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
n8n Workflow: Evaluate Hybrid Search for Legal Question Answering
This n8n workflow is designed to facilitate the evaluation of a hybrid search system, likely for legal question-answering applications. It allows for the iterative testing and aggregation of results from an external API, processing data in batches, and filtering based on specific criteria.
What it does
This workflow automates the following steps:
- Manual Trigger: Initiates the workflow upon manual execution.
- Edit Fields (Set): Prepares the input data by setting or modifying fields before further processing.
- Loop Over Items (Split in Batches): Divides the input data into manageable batches, allowing for iterative processing of items. This is crucial for handling large datasets or rate-limited APIs.
- HTTP Request: For each item in a batch, it sends an HTTP request to an external API (likely a search or evaluation endpoint).
- Filter: Filters the results received from the HTTP request based on a defined condition. This allows for focusing on relevant or successful responses.
- Aggregate: Combines the filtered results from all batches back into a single dataset.
- Split Out: Further processes the aggregated data, potentially splitting out nested arrays or objects into individual items for easier consumption or subsequent analysis.
- Merge: Merges data from different branches of the workflow, likely combining the initial input with the processed results.
Prerequisites/Requirements
- n8n Instance: A running instance of n8n to import and execute the workflow.
- External API Endpoint: Access to an API that performs the hybrid search or evaluation, which the "HTTP Request" node will call.
- API Key/Authentication: Any necessary API keys or authentication details for the external API. These should be configured as n8n credentials.
Setup/Usage
- Import the Workflow:
- Download the provided JSON file for this workflow.
- In your n8n instance, click on "Workflows" in the left sidebar.
- Click "New" and then "Import from JSON".
- Paste the JSON content or upload the file.
- Configure Credentials:
- Locate the "HTTP Request" node.
- If the external API requires authentication (e.g., API Key, Bearer Token), configure the appropriate credentials within the "HTTP Request" node.
- Adjust Node Settings:
- Edit Fields (Set): Modify the fields being set or transformed to match your input data structure.
- Loop Over Items (Split in Batches): Adjust the batch size as needed based on your data volume and API rate limits.
- HTTP Request:
- Set the correct URL for your hybrid search/evaluation API.
- Configure the HTTP Method (e.g., POST, GET).
- Define the Body of the request, mapping data from previous nodes (e.g.,
{{ $json.query }}).
- Filter: Define the Conditions for filtering the API responses based on your evaluation criteria.
- Aggregate: Ensure the aggregation method is suitable for combining your results.
- Split Out: Configure how you want to split out nested data from the aggregated results.
- Test the Workflow:
- Click "Execute Workflow" to run a test.
- Review the output of each node to ensure data is flowing and being transformed as expected.
- Activate the Workflow:
- Once configured and tested, activate the workflow to make it ready for use. Since this workflow uses a Manual Trigger, it will only run when explicitly executed.
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