

qa:
  model:
    provider: ollama
    name: gpt-oss
    temperature: 0.3
    max_tokens: 500

Available options:

temperature: Sampling temperature (0.0-1.0+). Defaults vary by task: 0.3 for QA and title generation, 0.0 for analysis and picture description.
Lower (0.0-0.3): Deterministic, focused responses
Medium (0.4-0.7): Balanced
Higher (0.8-1.0+): Creative, varied responses
max_tokens: Maximum tokens in response. Default: unset (provider default), except title generation (100).
enable_thinking: Control reasoning behavior (see below)
base_url: Custom endpoint for OpenAI-compatible servers (vLLM, LM Studio, etc.)
extra_body: Raw dict forwarded to the model SDK (see Raw Provider Pass-through)

Thinking Control

The enable_thinking setting controls whether models use explicit reasoning steps before answering.

qa:
  model:
    enable_thinking: true   # Better grounded answers

Values: - false: Disable reasoning for faster responses - true: Enable reasoning for complex tasks - Not set: Use model defaults

Provider support:

See the Pydantic AI thinking documentation for detailed provider support. haiku.rag supports thinking control for:

OpenAI: Reasoning models (o1, o3, gpt-oss)
Anthropic: All Claude models
Google: Gemini models with thinking support
Groq: Models with reasoning capabilities
Bedrock: Claude, OpenAI, and Qwen models
Ollama: Models supporting reasoning (gpt-oss, etc.)
vLLM: Models with a pydantic-ai reasoning profile (gpt-oss). Qwen3, Gemma, and similar templates ignore the OpenAI reasoning_effort that enable_thinking translates to — use extra_body to drive them.
LM Studio: Models supporting reasoning (gpt-oss, etc.)

When to use: - Enable for QA, complex reasoning, and mathematical problems - Disable for speed-critical applications, title generation, and simple tasks

vLLM-served models without a reasoning profile

On provider: openai with a custom base_url, enable_thinking only takes effect for models whose pydantic-ai profile advertises reasoning support (o-series, gpt-5, gpt-oss). For other vLLM-served models (Qwen3, Gemma family, …) the field is a silent no-op. Reach the chat template's thinking switch directly via extra_body.

Raw Provider Pass-through

The extra_body setting takes a dict that haiku.rag forwards verbatim to the underlying model SDK as ModelSettings.extra_body. Use it to reach provider-specific keys that haiku.rag does not model with a dedicated field.

Example: disable Qwen3 thinking on vLLM:

qa:
  model:
    provider: openai
    name: qwen3.6-35b
    base_url: http://localhost:11430/v1
    extra_body:
      chat_template_kwargs:
        enable_thinking: false

vLLM serves Qwen3 chat templates that read their thinking switch from chat_template_kwargs.enable_thinking. The high-level enable_thinking setting on the openai provider maps to vLLM's reasoning_effort parameter, which Qwen3 templates ignore, so the field is a no-op for this combination. extra_body reaches the chat template directly and disables thinking. With it off, Qwen3 returns the answer in content immediately instead of emitting a hidden reasoning trace first.

Example: enable Gemma-family thinking on vLLM:

qa:
  model:
    provider: openai
    name: nvidia/Gemma-4-26B-A4B-NVFP4
    base_url: http://localhost:11432/v1
    extra_body:
      chat_template_kwargs:
        enable_thinking: true

Same mechanism, opposite direction. Without extra_body the Gemma-4 chat template defaults to non-thinking and dumps a verbose answer straight into content. With it on, vLLM (started with --reasoning-parser) populates the parsed reasoning field and leaves content as the concise final answer.

Provider support: honored by openai, ollama, anthropic, and groq via pydantic-ai's ModelSettings.extra_body. Silently ignored by gemini and bedrock.

Embedding Providers

Embedding models require three settings: provider, name, and vector_dim. Optionally, use base_url for OpenAI-compatible servers.

