# Rag

> Browse 99 items tagged with Rag.

## Items

- [Agentic RAG](https://vectordb.ever.works/items/agentic-rag) — An advanced RAG architecture where an AI agent autonomously decides which questions to ask, which tools to use, when to retrieve information, and how to aggregate results. Represents a major trend in 2026 for more intelligent and adaptive retrieval systems.
- [DataRobot Vector Databases](https://vectordb.ever.works/items/datarobot-vector-databases) — The DataRobot vector databases feature provides FAISS-based internal vector databases and connections to external vector databases such as Pinecone, Elasticsearch, and Milvus. It supports creating and configuring vector databases, adding internal and external data sources, versioning internal and connected databases, and registering and deploying vector databases within the DataRobot AI platform to power retrieval-augmented generation and other AI use cases.
- [Multimodal RAG](https://vectordb.ever.works/items/multimodal-rag) — Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.
- [Building Applications with Vector Databases](https://vectordb.ever.works/items/building-applications-with-vector-databases) — DeepLearning.AI course teaching six practical vector database applications using Pinecone, including RAG for LLMs, recommender systems, and hybrid search combining images and text.
- [Cascading Retrieval](https://vectordb.ever.works/items/cascading-retrieval) — Advanced retrieval approach combining dense vectors, sparse vectors, and reranking in a multi-stage pipeline, achieving up to 48% better performance than single-method retrieval.
- [RecursiveCharacterTextSplitter](https://vectordb.ever.works/items/recursivecharactertextsplitter) — LangChain's hierarchical text chunking strategy achieving 85-90% accuracy by recursively splitting using progressively finer separators to preserve semantic boundaries.
- [Dify](https://vectordb.ever.works/items/dify) — Open-source LLM app development platform with an intuitive interface that combines AI workflow, RAG pipeline, agent capabilities, model management, and observability features for rapid prototyping and production deployment.
- [Mem0](https://vectordb.ever.works/items/mem0) — Knowledge engine for AI agent memory and memory layer for AI agents. Replaces complex RAG pipelines with serverless, single-file memory supporting instant retrieval and long-term memory.
- [LEANN](https://vectordb.ever.works/items/leann) — LEANN is a lightweight RAG-focused library for vector search on embeddings, achieving 97% storage savings via advanced compression and quantization techniques on personal devices. Implemented in Rust/Python, it supports efficient ANN indexing without full DB overhead. Ideal for embedded apps and private prototyping; far lighter than Milvus, more efficient on-device vs hnswlib.
- [Vectara](https://vectordb.ever.works/items/vectara) — Managed vector database platform for semantic search and retrieval augmented generation (RAG) in AI applications.
- [Haystack](https://vectordb.ever.works/items/haystack) — Haystack is a Python library for building vector search and embedding-based retrieval pipelines, integrating ANN indexes without requiring full databases. Key features include support for HNSW, FAISS indexes, quantization options, and multi-language embeddings. Perfect for prototyping RAG systems and embedded AI apps; more flexible than hnswlib, lighter than Milvus for development workflows.
- [LangChain4j](https://vectordb.ever.works/items/langchain4j) — LangChain4j is a Java library providing vector search and embedding capabilities for LLM applications via integrations with ANN indexes like HNSW and FAISS, without needing full vector databases. Features include support for quantization, tool calling, and seamless embedding in JVM environments like Spring Boot and Quarkus. Suited for prototyping RAG agents and embedded apps; lighter and more JVM-native than Milvus, easier integration vs hnswlib.
- [LlamaIndex](https://vectordb.ever.works/items/llamaindex) — LlamaIndex is a Python data framework library for vector search and embedding retrieval, integrating various ANN indexes like HNSW and FAISS without full database dependencies. Supports quantization, multi-modal embeddings, and advanced query engines in Python/Rust backends. Great for prototyping LLM apps and embedded RAG; more developer-friendly and lighter than Milvus, composable vs hnswlib.
