isaura 2.1.16
A lake of precalculated properties of biomedical entities based on the Ersilia Model Hub
Requires Python: >=3.10

<div align="center">

<img src="./isaura/assets/isaura_v2.png" height="160" alt="Isaura logo" />

### Ersilia’s Precalculation Store

Fast, reproducible access to **precalculated model outputs** from the **Ersilia Model Hub** — with a CLI and Python API built for batch workflows.

<br/>

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<br/>

[Quickstart](#quickstart) ·
[CLI](#cli) ·
[Python API](#python-api) ·
[Configuration](#configuration) ·
[Docs](#docs) ·
[Contributing](#contributing)

</div>

---

## Why Isaura?
Isaura is Ersilia’s precalculation store: it **precomputes and persistently stores model outputs** so researchers can retrieve results instantly instead of repeatedly running expensive inference. This delivers a major research speed-up—especially in low-resource settings where compute, bandwidth, or infrastructure are limited—by turning repeated calculations into reusable shared artifacts. To support equitable access, Ersilia also provides **free access to public precalculations**, making high-value model outputs available even when local compute isn’t.


Isaura provides a structured store for model results so you can:

- ⚡ **Skip recomputation** by reusing precalculated outputs
- 🧱 Keep artifacts **versioned and organized** (model → version → bucket/project)
- 📦 Store and retrieve results via **S3-compatible object storage (MinIO)**  
- 🔎 Enable **fast retrieval** using its fast engine developed on top of duckdb and for ANN uses vector search / indexing components (Milvus + NN service)

If you’re integrating Ersilia with Isaura, you typically (check Ersilia Model Hub for more info [here](https://github.com/ersilia-os/ersilia)):
1) run once (generate/store), then  
2) subsequent runs become fast (retrieve).

---
## Architecture (high level)

* 📝 **Write:** `CLI / Python API → MinIO`
  Precomputed outputs are stored as chunked artifacts (e.g., Parquet) under `model_id/version`, and Isaura updates lightweight registries (index/metadata/bloom) for deduplication and fast lookup.

* 📥 **Read(exact):** `CLI / Python API → DuckDB query on MinIO → results`
  Inputs are matched against the index, then the corresponding rows are fetched directly from the stored chunks.

* ⚡ **Read (approx / ANN, optional):** `CLI / Python API → NN service (+ Milvus) → nearest match → exact fetch from MinIO`
  For unseen inputs, the NN service finds the closest indexed compound(s); Isaura then retrieves the corresponding stored result from MinIO.


See the deep dive: **[How it works →](docs/HOW_IT_WORKS.md)**

---

## Quickstart

### 1) Install dependencies & setup env

We recommend using `uv`.

```bash
git clone https://github.com/ersilia-os/isaura.git
cd isaura
uv sync
source .venv/bin/activate
# if you have conda env
# use uv as below
uv pip install -e . 
````

### 2) Start local services (Docker required)

```bash
isaura engine --start
```

**Local dashboards**

* MinIO Console: `http://localhost:9001`

**Default MinIO credentials (local dev):**
```
Username: minioadmin123
Password: minioadmin1234
```

---

## CLI

### Common commands

#### Write (store outputs)

```bash
isaura write -i data/ersilia_output.csv -m eos8a4x -v v2 -pn myproject --access public
```

#### Read (retrieve outputs)

```bash
isaura read -i data/inputs.csv -m eos8a4x -v v2 -pn myproject -o data/outputs.csv
```

#### Copy artifacts to local directory

```bash
isaura copy -m eos8a4x -v v1 -pn myproject -o ~/Documents/isaura-backup/
```

#### Inspect available entries

```bash
isaura inspect -m eos8a4x -v v1 -o reports/available.csv
```


---

## Python API

```python
from isaura.manage import IsauraWriter, IsauraReader
```
Write the precalculation
```python
writer = IsauraWriter(
    input_csv="data/input.csv",
    model_id="eos8a4x",
    model_version="v1",
    bucket="my-project",
    access="public",
)
writer.write()
```
Read the stored calculation
```python
reader = IsauraReader(
    model_id="eos8a4x",
    model_version="v1",
    bucket="my-project",
    input_csv="data/query.csv",
    approximate=False,
)
reader.read(output_csv="results.csv")
```

More examples for CLI and API usage: **[API and CLI usage](docs/API_AND_CLI_USAGE.md)**

---

## Configuration

Isaura reads configuration from environment variables.

### Recommended: `.env`

Create a `.env` file in the repo root:

```bash
MINIO_ENDPOINT=http://127.0.0.1:9000
NNS_ENDPOINT=http://127.0.0.1:8080
DEFAULT_BUCKET_NAME=isaura-public
DEFAULT_PRIVATE_BUCKET_NAME=isaura-private
```

### Cloud credentials (optional)

```bash
export MINIO_CLOUD_AK="<access_key>"
export MINIO_CLOUD_SK="<secret_key>"

export MINIO_PRIV_CLOUD_AK="<access_key>"
export MINIO_PRIV_CLOUD_SK="<secret_key>"
```
> You can define those credentials in the .env as well

See the full list: **[CONFIGURATION](docs/CONFIGURATION.md)**

---

## MinIO Client (optional but recommended)

Install `mc` to manage buckets:

```bash
brew install minio/stable/mc   # macOS
# or Linux:
curl -O https://dl.min.io/client/mc/release/linux-amd64/mc && chmod +x mc && sudo mv mc /usr/local/bin/
```

Configure alias:

```bash
mc alias set local http://localhost:9000 minioadmin123 minioadmin1234
mc ls local
```

---

## Docs

* 📘 **How it works**: [here](docs/HOW_IT_WORKS.md)
* ⚙️ **Configuration**: [here](docs/CONFIGURATION.md)
* 🧰 **CLI and API reference**: [here](docs/API_AND_CLI_USAGE.md)
* 🧪 **Benchmark**: [here](docs/BENCHMARK.md)
* 🩹 **Troubleshooting / recovery**: [here](docs/TROUBLESHOOTING.md)

---

## Contributing

PRs are welcome. Please run format + lint before pushing:

```bash
uv run ruff format .
```

If you’re changing CLI behavior, please update **[here](docs/API_AND_CLI_USAGE.md)**.

---

## About the Ersilia Open Source Initiative

The [Ersilia Open Source Initiative](https://ersilia.io) is a tech-nonprofit organization fueling sustainable research in the Global South. Ersilia's main asset is the [Ersilia Model Hub](https://github.com/ersilia-os/ersilia), an open-source repository of AI/ML models for antimicrobial drug discovery.

![Ersilia Logo](isaura/assets/Ersilia_Brand.png)