# Quickstart

We support some 5 loaders out of the box from Langchain ETL they are

LangChain provides a set of default document loaders for extracting data from various sources. This document outlines the available default data source types, how to configure them using the provided code, and example usage for each type.

### Default Data Source Types

LangChain supports the following default data source types:

* **CSV**: Comma-separated values file.
* **PDF**: Portable Document Format file.
* **WEB**: Web-based content.
* **JSON**: JSON-formatted file.
* **MARKDOWN**: Markdown-formatted file.

### Configuration and Usage

The provided code includes a class named `FileDataSources`, which defines constants for each default data source type. It also includes a dictionary named `FILE_DATA_SOURCES_MAP`, which maps each data source type to its corresponding loader and default parameter name.

The function `get_config_from_source_kwargs` is provided to generate a configuration based on the data source type and provided source information.

#### Example Usage

Here's how you can use the provided code to configure and use each default data source type:

```python
from genai_stack.etl.langchain import LangchainETL
from genai_stack.stack.stack import Stack
from genai_stack.vectordb.chromadb import ChromaDB
from genai_stack.etl.utils import get_config_from_source_kwargs
from genai_stack.embedding.utils import get_default_embeddings
```

1. **CSV Source Example**:

```python
etl = LangchainETL.from_kwargs(
    **get_config_from_source_kwargs("csv", "/your/path/to/csv")
)

# Connect the ETL, Embedding and Vectordb component using Stack
stack = Stack(model=None, embedding=get_default_embeddings(), etl=etl, vectordb=ChromaDB.from_kwargs())

etl.run()
```

2. **PDF Source Example**:

```python
etl = LangchainETL.from_kwargs(
    **get_config_from_source_kwargs("pdf", "/your/path/to/pdf")
)

# Connect the ETL, Embedding and Vectordb component using Stack
stack = Stack(model=None, embedding=get_default_embeddings(), etl=etl, vectordb=ChromaDB.from_kwargs())

etl.run()
```

3. **Web Source Example**:

```python
etl = LangchainETL.from_kwargs(
    **get_config_from_source_kwargs("web", "a valid url")
)

# Connect the ETL, Embedding and Vectordb component using Stack
stack = Stack(model=None, embedding=get_default_embeddings(), etl=etl, vectordb=ChromaDB.from_kwargs())

etl.run()
```

4. **JSON Source Example**:

```python
etl = LangchainETL.from_kwargs(
    **get_config_from_source_kwargs("json", "/your/path/to/json")
)

# Connect the ETL, Embedding and Vectordb component using Stack
stack = Stack(model=None, embedding=get_default_embeddings(), etl=etl, vectordb=ChromaDB.from_kwargs())

etl.run()
```

5. **Markdown Source Example**:

```python
etl = LangchainETL.from_kwargs(
    **get_config_from_source_kwargs("markdown", "/your/path/to/markdown or valid url")
)

# Connect the ETL, Embedding and Vectordb component using Stack
stack = Stack(model=None, embedding=get_default_embeddings(), etl=etl, vectordb=ChromaDB.from_kwargs())

etl.run()
```

Please note that the examples assume the existence of the `LangchainETL` class and its associated methods, as well as the specific loaders mentioned in the provided code. You may need to adjust the code examples according to your implementation details and the specific functionalities of the loaders.


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