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https://github.com/bessouat40/raglight

RAGLight is a lightweight and modular Python library for implementing Retrieval-Augmented Generation (RAG), Agentic RAG and RAT (Retrieval augmented thinking)..
https://github.com/bessouat40/raglight

agent agentic-ai agentic-rag agentic-workflow artificial-intelligence automation data-science embeddings framework huggingface inference llm lmstudio mistral-api mistralai ollama rag retrieval-augmented retrieval-augmented-generation vector-database

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RAGLight is a lightweight and modular Python library for implementing Retrieval-Augmented Generation (RAG), Agentic RAG and RAT (Retrieval augmented thinking)..

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README 

          
# RAGLight



![License](https://img.shields.io/github/license/Bessouat40/RAGLight)

[![Downloads](https://static.pepy.tech/personalized-badge/raglight?period=total&units=international_system&left_color=grey&right_color=red&left_text=Downloads)](https://pepy.tech/projects/raglight)

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    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.



---



## 📚 Table of Contents



- [Requirements](#⚠️-requirements)



- [Features](#features)



- [Import library](#import-library-🛠️)



- [Chat with Your Documents Instantly With CLI](#chat-with-your-documents-instantly-with-cli-💬)



  - [Ignore Folders Feature](#ignore-folders-feature-🚫)

  - [Ignore Folders in Configuration Classes](#ignore-folders-in-configuration-classes-🚫)



- [Deploy as a REST API (raglight serve)](#deploy-as-a-rest-api-raglight-serve-🌐)



  - [Start the server](#start-the-server)

  - [Endpoints](#endpoints)

  - [Configuration via environment variables](#configuration-via-environment-variables)

  - [Deploy with Docker Compose](#deploy-with-docker-compose)



- [Environment Variables](#environment-variables)



- [Providers and Databases](#providers-and-databases)



  - [LLM](#llm)

  - [Embeddings](#embeddings)

  - [Vector Store](#vector-store)



- [Quick Start](#quick-start-🚀)



  - [Knowledge Base](#knowledge-base)

  - [RAG](#rag)

  - [Agentic RAG](#agentic-rag)

  - [MCP Integration](#mcp-integration)

  - [Use Custom Pipeline](#use-custom-pipeline)

  - [Override Default Processors](#override-default-processors)

  - [Hybrid Search](#hybrid-search-bm25--semantic--rrf-)



- [Use RAGLight with Docker](#use-raglight-with-docker)



  - [Build your image](#build-your-image)

  - [Run your image](#run-your-image)



---



> ## ⚠️ Requirements

>

> Actually RAGLight supports :

>

> - Ollama

> - Google

> - LMStudio

> - vLLM

> - OpenAI API

> - Mistral API

>

> If you use LMStudio, you 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 from different providers (Ollama and LMStudio supported).

- **RAG Pipeline**: Combines document retrieval and language generation in a unified workflow.

- **Agentic RAG Pipeline**: Use Agent to improve your RAG performances.

- 🔌 **MCP Integration**: Add external tool capabilities (e.g. code execution, database access) via MCP servers.

- **Flexible Document Support**: Ingest and index various document types (e.g., PDF, TXT, DOCX, Python, Javascript, ...).

- **Extensible Architecture**: Easily swap vector stores, embedding models, or LLMs to suit your needs.

- 🔍 **Hybrid Search (BM25 + Semantic + RRF)**: Combine keyword-based BM25 retrieval with dense vector search using Reciprocal Rank Fusion for best-of-both-worlds results.



---



## Import library 🛠️



To install the library, run:



```bash

pip install raglight

```



---



## Chat with Your Documents Instantly With CLI 💬



For the quickest and easiest way to get started, RAGLight provides an interactive command-line wizard. It will guide you through every step, from selecting your documents to chatting with them, without writing a single line of Python.

Prerequisite: Ensure you have a local LLM service like Ollama running.



