Inference
Run a single forecasting prediction
Submit a forecasting question and receive a typed structured prediction.
The model returns:
- question_type — binary, multiple_choice, numeric, or date
- predicted_answer — Yes/No, top option, or median estimate
- confidence — probability (0–1) for binary and multiple-choice answers
- options — per-option probabilities for multiple-choice questions
- range_forecast — p10/p50/p90 bounds for numeric and date questions
- rationale — 2–4 sentence explanation
- evidence_sources — news articles used as context
- market_analysis — model-vs-market edge when market_probability is provided
Choosing a variant
Start with variant0_neutral_baseline (the default). Switch to other variants to inject additional structure into the prompt — for example, variant3_reasoning_type asks the model to identify whether the prediction is based on speculation, an expert forecast, or a stated plan.
Providing your own evidence
Pass pre-fetched articles in news_articles to use them directly. Leave the list empty to trigger automatic news retrieval (if the evidence pipeline is configured on the server).
Example — binary request
curl -X POST /predict \
-H "Content-Type: application/json" \
-d '{
"question": "Will oil prices exceed $100 per barrel in 2026?",
"question_type": "binary",
"market_platform": "Kalshi",
"market_probability": 0.31
}'
Example — multiple choice
curl -X POST /predict \
-H "Content-Type: application/json" \
-d '{
"question": "Who will win the 2026 Formula 1 drivers championship?",
"question_type": "multiple_choice",
"options": ["Max Verstappen", "Lando Norris", "Charles Leclerc", "Lewis Hamilton", "Other"],
"attach_evidence": false
}'
Example — numeric forecast with context
curl -X POST /predict \
-H "Content-Type: application/json" \
-d '{
"question": "What will US CPI inflation be in December 2026?",
"question_type": "numeric",
"resolution_criteria": "Use the year-over-year CPI-U inflation rate for December 2026.",
"categories": ["Economics", "United States"],
"variant": "variant5_key_conditions"
}'
post/predict
Request
- Base URL: https://foresea.ink (the document declares no server; this is the origin it was published from)
- URL: https://foresea.ink/predict
- Auth: none declared
Query parameters
kb_user_idstring nullable
Request body
Example request
{
"market_platform": "Polymarket",
"market_probability": 0.54,
"question": "Will the Federal Reserve cut interest rates at least once before the end of 2025?",
"question_type": "binary"
}Response
Prediction returned successfully.
Example response
{
"confidence": 0.72,
"evidence_articles": [],
"evidence_sources": [
{
"publish_date": "2025-01-15T10:30:00Z",
"relevance_score": 0.91,
"source": "Reuters",
"title": "Fed signals rate cuts may slow in 2025",
"url": "https://reuters.com/example"
}
],
"market_analysis": {
"edge": 0.18,
"market_probability": 0.54,
"market_url": "https://polymarket.com/event/example",
"model_probability": 0.72,
"outcome": "Yes",
"platform": "Polymarket",
"stance": "model_above_market",
"summary": "Foresea is 18 percentage points above the market on Yes."
},
"model_key": "gpt-oss-120b",
"model_rationale": "Current Fed guidance suggests rates will remain elevated through mid-2025.",
"options": [],
"predicted_answer": "No",
"question_type": "binary",
"rationale": "Current Fed guidance suggests rates will remain elevated through mid-2025. Recent inflation data remains above the 2% target, reducing the likelihood of a cut in the near term. However, slowing economic growth introduces some downside risk that could prompt a cut by year-end.",
"served_model_name": "openai/gpt-oss-120b",
"variant": "variant0_neutral_baseline"
}Changes
No recorded changes to this operation across all 1 revision of this API.