This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
Qodo releases Qodo-Embed-1-1.5B, an open-source code embedding model that outperforms competitors from OpenAI and Salesforce, enhancing code search, retrieval, and understanding for enterprise development teams.
A mapping of Vespa terminology to equivalent concepts in Elasticsearch, OpenSearch, and Solr.
This blog post discusses strategies for staying up-to-date on the rapidly evolving field of AI, covering resources, tools, and techniques for tracking news, research, and developments.
Image Similarity Search Reverse Image Search Object Similarity Search Robust OCR Document Search Semantic Search Cross-modal Retrieval Probing Perceptual Similarity Comparing Model Representations Concept Interpolation Concept Space Traversal Image Similarity Search
LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
