7f7223e0ac
Replaces gpt-oss on ModelConfig, qa.model and processing.title_model, and ministral-3 on the picture-description model. qa.model.vision follows the model and is now true. enable_thinking was gated on the gpt-oss name, so it did nothing for qwen3.8. With title_model's max_tokens of 100 the reasoning consumed the whole budget and title generation returned an empty string. The mapping now applies to any ollama model via reasoning_effort(): false sends "none", true sends "high". Measured on qwen3.8:27b-mlx, "low" does not disable thinking and "none" does; gpt-oss is the inverse, its template has no "none" level, so it keeps "low". Picture description bypasses get_model -- docling posts the request itself from a params dict -- so the flag was inert on that path too. vlm_api_params() carries reasoning_effort into both converters' request bodies. At max_tokens 200 the description survived either way, but the switch cut completion tokens from 141 to 45. test_search_tool_skips_binary_content_when_qa_model_is_text_only asserted the vision default rather than setting it; it now configures vision=False itself. docs/benchmarks.md keeps ministral-3: those are recorded measurements.
86 lines
2.3 KiB
Markdown
86 lines
2.3 KiB
Markdown
# Quickstart
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Install haiku.rag, index a document, and chat with it.
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## Install
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```bash
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uv pip install haiku.rag
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```
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You also need [Ollama](https://ollama.com/) for the default embedding and answering models:
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```bash
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ollama pull qwen3-embedding:4b
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ollama pull qwen3.8
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```
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!!! note "Prefer OpenAI?"
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Drop this into a `haiku.rag.yaml` next to where you'll run the CLI:
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```yaml
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embeddings:
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model:
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provider: openai
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name: text-embedding-3-small
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vector_dim: 1536
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qa:
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model:
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provider: openai
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name: gpt-4o-mini
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```
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Then `export OPENAI_API_KEY="sk-..."` and continue with the rest of this page. Any provider Pydantic AI supports works the same way. See [Providers](configuration/providers.md).
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## Initialize
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```bash
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haiku-rag init
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```
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This creates a LanceDB database in your platform's user directory. Pass `--db` to any subcommand to use a different path:
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```bash
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haiku-rag init --db /tmp/test.lancedb
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```
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## Add a document
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Add a file, a URL, or a whole folder:
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```bash
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haiku-rag add-src https://arxiv.org/pdf/2408.09134
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haiku-rag add-src ~/Documents/papers/
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```
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Or paste text inline:
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```bash
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haiku-rag add "Yiorgis wrote haiku.rag in 2025."
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```
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Each `add-src` call converts the file with Docling, splits it into chunks, embeds them, and writes everything to LanceDB. Run `haiku-rag list` to see what you've added, `haiku-rag info` for a database summary.
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## Chat
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```bash
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haiku-rag chat
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```
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Ask a question. The agent searches your documents, expands context around the hits, and answers with citations pointing back to the source page and section. Citations are expandable, with visual grounding so you can see the chunk highlighted on the original page. Follow-ups continue within the same session. Start a new session when you switch topics.
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You can also ask a single question directly from the CLI without launching the TUI:
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```bash
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haiku-rag ask "Who wrote haiku.rag?"
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```
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## Where to go next
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- [Chat](chat.md): sessions, citations, and the full TUI.
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- [CLI reference](cli.md): every command.
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- [Python API](python.md): use haiku.rag in your own code.
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- [Capabilities](capabilities/index.md): native RAG and analysis components used by the client.
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- [Tuning](tuning.md): better retrieval.
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- [Configuration](configuration/index.md): every setting.
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