Index Legal Docs for Hybrid Search: Qdrant, OpenAI & BM25
Transforms legal Q&A dataset from Hugging Face into Qdrant vectors for hybrid search using dense embeddings (OpenAI or mxbai) and BM25 sparse vectors.
This n8n workflow indexes a legal Q&A corpus from Hugging Face (isaacus/LegalQAEval) into Qdrant, enabling hybrid search that combines semantic similarity via dense vectors and keyword matching via sparse vectors (BM25). It supports two embedding options: Qdrant Cloud Inference for direct vectorization or external providers like OpenAI's text-embedding-3-small or mxbai-embed-large-v1. After execution, your Qdrant collection is ready for advanced retrieval in the companion workflow.
Key benefits
- Platform
- n8n
- Category
- Utilities
- Price
- $24.99
- Creator
- Matt Buds
- Qdrant
- OpenAI
- Hybrid Search
- Legal AI
- BM25
- Embeddings
- Vector Database
- Hugging Face
- Data Indexing
- RAG
How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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