- [AnythingLLM](https://vectordb.ever.works/items/anythingllm) — AnythingLLM is an open-source, self-hosted AI application with integrated vector storage and retrieval for embeddings, enabling RAG and LLM workflows. Key features include built-in RAG, AI agent support, Docker deployment, and free MIT license. Ideal for RAG prototypes and local deployments, providing cost savings and full control compared to managed services like Pinecone.
- [DataRobot Vector Database](https://vectordb.ever.works/items/datarobot-vector-database) — DataRobot Vector Database is a managed vector store capability within the DataRobot AI Platform that allows users to create, register, deploy, and update vector databases for AI workloads, including RAG and semantic search. It integrates with NVIDIA NIM embeddings and supports both built-in and bring-your-own embeddings for building production-grade vector search solutions.
- [DataRobot Vector Databases (GenAI)](https://vectordb.ever.works/items/datarobot-vector-databases-genai) — A premium vector database capability within the DataRobot Generative AI platform that stores chunked unstructured text and their embeddings for retrieval-augmented generation (RAG). Users can create vector database objects, connect supported data sources from the DataRobot Data Registry, configure embeddings and chunking, and attach these vector databases to LLM blueprints in the playground to ground model responses in proprietary data.
- [EmbeddixDB](https://vectordb.ever.works/items/embeddixdb) — High-throughput vector database for RAG and LLM memory, utilizing HNSW/flat indexes with 256x quantization for memory efficiency and 65k QPS performance. Includes MCP server for AI agents, auto-embedding, and pluggable storage like BadgerDB. Fits real-time recommendations and analytics; lighter open-source option vs Milvus, adds MCP unlike standard Qdrant.
- [Nuclia](https://vectordb.ever.works/items/nuclia) — AI Search and RAG-as-a-Service platform with semantic search capabilities. Features NucliaDB open-source database. Acquired by Progress in 2025, now part of Progress Agentic RAG. This is a commercial service with OSS core (NucliaDB).
- [Redis LangCache](https://vectordb.ever.works/items/redis-langcache) — Redis as vector database via RediSearch module supports HNSW/Flat indexes for real-time vector search in key-value store. Features: sub-ms latency, JSON payloads, modules ecosystem; use cases: caching + search hybrids. Vs dedicated VDBs, Redis excels in low-latency but limited scale for pure vectors.
- [Amazon Bedrock Knowledge Bases](https://vectordb.ever.works/items/amazon-bedrock-knowledge-bases) — A fully managed service within Amazon Bedrock that automates the retrieval-augmented generation (RAG) workflow by ingesting unstructured and structured data, converting it into embeddings, and storing them in supported vector databases. It enables grounding generative AI responses with enterprise data without manual orchestration.
- [Inference](https://vectordb.ever.works/items/inference) — A powerful RAG application platform delivering OpenAI-compatible serverless inference APIs for top open-source LLM models. Offers specialized batch processing for large-scale async AI workloads and document extraction capabilities designed for RAG applications, balancing cost-efficiency with high performance.
- [Long-Context LLMs Meet RAG](https://vectordb.ever.works/items/long-context-llms-meet-rag) — A research paper examining the intersection of long-context LLMs and Retrieval-Augmented Generation, focusing on the challenges of combining long-context windows with RAG pipelines, including the 'hard negatives' problem where irrelevant retrieved documents can degrade LLM output quality.
- [RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines](https://vectordb.ever.works/items/ragops-operating-and-managing-retrieval-augmented-generation-pipelines) — Research paper on operating and managing Retrieval-Augmented Generation (RAG) pipelines at scale, covering production infrastructure patterns, monitoring, microservices decomposition, and multi-model architecture for enterprise embedding systems.
- [GraphRAG](https://vectordb.ever.works/items/graphrag) — Retrieval-Augmented Generation approach that combines graph databases with vector search for enhanced context retrieval. Uses graph structures to capture relationships between entities while leveraging vector embeddings for semantic search.
