• GuideRAG Pipelines & Patterns

  Implementing GraphRAG with Neo4j and LLMs

  This guide walks through implementing the GraphRAG (Graph Retrieval-Augmented Generation) pattern by integrating Neo4j graph databases with large language models. It provides step-by-step instructions and code snippets to build a scalable, knowledge-enriched question-answering system.

• GuideRAG Pipelines & Patterns

  Iterative RAG: Retrieval with Feedback Loops

  This guide explores iterative retrieval-augmented generation (RAG) techniques using feedback loops to refine responses for complex enterprise queries. It covers architecture patterns, feedback integration, and evaluation methods to enhance retrieval and generation accuracy in multi-step interactions.

• ComparisonRAG Pipelines & Patterns

  Managed vs. Self-Hosted Vector DB: Total Cost of Ownership Analysis

  This comparison evaluates the total cost of ownership (TCO) differences between managed and self-hosted vector databases for enterprise use. It considers licensing, infrastructure, maintenance, scalability, and operational overhead to guide buyers in the retrieval-augmented generation (RAG) and knowledge platform sectors.

• ComparisonRAG Pipelines & Patterns

  Metadata Filtering Strategies for Enterprise RAG

  This guide examines metadata filtering strategies used with vector databases in enterprise retrieval-augmented generation (RAG) workflows. It compares pre-filtering, post-filtering, and hybrid filtering approaches to help platform engineering leaders optimize relevance, performance, and operational overhead.

• GuideRAG Pipelines & Patterns

  Migrating Between Vector Databases: Export, Import, and Zero-Downtime

  This guide details a step-by-step approach to migrating production vector databases with minimal disruption. It covers data export and import strategies, schema compatibility considerations, and approaches to achieving zero-downtime migration for retrieval-augmented generation (RAG) and knowledge systems.

• GuideRAG Pipelines & Patterns

  Multi-Lingual Embeddings for Global Enterprises

  This guide examines multi-lingual embeddings tailored to enterprises managing non-English document collections. It covers key model architectures, vendor offerings, cost considerations, and implementation challenges for retrieval-augmented generation (RAG) and knowledge applications.

• GuideRAG Pipelines & Patterns

  Multi-Modal RAG: Retrieving Images, Tables, and Text Together

  This guide explores how multi-modal retrieval-augmented generation (RAG) architectures integrate images, tables, and text to enhance document AI capabilities. It outlines core components, challenges, and emerging vendor solutions supporting enterprise-scale deployments.

• GuideRAG Pipelines & Patterns

  Multi-Tenant RAG for B2B SaaS: Isolating Customer Knowledge

  This guide explains how product teams can implement Retrieval-Augmented Generation (RAG) in multi-tenant B2B SaaS environments to securely isolate customer knowledge bases. It covers architecture patterns, data segmentation strategies, and operational considerations for enterprise-grade knowledge management.

• GuideRAG Pipelines & Patterns

  Multimodal RAG: Retrieving Images, Charts, and Tables

  This guide explains how to implement and optimize multimodal Retrieval-Augmented Generation (RAG) workflows that retrieve not only text but also images, charts, and tables from documents. It covers architecture choices, indexing techniques, model integration, and operational considerations specific to enterprise AI use cases.

• ComparisonRAG Pipelines & Patterns

  OpenAI ada vs. Voyage vs. Cohere vs. BGE: 2026 Embedding Benchmark

  This comparison evaluates OpenAI's ada, Voyage, Cohere, and BGE embedding models on 2026 MTEB benchmark scores, inference latency, and cost per 1,000 requests. The data aids enterprise AI teams selecting embedding models optimized for retrieval-augmented generation (RAG) and knowledge management use cases.

• ComparisonRAG Pipelines & Patterns

  RAG Evaluation Frameworks: RAGAS, ARES, and TruLens

  Retrieval-augmented generation (RAG) has become a focal point for enterprise AI applications requiring relevant, accurate, and trustworthy outputs. This listicle examines three prominent open-source evaluation frameworks—RAGAS, ARES, and TruLens—that offer distinct approaches to measuring and improving RAG system performance.

• GuideRAG Pipelines & Patterns

  RAG over Confluence: Handling Pages, Spaces, and Attachments

  This guide details a stepwise approach to implementing Retrieval-Augmented Generation (RAG) over Confluence, focusing on effective handling of pages, spaces, and attachments for enterprise knowledge applications.

• GuideRAG Pipelines & Patterns

  RAG over SharePoint: Indexing, Permissions, and Search

  This guide examines best practices and considerations for implementing retrieval-augmented generation (RAG) over SharePoint content. It covers SharePoint indexing capabilities, permission handling complexities, and optimizing search to support enterprise AI solutions.

• GuideRAG Pipelines & Patterns

  RAG Routing: Directing Queries to Specialized Knowledge Bases

  This guide provides a detailed, step-by-step approach to implementing routing mechanisms in retrieval-augmented generation (RAG) systems. It explains best practices for directing user queries to the most relevant specialized knowledge bases, improving response quality and performance in enterprise AI deployments.

• GuideRAG Pipelines & Patterns

  RAG Routing: Directing Queries to Specialized Retrievers

  This guide explains retrieval-augmented generation (RAG) routing strategies to direct queries to specialized retrievers in multi-source knowledge systems. It covers architectural considerations, routing methods, and practical implementation details for enterprise AI deployments.

• InsightRAG Pipelines & Patterns

  Self-RAG: Training Models to Retrieve and Critique Their Own Output

  Self-Retrieval-Augmented Generation (Self-RAG) represents an emerging paradigm where models dynamically retrieve data sources and generate critiques of their own responses. This insight analyzes how Self-RAG adapts retrieval behavior through feedback loops, implications for knowledge consistency, and its role in scaling enterprise AI applications.

• GuideRAG Pipelines & Patterns

  Semantic caching for RAG: Reducing redundant retrieval

  Semantic caching offers a method to reduce repetitive data retrievals in Retrieval-Augmented Generation (RAG) systems by storing and reusing embedding-based vectors. This guide details the architecture, tradeoffs, and deployment considerations for enterprises focused on lowering latency and operational costs in advanced RAG applications.

• ComparisonRAG Pipelines & Patterns

  Serverless vector databases: Aurora pgvector, Pinecone Serverless

  This insight compares two serverless vector database options—Amazon Aurora with pgvector extension and Pinecone's Serverless product—focusing on their suitability for variable workloads common in retrieval-augmented generation (RAG) and knowledge search. It analyzes cost, scalability, latency, and operational complexity to guide enterprise AI buyers and platform engineering leads.

• ComparisonRAG Pipelines & Patterns

  Updating Embeddings for Changing Corpora: Incremental vs. Full Recompute

  This guide evaluates strategies for updating vector embeddings when a document corpus shifts over time. It contrasts incremental embedding updates with full recompute approaches, emphasizing trade-offs around latency, accuracy, complexity, and cost for enterprise knowledge management.

• ToolRAG Pipelines & Patterns

  Vector Database Selection Wizard

  This wizard helps enterprise architects and AI platform leads select an optimal vector database by evaluating scale, latency requirements, and deployment preferences. It balances performance demands with operational considerations to recommend appropriate database solutions.

• InsightRAG Pipelines & Patterns

  Vector database storage costs: Index size, replication, and tiering

  Vector databases form a critical component of retrieval-augmented generation (RAG) pipelines but introduce complex storage cost factors. This insight analyzes index size inflation, replication overhead, and tiered storage trade-offs with real vendor metrics and benchmarks.