Just run this one command in your terminal:



```bash

raglight chat

```



You can also launch the Agentic RAG wizard with:



```bash

raglight agentic-chat

```



The wizard will guide you through the setup process. Here is what it looks like:





    RAGLight




The wizard will ask you for:



- 📂 Data Source: The path to your local folder containing the documents.

- 🚫 Ignore Folders: Configure which folders to exclude during indexing (e.g., `.venv`, `node_modules`, `__pycache__`).

- 💾 Vector Database: Where to store the indexed data and what to name it.

- 🧠 Embeddings Model: Which model to use for understanding your documents.

- 🤖 Language Model (LLM): Which LLM to use for generating answers.



After configuration, it will automatically index your documents and start a chat session.



### Ignore Folders Feature 🚫



RAGLight automatically excludes common directories that shouldn't be indexed, such as:



- Virtual environments (`.venv`, `venv`, `env`)

- Node.js dependencies (`node_modules`)

- Python cache files (`__pycache__`)

- Build artifacts (`build`, `dist`, `target`)

- IDE files (`.vscode`, `.idea`)

- And many more...



You can customize this list during the CLI setup or use the default configuration. This ensures that only relevant code and documentation are indexed, improving performance and reducing noise in your search results.



### Ignore Folders in Configuration Classes 🚫



The ignore folders feature is also available in all configuration classes, allowing you to specify which directories to exclude during indexing:



- **RAGConfig**: Use `ignore_folders` parameter to exclude folders during RAG pipeline indexing

- **AgenticRAGConfig**: Use `ignore_folders` parameter to exclude folders during AgenticRAG pipeline indexing

- **VectorStoreConfig**: Use `ignore_folders` parameter to exclude folders during vector store operations



All configuration classes use `Settings.DEFAULT_IGNORE_FOLDERS` as the default value, but you can override this with your custom list:



```python

# Example: Custom ignore folders for any configuration

custom_ignore_folders = [

    ".venv",

    "venv",

    "node_modules",

    "__pycache__",

    ".git",

    "build",

    "dist",

    "temp_files",  # Your custom folders

    "cache"

]



# Use in any configuration class

config = RAGConfig(

    llm=Settings.DEFAULT_LLM,

    provider=Settings.OLLAMA,

    ignore_folders=custom_ignore_folders  # Override default

)

```



See the complete example in [examples/ignore_folders_config_example.py](examples/ignore_folders_config_example.py) for all configuration types.



---



## Deploy as a REST API (raglight serve) 🌐



`raglight serve` starts a **FastAPI** server configured entirely via environment variables — no Python code required.



### Start the server



```bash

raglight serve

```



Options :



```

--host    Host to bind (default: 0.0.0.0)

--port    Port to listen on (default: 8000)

--reload  Enable auto-reload for development (default: false)

--workers Number of worker processes (default: 1)

```



Example :



```bash

RAGLIGHT_LLM_MODEL=mistral-small-latest \

RAGLIGHT_LLM_PROVIDER=Mistral \

raglight serve --port 8080

```



### Endpoints



| Method | Path | Body | Response |

|---|---|---|---|

| `GET` | `/health` | — | `{"status": "ok"}` |

| `POST` | `/generate` | `{"question": "..."}` | `{"answer": "..."}` |

| `POST` | `/ingest` | `{"data_path": "...", "file_paths": [...], "github_url": "...", "github_branch": "main"}` | `{"message": "..."}` |

| `POST` | `/ingest/upload` | `multipart/form-data` — champ `files` (un ou plusieurs fichiers) | `{"message": "..."}` |

| `GET` | `/collections` | — | `{"collections": [...]}` |



The interactive API documentation (Swagger UI) is automatically available at `http://localhost:8000/docs`.



