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The tool splits text from PDFs into smaller chunks, embeds the chunks using HuggingFace's sentence-transformers, and stores them in a Chroma vector database. The stored embeddings are then queried using various retrieval mechanisms, including contextual compression and filtering, to provide accurate and relevant responses to user queries.\n\nThis tool is ideal for tasks such as document indexing, information retrieval, and knowledge management across large document sets.\n\n## Features\nPDF Parsing: Automatically loads and parses multiple PDF files from a specified directory.\nText Chunking: Splits parsed text into manageable chunks for efficient processing.\nEmbeddings Generation: Uses sentence-transformers from HuggingFace to generate high-quality embeddings for each text chunk.\nVector Store Management: Stores and retrieves embeddings using Chroma, a high-performance vector database.\nContextual Compression: Utilizes contextual compression techniques to retrieve the most relevant information from large document sets.\nCustom Retrieval Chains: Includes multiple retrieval chains like LLMChainExtractor, LLMChainFilter, EmbeddingsFilter, and DocumentCompressorPipeline for flexible query processing.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruntime-error786%2Fcontext-compression-retriever","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fruntime-error786%2Fcontext-compression-retriever","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruntime-error786%2Fcontext-compression-retriever/lists"}