concept

vector database

Also known as: vector stores, vector store, vector databases

Facts (23)

Sources

10 RAG examples and use cases from real companies - Evidently AI evidentlyai.com Evidently AI Feb 13, 2025 4 facts

procedureIn Thomson Reuters' customer service solution, user questions are converted into vector embeddings and queried against a vector database to retrieve relevant documents, which are then used by a seq-to-seq model to generate a response.

procedureTo respond to student questions, ChatLTV provides the LLM with the user query and relevant context retrieved from a vector database, with content chunks served via OpenAI's API.

procedureVimeo's video chatbot implementation follows a bottom-up approach for transcript database registration: first transforming video content into text, then processing and saving the transcript in a vector database, and finally using multiple context window sizes to summarize long context and create descriptions for the entire video.

procedureThomson Reuters' customer service solution uses embeddings to find relevant documents by splitting text into small chunks, embedding each chunk, and storing them in a vector database.

Knowledge Graphs vs RAG: When to Use Each for AI in 2026 - Atlan atlan.com Atlan Feb 12, 2026 4 facts

claimKnowledge graph integration requires a graph database such as Neo4j or Amazon Neptune, while RAG integration works with vector stores such as Pinecone or Weaviate.

procedureTraditional RAG systems process documents by splitting them into chunks, converting those chunks into numerical embeddings, and storing them in vector databases.

claimRAG systems require minimal infrastructure, specifically a vector database, an embedding model, and a retrieval pipeline.

referenceGraphRAG infrastructure requires graph databases (such as Neo4j or Amazon Neptune), vector stores (such as Pinecone or Weaviate), and integration layers connecting both components.

Combining Knowledge Graphs With LLMs | Complete Guide - Atlan atlan.com Atlan Jan 28, 2026 2 facts

procedureMaintaining consistency between graph databases, vector stores, and LLM inference infrastructure requires monitoring data freshness, handling partial failures, and implementing retry logic for transient errors.

claimOrganizations report faster implementation timelines when using integrated platforms for knowledge graphs and LLMs compared to assembling separate graph databases, vector stores, and LLM infrastructure.

RAG Using Knowledge Graph: Mastering Advanced Techniques procogia.com Procogia Jan 15, 2025 2 facts

procedureThe process for building a vector retriever model involves sending chunked documents to an embedding model (such as nomic-embed-text) to generate numerical representations (embeddings) that capture semantic meaning, and then storing these embeddings in a vector database like Neo4j for efficient similarity searches.

claimVectorRAG is a retrieval method that uses vector databases for similarity-based text retrieval.

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv May 20, 2024 2 facts

procedureThe KG-RAG pipeline creates a knowledge graph, computes embeddings for all nodes, hypernodes, and relationships, and stores them in a vector database with corresponding metadata to enable dense vector similarity search during the retrieval stage.

Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org arXiv Jun 29, 2025 2 facts

claimMost Retrieval Augmented Generation (RAG) systems rely on static vector stores and cannot explain answers in terms of explicit biomedical relations.

referenceThe system stores text chunk embeddings in the Chroma vector database, including metadata such as document filenames and chunk IDs to maintain document traceability.

Reducing hallucinations in large language models with custom ... aws.amazon.com Amazon Web Services Nov 26, 2024 1 fact

procedureThe cleanup process for the Amazon Bedrock Agents hallucination detection infrastructure follows this specific order: disable the action group, delete the action group, delete the alias, delete the agent, delete the Lambda function, empty the S3 bucket, delete the S3 bucket, delete AWS Identity and Access Management (IAM) roles and policies, delete the vector database collection policies, and delete the knowledge bases.

Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org arXiv Aug 7, 2025 1 fact

referenceThe knowledge graph construction and retrieval system described in the arXiv paper 'Efficient Knowledge Graph Construction and Retrieval from ...' uses the Milvus vector database to store and retrieve both chunk and relation embeddings, which are then used to compute cosine similarity with a query.

Evaluating RAG applications with Amazon Bedrock knowledge base ... aws.amazon.com Amazon Web Services Mar 14, 2025 1 fact

claimThe Amazon Bedrock knowledge base evaluation feature allows users to assess RAG application performance by analyzing how different components, such as knowledge base configuration, retrieval strategies, prompt engineering, model selection, and vector store choices, impact metrics.

Integrating Knowledge Graphs & Vector RAG for Efficient ... - YouTube youtube.com YouTube Sep 30, 2024 1 fact

claimThe system described in the paper 'HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction' integrates GraphRAG (graph-based retrieval-augmented generation) to optimize review retrieval, moving beyond simple vector storage.

Enhancing LLMs with Knowledge Graphs: A Case Study - LinkedIn linkedin.com LinkedIn Nov 7, 2023 1 fact

accountThe authors created a taxonomy to express the relationship between services offered by a healthcare provider, which was then vectorized and stored in Weaviate, an open-source vector database, for similarity checking.

LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org arXiv Mar 11, 2025 1 fact

procedureThe graph construction process involves transforming extracted entities and relationships into vector embeddings via embedding models, while simultaneously processing existing entities and relationships from the graph database into vector representations stored in a vector store for efficient retrieval.

Enterprise AI Requires the Fusion of LLM and Knowledge Graph stardog.com Stardog Dec 4, 2024 1 fact

claimAli Ghodsi, the CEO of Databricks, suggests that Retrieval-Augmented Generation (RAG) is inadequate for enterprise use because most LLMs struggle to leverage the context pulled from vector databases.