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

1. Setup Configuration

   Import the necessary classes and set up logging to see the pipeline in action.

2. Define Knowledge Base

   Point the pipeline to your data directory. RAGLight handles PDF and text files automatically.

3. Build & Query

   The build() method processes documents and creates the vector store.

main.py

from raglight.rag.simple_rag_api import RAGPipeline
from raglight.config.settings import Settings
from raglight.config.rag_config import RAGConfig
from raglight.models.data_source_model import FolderSource

# 1. Initialize Settings
Settings.setup_logging()

# 2. Configure the Pipeline
# We use Ollama with llama3 by default, but you can switch to OpenAI/Mistral.
config = RAGConfig(
    provider=Settings.OLLAMA,
    llm="llama3",
    knowledge_base=[FolderSource(path="./data")]
)

# 3. Build the Pipeline
# This will ingest documents and create the local ChromaDB
pipeline = RAGPipeline(config)
pipeline.build()

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