verirerag

What is this model?

A self-hosted reranking model based on BAAI/bge-reranker-large. It takes a query and a list of documents and returns a relevance score per document, surfacing the most relevant passages for RAG (retrieval-augmented generation).

Capability & type

Capability	Rerank — Capability page
Model type	Rerank (type=10)
Base model	BAAI/bge-reranker-large
Provider	arpanet (local / self-hosted)
Streaming	—

Endpoint

POST /v1/rerank

Base URL: https://app.qevron.ai/v1

Authentication

Every request needs the Authorization: Bearer <KEY> header.

Authorization: Bearer <KEY>

Request schema & parameters

Parameter	Type	Required	Description
model	string	✓	verirerag
query	string	✓	The ranking query
documents	string[]	✓	Documents to rank
top_n	int	—	Return top N

Response schema

{
  "results": [
    {"index": 0, "relevance_score": 0.98, "document": {"text": "..."}},
    {"index": 2, "relevance_score": 0.71, "document": {"text": "..."}}
  ]
}

Code examples

curl

curl https://app.qevron.ai/v1/rerank \
  -H "Authorization: Bearer $QEVRON_KEY" -H "Content-Type: application/json" \
  -d '{
    "model": "verirerag",
    "query": "What is the capital of France?",
    "documents": ["Paris is the capital of France.", "Berlin is in Germany.", "The Eiffel Tower is in Paris."],
    "top_n": 2
  }'

Python

import requests
r = requests.post("https://app.qevron.ai/v1/rerank",
    headers={"Authorization": "Bearer $QEVRON_KEY"},
    json={"model": "verirerag", "query": "What is the capital of France?",
          "documents": ["Paris is the capital of France.", "Berlin is in Germany."], "top_n": 2})
print(r.json())

Pricing

Input	Output	Unit
$0.00003	—	1K tokens

Rate limits

RPM/TPM are governed by your group quota. Check your current quota with: GET /v1/dashboard/billing/quota.

Notes & quirks

• The public API endpoint is OpenAI/Cohere-style: send query + documents (an array of strings) in the body.
• Results come back sorted by relevance_score descending; limit with top_n.

TEI compatibility

On some internal TEI-based channels the fields are renamed to texts / return_text — Qevron does that conversion for you; on the public endpoint use documents.

Limits & constraints

• The output is relevance scores for query–document pairs, not an embedding; for direct vector search use veriEmbedding.

Related

• Capability page: Rerank
• All local models: Local Models