- [HybridRAG](https://vectordb.ever.works/items/hybridrag) — Next evolution in RAG systems that combines vector databases for semantic similarity with graph databases for relationship exploration and multi-hop reasoning.
- [Wren AI](https://vectordb.ever.works/items/wren-ai) — Open-source GenBI platform that queries databases in natural language, generates SQL (Text-to-SQL), charts (Text-to-Chart), and AI-powered business intelligence using RAG architecture.
- [Vanna AI](https://vectordb.ever.works/items/vanna-ai) — RAG-powered text-to-SQL framework that enables natural language querying of SQL databases using vector search for retrieval of relevant schema, documentation, and example queries.
- [Chunk Size Optimization](https://vectordb.ever.works/items/chunk-size-optimization) — The process of determining optimal text segment sizes for embedding and retrieval in vector databases. Chunk size significantly impacts RAG quality, balancing between capturing complete context (larger chunks) and retrieval precision (smaller chunks), typically ranging from 256 to 1024 tokens.
- [Context Window Strategies](https://vectordb.ever.works/items/context-window-strategies) — Techniques for managing limited LLM context windows in RAG systems, including chunk selection, summarization, and iterative retrieval. As context windows fill with retrieved documents, strategies ensure the most relevant information reaches the model while respecting token limits.
- [LLM-as-Judge Evaluation](https://vectordb.ever.works/items/llm-as-judge-evaluation) — Using language models to automatically evaluate RAG system outputs, retrieval quality, and answer correctness. LLM-as-judge provides scalable, consistent evaluation of aspects like faithfulness, relevance, and coherence that are difficult to measure with traditional metrics, enabling rapid iteration on RAG systems.
- [Reranking](https://vectordb.ever.works/items/reranking) — A two-stage retrieval process where initial candidates from vector search are reordered using more sophisticated models like cross-encoders. Reranking significantly improves result quality by applying computationally expensive models to a small set of candidates, commonly used in RAG systems and search applications.
- [Aryn DocParse](https://vectordb.ever.works/items/aryn-docparse) — A compound AI system for parsing, chunking, enriching, and storing unstructured documents at scale, trained on 80k+ enterprise documents and delivering up to 6x better accuracy and 5x cost savings compared to alternative systems.
- [Agentic Chunking](https://vectordb.ever.works/items/agentic-chunking-strategy) — An advanced RAG chunking strategy that uses LLMs to dynamically determine optimal document splitting based on semantic meaning and content structure. Agentic chunking analyzes document characteristics and adapts the chunking approach per document for superior retrieval accuracy.
- [ARES](https://vectordb.ever.works/items/ares-rag-evaluation) — Automatic RAG Evaluation System - a framework for assessing RAG system quality through automated evaluation of retrieval relevance and generation accuracy without human labels.
- [Contextual Retrieval](https://vectordb.ever.works/items/contextual-retrieval-anthropic) — A RAG enhancement technique from Anthropic that adds chunk-specific explanatory context to each document chunk before embedding. Contextual Retrieval reduces retrieval failure rates by 49% and improves accuracy by 67% compared to traditional RAG methods.
- [Unstructured](https://vectordb.ever.works/items/document-parsing-unstructured) — Open-source library for preprocessing unstructured documents (PDFs, Word, HTML, images) for RAG and LLM applications. Handles extraction, chunking, and cleaning of diverse document types.
- [GraphRAG](https://vectordb.ever.works/items/graphrag-microsoft) — Microsoft's approach to RAG that uses knowledge graphs to enhance retrieval. GraphRAG builds structured representations of documents enabling better context understanding and multi-hop reasoning for complex queries.
- [Hybrid Chunking Strategies](https://vectordb.ever.works/items/hybrid-chunking-strategies) — Advanced document chunking approaches that combine multiple chunking methods (fixed-size, semantic, structural) to optimize retrieval in RAG systems. Hybrid strategies adapt to document characteristics for superior performance.
