Key Length Constraints: Minimum length of 1. Maximum length of 100. Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: Document Conventions VectorSearchBedrockRerankingConfiguration ...
Vector: All the chunks were embedded using Ada-002 and an ANN index was built. Each query was also embedded with Ada-002 and searched using cosine similarity. The top 50 were labeled. \n Hybrid: The keyword index and vector index of the chunks...
Text Splitters:提供多种文本分割策略(如按 Token 数、递归分块、语义分块),可结合句向量或标点分割。 Vector Stores:集成 FAISS(Facebook 的向量检索库)、Chroma(轻量级向量数据库)、Pinecone(云端向量数据库)等,支持混合检索(关键词+语义)。 LLM Wrappers:封装 OpenAI GPT、Anthropic Claude、开源模型(LLaMA、Chat...
63 71 # Search chunks with vector similarity 64 - chunks = await self.vector_store.query_similar(query_embedding, k=k, doc_ids=doc_ids) 72 + chunks = await self.vector_store.query_similar( 73 + query_embedding, k=10 * k if should_rerank else k, doc_ids=doc_ids 74 + ) ...
Types of Rerankers There is no single recipe for implementing a reranker. Several approaches have been established, some of which are: Multi-vector rerankers: This type of reranker assigns multiple vector representations to documents and user queries, using vector similarity to rerank results. Lea...
Embedding + vector search is fast, but much less accurate than reranker, so this embedding+reranker+LLM workflow works very well in practice. 👍 3 Contributor foldl commented Jul 21, 2024 FYI: chatllm.cpp supports 2 re-ranker models, and RAG of course. 👍 3 Contributor foldl ...
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 impact...
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 ...
Engel, C., Baumgartner, P., Holzmann, M, Nutzel, J.F.: Person re-identification by support vector ranking. In: Proceedings of British Machine Vision Conference, BMVC 2010, Aberystwyth, 31 August–3 September 2010, pp. 1–11 (2010) ...
Constructing positive and negative samples directly at the dialogue level and representing them with a single vector may lead to the loss of important semantic details. Moreover, lengthy dialogues often include much unimportant information, such as polite phrases, inaccurate descriptions of requirements,...