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Building RAG Pipelines

Retrieval Augmented Generation.

RAG is an approach in developing generative AI applications. Embeddings represent data in a way that captures meaning and relationships, and vector databases are the specialized storage and retrieval engines that make it possible to leverage these embeddings effectively in real-world applications.

HippoRAG is a novel RAG framework that aims to improve knowledge integration capabilities for large language models (LLMs). HippoRAG represents a significant advancement in RAG systems, offering a more brain-inspired approach to knowledge integration for LLMs. Its ability to perform multi-hop reasoning efficiently makes it a promising tool for complex information retrieval and question answering tasks that require integrating knowledge from multiple sources.

  • Inspiration and Goal:
    • Inspired by the neurobiology of human long-term memory, particularly the hippocampal indexing theory.
    • Aims to enable LLMs to continuously integrate knowledge across external documents more effectively than traditional RAG systems.
  • Key Components:
    • Uses a graph-based "hippocampal index" to create a network of associations between concepts and passages.
    • Employs an LLM for information extraction and a retrieval encoder to build the knowledge graph.
    • Utilizes the Personalized PageRank algorithm for efficient retrieval.
  • Advantages:
    • Outperforms state-of-the-art methods on multi-hop question answering benchmarks by up to 20%.
    • Single-step retrieval with HippoRAG achieves comparable or better performance than iterative methods like IRCoT.
    • 10-30 times cheaper and 6-13 times faster than iterative retrieval methods.
  • Implementation:
    • Works in two phases: offline indexing (for storing information) and online retrieval (for integrating knowledge into user requests).
    • Can be integrated with existing RAG pipelines and LLM frameworks like LangChain.
  • Setup and Usage:
    • Requires setting up a Python environment with specific dependencies.
    • Supports indexing with different retrieval models like ColBERTv2 or Contriever.
    • Provides scripts for indexing, retrieval, and integration with custom datasets.
  • Applications:
    • Particularly useful for tasks requiring complex reasoning and integration of information from multiple sources.
    • Potential applications in scientific literature reviews, legal case briefings, and medical diagnoses.
  • Future Directions:
    • The researchers suggest potential improvements like fine-tuning components and validating scalability to larger knowledge graphs.
    • Integration with other techniques like graph neural networks (GNNs) could further enhance its capabilities.

Source Code

Intentions​

Building production-ready RAG-powered applications

  • Decrease cycle time
  • Increase accuracy
  • Maximize value

Embeddings​

Embeddings are dense vector representations of data that capture semantic and contextual information. They map raw data (text, images, etc.) into high-dimensional vector spaces, where similar items are positioned closer together based on their meaning or characteristics. This allows machines to understand and process data more effectively.

Vector Databases​

Vector databases are specialized databases designed to efficiently store, index, and query large collections of high-dimensional vector embeddings. They provide the following key capabilities:

  1. Storage: Vector databases allow inserting, updating, and deleting vector embeddings along with associated metadata.
  2. Indexing: They index the embeddings using specialized data structures and algorithms, enabling fast similarity searches based on vector distances
  3. Querying: Vector databases support querying the embeddings by providing a vector representation of the query and retrieving the most similar embeddings from the database. This enables applications like semantic search, recommendation systems, and clustering.
  4. Scalability: They are designed to handle massive volumes of high-dimensional vector data efficiently, scaling horizontally as needed.

Vendors:

Challenges​

Challenges building error-free RAG systems include:

  • Chunk and document quality
  • Refining prompts
  • Output hallucinations

Articles