Actions13
- Agent Actions
- Datastore Actions
- Query Actions
- Reranker Actions
- LMUnit Actions
- Parser Actions
Overview
This node provides access to a "Reranker" operation that reorders a list of documents based on their relevance to a given query. It is useful in scenarios where you have multiple text documents and want to prioritize or filter them according to how well they match a specific question or topic. For example, it can be used in search engines, recommendation systems, or any application requiring ranking of textual data by relevance.
The "Rerank documents" operation takes a query string and a set of documents, optionally with metadata and instructions, and returns the documents sorted by their relevance scores. You can also limit the output to the top N results.
Properties
| Name | Meaning |
|---|---|
| Query | The input question or statement to which the documents should be ranked for relevance. |
| Documents | A comma-separated list of document texts to be reranked based on the query. |
| Instruction | Optional additional instruction to guide the reranking process (e.g., focus on certain aspects). |
| Model | The reranking model to use. Options: ctxl-rerank-v2-instruct-multilingual, ctxl-rerank-v2-instruct-multilingual-mini, ctxl-rerank-v1-instruct. |
| Top N | Number of top-ranked documents to return. Set to 0 to return all documents after reranking. |
| Metadata | Comma-separated metadata strings corresponding to each document, useful for additional context or identification. Must match the number of documents. |
| Authentication | Method of authentication to use; currently supports API Key authentication. |
Output
The node outputs an array of JSON objects representing the reranked documents. Each object typically contains the document text along with its associated metadata and a relevance score indicating how well it matches the query.
If binary data were involved, it would be summarized here, but this node focuses on textual JSON output only.
Dependencies
- Requires an active API key credential for the Contextual AI service.
- The node depends on external Contextual AI APIs for performing the reranking operation.
- Proper network connectivity and valid API credentials are necessary for successful execution.
Troubleshooting
- Common issues:
- Mismatch between the number of documents and metadata entries can cause errors or unexpected behavior.
- Providing empty or invalid query or documents fields will likely result in errors or no meaningful output.
- Using an invalid or expired API key will cause authentication failures.
- Error messages:
- Authentication errors indicate problems with the API key; verify and update credentials.
- Input validation errors may occur if required fields like Query or Documents are missing or improperly formatted.
- API rate limits or service unavailability might cause request failures; retry after some time or check service status.
Links and References
- Contextual AI Documentation (hypothetical link)
- n8n Documentation on Creating Custom Nodes
- General information on text reranking and relevance scoring techniques in NLP contexts.