type The type of reranker model. Type: String Valid Values: BEDROCK_RERANKING_MODEL Required: Yes bedrockRerankingConfiguration Contains configurations for an Amazon Bedrock reranker model. Type: VectorSearchBedrockRerankingConfiguration object Required: No...
Vector search has a per-model limit to how much content can be embedded into each vector. Embedding each chunk into its own vector keeps the input within the embedding model’s token limit and enables the entire document to be searchable in an ANN search index without trun...
In fact, the high recall without any configuration is a huge advantage to using a zero-shot vector model (like the one Vectara has created and builds in) for the initial retrieval step. Being Precise Precision is on the flip side of the search coin to recall. The way to think about ...
Rust library for generating vector embeddings, reranking locally docs.rs/fastembed Topics retrieval embeddings reranking reranker rag vector-search retrieval-augmented-generation fastembed Resources Readme License Apache-2.0 license Activity Stars 341 stars Watchers 4 watching Forks 50 forks ...
Embedding models can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. The most known architecture are encoder-only transformers such as BERT, and ...
on the user question, and return the k results. However, because the similarity algorithm in a vector database works on vectors and not documents, vector search doesn’t always return the most relevant information in the top k results. This directly impacts...
encodes it into a vector numerical representation, and uses it to search in a vast knowledge base for documents that strongly match the query. After that, the original query is augmented by adding additional context information resulting from the retrieved documents, finally sending the augmented inp...
Vector Databases for Embeddings with Pinecone 3 hr 473Discover how the Pinecone vector database is revolutionizing AI application development! See DetailsStart Course course Working with Llama 3 4 hr 633Explore the latest techniques for running the Llama LLM locally, fine-tuning it, and integrating ...
To improve the discriminant ability of selected feature vector, we can transform traditional gait features, e.g., Gait Energy Image (GEI), to a new feature space to improve robustness to influencing factors. We encode the original gait features in another space and complete feature selection in...
Our approach is to learn a user profile representing user's interests using Machine Learning techniques and to re-rank the search results based on collaborative filtering techniques. In particular, we investigate the use of Support Vector Machines(SVMs) and k-Nearest Neighbour methods (kNN) for ...