- [Hybrid Search Techniques](https://vectordb.ever.works/items/hybrid-search-techniques) — Best practices for combining vector and keyword search using RRF and weighted fusion for improved retrieval accuracy in RAG systems.
- [LlamaParse](https://vectordb.ever.works/items/llama-parse-document) — Advanced document parsing service from LlamaIndex for extracting structured data from PDFs, PowerPoints, and Word documents. Uses LLMs to understand document structure and maintain layout information.
- [Neo4j Vector Search](https://vectordb.ever.works/items/neo4j-graph-vector-search) — Vector similarity search in Neo4j enabling GraphRAG by combining knowledge graphs with vector embeddings.
- [RAGAS](https://vectordb.ever.works/items/ragas-rag-evaluation) — Retrieval Augmented Generation Assessment framework for reference-free evaluation of RAG pipelines. RAGAS provides automated metrics for retrieval quality, context relevance, and generation faithfulness.
- [SFR-Embedding](https://vectordb.ever.works/items/sfr-embedding-salesforce) — Salesforce's family of state-of-the-art embedding models including SFR-Embedding-Mistral for text and SFR-Embedding-Code for code retrieval. SFR-Embedding-Mistral achieved #1 on the MTEB benchmark with a 67.6 average score, surpassing OpenAI and Cohere models.
- [TruLens](https://vectordb.ever.works/items/trulens-llm-evaluation) — An evaluation framework for LLM applications including RAG systems, providing observability, debugging, and guardrails. TruLens tracks retrieval quality, LLM performance, and hallucinations with detailed tracing.
- [AutoRAG](https://vectordb.ever.works/items/autorag-optimization-framework) — Automated framework for optimizing Retrieval Augmented Generation pipelines using AutoML-style techniques to find the best RAG module combinations and parameters for specific datasets.
- [Canopy](https://vectordb.ever.works/items/canopy-rag-framework) — Open-source Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone, providing automatic chunking, embedding, chat history management, and query optimization.
- [Context Window Management in RAG](https://vectordb.ever.works/items/context-window-management-in-rag) — Strategies for managing LLM context windows in RAG applications including chunk selection, context compression, and techniques for working within token limits while maintaining answer quality.
- [Document Parsing for RAG](https://vectordb.ever.works/items/document-parsing-for-rag) — Critical preprocessing step for RAG systems involving extraction of text, tables, and images from various document formats (PDF, DOCX, HTML) using tools like Unstructured, LlamaParse, and PyPDF.
- [Graph RAG](https://vectordb.ever.works/items/graph-rag) — RAG architecture that combines knowledge graphs with vector databases, enabling multi-hop reasoning, relationship traversal, and structured knowledge representation for more accurate and explainable AI responses.
- [Late Chunking](https://vectordb.ever.works/items/late-chunking) — Advanced chunking technique for long-context embeddings where documents are embedded first as a whole, then chunked, preserving contextual information and improving retrieval quality especially for technical documents.
- [Prompt Engineering for RAG](https://vectordb.ever.works/items/prompt-engineering-for-rag) — Best practices and techniques for crafting effective prompts in RAG systems including context formatting, instruction design, few-shot examples, and prompt optimization strategies.
- [Query Expansion for Vector Search](https://vectordb.ever.works/items/query-expansion-for-vector-search) — Techniques to improve retrieval by expanding user queries with synonyms, related terms, and reformulations including HyDE, query rewriting, and multi-query approaches.
- [RAG Evaluation Frameworks](https://vectordb.ever.works/items/rag-evaluation-frameworks) — Comprehensive overview of frameworks and tools for evaluating RAG systems including RAGAS, TruLens, LangSmith, and ARES with metrics for retrieval quality, generation accuracy, and end-to-end performance.
- [RAG Production Readiness Checklist](https://vectordb.ever.works/items/rag-production-readiness-checklist) — Comprehensive checklist for deploying RAG systems to production covering data quality, retrieval performance, LLM integration, monitoring, security, and operational requirements.
