> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datalinks.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Components

> Core building blocks of the DataLinks Python SDK.

### 1. **DLConfig**

`DLConfig` reads configurations (e.g., API keys) via environment variables or `.env` files. This enables dynamic adaptation across deployment environments.

### 2. **DataLinksAPI**

`DataLinksAPI` handles interactions with the API. You can:

* Ingest data directly or via multipart upload for large files.
* Track and wait for async ingestion completion.
* Query or retrieve data with complex parameters.
* Manage namespaces.

### 3. **Inference Workflow**

Use a chain of inference and validation steps defined through classes like `ProcessUnstructured`, `Normalize`, and `Validate` to automate data preparation workflows.

```python theme={null}
from datalinks.pipeline import Pipeline, ProcessUnstructured, Normalize, Validate, ValidateModes

# Define an inference pipeline
inference_steps = Pipeline(
   ProcessUnstructured(derive_from="source_field", helper_prompt="This extracts tables."),
   Normalize(target_cols={"email": "email_address"}, mode="all-in-one"),
   Validate(mode=ValidateModes.FIELDS, columns=["email", "phone"]),
)
```

### 4. **Entity Resolution**

Supports multiple resolution strategies, configurable via `MatchTypeConfig`:

```python theme={null}
from datalinks.links import MatchTypeConfig, ExactMatch

entity_resolution = MatchTypeConfig(
   # parameters are optional
    exact_match=ExactMatch(minVariation=0.2, minDistinct=0.3)
)
```

### 5. **Loaders**

Abstract base loaders (e.g., `JSONLoader`) allow seamless data ingestion from custom file formats like `.json`.

### 6. **Parametrize LLMs**

You can choose the model and provider to be used in inference steps (eg.: `ProcessUnstructured`, `Normalize`, `Validate`).

```python theme={null}

from datalinks.pipeline import Pipeline, ProcessUnstructured

steps = Pipeline(
        ProcessUnstructured(
            derive_from="text",
            helper_prompt="If you find a numeric field use only the value and omit the rest.",
            model="gpt-4.1-nano-2025-04-14",
            provider="openai"
        )
    )
```