Batch Size

embeddings.batch_size (default 512) sets how many text chunks are sent per /v1/embeddings call during ingest. Lower it if your provider caps total tokens per request. Picture embeddings are always sent one image per call and are unaffected.

Ollama (Default)

embeddings:
  model:
    provider: ollama
    name: mxbai-embed-large
    vector_dim: 1024

The Ollama base URL can be configured in your config file or via environment variable:

providers:
  ollama:
    base_url: http://localhost:11434

Or via environment variable:

export OLLAMA_BASE_URL=http://localhost:11434

If not configured, it defaults to http://localhost:11434.

VoyageAI

If you installed haiku.rag (full package), VoyageAI is already included. If you installed haiku.rag-slim, install with VoyageAI extras:

uv pip install haiku.rag-slim[voyageai]

embeddings:
  model:
    provider: voyageai
    name: voyage-3.5
    vector_dim: 1024

Set your API key via environment variable:

export VOYAGE_API_KEY=your-api-key

OpenAI

OpenAI embeddings are included in the default installation:

embeddings:
  model:
    provider: openai
    name: text-embedding-3-small  # or text-embedding-3-large
    vector_dim: 1536

Set your API key via environment variable:

export OPENAI_API_KEY=your-api-key

Cohere

Cohere embeddings are available via pydantic-ai:

embeddings:
  model:
    provider: cohere
    name: embed-v4.0
    vector_dim: 1024

Set your API key via environment variable:

export CO_API_KEY=your-api-key

SentenceTransformers

For local embeddings using HuggingFace models:

embeddings:
  model:
    provider: sentence-transformers
    name: all-MiniLM-L6-v2
    vector_dim: 384

OpenAI-Compatible Servers (vLLM, LM Studio, etc.)

For local inference servers with OpenAI-compatible APIs, use the openai provider with a custom base_url:

# vLLM example
embeddings:
  model:
    provider: openai
    name: mixedbread-ai/mxbai-embed-large-v1
    vector_dim: 512
    base_url: http://localhost:8000/v1

# LM Studio example
embeddings:
  model:
    provider: openai
    name: text-embedding-qwen3-embedding-4b
    vector_dim: 2560
    base_url: http://localhost:1234/v1

Note: The base_url must include the /v1 path for OpenAI-compatible endpoints.

vLLM (multimodal)

For cross-modal retrieval (text and pictures share a single vector space), use the dedicated vllm provider against a vLLM server hosting a multimodal embedding model:

embeddings:
  model:
    provider: vllm
    name: Qwen/Qwen3-VL-Embedding-8B
    vector_dim: 4096
    base_url: http://localhost:8000/v1

Tested with Qwen/Qwen3-VL-Embedding-8B (4096-dim) and jinaai/jina-embeddings-v4 (2048-dim). Run vLLM separately. haiku.rag adds no Python ML dependencies for this path. Text inputs use the standard OpenAI input field. Image inputs use vLLM's messages-with-image_url superset, transparently to the caller.

Picture chunks for retrieval are emitted at ingest under any embedder reporting supports_images=True. See Picture Handling.

Question Answering Providers

Configure which LLM provider to use for question answering. Any provider and model supported by Pydantic AI can be used.

Ollama (Default)

qa:
  model:
    provider: ollama
    name: gpt-oss

The Ollama base URL can be configured via the OLLAMA_BASE_URL environment variable, config file, or defaults to http://localhost:11434:

export OLLAMA_BASE_URL=http://localhost:11434

Or in your config file:

providers:
  ollama:
    base_url: http://localhost:11434

OpenAI

OpenAI QA is included in the default installation:

qa:
  model:
    provider: openai
    name: gpt-4o-mini  # or gpt-4, gpt-3.5-turbo, etc.

Set your API key via environment variable:

export OPENAI_API_KEY=your-api-key

Anthropic

Anthropic QA is included in the default installation:

qa:
  model:
    provider: anthropic
    name: claude-3-5-haiku-20241022  # or claude-3-5-sonnet-20241022, etc.