- [LazyGraphRAG](https://vectordb.ever.works/items/lazygraphrag) — Cost-optimized variant of GraphRAG that reduces indexing cost to 0.1% of full GraphRAG while maintaining retrieval quality. Designed for resource-constrained deployments where traditional GraphRAG's 100-1000x higher indexing cost is prohibitive.
- [Contextual Compression](https://vectordb.ever.works/items/contextual-compression) — A RAG optimization technique that compresses retrieved documents by extracting only the most relevant portions relative to the query. Reduces token usage and improves LLM response quality by removing irrelevant context.
- [Document Loaders](https://vectordb.ever.works/items/document-loaders) — Components in LLM frameworks that fetch and parse data from various sources (PDFs, websites, databases) into a standardized format for processing. Essential first step in RAG pipelines for converting raw data into processable documents.
- [Neo4j GraphRAG Python](https://vectordb.ever.works/items/neo4j-graphrag-python) — Official Neo4j package for building graph retrieval augmented generation (GraphRAG) applications in Python. Enables developers to create knowledge graphs and implement advanced retrieval methods including graph traversals, text-to-Cypher, and vector searches.
- [Parent Document Retriever](https://vectordb.ever.works/items/parent-document-retriever) — A RAG technique that indexes small chunks for precise matching but retrieves larger parent documents for LLM context. Balances retrieval precision with comprehensive context by separating indexing granularity from context size.
- [Self-Querying Retriever](https://vectordb.ever.works/items/self-querying-retriever) — An intelligent retrieval technique where an LLM decomposes natural language queries into semantic search components and metadata filters. Enables more precise retrieval by automatically extracting structured filters from unstructured queries.
- [Sentence Window Retrieval](https://vectordb.ever.works/items/sentence-window-retrieval) — A RAG technique that indexes individual sentences for precise matching but retrieves surrounding sentences (a window) for context. Provides fine-grained retrieval precision while maintaining adequate context for LLM generation.
- [MeMemo](https://vectordb.ever.works/items/mememo) — A JavaScript library that brings vector search and RAG (Retrieval-Augmented Generation) to browser environments, enabling efficient searching through millions of vectors using HNSW algorithm with IndexedDB and Web Workers.
- [RAG Evaluation Metrics](https://vectordb.ever.works/items/rag-evaluation-metrics) — Industry-standard metrics for evaluating Retrieval-Augmented Generation systems, including Answer Relevancy, Faithfulness, Context Relevance, Context Recall, and Context Precision to ensure quality and reliability.
- [Ragas](https://vectordb.ever.works/items/ragas) — RAG Assessment framework for Python providing reference-free evaluation of RAG pipelines using LLM-as-a-judge, measuring context relevancy, context recall, faithfulness, and answer relevancy with automatic test data generation.
- [Text Chunking Strategies for RAG](https://vectordb.ever.works/items/text-chunking-strategies-for-rag) — Essential techniques for splitting documents into optimal-sized chunks for Retrieval-Augmented Generation, including fixed-size, recursive, semantic, and document-based chunking with overlap strategies to preserve context.
- [Embedchain](https://vectordb.ever.works/items/embedchain) — Open Source RAG Framework designed to be 'Conventional but Configurable', streamlining the creation of RAG applications with efficient data management, embeddings generation, and vector storage.
- [FlashRAG](https://vectordb.ever.works/items/flashrag) — Python toolkit for efficient RAG research providing 36 pre-processed benchmark datasets and 23 state-of-the-art RAG algorithms in a unified, modular framework for reproduction and development.
- [NVIDIA NeMo Retriever](https://vectordb.ever.works/items/nvidia-nemo-retriever) — Collection of industry-leading Nemotron RAG models delivering 50% better accuracy, 15x faster multimodal PDF extraction, and 35x better storage efficiency for building enterprise-grade retrieval-augmented generation pipelines.
- [Semantic Chunking](https://vectordb.ever.works/items/semantic-chunking) — Advanced text splitting technique using embeddings to divide documents based on semantic content instead of arbitrary positions, preserving cohesive ideas within chunks for improved RAG performance.
