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