{"uid":"cap_PQFDq8OM_182YZJNsKAJG","slug":"citiview-bge-reranker-40a7fa94","name":"Citiview BGE Reranker","description":"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.","url":"https://agents.citiviewgroup.com/v1/rerank?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["query","documents"],"properties":{"query":{"type":"string"},"top_k":{"type":"integer"},"documents":{"type":"string","description":"separate items with |"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_QU_1aqEP7HQJHmzkkpYBE","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Reranks up to 100 documents by relevance to a query using the BGE reranker model, returning ranked indices and scores.","exampleAgentPrompt":"Rerank these five documents by relevance to my query 'what causes inflation' and give me the top 3: 'Central banks raise rates to control inflation|Supply chain disruptions drive up prices|The history of ancient Rome|Interest rate policy explained|Grocery store loyalty programs'","exampleUseCases":[{"title":"RAG pipeline context selection","prompt":"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."},{"title":"Search result quality improvement","prompt":"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?"},{"title":"Document relevance scoring for QA","prompt":"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'."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-14T06:43:42.062Z","isFirstParty":false,"canonicalSlug":"citiview-bge-reranker-40a7fa94"}