Summary
LLMs and vector databases are powerful tools in information retrieval, but they still need a search engine to perform optimally. Vectors provide predictions based on the most likely context within the vector space, but without additional context, the interpretation can be difficult. LLMs understand language patterns and allow for semantic search without exact terms. Vector databases use coordinates to find content matches and determine relevance, but they lack the user's context. Elasticsearch as a vector database allows for additional context and combines multiple search modalities for better results.

Keywords:
LLMs, vector databases, search engine, information retrieval, context, semantic search, relevance,


ElasticsearchTakeaways

LLMs and vector databases need a search engine to perform optimally
Vectors provide predictions based on the most likely context within the vector space
LLMs allow for semantic search without exact terms
Vector databases lack the user's context, which affects relevance
Elasticsearch as a vector database allows for additional context and combines multiple search modalities


Understanding Context in Information Retrieval
The Power of Elasticsearch as a Vector Database


"LLMs and vectors databases and vector search and retrieval augmented generation, all the above, still need a search engine to perform to their optimal accuracy and efficiency."
"LLMs are trained on a large amount of content, so they understand the patterns of language usage."
"With Elasticsearch as your vector database, you can vectorize your content using third-party models and then bring to bear your additional context that LLMs don't have any knowledge of."


Chapters
00:00 The Role of Search Engines in Optimizing LLMs and Vector Databases
02:16 Limitations of Vector Databases and the Need for Additional Context
04:12 Elasticsearch: A Superior Vector Database with Multiple Search Modalities

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