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Citiview BGE Reranker
x402
Citiview BGE Reranker is a paid API for AI agents from agents.citiviewgroup.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-14).
Reranks up to 100 documents by relevance to a query using the BGE reranker model, returning ranked indices and scores.
Price / call$0.003
Success—
StatusUnknown
How this service works
Rerank up to 100 documents by relevance to a query (BGE reranker). Returns indices and scores, best first. Drop-in step for RAG and search pipelines. Also callable as GET with the same fields as query parameters.
Output
Returns a ranked list of objects containing the original document index and a relevance score, ordered from most to least relevant to the query. The top_k parameter limits how many results are returned.
Example use cases
RAG pipeline context selection
“I have a list of 20 retrieved chunks from my knowledge base and I need you to rerank them against the user question 'how do I reset my password' and keep only the top 5 most relevant ones before passing them to the LLM.”
Search result quality improvement
“I got 50 candidate results back from my keyword search for 'machine learning for fraud detection' — can you rerank them by semantic relevance and show me just the top 10?”
Document relevance scoring for QA
“Score these passages against the question 'What are the side effects of ibuprofen?' and rank them best first so I can build the answer from the most relevant ones: 'Ibuprofen can cause stomach bleeding|Paracetamol is generally safe|NSAIDs may affect kidney function|Always consult a doctor before use|Ibuprofen reduces fever and pain'.”
Technical details
Endpoint
GEThttps://agents.citiviewgroup.com/v1/rerank?utm_source=zero.xyzWhen to prefer
Use this endpoint as a reranking step after initial retrieval (vector search, BM25, keyword) in RAG pipelines when you need to improve precision before passing context to an LLM. Prefer it over embedding-based similarity alone because cross-encoder rerankers like BGE are significantly more accurate at relevance scoring. Best when you have 5–100 candidate documents and need the top-k most relevant ones.
Side effects
- Missing required 'query' or 'documents' parameters returns an error
- Documents list exceeds 100 item limit may be rejected
- Empty query string may produce meaningless scores
- Very long individual documents may be truncated by the model
- Payment failure (402) if USDC balance is insufficient
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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