Set your API key via environment variable:

export ANTHROPIC_API_KEY=your-api-key

OpenAI-Compatible Servers (vLLM, LM Studio, etc.)

For local inference servers with OpenAI-compatible APIs, use the openai provider with a custom base_url:

# vLLM example
qa:
  model:
    provider: openai
    name: Qwen/Qwen3-4B
    base_url: http://localhost:8002/v1

# LM Studio example
qa:
  model:
    provider: openai
    name: gpt-oss-20b
    base_url: http://localhost:1234/v1
    enable_thinking: false

Note: The server must be running with a model that supports tool calling. The base_url must include the /v1 path.

Other Providers

Any provider supported by Pydantic AI can be used. Examples:

# Google Gemini
qa:
  model:
    provider: gemini
    name: gemini-1.5-flash

# Groq
qa:
  model:
    provider: groq
    name: llama-3.3-70b-versatile

# Mistral
qa:
  model:
    provider: mistral
    name: mistral-small-latest

See the Pydantic AI documentation for the complete list of supported providers and models.

Reranking Providers

Reranking improves search quality by re-ordering the initial search results using specialized models. When enabled, the system retrieves more candidates (10x the requested limit) and then reranks them to return the most relevant results.

Reranking is disabled by default (provider: "") for faster searches. You can enable it by configuring one of the providers below.

MixedBread AI

If you installed haiku.rag (full package), MxBAI is already included. If you installed haiku.rag-slim, add the mxbai extra:

uv pip install haiku.rag-slim[mxbai]

Then configure:

reranking:
  model:
    provider: mxbai
    name: mixedbread-ai/mxbai-rerank-base-v2

Cohere

If you installed haiku.rag (full package), Cohere is already included. If you installed haiku.rag-slim, add the cohere extra:

uv pip install haiku.rag-slim[cohere]

Then configure:

reranking:
  model:
    provider: cohere
    name: rerank-v3.5

Set your API key via environment variable:

export CO_API_KEY=your-api-key

Zero Entropy

If you installed haiku.rag (full package), Zero Entropy is already included. If you installed haiku.rag-slim, add the zeroentropy extra:

uv pip install haiku.rag-slim[zeroentropy]

Then configure:

reranking:
  model:
    provider: zeroentropy
    name: zerank-1  # Currently the only available model

Set your API key via environment variable:

export ZEROENTROPY_API_KEY=your-api-key

vLLM

For high-performance local reranking using dedicated reranking models:

reranking:
  model:
    provider: vllm
    name: mixedbread-ai/mxbai-rerank-base-v2
    base_url: http://localhost:8001

Note: vLLM reranking uses the /v1/rerank API endpoint. You need to run a vLLM server separately with a reranking model loaded.

Jina AI

Jina provides high-quality reranking with two deployment options: API mode and local inference.

API Mode

Use the Jina Reranker API for cloud-based reranking:

reranking:
  model:
    provider: jina
    name: jina-reranker-v3

Set your API key via environment variable:

export JINA_API_KEY=your-api-key

Local Mode

For local inference, install the jina extra:

uv pip install haiku.rag-slim[jina]

Then configure:

reranking:
  model:
    provider: jina-local
    name: jinaai/jina-reranker-v3

Note: The Jina Reranker v3 local model is licensed under CC BY-NC 4.0, which restricts commercial use. For commercial applications, use the API mode instead.

Cross-Encoder (sentence-transformers)

Run any HuggingFace cross-encoder reranker in-process via sentence-transformers. No separate server required. Useful when you want a specific model (BGE, Qwen3-Reranker, MS-MARCO MiniLM, etc.) without running vLLM.

Install the extra:

uv pip install haiku.rag-slim[cross-encoder]

Then configure with any HuggingFace model id:

reranking:
  model:
    provider: cross-encoder
    name: BAAI/bge-reranker-v2-m3

Other tested models: Qwen/Qwen3-Reranker-0.6B, cross-encoder/ms-marco-MiniLM-L-6-v2. Any model exposed as a sentence_transformers.CrossEncoder works.