Source: https://ailoot.ai/models/en/voyageai-rerank-2-5/ Canonical: https://ailoot.ai/models/en/voyageai-rerank-2-5/ Language: en # VoyageAI by MongoDB: rerank-2.5 [Markdown](https://ailoot.ai/models/en/voyageai-rerank-2-5/index.md) · [JSON](https://ailoot.ai/models/en/voyageai-rerank-2-5/facts.json) ## [VoyageAI by MongoDB: rerank-2.5](https://ailoot.ai/models/en/voyageai-rerank-2-5/) rerank-2.5 reorders a supplied set of text candidates by relevance to a query and optional natural-language instructions. It is a multilingual, quality-oriented reranker with a 32,000-token query–document context and ranked scores tied to the original candidate indices. - [**Ailoot Ultra · Rerank** `POST /v1/rerank` `voyageai/rerank-2.5`](https://ailoot.ai/models/en/voyageai-rerank-2-5/#method-0) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. rerank-2.5 is Voyage AI's instruction-following reranker, designed to refine a set of search candidates. It jointly examines the query and each document as a cross-encoder, then returns a relevance ordering. The maker's release emphasizes natural-language control over what counts as relevant, multilingual retrieval and a longer query–document context than the preceding rerank-2 edition. #### Give the model the candidates to compare The input is a query and a list of candidate text documents. These can be passages selected by embedding search or a lexical first-stage search. The reranker evaluates the supplied text rather than creating corpus embeddings, so its practical job is to refine the candidates the initial search already found. Keep the original candidate ordering and source identifiers when preparing that list. The returned indices refer to those input positions. For a RAG task, supply the passages the answer stage might actually use, including the title or qualifications needed to interpret them. A relevant document that is absent from the submitted candidate set cannot appear as a newly found result of reranking. #### Specify the intended notion of relevance Instructions can be appended or prepended to the query. The release describes three useful roles: emphasize a particular part of a document, prefer a type of source and disambiguate the query with application context. These alter the relevance task presented to the model. For a technical search, an illustrative instruction is “Prioritize passages that explain the failure and recovery behavior; introductory overviews are secondary.” For a research collection, specify whether the title, method or result matters most. For an ambiguous term, state the subject area the search is about. Keep the question and the instruction compatible, so one does not ask for a broad topic while the other silently changes the task. These preferences guide relevance scoring. If the application has strict eligibility requirements—such as source version, date or access scope—prepare the eligible candidate set separately. The model's relevance instruction and a deterministic collection restriction serve different purposes. #### Ranked output and later result use Results are ordered by descending relevance and include a floating-point relevance score and the candidate's original input index. The top-k option selects how many leading results to return; without it, the manufacturer specification returns results for all submitted candidates. An optional return-documents setting includes the corresponding source text; otherwise the response contains indices and scores. Use the ordering to select passages for display or a later answer stage, keeping their source identity intact. Inspect the actual text when evaluating the ranking: the score is a measure of relevance to the submitted query, not a generated answer or a verification that the document's claims are correct. This makes the model useful for refining technical, legal, financial, medical, conversation and multilingual search material without converting ranking into a domain conclusion. #### Context, total-token budget and truncation The documented query limit is 8,000 tokens, including instructions placed in the query. The query plus any one candidate document must fit within 32,000 tokens, and the document list can contain at most 1,000 items. The total allowance is 600,000 tokens, counted as query tokens multiplied by the number of documents, plus all document tokens. A long instruction is therefore counted repeatedly across the candidate pairs. The output top-k count does not change the number of candidates in that input-token calculation. Default truncation shortens over-length queries or documents to meet the context limit; disabling it makes an over-length query or pair return an error. Prepare answer-bearing passages deliberately and keep the relevant qualification within the text the model will see. Evaluate the refined order on representative questions and instructions, then pass the selected original passages to the next stage. **Ailoot Ultra · Rerank** `POST /v1/rerank` `voyageai/rerank-2.5` [`voyageai/rerank-2.5`](https://ailoot.ai/models/en/#m=voyageai%2Frerank-2.5&route=Ultra&caller=voyageai%2Frerank-2.5&fmt=rerank) VoyageAI by MongoDB /`voyageai/rerank-2.5` ### Overview rerank-2.5 reorders a supplied set of text candidates by relevance to a query and optional natural-language instructions. It is a multilingual, quality-oriented reranker with a 32,000-token query–document context and ranked scores tied to the original candidate indices. Accepted inputs Text Results Ranked results ### Available in Ailoot · inputs & methods **Ailoot Ultra · Rerank** `POST /v1/rerank` Input types `text` Output types `rerank` Output format json | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`documents` | Type`array` | RequirementRequired | Values & limits | Default— | | Field`model` | Type`string` | RequirementRequired | Values & limits voyageai/rerank-2.5 | Default— | | Field`provider` | Type`schema` | RequirementOptional | Values & limits | Default— | | Field`query` | Type`string` | RequirementRequired | Values & limits | Default— | | Field`session_id` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`top_n` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 | Default— | | Field`trace` | Type`schema` | RequirementOptional | Values & limits | Default— | | Field`user` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`documents[].text` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`documents[].image` | Type`string` | RequirementOptional | Values & limits | Default— | Input material combinations **text\_rerank** · Required: query, documents array with minItems1 Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, query, documents | | Parameter`material_types` | Contracttext | | Parameter`optional_controls` | Contractprovider, session\_id, top\_n, trace, user | | Parameter`multimodal_input_binding` | Contractnull | Lifecycle: `synchronous` Send the exact model ID and retain the complete native response. ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration Match each charge to the conditions shown. Use one rate for each set of alternative configurations. | Configuration | Rate schedule | Billing unit | Charge | | --- | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Billing unit— | Charge Input · Input tokens **—** / 1K tokens Output · Output tokens **—** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | | ConfigurationUltra | Rate schedule— | Billing unit/ 1K tokens | Charge Input · input token **$0.0001** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Rerank Retained official request structure; documentary exampleUse your actual request material and retain the complete result ```json { "documents": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "model": "voyageai/rerank-2.5", "query": "What is the capital of France?", "top_n": 3 } ``` ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_REQUEST_FILE:?Set your actual JSON request file}" export AILOOT_REQUEST_FILE python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8')) if not isinstance(p,dict): raise SystemExit('Request must be a JSON object') p['model']='voyageai/rerank-2.5' Path('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8') PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path print(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2)) PY_RESULT ``` ### Receive and save results #### Ailoot Ultra · Rerank json ##### Keep all completed results Keep every complete returned response, file and content part. Every actual result index, finite relevance\_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement. ##### If the original response needs checking Preserve original operation and full/partial reply; do not replay POST. Response fields | Field | Type | | --- | --- | | Field`model` | Typestring | | Field`id` | Typestring | | Field`provider` | Typestring | | Field`results[].index` | Typeinteger | | Field`results[].relevance_score` | Typenumber | | Field`results[].document.text` | Typestring | | Field`results[].document.image` | Typestring | | Field`usage.search_units` | Typeinteger | | Field`usage.total_tokens` | Typeinteger | | Field`usage.cost` | Typenumber | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionInvalid required field, representation or value | Next stepCorrect the indicated request field. | ### Aliases #### Model aliases **Ailoot Ultra** [`voyageai/rerank-2.5`](https://ailoot.ai/models/en/#m=voyageai%2Frerank-2.5&route=Ultra&caller=voyageai%2Frerank-2.5&fmt=rerank) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`voyageai/rerank-2.5`](https://ailoot.ai/models/en/#m=voyageai%2Frerank-2.5&route=Ultra&caller=voyageai%2Frerank-2.5&fmt=rerank) FAQ · VoyageAI by MongoDB: rerank-2.5 What inputs and results are available? `POST /v1/rerank` · `voyageai/rerank-2.5`: `text` → `rerank` Which fields does the request use? `POST /v1/rerank` · `voyageai/rerank-2.5`: `documents`, `model`, `provider`, `query`, `session_id`, `top_n`, `trace`, `user` What limits apply? `POST /v1/rerank` · `voyageai/rerank-2.5`: `top_n.minimum`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. Subscription discounts for regular use. Promo codes offer up to 50% off token purchases. Subscription Choose a discounted plan for your regular requests. Promo code: up to 50% off Enter your code at checkout. The price updates to reflect the current promotion. Spending in your history The model, tokens and amount are recorded for each request. [Choose a subscription](https://ailoot.ai/docs/en/billing/subscriptions/) · [View model prices](https://ailoot.ai/models/en/) ## Connect the API to a product you sell. Use Ailoot in SaaS, apps and bots under its official service offer and licence agreement. Request a reseller licence for resale. Commercial application Add text, image, video and audio generation to paid features in your service. Reseller licence Send a description of your project and how you provide access. Request written terms for use and resale. Documents on the website The service offer, licence agreement and data processing policy are available before you connect. [Request a reseller licence](mailto:support@ailoot.ai) · [Service offer and licence agreement](https://ailoot.ai/legal/offer?lang=en) ## Ailoot · The company and its project portfolio Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control. The company and its project portfolio: millions of customers; monthly sales in the billions. Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.