RAGLight

RAGLight is a lightweight and modular Python library for implementing Retrieval-Augmented Generation (RAG). It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference.

Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context-aware AI solutions. ✨

---

⚠️ Requirements

Actually RAGLight supports :

• Ollama
• LMStudio

You need to have Ollama or LMStudio running on your computer.

If you use LMStudio, ou need to have the model you want to use loaded in LMStudio.

Features 🔥

• 🌐 Embeddings Model Integration: Plug in your preferred embedding models (e.g., HuggingFace all-MiniLM-L6-v2) for compact and efficient vector embeddings.
• 🧙🏽 LLM Agnostic: Seamlessly integrates with different LLMs, such as llama3 or custom providers, for natural language inference.
• ⚖️ RAG Pipeline: Combines document retrieval and language generation in a unified workflow.
• ⚖️ RAT Pipeline: Combines document retrieval and language generation in a unified workflow. Add reflection loops using a reasoning model like Deepseek-R1 or o1.
• 🖋️ Flexible Document Support: Ingest and index various document types (e.g., PDF, TXT, DOCX).
• 🛠️ Extensible Architecture: Easily swap vector stores, embedding models, or LLMs to suit your needs.

---

Import library 🛠️

If you want to install library, use :

pip install raglight

---

Quick Start 🚀

Use Default Pipeline ⭐️

You can setup with two lines your RAG with RAGLight:

from raglight.rag.simple_rag_api import RAGPipeline
from raglight.models.data_source_model import FolderSource, GitHubSource
from raglight.config.settings import Settings

Settings.setup_logging()

pipeline = RAGPipeline(knowledge_base=[
FolderSource(path="<path to your folder with pdf>/knowledge_base"),
GitHubSource(url="https://github.com/Bessouat40/RAGLight")
], model_name="llama3")

pipeline.build()

response = pipeline.generate("How can I create an easy RAGPipeline using raglight framework ? Give me python implementation")
print(response)

Try using RAT (Retrieval Augmented Thinking)

This pipeline extends the Retrieval-Augmented Generation (RAG) concept by incorporating an additional reasoning step using a specialized reasoning language model (LLM).

from raglight.rat.simple_rat_api import RATPipeline
from raglight.models.data_source_model import FolderSource, GitHubSource
from raglight.config.settings import Settings

Settings.setup_logging()

pipeline = RATPipeline(knowledge_base=[
FolderSource(path="<path to your folder with pdf>/knowledge_base"),
GitHubSource(url="https://github.com/Bessouat40/RAGLight")
], model_name="llama3", reasoning_model_name="deepseek-r1:1.5b", reflection=2, provider=SETTINGS.OLLAMA, k=5) # default : provider = Settings.Ollama
# ], model_name="llama3", reasoning_model_name="deepseek-r1:1.5b", reflection=1, provider=SETTINGS.LMSTUDIO)

pipeline.build()

response = pipeline.generate("How can I create an easy RAGPipeline using raglight framework ? Give me the the easier python implementation")
print(response)

Configure Your Own Pipeline

1. Configure Your Pipeline

Set up the components of your RAG pipeline:

from raglight.rag.builder import Builder
from raglight.config.settings import Settings

rag = Builder() \
    .with_embeddings(Settings.HUGGINGFACE, model_name=model_embeddings) \
    .with_vector_store(Settings.CHROMA, persist_directory=persist_directory, collection_name=collection_name) \
    .with_llm(Settings.OLLAMA, model_name=model_name, system_prompt_file=system_prompt_directory, provider=Settings.LMStudio) \
    .build_rag(k = 5)

Ingest Documents Inside Your Vector Store

Use the pipeline to ingest documents into the vector store:

rag.vector_store.ingest(file_extension='**/*.pdf', data_path='./data')

3. Query the Pipeline

Retrieve and generate answers using the RAG pipeline:

response = rag.question_graph("How can I optimize my marathon training?")
print(response)

Use RAGLight with Docker

You can use RAGLight inside a Docker container easily. Find Dockerfile to do it here : examples/Dockerfile.example

Build you image

Just go to examples directory and run :

docker build -t docker-raglight -f Dockerfile.example .

Run you image

In order your container can communicate with Ollama or LMStudio, you need to add a custom host-to-IP mapping :

docker run --add-host=host.docker.internal:host-gateway docker-raglight

UI For RAGLight

You can try this project : LLMChat if you want a UI running locally.