ElasticSearch in Python #36 - Hybrid search with Reciprocal Rank Fusion скачать в хорошем качестве

ElasticSearch in Python #36 - Hybrid search with Reciprocal Rank Fusion

Hi everyone! In this video, I'll show you how to perform hybrid search with Elasticsearch and Python by combining semantic (kNN) and full-text search results using a library called ranx. Hybrid search leverages the strengths of both semantic search, which understands the meaning and context of a query, and traditional full-text search, which excels at keyword matching. We'll start by explaining the concept and the main challenge: how to effectively combine the results from these two different search methods, especially when their relevance scores are on completely different scales. I'll introduce the solution, a fusion algorithm called Reciprocal Rank Fusion (RRF). We'll discuss how RRF works to merge ranked lists from multiple sources into a single, more relevant list. While Elasticsearch offers a built-in RRF feature, it requires a paid Enterprise license. To work around this, I'll demonstrate a free and open-source solution using the ranx Python library. You'll learn how to set up your Elasticsearch index, embed your documents using a sentence-transformer model, run kNN and full-text queries independently, and then use ranx to fuse the results into a unified ranking. Here is the link to the GitHub repository for the course: https://github.com/ImadSaddik/Elastic... Useful links: ranx library: https://github.com/AmenRa/ranx Elasticsearch documentation on RRF: https://www.elastic.co/docs/reference... The embedding model: https://huggingface.co/sentence-trans... Don't forget to like, subscribe, and leave a comment if you have any questions or feedback! ⭐️ Contents ⭐️ (00:00) Intro to Hybrid Search (01:25) Combining results with Reciprocal Rank Fusion (RRF) (02:07) RRF licensing requirements in Elasticsearch (02:43) Using the 'ranx' library as a free alternative (03:54) Connecting to Elasticsearch with the Python client (04:23) Preparing the index for vector search (04:45) Loading the sentence-transformer embedding model (05:33) Loading and embedding the documents (07:07) Performing a k-Nearest Neighbor (kNN) search (08:11) Performing a full-text search (09:52) Paid Solution: How to use the built-in RRF feature (11:02) Free Solution: Fusing results with the ranx library (12:43) Implementing the RRF fusion logic in Python (14:47) Reviewing the final combined search results (16:30) Outro #Elasticsearch #Python #HybridSearch #SemanticSearch #VectorSearch #RRF #ReciprocalRankFusion #DataScience #ranx