This tutorial demonstrates how to combine LLM embeddings, TF-IDF vectors, and metadata features into a single Scikit-learn pipeline for document retrieval and search. It covers generating embeddings with Sentence Transformers, calculating TF-IDF, handling metadata, and building a combined retrieval system.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
A mapping of Vespa terminology to equivalent concepts in Elasticsearch, OpenSearch, and Solr.
LangChain's ElasticsearchRetriever enables full flexibility in defining retrieval strategies, allowing users to experiment with different approaches.
