Qdrant と n8n を使用したハイブリッド検索、法務 AI：インデックス

• •Qdrantサーバー接続情報
• •ターゲットAPIの認証情報が必要な場合あり

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        "content": "## Index Legal Dataset to Qdrant for Hybrid Retrieval\n*This pipeline is the first part of **\"Hybrid Search with Qdrant & n8n, Legal AI\"**.  \nThe second part, **\"Hybrid Search with Qdrant & n8n, Legal AI: Retrieval\"**, covers retrieval and simple evaluation.* \n\n### Overview\nThis pipeline transforms a [Q&A legal corpus from Hugging Face (isaacus)](https://huggingface.co/datasets/isaacus/LegalQAEval) into vector representations and indexes them to Qdrant, providing the foundation for running [**Hybrid Search**](https://qdrant.tech/articles/hybrid-search/), combining:\n\n- [**Dense vectors**](https://qdrant.tech/documentation/concepts/vectors/#dense-vectors) (embeddings) for semantic similarity search;  \n- [**Sparse vectors**](https://qdrant.tech/documentation/concepts/vectors/#sparse-vectors) for keyword-based exact search.\n\n\nAfter running this pipeline, you will have a Qdrant collection with your legal dataset ready for hybrid retrieval on [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) and dense embeddings: either [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) or [`text-embedding-3-small`](https://platform.openai.com/docs/models/text-embedding-3-small).\n\n#### Options for Embedding Inference\nThis pipeline equips you with two approaches for generating dense vectors:\n\n1. Using [**Qdrant Cloud Inference**](https://qdrant.tech/documentation/cloud/inference/), conversion to vectors handled directly in Qdrant;\n2. Using external provider, e.g. OpenAI for generating embeddings.\n\n#### Prerequisites\n- A cluster on [Qdrant Cloud](https://cloud.qdrant.io/)  \n  - Paid cluster in the US region if you want to use **Qdrant Cloud Inference**  \n  - Free Tier Cluster if using an external provider (here OpenAI)  \n- Qdrant Cluster credentials: \n  - You'll be guided on how to obtain both the **URL** and **API_KEY** from the Qdrant Cloud UI when setting up your cluster;  \n- An **OpenAI API key** (if you’re not using Qdrant’s Cloud Inference);  \n\n#### P.S.\n- To ask retrieval in Qdrant-related questions, join the [Qdrant Discord](https://discord.gg/ArVgNHV6).  \n- Star [Qdrant n8n community node repo](https://github.com/qdrant/n8n-nodes-qdrant) <3"
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        "content": "## Get Dataset from Hugging Face\n\nFetching a sample dataset from Hugging Face using the [Dataset Viewer API](https://huggingface.co/docs/dataset-viewer/quick_start).\n**Dataset:** [LegalQAEval from isaacus](https://huggingface.co/datasets/isaacus/LegalQAEval).\n\n1. **Retrieve dataset splits**.  \n2. **Fetch all items with pagination**  \n   - Apply [pagination in HTTP node](https://docs.n8n.io/code/cookbook/http-node/pagination/#enable-pagination) to retrieve the full dataset.  \n3. **Deduplicate text chunks**  \n   - The dataset contains duplicate `text` chunks, since multiple questions may belong to each passage.  \n   - Deduplicate before indexing into Qdrant to avoid storing duplicates.  \n   - Aggregate the corresponding **question–answer IDs** so they can be reused later during retrieval evaluation.  \n4. **Format data for batching** (embeddings inference & indexing to Qdrant)  \n"
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        "content": "## Estimate Average Length of Text Chunks\n\nAverage length of texts in the dataset is a part of the [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) formula used for keyword-based retrieval.\n\n1. **Select a subsample**  \n2. **Count words per text chunk**  \n3. **Compute average length**  \n   - Calculate the mean across all chunks in the subsample.  \n   - This value will be used as the **average document length (avg_len)** parameter in BM25."
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        "content": "## Create [Qdrant Collection](https://qdrant.tech/documentation/concepts/collections/) for Hybrid Search\nThe collection used for **Hybrid Search** is configured here with two types of vectors:\n\n**1. [Dense Vectors](https://qdrant.tech/documentation/concepts/vectors/#dense-vectors)**\nIn this pipeline, we're using the [**mxbai-embed-large-v1**](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) embedding model through Qdrant's Cloud Inference. Hence, we need to specify during the collection configuration its:\n- **Dimensions**: 1024  \n- **Similarity metric**: `cosine`\n\n\n**2. [Sparse Vectors](https://qdrant.tech/documentation/concepts/vectors/#sparse-vectors)**\nQdrant’s main mechanism for setting up **keyword-based retrieval**. \nFor example, you can set up retrieval with:\n  - [**BM25**](https://en.wikipedia.org/wiki/Okapi_BM25) (used in this pipeline);\n    - Qdrant provides an [**`IDF` modifier**](https://qdrant.tech/documentation/concepts/indexing/#idf-modifier) for sparse vectors. This enables Qdrant to calculate **inverse document frequency (IDF)** statistics on the server side. These statistics evaluate the importance of keywords, for example, in BM25.  \n  - SPLADE, miniCOIL and other sparse neural retrievers.  \n\n"
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        "content": "## (Option №1) Index Text Chunks to Qdrant Using [Cloud Inference](https://qdrant.tech/documentation/cloud/inference/)\n\n- **Embed & upsert text chunks in batches**  \n  - **Dense embeddings inference + upsert handled by Qdrant node**, it takes care of generating embeddings and inserting them into the collection.  \n  - **Sparse representations for BM25** are created automatically under the hood by Qdrant.  \n"
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        "content": "## (Option №2) 1. Configure a Collection for OpenAI Embeddings & BM25 Retrieval\nSince [`text-embedding-3-small`] OpenAI embeddings have a different dimensionality (1536) than mxbai embeddings (1024), you need to account for this when configuring the collection. \n \nFor simplicity, create a **separate collection** dedicated to OpenAI embeddings. This collection will be used to index texts in this block.  "
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