For a day

Find semantically related events inside one dated World archive slice.

POST/api/v2.0-preview/search/{date}/semantic

Authorizations

Bearer

Authorizationstringrequiredheader

Required Bearer API key. Programmatic access to the World API needs a key; browsing nosible.world in a browser does not.

Example: Bearer nos_sk_...

Parameters

query & path

datedaterequiredpath

Archive date in YYYY-MM-DD format.

Example: 2026-07-20

Body

application/json

querystringrequiredbody

Natural-language semantic query.

Example: AI chip supply constraints

limitintegerbody

Maximum events to return.

Example: 20

embedding_modelenumbody

Embedding model. The supported value is openai.

Example: openai

How to use this endpoint

guidance

Use semantic search for concept-level recall when exact keywords are too brittle. Keep the query focused and compare similarity scores across a consistent date and limit. The route returns the same lite event records used by day search, plus explicit HNSW score metadata.

Response body

application/json

These are the fields you can build against. Nested names use dot notation; optional sections are called out in their descriptions.

totalinteger

Semantic matches above the similarity floor.

countinteger

Events in this response.

limitinteger

Result cap applied: 50 unless limit is sent.

offsetinteger

Always 0.

eventsobject[]

The events in the list format: the V2.0-preview record without its evidence and embedding (provenance, triples, availability, each ticker's why and oai_vector). Fetch Event details for those.

events[].event_idstring

Stable event identifier.

events[].versionstring

Event record version: "2.0-preview".

events[].eventobject

The event in its original language: date, language, title, description and points.

events[].event.datedate

World archive date of the event.

events[].event.languagestring

ISO 639-1 code of the event's original language.

events[].event.titlestring

Headline, in the original language.

events[].event.descriptionstring

Short narrative, in the original language.

events[].event.pointsstring[]

Key facts of the event, one sentence each.

events[].englishobject

The event in English: title, description and points, as in Event details.

events[].coverageobject

total_coverage, total_netlocs, coverage_concentration_index, first_seen, last_seen and most_seen, as in Event details.

events[].locationsobject[]

Places of the event, its own place first, then places named in it, as in Event details.

events[].signalsobject

Sentiment, materiality, time horizon and forward-looking signals with their class probabilities, as in Event details.

events[].tickersobject[]

Listed companies the event concerns, in the Event details format without why. Empty when has_tickers is false.

events[].has_tickersboolean

Whether tickers is non-empty.

events[].entitiesobject

Named entities by NER type, {TYPE: [{name, mention_count, confidence}]}, as in Event details.

events[].ontologiesobject[]

Classifications of the event, most probable first, as in Event details.

events[]._scorenumber

Rank-derived score, 1 / (rank + 1).

events[]._hnsw_distancenumber

Distance in the day's vector index; lower is closer.

events[]._cosine_similaritynumber

Cosine similarity to the query; higher is closer.

facetsobject

Always empty on this route.

query_took_msinteger

Total time in milliseconds.

embedding_msinteger

Time spent embedding the query.

search_msinteger

Time spent searching the day's index.

embedding_modelstring

Embedding model requested.

models_usedstring[]

Embedding models used.

per_index_kinteger

Candidates requested from the day's index.

min_cosine_similaritynumber

Similarity floor applied.

matched_tokensstring[]

The query, echoed.

errorsobject[]

Non-fatal errors; empty when none.

Responses and errors

HTTP

200Request succeeded. The response body is shown in the panel on the right.

400invalid_request — The date, filter, cursor, identifier, or request combination is invalid.

401api_key_required — No API key was sent. Send it as Authorization: Bearer <key> or the api-key header.

401invalid_api_key — The API key is invalid or has been revoked.

403access_denied — The requested archive date is outside the caller's World tier.

404not_found — The requested date, entity, ticker, or event does not exist in the accessible index.

410cursor_expired — The cursor was minted against a replaced index build. Restart pagination from the first page.

429rate_limited — The request exceeded the endpoint or account rate limit. Respect Retry-After when supplied.

501backend_not_configured — The World backend is not configured for this deployment.

503api_key_auth_unavailable — API key authentication is temporarily unavailable; retry shortly.

502backend_error — The BFF could not obtain a valid response from the World backend.

504backend_timeout — The World backend or an upstream data source timed out.

curl --request POST \ --url 'https://nosible.world/api/v2.0-preview/search/YYYY-MM-DD/semantic' \ --header 'Accept: application/json' \ --header 'Authorization: Bearer nos_sk_...' \ --header 'Content-Type: application/json' \ --data-raw '{ "query": "AI chip supply constraints" }'

