Semantic search vs vector search reddit Also, with the vector or semantic search, I am assuming we would need to deploy an embedding model to do the embeddings of the documents (data source) into the vector db? I'm getting using an E2-standard instance, 1000 embeddings of dim 1024, and hybrid text / vector search. While they approach the problem from different angles – semantic search focusing on meaning and context, and vector search on mathematical representations – they often work in tandem to provide powerful, accurate search capabilities. Feb 19, 2024 · Yet, there's some confusion surrounding vector similarity search, its capabilities, and its relationship with semantic search. I'm using a vector and a GIN index for the search. I get: ~14 QPS with inner product and vectors only ~6 QPS with L2 distance and vectors only ~2 QPS with L2 distance and vectors + text ~2 QPS with inner product and vectors + text FAISS is my favorite open source vector db. Pretty much a set it and forget it type of thing. I actually ended up doing the vector db memory thing and it wasn't as involved as I thought. Given a domain keyword, I want to find the top 5 most similar matches. Milvus, Jina, and Pinecone do support vector search. May 21, 2024 · Hybrid + Semantic ranker ; Optimal search strategy . . Sep 8, 2024 · Semantic search and vector search represent significant advancements in information retrieval technology. Basically a vector store will let you find parts of the text that is similar to your search. Our sys admin did a lot of the setup, so I am playing catch up. I am currently doing this: Document -> Open AI Embeddings -> Pinecone Upsert -> Pinecone Query -> Process Answer -> Delete pinecone data. I have been using ElasticSearch to store vectors that were generated by Azure OpenAI's embeddings endpoint. I used same embedding model text-embedding-3-small for embedding the test document ( 300 character small chunks) . Can also do things like composite scoring so you can rerank results by other criteria, etc. Now I am planning to implement semantic search similarity. Generally speaking the retrieval part of semantic search is more complicated than usually meets the eye, and it feels like you're running into some of these issues. Now I'm confused if semantic search is actually the same as vector search. Keyword search - Uses traditional full-text search methods – content is broken into terms through language-specific text analysis, inverted indexes are created for fast retrieval, and the BM25 probabilistic model is used for scoring. Second, is there a way to configure a good mix between vector semantic search and keyword search? Namely, between vector/keyword search we are looking at a possible mix of 20/80, 35/65, and 50/50 solutions. Both have a ton of support in the langchain libraries. For my use case, I only need to process a document once, then delete it. To put it simply, vector search and semantic search are interconnected but fundamentally different concepts. Pinecone costs 70 stinking dollars a month for the cheapest sub and isn't open source, but if you're only using it for very small scale applications for yourself, you can get away with the free version, assuming that you don't mind waitlists. First of all, what would be the best services to use keyword search? I have heard about Algolia, Elasticsearch, and Typesense. I then performed Vector Searching within ElasticSearch on these vectors without having to have a subscription, this is working really well. If you ask (search) for a term, the vector store will find chucks of text that are most similar to your search. Hi, I have converted some domain-specific name vectors into embeddings, with a dataset size of 200k words. Think of it like an index at the back of a textbook. Sometimes you may want both, which Pinecone supports via single-stage filtering. The embedding models are expensive to run, chunking is flaky, the search is approximate and noisy to the point you go back to hybrid keyword vector search, filtering is a best effort service and when there is a better embedding model you have to start again. Also check out opensearch, an AWS fork of es. So I am not sure. That search will just return chunks of text from the vector store. Easy to deploy on kubernetes, just make sure to provision enough memory&storage for it to actually run I see the option, but I assumed it would have been part of the cognitive search setup instead. I was thinking that Azure AI search should easily outperform chroma DB , So I configured both Chroma DB and Azure AI search Index with same configuration ( HNSW with Cosin similarity ) . All the embeddings were generated using OpenAI's embedding model 3 (3072 dim per embedding) . Vector search acts as a building block for semantic search, enabling data retrieval based on relevance. Followed by chroma. Thanks for this info. Vector search - is best for finding semantically related The experience of building a document based RAG app just seems to be overkill. It is hard to compare but dense vs sparse vector retrieval is like search based on meaning and semantics (dense) vs search on words/syntax (sparse). Nov 26, 2023 · I tried to find some existing services for semantic search and AWS came up: Semantic search in Amazon OpenSearch Service. It's about picking the right chunking strategy, the best embedding model, and just running the whole retreival engine properly. But it seems to be just a vector search with extra sauce, not really a semantic search using knowledge graph in a graph database. We would like to show you a description here but the site won’t allow us. May 23, 2023 · “Vector Database” is not technically a database; rather, it is a search tool for similarity, similar to other search tools such as “ElasticSearch”, “Algolia”, or “Typesense”. Database that supports knn vector search, fuzzy text search, and exact keyword search. This seems like something I'd really enjoy playing with and if it has benefits over vector databases then I'm certainly willing to swap out the vector db for it. In some cases the former is preferred, and in others the latter. I had assumed vector search was semantic search. rlio seiisxnt mdlrgkb qya wzeja yjnszh tgkqrz tobhagp ntlzbx rlkpry