- [ViDoRe](https://vectordb.ever.works/items/vidore) — Visual Document Retrieval Benchmark defining standard evaluation protocols for vision-centric document and video retrieval with 26,000 pages and 3,099 queries across 6 languages from 12,000 man-hours of annotations.
- [ARES](https://vectordb.ever.works/items/ares) — RAG evaluation framework that trains lightweight judges for retrieval and generation scoring, refining evaluation by training specialized LLM judges on synthetic datasets to provide more reliable, confidence-aware judgments.
- [BGE Reranker Base](https://vectordb.ever.works/items/bge-reranker-base) — Open-source cross-encoder reranking model from BAAI that enhances RAG retrieval quality by examining query-document pairs individually. Self-hostable with Apache 2.0 licensing for cost-effective production deployments.
- [Docling](https://vectordb.ever.works/items/docling) — Open-source document parsing framework from IBM with 97.9% accuracy in complex table extraction and excellent text fidelity. Self-hostable solution for converting PDFs, spreadsheets, and scanned images into structured data for RAG pipelines.
- [LightRAG](https://vectordb.ever.works/items/lightrag) — Simple and efficient retrieval-augmented generation framework that combines document retrieval with generation, focusing on speed and ease of use. Designed to run on standard CPUs and laptops with minimal resource requirements.
- [LlamaParse](https://vectordb.ever.works/items/llamaparse) — High-performance document parsing service by LlamaIndex that consistently processes documents in about 6 seconds regardless of size. Returns rich Markdown and optional HTML tables with wide format support through hosted API.
- [LLMWare](https://vectordb.ever.works/items/llmware) — Retrieval-augmented generation framework that utilizes small, specialized models instead of large language models, significantly reducing computational and financial costs while offering cost-effective RAG solutions that can run on standard hardware.
- [Pathway](https://vectordb.ever.works/items/pathway) — Python ETL framework for stream processing and real-time analytics with built-in vector search capabilities. Features real-time document synchronization, in-memory vector index, and adaptive RAG technology for always-current AI applications.
- [Recursive Character Text Splitter](https://vectordb.ever.works/items/recursive-character-text-splitter) — Document chunking strategy that splits text at hierarchical boundaries like paragraphs, sentences, or headings. Industry-standard approach recommended as starting point with 400-512 tokens and 10-20% overlap for optimal RAG performance.
- [Unstructured](https://vectordb.ever.works/items/unstructured) — Document parsing platform delivering strong content fidelity and precision with low hallucination rates. Achieves 100% accuracy on simple tables and 75% on complex structures with comprehensive enterprise document support.
- [Chunking Strategies for RAG](https://vectordb.ever.works/items/chunking-strategies-for-rag) — Methods for splitting documents into optimal pieces for vector embedding and retrieval. Includes fixed-size, recursive, semantic, and agentic chunking approaches.
- [Contextual Retrieval](https://vectordb.ever.works/items/contextual-retrieval) — Anthropic's RAG technique that prepends chunk-specific explanatory context before embedding, reducing failed retrievals by 49% (67% with reranking). Uses Contextual Embeddings and Contextual BM25.
- [PrivateGPT](https://vectordb.ever.works/items/privategpt) — Production-ready AI project for private, local document Q&A using RAG. 100% private with no data leaving your environment, supporting offline operation with local LLMs and vector databases.
- [RAG (Retrieval-Augmented Generation)](https://vectordb.ever.works/items/rag-retrieval-augmented-generation) — AI technique combining information retrieval with LLM generation. Retrieves relevant context from knowledge base before generating responses, reducing hallucinations and enabling grounded answers.
- [Reranking Models](https://vectordb.ever.works/items/reranking-models) — Cross-encoder models that rerank initial retrieval results for improved relevance. More accurate than bi-encoders but slower, typically applied to top-k candidates.