#### Examples with curl



```bash

# Health check

curl http://localhost:8000/health



# Ask a question

curl -X POST http://localhost:8000/generate \

  -H "Content-Type: application/json" \

  -d '{"question": "What is RAGLight?"}'



# Ingest a local folder

curl -X POST http://localhost:8000/ingest \

  -H "Content-Type: application/json" \

  -d '{"data_path": "./my_documents"}'



# Ingest a GitHub repository

curl -X POST http://localhost:8000/ingest \

  -H "Content-Type: application/json" \

  -d '{"github_url": "https://github.com/Bessouat40/RAGLight", "github_branch": "main"}'



# Upload files directly (multipart)

curl -X POST http://localhost:8000/ingest/upload \

  -F "files=@./rapport.pdf" \

  -F "files=@./notes.txt"



# List collections

curl http://localhost:8000/collections

```



### Configuration via environment variables



All server settings are read from `RAGLIGHT_*` environment variables. Copy `examples/serve_example/.env.example` to `.env` and adjust the values.



| Variable | Default | Description |

|---|---|---|

| `RAGLIGHT_LLM_MODEL` | `llama3` | LLM model name |

| `RAGLIGHT_LLM_PROVIDER` | `Ollama` | LLM provider (`Ollama`, `Mistral`, `OpenAI`, `LmStudio`, `GoogleGemini`) |

| `RAGLIGHT_LLM_API_BASE` | `http://localhost:11434` | LLM API base URL |

| `RAGLIGHT_EMBEDDINGS_MODEL` | `all-MiniLM-L6-v2` | Embeddings model name |

| `RAGLIGHT_EMBEDDINGS_PROVIDER` | `HuggingFace` | Embeddings provider (`HuggingFace`, `Ollama`, `OpenAI`, `GoogleGemini`) |

| `RAGLIGHT_EMBEDDINGS_API_BASE` | `http://localhost:11434` | Embeddings API base URL |

| `RAGLIGHT_PERSIST_DIR` | `./raglight_db` | Local ChromaDB persistence directory |

| `RAGLIGHT_COLLECTION` | `default` | ChromaDB collection name |

| `RAGLIGHT_K` | `5` | Number of documents retrieved per query |

| `RAGLIGHT_SYSTEM_PROMPT` | *(default prompt)* | Custom system prompt for the LLM |

| `RAGLIGHT_CHROMA_HOST` | — | Remote Chroma host (leave unset for local storage) |

| `RAGLIGHT_CHROMA_PORT` | — | Remote Chroma port |



### Deploy with Docker Compose



The quickest way to deploy in production :



```bash

cd examples/serve_example

cp .env.example .env   # edit values as needed

docker-compose up

```



The `docker-compose.yml` uses `extra_hosts: host.docker.internal:host-gateway` so the container can reach an Ollama instance running on the host machine.



---



## Environment Variables



You can set several environment variables to change **RAGLight** settings :



**Provider credentials & URLs**



- `MISTRAL_API_KEY` if you want to use Mistral API

- `OLLAMA_CLIENT_URL` if you have a custom Ollama URL

- `LMSTUDIO_CLIENT` if you have a custom LMStudio URL

- `OPENAI_CLIENT_URL` if you have a custom OpenAI URL or vLLM URL

- `OPENAI_API_KEY` if you need an OpenAI key

- `GEMINI_API_KEY` if you need a Google Gemini API key



**REST API server (`raglight serve`)**



See the full list in the [Configuration via environment variables](#configuration-via-environment-variables) section above.



## Providers and databases



### LLM



For your LLM inference, you can use these providers :



- LMStudio (`Settings.LMSTUDIO`)

- Ollama (`Settings.OLLAMA`)

- Mistral API (`Settings.MISTRAL`)

- vLLM (`Settings.VLLM`)

- OpenAI (`Settings.OPENAI`)

- Google (`Settings.GOOGLE_GEMINI`)



### Embeddings



For embeddings models, you can use these providers :



- Huggingface (`Settings.HUGGINGFACE`)

- Ollama (`Settings.OLLAMA`)

- vLLM (`Settings.VLLM`)

- OpenAI (`Settings.OPENAI`)

- Google (`Settings.GOOGLE_GEMINI`)



### Vector Store



For your vector store, you can use :



- Chroma (`Settings.CHROMA`)



## Quick Start 🚀



### Knowledge Base



Knowledge Base is a way to define data you want to ingest inside your vector store during the initialization of your RAG.

