Hybrid Search with Qdrant & n8n, Legal AI: Retrieval

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