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# ragoon

📚 Modules

- ragoon.chunks
  - `ChunkMetadata`
  - `DatasetChunker`
- ragoon.datasets
  - `dataset_loader()`
  - `load_datasets()`
- ragoon.embeddings
  - `EmbeddingsDataLoader`
  - `EmbeddingsVisualizer`
- ragoon.similarity_search
  - `SimilaritySearch`
- ragoon.web_rag
  - `WebRAG`

This module provides the following submodules:

- ragoon.chunks: Contains functions for handling text chunks.

- ragoon.datasets: Contains functions for loading datasets concurrently.

- ragoon.embeddings: Provides methods for working with text embeddings.

- ragoon.similarity_search: Implements algorithms for similarity search.

- ragoon.web_rag: Offers functionality for web-based retrieval augmented generation.

Please refer to the individual submodules for detailed documentation and usage instructions.

## Key Features

- Query Generation: RAGoon generates search queries tailored to retrieve results that directly address the user’s intent, enhancing the context for subsequent language model interactions.

- Web Scraping and Data Retrieval: RAGoon leverages web scraping capabilities to extract relevant content from various websites, providing language models with domain-specific knowledge.

- Parallel Processing: RAGoon utilizes parallel processing techniques to efficiently scrape and retrieve data from multiple URLs simultaneously.

- Language Model Integration: RAGoon integrates with language models, such as OpenAI’s GPT-3 or LLama 3 on Groq Cloud, enabling users to leverage natural language processing capabilities for their applications.

- Extensible Design: RAGoon’s modular architecture allows for the integration of new data sources, retrieval methods, and language models, ensuring future extensibility.