It's the data ingest when you call `build` function :



```python

from raglight import RAGPipeline

pipeline = RAGPipeline(knowledge_base=[

    FolderSource(path="/knowledge_base"),

    GitHubSource(url="https://github.com/Bessouat40/RAGLight")

    ],

    model_name="llama3",

    provider=Settings.OLLAMA,

    k=5)



pipeline.build()

```



You can define two different knowledge base :



1. Folder Knowledge Base



All files/folders into this directory will be ingested inside the vector store :



```python

from raglight import FolderSource

FolderSource(path="/knowledge_base"),

```



2. Github Knowledge Base



You can declare Github Repositories you want to store into your vector store :



```python

from raglight import GitHubSource

GitHubSource(url="https://github.com/Bessouat40/RAGLight")

```



### RAG



You can setup easily your RAG with RAGLight :



```python

from raglight.rag.simple_rag_api import RAGPipeline

from raglight.models.data_source_model import FolderSource, GitHubSource

from raglight.config.settings import Settings

from raglight.config.rag_config import RAGConfig

from raglight.config.vector_store_config import VectorStoreConfig



Settings.setup_logging()



knowledge_base=[

    FolderSource(path="/knowledge_base"),

    GitHubSource(url="https://github.com/Bessouat40/RAGLight")

    ]



vector_store_config = VectorStoreConfig(

    embedding_model = Settings.DEFAULT_EMBEDDINGS_MODEL,

    api_base = Settings.DEFAULT_OLLAMA_CLIENT,

    provider=Settings.HUGGINGFACE,

    database=Settings.CHROMA,

    persist_directory = './defaultDb',

    collection_name = Settings.DEFAULT_COLLECTION_NAME

)



config = RAGConfig(

        llm = Settings.DEFAULT_LLM,

        provider = Settings.OLLAMA,

        # k = Settings.DEFAULT_K,

        # cross_encoder_model = Settings.DEFAULT_CROSS_ENCODER_MODEL,

        # system_prompt = Settings.DEFAULT_SYSTEM_PROMPT,

        # knowledge_base = knowledge_base

    )



pipeline = RAGPipeline(config, vector_store_config)



pipeline.build()



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

print(response)

```



You just have to fill the model you want to use.



> ⚠️

> By default, LLM Provider will be Ollama



### Agentic RAG



This pipeline extends the Retrieval-Augmented Generation (RAG) concept by incorporating

an additional Agent. This agent can retrieve data from your vector store.



You can modify several parameters in your config :



- `provider` : Your LLM Provider (Ollama, LMStudio, Mistral)

- `model` : The model you want to use

- `k` : The number of document you'll retrieve

- `max_steps` : Max reflexion steps used by your Agent

- `api_key` : Your Mistral API key

- `api_base` : Your API URL (Ollama URL, LM Studio URL, ...)

