103 lines
2.4 KiB
Markdown
103 lines
2.4 KiB
Markdown
# Installation
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## Basic Installation
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```bash
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uv pip install haiku.rag
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```
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This includes support for:
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- **Ollama** (default embedding provider using `mxbai-embed-large`)
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- **OpenAI** (GPT models for QA and embeddings)
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- **Anthropic** (Claude models for QA)
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- **Cohere** (reranking models)
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- **vLLM** (high-performance local inference for embeddings, QA, and reranking)
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## Provider-Specific Installation
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For additional embedding providers, install with extras:
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### VoyageAI
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```bash
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uv pip install haiku.rag[voyageai]
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```
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### MixedBread AI Reranking
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```bash
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uv pip install haiku.rag[mxbai]
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```
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### vLLM Setup
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vLLM requires no additional installation - it works with the base haiku.rag package. However, you need to run vLLM servers separately:
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```bash
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# Install vLLM
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pip install vllm
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# Serve an embedding model
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vllm serve mixedbread-ai/mxbai-embed-large-v1 --port 8000
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# Serve a model for QA (requires tool calling support)
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vllm serve Qwen/Qwen3-4B --port 8002 --enable-auto-tool-choice --tool-call-parser hermes
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# Serve a model for reranking
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vllm serve mixedbread-ai/mxbai-rerank-base-v2 --hf_overrides '{"architectures": ["Qwen2ForSequenceClassification"],"classifier_from_token": ["0", "1"], "method": "from_2_way_softmax"}' --port 8001
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```
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Then configure haiku.rag to use the vLLM servers. Create a `haiku.rag.yaml` file:
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```yaml
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embeddings:
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provider: vllm
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model: mixedbread-ai/mxbai-embed-large-v1
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vector_dim: 512
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qa:
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provider: vllm
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model: Qwen/Qwen3-4B
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reranking:
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provider: vllm
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model: mixedbread-ai/mxbai-rerank-base-v2
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providers:
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vllm:
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embeddings_base_url: http://localhost:8000
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qa_base_url: http://localhost:8002
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rerank_base_url: http://localhost:8001
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```
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See [Configuration](configuration.md) for all available options.
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## Requirements
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- Python 3.12+
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- Ollama (for default embeddings)
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- vLLM server (for vLLM provider)
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## Pre-download Models (Optional)
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You can prefetch all required runtime models before first use:
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```bash
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haiku-rag download-models
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```
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This will download Docling models and pull any Ollama models referenced by your current configuration.
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## Docker
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```bash
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docker pull ghcr.io/ggozad/haiku.rag:latest
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```
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Run the container with all services:
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```bash
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docker run -p 8000:8000 -p 8001:8001 -v $(pwd)/data:/data ghcr.io/ggozad/haiku.rag:latest
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```
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This starts the MCP server on port 8001 and A2A server on port 8000, with data persisted to `./data`.
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