Vector Databases Are the Wrong Abstraction
"Your embeddings are out of sync again." It's a message that haunts engineering teams trying to build AI applications. What starts as a simple vector search implementation inevitably evolves into a complex orchestra of monitoring, synchronization, and firefighting. We've spent the past year talking to dozens of engineering teams building AI systems with vector databases, whether semantic search, r
Yusuke Matsui (@matsui_528) on Speaker Deck
A History of Approximate Nearest Neighbor Search from an Applications Perspective
外部データをRetrievalã—ã¦LLM活用ã™ã‚‹ä¸Šã§ã®èª²é¡Œã¨å¯¾ç–案 - ABEJA Tech Blog
ã¯ã˜ã‚ã« ABEJAã§ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆã‚’ã—ã¦ã„ã‚‹æœéƒ¨ã§ã™ã€‚ 今回ã¯LLMã§å¤–部データを使ã†ã‚±ãƒ¼ã‚¹ã«ã¤ã„ã¦ã®ãŠè©±ã‚’ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ ã¯ã˜ã‚ã« LLMã¨å¤–部データã®åˆ©ç”¨ Retrievalã¨LLM 0. (事å‰æº–å‚™)å‚ç…§ã—ãŸã„テã‚ストデータをDBã«æ ¼ç´ 1. ユーザã®å…¥åŠ›æ–‡ã¨ã®ãƒ†ã‚スト類似度を計算ã—ã¦ã€é–¢é€£ãƒ†ã‚ストを抽出ã™ã‚‹(Retrieval) 2. 関連テã‚ストをLLMã®ãƒ—ãƒãƒ³ãƒ—トã«å…¥ã‚Œè¾¼ã¿ã€ãƒ¦ãƒ¼ã‚¶ã®å…¥åŠ›æ–‡ã«å›žç”ã™ã‚‹ã€‚ Retrieval時ã®èª²é¡Œ LangChainã§ã®ç”¨æ„ Case1: ãã‚Œãžã‚Œã®æ–‡ç« ãŒRetrievalã—ã«ãã„å½¢ã§ä¿å˜ã•ã‚Œã¦ã„ã‚‹ 対ç–案: ãƒšãƒ¼ã‚¸æ§‹é€ ã‚’æ„è˜ã—ãŸå½¢ã§å„æ–‡ç« ã‚’æ ¼ç´ã™ã‚‹ ä»–ã®å¯¾ç–案 èžã方を明確ã«ã™ã‚‹ é¡žä¼¼åº¦ã‚’æ¸¬ã‚‹ã‚¯ã‚¨ãƒªæ–‡ç« ã‚’ç½®ãæ›ãˆã‚‹ ä¸è¦ãã†ãªæ–‡ç« をデータã‹ã‚‰å‰Šé™¤ã™ã‚‹ データ自体をLLMã§æ•´å½¢ã—ç›´ã™ Case2: 未知ã®å˜èªžã‚’å«ã‚€ 仮説: ニャオãƒ
Postgres as a search engine / anyblockers
This method ensures that items ranked high in multiple lists are given a high rank in the final list. It also ensures that items ranked high in only a few lists but low in others are not given a high rank in the final list. Placing the rank in the denominator when calculating score helps penalize the low ranking records. It’s also worth noting: $rrf_k: To prevent extremely high scores for items ra
ベクトルデータã®å®¹é‡ã‚’96%削減ã™ã‚‹Binary Embedding
å°Žå…¥ ã“ã‚“ã«ã¡ã¯ã€æ ªå¼ä¼šç¤¾ãƒŠãƒ¬ãƒƒã‚¸ã‚»ãƒ³ã‚¹ã®é ˆè—¤è‹±å¯¿ã§ã™ã€‚普段ã¯ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã¨ã—ã¦ã€LLMを使用ã—ãŸãƒãƒ£ãƒƒãƒˆã®ã‚µãƒ¼ãƒ“スをæä¾›ã—ã¦ãŠã‚Šã€ã¨ã‚Šã‚ã‘RAGシステムã®æ”¹å–„ã¯æ—¥ã€…ã®èª²é¡Œã«ãªã£ã¦ã„ã¾ã™ã€‚ RAGã®ã‚·ã‚¹ãƒ†ãƒ ã®ä¸ã§ã¯ã€ã©ã‚“ãªæƒ…å ±ã«ã‚¢ã‚¯ã‚»ã‚¹ã™ã‚‹ã‹ã‚’決定ã™ã‚‹éš›ã«ã€Embeddingã¨å‘¼ã°ã‚Œã‚‹æ–‡ç« をベクトル化ã™ã‚‹æŠ€è¡“ãŒä½¿ç”¨ã•ã‚Œã¦ã„ã¾ã™ã€‚ãã—ã¦å¤šãã®å ´åˆã§ã¯å°æ•°(float)ã®å¤šæ¬¡å…ƒãƒ™ã‚¯ãƒˆãƒ«ãŒæŽ¡ç”¨ã•ã‚Œã¦ã„ã¾ã™ã€‚ ã—ã‹ã—ã€Embeddingã®ä¸ã«ã¯å„ベクトルã®æ•°å€¤ã‚’1Bitã®ãƒ‡ãƒ¼ã‚¿ã¨ã—ã¦æ‰±ã†Binary Embeddingã¨ã„ã†ã‚‚ã®ãŒå˜åœ¨ã—ã¾ã™ã€‚ 本記事ã§ã¯ã€Embeddingã®æ‰‹æ³•ã®ä¸€ã¤ã§ã‚ã‚‹ãã®Binary Embeddingã«ã¤ã„ã¦è§£èª¬ã¨æ¤œè¨¼ã‚’è¡Œã„ã¾ã™ã€‚ サマリー Binary Embeddingを採用ã™ã‚‹ã“ã¨ã§ä»¥ä¸‹ã®ã‚ˆã†ãªåŠ¹æžœã‚’å¾—ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ ä¿ç®¡ã™ã‚‹ãƒ™ã‚¯ãƒˆãƒ«ãƒ‡ãƒ¼ã‚¿ã®å®¹é‡ã‚’96%ã»ã©å‰Šæ¸›ã§
Abusing CSS Selectors to Perform UI Redressing Attacks
Engineering Blog Technology that sparks innovation to ignite growth Search Reimagining LinkedIn’s search tech stack We share how we transformed our overarching search experience at LinkedIn, including the challenges and decisions that went into creating a scalable LLM-based stack and how the technology is powering a smarter, faster, and more personalized experience that helps every member find the
Picking a vector database: a comparison and guide for 2023
In an era where semantic search and retrieval-augmented generation (RAG) are redefining our online interactions, the backbone supporting these advancements is often overlooked: vector databases. If you're diving into applications like large language models, RAG, or any platform leveraging semantic search, you're in the right place. Picking a vector database can be hard. Scalability, latency, costs
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Vector Indexes | VectorHub by Superlinked
Announcing the PlanetScale vectors public beta — PlanetScale
近似最近å‚探索ã®æœ€å‰ç·š
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