- `num_ctx` : Your context max_length

- `verbosity_level` : Your logs' verbosity level

- `ignore_folders` : List of folders to exclude during indexing (e.g., [".venv", "node_modules", "**pycache**"])



```python

from raglight.config.settings import Settings

from raglight.rag.simple_agentic_rag_api import AgenticRAGPipeline

from raglight.config.agentic_rag_config import AgenticRAGConfig

from raglight.config.vector_store_config import VectorStoreConfig

from raglight.config.settings import Settings

from dotenv import load_dotenv



load_dotenv()

Settings.setup_logging()



persist_directory = './defaultDb'

model_embeddings = Settings.DEFAULT_EMBEDDINGS_MODEL

collection_name = Settings.DEFAULT_COLLECTION_NAME



vector_store_config = VectorStoreConfig(

    embedding_model = model_embeddings,

    api_base = Settings.DEFAULT_OLLAMA_CLIENT,

    database=Settings.CHROMA,

    persist_directory = persist_directory,

    # host='localhost',

    # port='8001',

    provider = Settings.HUGGINGFACE,

    collection_name = collection_name

)



# Custom ignore folders - you can override the default list

custom_ignore_folders = [

    ".venv",

    "venv",

    "node_modules",

    "__pycache__",

    ".git",

    "build",

    "dist",

    "my_custom_folder_to_ignore"  # Add your custom folders here

]



config = AgenticRAGConfig(

            provider = Settings.MISTRAL,

            model = "mistral-large-2411",

            k = 10,

            system_prompt = Settings.DEFAULT_AGENT_PROMPT,

            max_steps = 4,

            api_key = Settings.MISTRAL_API_KEY, # os.environ.get('MISTRAL_API_KEY')

            ignore_folders = custom_ignore_folders,  # Use custom ignore folders

            # api_base = ... # If you have a custom client URL

            # num_ctx = ... # Max context length

            # verbosity_level = ... # Default = 2

            # knowledge_base = knowledge_base

        )



agenticRag = AgenticRAGPipeline(config, vector_store_config)

agenticRag.build()



response = agenticRag.generate("Please implement for me AgenticRAGPipeline inspired by RAGPipeline and AgenticRAG and RAG")



print('response : ', response)

```



### MCP Integration



RAGLight supports MCP Server integration to enhance the reasoning capabilities of your agent. MCP allows the agent to interact with external tools (e.g., code execution environments, database tools, or search agents) via a standardized server interface.



To use MCP, simply pass a mcp_config parameter to your AgenticRAGConfig, where each config defines the url (and optionally transport) of the MCP server.



Just add this parameter to your AgenticRAGPipeline :



```python

config = AgenticRAGConfig(

    provider = Settings.OPENAI,

    model = "gpt-4o",

    k = 10,

    mcp_config = [

        {"url": "http://127.0.0.1:8001/sse"}  # Your MCP server URL

    ],

    ...

)

```



> 📚 Documentation: Learn how to configure and launch an MCP server using [MCPClient.server_parameters](https://huggingface.co/docs/smolagents/en/reference/tools#smolagents.MCPClient.server_parameters)



### Use Custom Pipeline



**1. Configure Your Pipeline**



You can also setup your own Pipeline :



```python

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)

```



**2. Ingest Documents Inside Your Vector Store**



Then you can ingest data into your vector store.



1. You can use default pipeline that'll ingest no code data :



```python

rag.vector_store.ingest(data_path='./data')

```



2. Or you can use code pipeline :



```python

rag.vector_store.ingest(repos_path=['./repository1', './repository2'])

```



This pipeline will ingest code embeddings into your collection : **collection_name**.

But this pipeline will also extract all signatures from your code base and ingest it into : **collection_name_classes**.



You have access to two different functions inside `VectorStore` class : `similarity_search` and `similarity_search_class` to search into different collection.



**3. Query the Pipeline**



Retrieve and generate answers using the RAG pipeline:



```python

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

print(response)

```



> ### ✚ More Examples

>

> You can find more examples for all these use cases in the [examples](https://github.com/Bessouat40/RAGLight/blob/main/examples) directory.



### Override Default Processors



RAGLight ships with built-in document processors based on file extension:



- `pdf` → `PDFProcessor`

- `py`, `js`, `ts`, `java`, `cpp`, `cs` → `CodeProcessor`

- `txt`, `md`, `html` → `TextProcessor`



You can override these defaults using the `custom_processors` argument when building your vector store. This is especially useful if you want to handle certain file types with a custom logic, such as using a **Vision-Language Model (VLM)** for PDFs with diagrams and images. RAGLight provides a VLM based Processor too.