{ "total": 50, "count": 50, "limit": 50, "offset": 0, "events": [ { "event_id": "2026-07-20_en_JP_v1_NQA5mXKS5x7MUUbwQAAAABDCQAAAInpsKAEpoAAAAAA_ehrkVV2W", "version": "2.0-preview", "event": { "date": "2026-07-20", "language": "en", "title": "Nvidia and Japan Launch $2.4B Physical AI Factory for Global Robot Revolution", "description": "Jensen Huang secured major deals in Tokyo to establish a $2.4 billion AI factory, shifting focus from cloud models to embedded machine intelligence. This partnership aims to accelerate Japan's industrial robotics strategy and achieve...", "points": [ "Nvidia and Japan launched a $2.4 billion Physical AI factory initiative." ] }, "english": { "date": "2026-07-20", "language": "en", "title": "Nvidia and Japan Launch $2.4B Physical AI Factory for Global Robot Revolution", "description": "Jensen Huang secured major deals in Tokyo to establish a $2.4 billion AI factory, shifting focus from cloud models to embedded machine intelligence. This partnership aims to accelerate Japan's industrial robotics strategy and achieve...", "points": [ "Nvidia and Japan launched a $2.4 billion Physical AI factory initiative." ] }, "coverage": { "total_coverage": 20, "total_netlocs": 20, "coverage_concentration_index": 0.258503, "first_seen": "2026-07-19T21:28:02+00:00", "last_seen": "2026-07-20T15:22:30+00:00", "most_seen": "2026-07-19T21:28:02+00:00" }, "ontologies": [ { "ontology": "plover", "context": "technology", "probability": 0.9999 }, { "ontology": "schema_org_event", "label": "BusinessEvent", "probability": 0.9988 } ], "locations": [ { "continent": "Asia", "region": "East Asia", "country": "Japan", "lat": 35.6895, "lng": 139.6917, "source": "llm", "matched_on": "{event.title}+{event.description}" }, { "matched_on": "Tokyo", "entity_type": "GPE", "lat": 35.6895, "lng": 139.69171, "region": "Tokyo", "country": "Japan", "continent": "Asia", "source": "ner" } ], "signals": { "sentiment": "positive", "sentiment_prob_positive": 0.9996, "sentiment_prob_neutral": 0.0003, "sentiment_prob_negative": 0, "materiality": "medium", "materiality_score": 0.470755, "time_horizon": "years", "forward_looking": "forward", "forward_looking_prob_forward": 0.9579, "forward_looking_prob_not_forward": 0.0421 }, "tickers": [ { "ticker": "NVDA.US", "companyv5_id": "c5-1b5479ea345b", "name": "NVIDIA Corporation", "website": "https://nvidia.com", "wiki_qid": "Q182477", "isin": "US67066G1040", "lei": "549300S4KLFTLO7GSQ80", "figi": "BBG000BBJQV0", "sector": "Information Technology", "industry": "Semiconductors & Semiconductor Equipment", "country_iso": "US", "exchange": "NASDAQ", "mic": "XNAS", "score": 0.994437 } ], "has_tickers": true, "entities": { "PERSON": [ { "name": "Jensen Huang", "mention_count": 2, "confidence": 0.9844 } ], "ORG": [ { "name": "Nvidia", "mention_count": 4, "confidence": 1 } ], "GPE": [ { "name": "Tokyo", "mention_count": 2, "confidence": 1 } ] }, "_score": 0.05882352963089943, "_hnsw_distance": 0.5715935230255127, "_cosine_similarity": 0.4284064769744873 }, { "event_id": "2026-07-20_en_US_v1_NYAUbHOS5x7MUTavnIgAABDCQAAAIsSVuAAAAAAAAAA_EcwnN6mq", "version": "2.0-preview", "event": { "date": "2026-07-20", "language": "en", "title": "Microsoft Deploys AMD Helios AI Systems to Expand Azure Infrastructure", "description": "Microsoft and AMD have announced an expanded partnership to deploy the new Helios AI rack-scale system and next-generation EPYC processors on Azure. This strategic move aims to bolster high-performance computing and artificial...", "points": [ "Microsoft and AMD announced an expanded partnership to deploy Helios AI systems on Azure." ] }, "english": { "date": "2026-07-20", "language": "en", "title": "Microsoft Deploys AMD Helios AI Systems to Expand Azure Infrastructure", "description": "Microsoft and AMD have announced an expanded partnership to deploy the new Helios AI rack-scale system and next-generation EPYC processors on Azure. This strategic move aims to bolster high-performance computing and artificial...", "points": [ "Microsoft and AMD announced an expanded partnership to deploy Helios AI systems on Azure." ] }, "coverage": { "total_coverage": 97, "total_netlocs": 73, "coverage_concentration_index": 0.052743, "first_seen": "2026-07-20T05:00:00+00:00", "last_seen": "2026-07-21T04:33:00+00:00", "most_seen": "2026-07-20T13:00:00+00:00" }, "ontologies": [ { "ontology": "plover", "context": "technology", "probability": 0.9964 }, { "ontology": "nosible", "category": "Business Operations", "event": "Major Partnership Announced", "subcategory": "Sales Efforts", "probability": 0.996 } ], "locations": [ { "continent": "North America", "region": "North America", "country": "United States", "lat": 37.7749, "lng": -122.4194, "source": "llm", "matched_on": "{event.title}+{event.description}" } ], "signals": { "sentiment": "positive", "sentiment_prob_positive": 0.9999, "sentiment_prob_neutral": 0.0001, "sentiment_prob_negative": 0, "materiality": "high", "materiality_score": 0.811903, "time_horizon": "months", "forward_looking": "forward", "forward_looking_prob_forward": 0.9149, "forward_looking_prob_not_forward": 0.0851 }, "tickers": [ { "ticker": "MSFT.US", "companyv5_id": "c5-37834c8f281e", "name": "Microsoft Corporation", "website": "https://microsoft.com", "wiki_qid": "Q2283", "isin": "US5949181045", "lei": "INR2EJN1ERAN0W5ZP974", "figi": "BBG000BPH459", "sector": "Information Technology", "industry": "Software", "country_iso": "US", "exchange": "NASDAQ", "mic": "XNAS", "score": 0.999559 } ], "has_tickers": true, "entities": { "ORG": [ { "name": "Microsoft", "mention_count": 9, "confidence": 1 } ], "PRODUCT": [ { "name": "Azure", "mention_count": 11, "confidence": 0.7051 } ] }, "_score": 0.043478261679410934, "_hnsw_distance": 0.5872632265090942, "_cosine_similarity": 0.41273677349090576 } ], "facets": {}, "query_took_ms": 633, "embedding_ms": 619, "search_ms": 9, "embedding_model": "openai", "models_used": [ "openai" ], "per_index_k": 200, "min_cosine_similarity": 0.20000000298023224, "matched_tokens": [ "AI chip supply constraints" ], "errors": [] }