211 lines
7.7 KiB
Markdown
211 lines
7.7 KiB
Markdown
# Tutorial
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This tutorial provides quickstart instructions for getting familiar with `haiku.rag`. This tutorial is intended for people who are familiar with command line and Python, but not different AI ecosystem tools.
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The tutorial covers:
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- RAG and embeddings basics
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- Installing `haiku.rag` Python package
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- Configuring `haiku.rag` with YAML
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- Adding and retrieving items
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- Inspecting the database
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The tutorial uses OpenAI API service - no local installation needed and will work on computers with any amount of RAM and GPU. The OpenAI API is pay-as-you-go, so you need to top it up with at least ~$5 when creating the API key.
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## Introduction
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Retrieval-Augmented Generation (RAG) lets you give AI models access to your own documents and data. Instead of relying solely on the model's training data, RAG finds relevant information from your documents and includes it in the AI's responses.
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`haiku.rag` handles the mechanics: it converts your documents into searchable embeddings, stores them locally, and retrieves relevant chunks when you ask questions. You provide the documents and questions, and it coordinates between the embedding service (like OpenAI) and the AI model to give you accurate, grounded answers.
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## Setup
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First, [get an OpenAI API key](https://platform.openai.com/api-keys).
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Install `haiku.rag` Python package using [uv](https://docs.astral.sh/uv/getting-started/installation/) or your favourite Python package manager:
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```bash
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# Python 3.12+ needed
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uv pip install haiku.rag
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```
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Configure haiku.rag to use OpenAI. Create a `haiku.rag.yaml` file:
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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 # or text-embedding-3-large
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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 # or gpt-4o, gpt-4, etc.
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```
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Set your OpenAI API key as an environment variable (API keys should not be stored in the YAML file):
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```bash
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export OPENAI_API_KEY="<your OpenAI API key>"
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```
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For the list of available OpenAI models and their vector dimensions, see the [OpenAI documentation](https://platform.openai.com/docs/guides/embeddings).
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See [Configuration](configuration/index.md) for all available options.
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## Initialize the database
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Before adding documents, initialize the database:
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```bash
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haiku-rag init
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```
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This creates an empty database with the configured settings.
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## Adding the first documents
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Now you can add some pieces of text in the database:
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```bash
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haiku-rag add "Python is the best programming language in the world, because it is flexible, with robust ecosystem, open source licensing and thousands of contributors"
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haiku-rag add "JavaScript is a popular programming language, but has a lot of warts"
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haiku-rag add "PHP is a bad programming language, because of spotted security history, horrible syntax and declining popularity"
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```
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What will happen:
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- The piece of text is sent to OpenAI `/embeddings` API service
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- OpenAI translates the free form text to RAG embedding vectors needed for the retrieval
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- The vector values will be stored in a local database
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Now you can view your [LanceDB](https://lancedb.com/) database, and the embeddings it is configured for:
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```bash
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haiku-rag info
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```
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You should see output similar to:
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```
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haiku.rag database info
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path: /Users/moo/Library/Application Support/haiku.rag/haiku.rag.lancedb
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haiku.rag version (db): x.y.z
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embeddings: openai/text-embedding-3-small (dim: 1536)
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documents: 3 (storage: 48.0 KB)
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chunks: 3 (storage: 52.0 KB)
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vector index: not created
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──────────────────────────────────────────────────────────────────────────────────
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Versions
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haiku.rag: x.y.z
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lancedb: ...
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docling: ...
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```
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## Asking questions and retrieving information
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Now we can use OpenAI LLMs to retrieve information from our embeddings database.
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In this example, we connect to a remote OpenAI API.
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Behind the scenes [pydantic-ai](https://ai.pydantic.dev/) query is created
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using `OpenAIChatModel.request()`.
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The easiest way to do this is `ask` CLI command:
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```bash
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haiku-rag ask "What is the best programming language in the world"
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```
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```
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Question: What is the best programming language in the world
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Answer:
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According to the document, Python is considered the best programming language in the world due to its flexibility, robust ecosystem, open-source licensing, and thousands of contributors.
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```
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## Programmatic interaction in Python
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You can interact with haiku.rag from Python. Since the API is async, we'll use IPython which supports async/await directly.
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```bash
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uv pip install ipython
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ipython
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```
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Then run:
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```python
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from haiku.rag.client import HaikuRAG
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# Uses database from default location (must be initialized first)
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async with HaikuRAG() as client:
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answer, citations = await client.ask("What is the best programming language in the world?")
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print(answer)
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```
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You should see:
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```
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According to the document, Python is considered the best programming language in the world due to its flexibility, robust ecosystem, open-source licensing, and support from thousands of contributors.
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```
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## Complex documents
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Haiku RAG can also handle types beyond plain text, including PDF, DOCX, HTML, and 40+ other file formats.
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Here we add research papers about Python from [arxiv](https://arxiv.org/search/?query=python&searchtype=all&source=header) using URL retriever.
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```bash
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# Better Python Programming for all: With the focus on Maintainability
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haiku-rag add-src --meta collection="Interesting Python papers" "https://arxiv.org/pdf/2408.09134"
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# Interoperability From OpenTelemetry to Kieker: Demonstrated as Export from the Astronomy Shop
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haiku-rag add-src --meta collection="Interesting Python papers" "https://arxiv.org/pdf/2510.11179"
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```
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Then we can query this:
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```bash
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haiku-rag ask "Who wrote a paper about OpenTelemetry interoperability, and what was his take"
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```
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We should get something along the lines:
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```
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Answer:
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David Georg Reichelt from Lancaster University wrote a paper titled "Interoperability From OpenTelemetry to Kieker: Demonstrated as Export from the Astronomy Shop." In his work, he indicates that there is a structural difference between Kieker’s synchronous traces and OpenTelemetry’s asynchronous traces, leading to limited compatibility between the two systems. This highlights the challenges of interoperability in observability frameworks.
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```
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We can also add offline files, like PDFs. Here we add a local file to ensure OpenAI does not cheat - a file we know that should not be very well known in Internet:
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```bash
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# This static file is supplied in haiku.rag repo
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haiku-rag add-src "examples/samples/PyCon Finland 2025 Schedule.html"
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```
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And then:
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```bash
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haiku-rag ask "Who were presenting talks in Pycon Finland 2025? Can you give at least five different people."
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```
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```
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The following people are presenting talks at PyCon Finland 2025:
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1 Jeremy Mayeres - Talk: The Limits of Imagination: An Open Source Journey
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2 Aroma Rodrigues - Talk: Python and Rust, a Perfect Pairing
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3 Andreas Jung - Talk: Guillotina Volto: A New Backend for Volto
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4 Daniel Vahla - Talk: Experiences with AI in Software Projects
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5 Andreas Jung (also presenting another talk) - Talk: Debugging Python
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```
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## Next Steps
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- **[Chat](apps.md#chat-tui)** - Interactive conversations with `haiku-rag chat`
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- **[CLI Reference](cli.md)** - All available commands and options
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- **[Python API](python.md)** - Use haiku.rag in your Python applications
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- **[Skills](skills/index.md)** - The RAG and analysis skills the client wraps
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- **[Configuration](configuration/index.md)** - Complete YAML configuration reference
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- **[Server Mode](server.md)** - File monitoring and MCP server
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