#### Register the Custom Processor in the Builder



```python

from raglight.document_processing.vlm_pdf_processor import VlmPDFProcessor

from raglight.llm.ollama_model import OllamaModel

from raglight.rag.builder import Builder

from raglight.config.settings import Settings



from dotenv import load_dotenv

import os



load_dotenv()

Settings.setup_logging()



persist_directory = './defaultDb'

model_embeddings = Settings.DEFAULT_EMBEDDINGS_MODEL

collection_name = Settings.DEFAULT_COLLECTION_NAME

data_path = os.environ.get('DATA_PATH')



# Vision-Language Model (example with Ollama)

vlm = OllamaModel(

    model_name="ministral-3:3b",

    system_prompt="You are a technical documentation visual assistant.",

)



custom_processors = {

    "pdf": VlmPDFProcessor(vlm),  # Override default PDF processor

}



vector_store = Builder() \

    .with_embeddings(Settings.HUGGINGFACE, model_name=model_embeddings) \

    .with_vector_store(

        Settings.CHROMA,

        persist_directory=persist_directory,

        collection_name=collection_name,

        custom_processors=custom_processors,

    ) \

    .build_vector_store()



vector_store.ingest(data_path=data_path)

```



With this setup, all `.pdf` files will be processed by your custom `VlmPDFProcessor`, while other file types keep using the default processors.



### Hybrid Search (BM25 + Semantic + RRF) 🔍



RAGLight supports three retrieval strategies, configurable via the `search_type` parameter:



| Mode | Description |

|---|---|

| `"semantic"` | Dense vector similarity search (default) |

| `"bm25"` | Keyword-based BM25 search |

| `"hybrid"` | BM25 + semantic merged with Reciprocal Rank Fusion (RRF) |



#### With the Builder API



```python

from raglight.rag.builder import Builder

from raglight.config.settings import Settings



rag = (

    Builder()

    .with_embeddings(Settings.HUGGINGFACE, model_name="all-MiniLM-L6-v2")

    .with_vector_store(

        Settings.CHROMA,

        persist_directory="./myDb",

        collection_name="my_collection",

        search_type=Settings.SEARCH_HYBRID,  # "semantic" | "bm25" | "hybrid"

        alpha=0.5,                           # weight between semantic and BM25 in RRF

    )

    .with_llm(Settings.OLLAMA, model_name="llama3.1:8b")

    .build_rag(k=5)

)



rag.vector_store.ingest(data_path="./docs")

response = rag.generate("What is Reciprocal Rank Fusion?")

print(response)

```



#### With the high-level RAGPipeline API



```python

from raglight.rag.simple_rag_api import RAGPipeline

from raglight.config.rag_config import RAGConfig

from raglight.config.vector_store_config import VectorStoreConfig

from raglight.config.settings import Settings

from raglight.models.data_source_model import FolderSource



vector_store_config = VectorStoreConfig(

    embedding_model=Settings.DEFAULT_EMBEDDINGS_MODEL,

    provider=Settings.HUGGINGFACE,

    database=Settings.CHROMA,

    persist_directory="./myDb",

    collection_name="my_collection",

    search_type=Settings.SEARCH_HYBRID,   # or SEARCH_SEMANTIC / SEARCH_BM25

    hybrid_alpha=0.5,

)



config = RAGConfig(

    llm="llama3.1:8b",

    provider=Settings.OLLAMA,

    k=5,

    knowledge_base=[FolderSource(path="./docs")],

)



pipeline = RAGPipeline(config, vector_store_config)

pipeline.build()

response = pipeline.generate("Explain the retrieval pipeline")

print(response)

```



> **How RRF works**: each search mode returns its own ranked list of documents. RRF assigns a score of `1 / (k + rank)` to each document per list and sums them — documents appearing high in both lists are promoted, while documents unique to one list are kept but ranked lower. This gives the hybrid mode better recall and precision than either mode alone.



> See the full working example in [examples/hybrid_search_example.py](examples/hybrid_search_example.py).



---



## Use RAGLight with Docker



You can use RAGLight inside a Docker container easily.

Find Dockerfile example here : [examples/Dockerfile.example](https://github.com/Bessouat40/RAGLight/blob/main/examples/Dockerfile.example)



### Build your image



Just go to **examples** directory and run :



```bash

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

```



## Run your image



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



```bash

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

```



We use `--add-host` flag to allow Ollama call.