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.