Evaluate hybrid search for legal question-answering using Qdrant & BM25/mxbai

Evaluate Hybrid Search on Legal Dataset This is the second part of "Hybrid Search with Qdrant & n8n, Legal AI." The first part, "Indexing", covers preparing and uploading the dataset to Qdrant. Overview This pipeline demonstrates how to perform Hybrid Search on a Qdrant collection using questions and text chunks (containing answers) from the LegalQAEval dataset (isaacus). On a small subset of questions, it shows: - How to set up hybrid retrieval in Qdrant with: - BM25-based keyword retrieval; - mxbai-embed-large-v1 semantic retrieval; - Reciprocal Rank Fusion (RRF), a simple zero-shot fusion of the two searches; - How to run a basic evaluation: - Calculate hits@1 — the percentage of evaluation questions where the top-1 retrieved text chunk contains the correct answer After running this pipeline, you will have a quality estimate of a simple hybrid retrieval setup. From there, you can reuse Qdrant’s Query Points node to build a legal RAG chatbot. Embedding Inference - By default, this pipeline uses Qdrant Cloud Inference to convert questions to embeddings. - You can also use an external embedding provider (e.g. OpenAI). - In that case, minimally update the pipeline, similar to the adjustments showed in Part 1: Indexing. Prerequisites - Completed Part 1 pipeline, "Hybrid Search with Qdrant & n8n, Legal AI: Indexing", and the collection created in it; - All the requirements of Part 1 pipeline; Hybrid Search The example here is a basic hybrid query. You can extend/enhance it with: - Reranking strategies; - Different fusion techniques; - Score boosting based on metadata; - ... More details: Hybrid Queries in Qdrant. P.S. - To ask retrieval in Qdrant-related questions, join the Qdrant Discord. - Star Qdrant n8n community node repo <3