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Nappai Documentation

Contextual Compression Retriever

The Contextual Compression Retriever is a component in the Nappai automation system that helps users find documents more efficiently. By using advanced compression techniques, it improves the accuracy and speed of document searches.

Relationship with Contextual Compression Technology

This component leverages contextual compression technology to optimize document retrieval. It uses language models and embeddings to compress and decompress data, making searches faster and more precise.

Inputs

  • Search Query: The text you enter to find specific documents.
  • Compressor: Choose the type of compression to apply, such as “LLMChainExtractor” or “EmbeddingsFilter”.
  • Base Retriever: The foundational tool used for document retrieval.
  • LLM: The language model that aids in compressing and decompressing data.
  • Embedding: The embedding technique used to enhance the compression process.

Outputs

The component produces two main outputs:

  • Retriever: A configured tool that can be used for future document searches.
  • Search Results: The list of documents that match your search query, ready for review or further action.

Usage Example

Imagine you need to find specific reports from a large database. You enter your search terms in the “Search Query” field, select your preferred compression method, and let the Contextual Compression Retriever do the rest. It quickly provides a list of relevant documents, saving you time and effort.

Templates

Currently, there are no specific templates where this component is pre-configured. However, it can be integrated into various workflows within the Nappai system.

  • NVIDIA Rerank: Enhances document ranking using the NVIDIA API.
  • Multi Query Retriever: Allows multiple search queries for comprehensive results.
  • Ensemble Retriever: Combines results from multiple retrievers for improved accuracy.

Tips and Best Practices

  • Use specific and clear search queries to get the best results.
  • Experiment with different compression types to see which yields the most accurate results for your needs.
  • Regularly update your language models and embeddings to maintain optimal performance.

Security Considerations

Ensure that any sensitive data used in searches is handled in compliance with your organization’s data protection policies. Always verify that the language models and embeddings are sourced from trusted providers to avoid potential security risks.