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

# Basic RAG

> Create a simple Q&A pipeline with local documents.

This example demonstrates the core functionality of RAGLight: loading documents from a local folder, indexing them, and asking questions using a local LLM (Ollama).

## Code Example

<Steps>
  <Step title="Setup Configuration">
    Import the necessary classes and set up logging to see the pipeline in
    action.
  </Step>

  <Step title="Define Knowledge Base">
    Point the pipeline to your data directory. RAGLight handles PDF and text
    files automatically.
  </Step>

  <Step title="Build & Query">
    The `build()` method processes documents and creates the vector store.
  </Step>
</Steps>

```python main.py theme={null}
from raglight.rag.simple_rag_api import RAGPipeline
from raglight.config.settings import Settings
from raglight.config.rag_config import RAGConfig
from raglight.config.vector_store_config import VectorStoreConfig
from raglight.models.data_source_model import FolderSource

# 1. Initialize Settings
Settings.setup_logging()

# 2. Configure the vector store (embeddings + ChromaDB)
vector_store_config = VectorStoreConfig(
    embedding_model=Settings.DEFAULT_EMBEDDINGS_MODEL,
    provider=Settings.HUGGINGFACE,
    database=Settings.CHROMA,
    persist_directory="./defaultDb",
    collection_name=Settings.DEFAULT_COLLECTION_NAME,
)

# 3. Configure the RAG pipeline (LLM + retrieval)
config = RAGConfig(
    provider=Settings.OLLAMA,
    llm=Settings.DEFAULT_LLM,
    k=5,
    knowledge_base=[FolderSource(path="./data")],
)

# 4. Build the Pipeline (ingests documents and creates the vector store)
pipeline = RAGPipeline(config, vector_store_config)
pipeline.build()

# 5. Ask a Question
response = pipeline.generate("What are the key takeaways from these documents?")
print("\n🤖 Answer:", response)
```