AI search
AI search (also called AI-powered search, generative search, or an answer engine) is a class of search engine and search feature that uses large language models (LLMs) and generative AI to produce a direct
Natural Language Processing
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Browse subtopics: Information Retrieval
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Showing 1-29 of 29 articles
Agent memory
Agent memory is the set of systems that let an AI agent retain and recall information beyond a single context window, so it can carry knowledge, preferences, and experience from one interaction or session into…
Agentic RAG
Agentic RAG (agentic retrieval-augmented generation) is a retrieval-augmented generation design pattern in which one or more autonomous large language model AI agents plan, execute, and revise a sequence of…
Chunking (information retrieval)
Chunking is the preprocessing step in document indexing and retrieval-augmented generation (RAG) systems in which a long document or corpus is split into smaller passages, called chunks, that are then embedded…
ColBERT
ColBERT (Contextualized Late Interaction over BERT) is a neural information retrieval model that encodes queries and documents into matrices of token-level vectors and scores them with a late-interaction…
Contextual AI
Contextual AI is an American enterprise artificial intelligence company headquartered in Mountain View, California, that builds production-grade systems based on retrieval-augmented generation.
Cross-encoder
A cross-encoder is a neural network architecture that scores a pair of texts by feeding them jointly into a single transformer (such as BERT) and reading out one scalar score for the pair, for example a…
Dense Passage Retrieval (DPR)
Dense Passage Retrieval (DPR) is a neural information retrieval method that uses a dual-encoder BERT architecture to map questions and passages into dense vectors
Embeddings
In machine learning, an embedding is a learned representation that maps an input into a vector, usually so that a downstream model or comparison rule can use relationships encoded in the vector's geometry.
Haystack (framework)
Haystack is an open-source AI orchestration framework developed by deepset, a Berlin-based company, for building production-ready natural language processing (NLP), retrieval-augmented generation (RAG), and AI…
HyDE (Hypothetical Document Embeddings)
HyDE (Hypothetical Document Embeddings) is a zero-shot dense retrieval technique that, instead of searching with the user's query, first prompts an instruction-following large language model to write a…
Information Retrieval
Information retrieval (IR) is the study and engineering of systems that identify material likely to satisfy an information need.
Jina Embeddings v3
Jina Embeddings v3 is a multilingual text embedding model released by Jina AI on September 18, 2024, with 570 million parameters, support for 89 languages, an 8,192 token context window, and a stack of…
Latent semantic analysis (LSA)
Latent semantic analysis (LSA), called latent semantic indexing (LSI) in information retrieval contexts, is an unsupervised technique that maps both terms and documents into a shared low-dimensional vector…
LlamaIndex
LlamaIndex is an open-source data framework for building large language model (LLM) applications, with a particular focus on retrieval-augmented generation (RAG) and document processing.
MMTEB
MMTEB (Massive Multilingual Text Embedding Benchmark) is a large, community-built suite for evaluating text embedding models across more than 500 quality-controlled tasks and over 250 languages
MTEB (Massive Text Embedding Benchmark)
MTEB, short for Massive Text Embedding Benchmark, is the standard public leaderboard for evaluating text embedding models across many task types at once.
Matryoshka representation learning
Matryoshka Representation Learning (MRL) is a representation learning technique that trains a single neural model to produce embedding vectors which remain useful when truncated to many smaller dimensionalities
Question answering
Question answering (QA) is the task of producing an answer to a question, usually expressed in natural language.
Reranker
A reranker (also called a cross-encoder reranker or rerank model) is a neural model used in retrieval augmented generation and information retrieval pipelines to re-score an initial set of candidate documents…
SPLADE
SPLADE (Sparse Lexical and Expansion model) is a learned sparse retrieval model that encodes a query or document as a weighted
Semantic Scholar
Semantic Scholar is a free, AI-powered academic search engine and open research-data platform built by the Allen Institute for AI (AI2, also styled Ai2)
Semantic chunking
Semantic chunking is a family of document-segmentation strategies for retrieval augmented generation pipelines that places chunk boundaries based on similarity between sentence embeddings rather than fixed…
Similarity Measure
A similarity measure (also called a similarity function or similarity metric) is a real-valued function that quantifies the degree of resemblance between two objects, returning a large value when the objects…
TF-IDF (Term Frequency-Inverse Document Frequency)
TF-IDF (term frequency-inverse document frequency) is a numerical statistic that measures how important a word is to a single document within a larger collection or corpus, computed as the product of two…
Vector embeddings
Vector embeddings are dense numerical representations of objects (text, images, audio, video, code, graphs, or any structured data) that map them into a continuous vector space such that semantic similarity…
Voyage AI
Voyage AI is an artificial intelligence company that builds state-of-the-art embedding and reranking models for retrieval-augmented generation and semantic search.
Voyage-3
Voyage-3 is a family of general-purpose text embedding models developed by Voyage AI, launched in September 2024 with voyage-3 and voyage-3-lite , expanded in January 2025 with voyage-3-large , and refreshed…
text-embedding-3
text-embedding-3 is a family of third-generation text embedding models that OpenAI released on January 25, 2024.