---

title: 'Usage'

description: 'Use AItrika'

icon: 'code'

---

## 🔍 Usage

You can easily get informations of a paper by passing a PubMed ID:

```python

from aitrika.engine.aitrika import OnlineAItrika

aitrika_engine = OnlineAItrika(pubmed_id=pubmed_id)

title = aitrika_engine.get_title()

print(title)

```

Or you can parse a local pdf:

```python

from aitrika.engine.aitrika import LocalAItrika

aitrika_engine = LocalAItrika(pdf_path = pdf_path)

title = aitrika_engine.get_title()

print(title)

```

```

Breast cancer genes: beyond BRCA1 and BRCA2.

```

You can get other informations, like the associations between genes and diseases:

```python

associations = aitrika_engine.get_associations()

```

```

[

  {

    "gene": "BRIP1",

    "disease": "Breast Neoplasms"

  },

  {

    "gene": "PTEN",

    "disease": "Breast Neoplasms"

  },

  {

    "gene": "CHEK2",

    "disease": "Breast Neoplasms"

  },

]

...

```

Or you can get a nice formatted DataFrame:

```python

associations = aitrika_engine.associations(dataframe = True)

```

```

      gene                          disease

0    BRIP1                 Breast Neoplasms

1     PTEN                 Breast Neoplasms

2    CHEK2                 Breast Neoplasms

...

```

With the power of RAG, you can query your document:

```python

## Prepare the documents

documents = generate_documents(content=abstract)

## Set the LLM

llm = GroqLLM(documents=documents, api_key=os.getenv("GROQ_API_KEY"))

## Query your document

query = "Is BRCA1 associated with breast cancer?"

print(llm.query(query=query))

```

```

The provided text suggests that BRCA1 is associated with breast cancer, as it is listed among the high-penetrance genes identified in family linkage studies as responsible for inherited syndromes of breast cancer.

```

Or you can extract other informations:

```python

results = engine.extract_results(llm=llm)

print(results)

```

```

** RESULTS **

- High-penetrance genes - BRCA1, BRCA2, PTEN, TP53 - responsible for inherited syndromes

- Moderate-penetrance genes - CHEK2, ATM, BRIP1, PALB2, RAD51C - associated with moderate BC risk

- Low-penetrance alleles - common alleles - associated with slightly increased or decreased risk of BC

- Current clinical practice - high-penetrance genes - widely used

- Future prospect - all familial breast cancer genes - to be included in genetic test

- Research need - clinical management - of moderate and low-risk variants

```