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Visual search or similarity search has the following limitations: Visual search works best with a large repository. While there is no minimum number of images required for good results, the quality of matches with a few images is not as good as the matches from a large repository. You cannot...
Embeddings can be stored in a vector database, such as ChromaDB or Facebook AI Similarity Search (FAISS), designed specifically for efficient storage, indexing, and retrieval of vector embeddings. The Chat Completion API, which is part of the Azure OpenAI Service, provides a dedicated interface...
Ahybrid approachthat includes full text search can mitigate this problem. Another mitigation is to set a minimum threshold on the search score, but only if the query is a pure single vector query. Hybrid queries aren't conducive to minimum thresholds because the RRF ranges are so much smaller...
Question Validation I have searched both the documentation and discord for an answer. Question When I build Index using faiss vector store, I get the following error telling me that something is wrong with CUDA, what could be the cause o...
Kernel memory provides the functionality to ingest and index data in a way that makes it possible to answer questions later. The interface IMemoryDb and the data structure MemoryRecord are how Kernel Memory connects to vector databases and storage systems capable of performing vector similarity search...
Command R is a highly performant generative large language model, optimized for a variety of use cases including reasoning, summarization, and question answering. Model Architecture:This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model ...
I will discuss indexing in vector databases in my next blog post, so I won’t go into much detail here, but we knowHNSWhas better query performance than IVFFlat. Also, forIVFFlatindexes, it is best to create the index after the table has some data. For HNSW indexes, there is no tra...
Milvus: Milvus is an open source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with ...
To retrieve the corresponding embeddings for a set of documents from a VectorStoreIndex, you can use the query method of the OpensearchVectorStore class. This method takes a VectorStoreQuery object as an argument, which encapsulates the query parameters. Here is an example of how you can use ...