Skip to main content

Building RAG Pipelines

Retrieval Augmented Generation.

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external data sources with generative AI capabilities, enabling domain-specific accuracy and reducing hallucinations.

Concepts​

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

Context​

Challenges​

RAG is a hack: Challenges building error-free RAG systems include:

  • Chunk and document quality
  • Refining prompts
  • Output hallucinations

Architecture​

Core Components

  • Indexing
  • Retrieval
  • Augmentation
  • Generation

Indexing​

Convert unstructured/semi-structured data (documents, databases) into vector embeddings stored in specialized databases. Proprietary data requires advanced chunking strategies (semantic vs. fixed-length) to preserve context.

Retrieval​

Hybrid search combining:

  • Vector search (semantic similarity)
  • Keyword search (exact matches) Optimized with re-ranking models (e.g., Cohere Rerank) to prioritize critical documents.

Augmentation​

Advanced implementations use query expansion and multi-hop retrieval for complex questions. Inject retrieved context into prompts using templates like:

Answer using ONLY this context: {context}
Question: {query}

Generation​

LLMs (GPT-4, Claude 3) generate responses grounded in retrieved data. Guardrails filter hallucinations and enforce compliance.

RAG Pipelines​

Key Differentiators​

The differentiation lies in context precision through building data pipelines that deliver insights impossible without deep internal data integration.

  1. Treat proprietary data as core IP - implement strict access controls
  2. Design pipelines for milliseconds-fresh data (not batch updates)
  3. Combine statistical retrieval with business rule engines
  4. Continuously validate outputs against ground-truth SME knowledge

Open vs Proprietary Domain Specific Pipelines.

FeatureOpen-Data PipelinesProprietary Pipelines
Data SourcesPublic datasets (Wikipedia, Common Crawl)Internal docs, CRM systems, IoT sensors
FreshnessStatic snapshotsReal-time CDC (Change Data Capture)
CustomizationGeneric embeddingsDomain-tuned embedding models
SecurityBasic encryptionZero-trust architecture with data masking
ObservabilityBasic metricsFull data lineage tracking & LLM response audits

Implementation​

Proprietary Data Integration

  • Ingest from niche sources: ERP systems (SAP), lab notebooks (Benchling), or manufacturing logs
  • Use synthetic data generators to fill knowledge gaps while maintaining IP control

Real-Time Processing

  • Implement Kafka/Pulsar for streaming updates to vector DBs
  • Example: Medical RAG agents updating with latest trial results < 5min after publication

Continuous Learning

  • Feedback loops where user corrections auto-update knowledge bases
  • A/B test multiple retrieval strategies (HyDE, Sub-Query)

Performance​

  1. Accuracy: Domain-specific fine-tuning achieves 92% accuracy vs. 67% for generic RAG in pharma use cases
  2. Latency: Sub-200ms response times via pre-computed semantic indexes of critical documents
  3. Compliance: Built-in redaction of PII/PHI during ingestion using NLP models
  4. Cost: 40% lower than fine-tuning LLMs through selective retrieval (only query-relevant data processed)

Hippo RAG​

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

Tech Stack​

Vector Databases​

Vector Database requirements:

  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:

Data Inference​