- [Chunk Overlap Strategy](https://vectordb.ever.works/items/chunk-overlap-strategy) — Text chunking technique using 10-20% overlap between consecutive chunks to preserve context continuity and prevent information loss at chunk boundaries for improved retrieval.
- [Context Precision](https://vectordb.ever.works/items/context-precision) — RAG evaluation metric assessing retriever's ability to rank relevant chunks higher than irrelevant ones, measuring context relevance and ranking quality for optimal retrieval.
- [Context Recall](https://vectordb.ever.works/items/context-recall) — RAG evaluation metric measuring whether retrieved context contains all information required to produce ideal output, assessing completeness and sufficiency of retrieval.
- [DSPy](https://vectordb.ever.works/items/dspy) — Programming framework for RAG and AI applications with cutting-edge optimization capabilities, featuring the lowest framework overhead and automatic improvement based on example data.
- [Faithfulness](https://vectordb.ever.works/items/faithfulness) — RAG evaluation metric measuring whether generated answers accurately align with retrieved context without hallucination, ensuring factual grounding of LLM responses.
- [LangChain & Vector Databases in Production](https://vectordb.ever.works/items/langchain-vector-databases-in-production) — Free comprehensive course from Activeloop with 60+ lessons and 10+ practical projects, teaching production-ready LLM applications with vector databases, trusted by 10,000+ engineers.
- [Hybrid Search Best Practices](https://vectordb.ever.works/items/hybrid-search-best-practices) — Comprehensive guide to combining BM25 keyword search with vector semantic search using reciprocal rank fusion and reranking. Essential pattern for production RAG systems in 2026.
- [Instaclustr Vector Database Management](https://vectordb.ever.works/items/instaclustr-vector-database-management) — A managed service and tooling offering from Instaclustr that helps teams operate and optimize vector databases for GenAI and Retrieval-Augmented Generation (RAG) workloads, providing expertise and infrastructure management for production deployments.
- [Elysia](https://vectordb.ever.works/items/elysia) — Elysia is an open-source, decision-tree-based agentic system built on top of Weaviate that orchestrates tools and vector-search workflows, demonstrating how to build complex AI agents that leverage a vector database as a core component.
- [Implement two-tower retrieval for large-scale candidate generation](https://vectordb.ever.works/items/implement-two-tower-retrieval-for-large-scale-candidate-generation) — A Google Cloud reference architecture demonstrating an end-to-end two-tower retrieval system for large-scale candidate generation that uses Vertex AI and vector similarity search concepts to learn and serve semantic similarity between entities.
- [Verba](https://vectordb.ever.works/items/verba) — Verba is a community-driven, open-source Retrieval-Augmented Generation (RAG) application that provides an end-to-end, user-friendly interface for building RAG workflows on top of a vector database, showcasing practical semantic search and retrieval patterns with Weaviate.
- [Mastering Multimodal RAG](https://vectordb.ever.works/items/mastering-multimodal-rag) — A course focused on mastering multimodal Retrieval Augmented Generation (RAG) and embeddings, which are fundamental components often stored and managed by vector databases.
- [Vector Databases](https://vectordb.ever.works/items/vector-databases) — A critical emerging technology focused on processing, storing, and retrieving vast amounts of high-dimensional vector data rapidly and efficiently. Unlike traditional databases, they offer unique advantages for use cases such as image and video recognition, natural language processing (NLP), and Retrieval-Augmented Generation (RAG).
- [Trieve](https://vectordb.ever.works/items/trieve) — Trieve provides an all-in-one infrastructure for vector search, recommendations, retrieval-augmented generation (RAG), and analytics, accessible via API for seamless integration.
- [RETA-LLM](https://vectordb.ever.works/items/reta-llm) — RETA-LLM is a toolkit designed for retrieval-augmented large language models. It is directly relevant to vector databases as it involves retrieval-based methods that typically leverage vector search and vector databases to enhance language model capabilities through external knowledge retrieval.

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_Canonical page: https://vectordb.ever.works/tags/rag_