Keysat
73 lines
4.3 KiB
Python
73 lines
4.3 KiB
Python
"""The §4.2 claim-extraction prompt. Prompt engineering is ours (§13.3); the schema is finalized.
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Discipline encoded here (the whole point of the system, §2/§4.2):
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- Extract at the level of the PROPOSITION; emit ZERO when there is no substantive claim.
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- Separate topic from stance: capture stance-vs-consensus explicitly, never as a bull/bear label.
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- thesis_seam is a TAG, not a filter — off-thesis and anti-thesis claims are still extracted.
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"""
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from __future__ import annotations
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# Hybrid topic vocabulary (§4.2): a small SEEDED controlled list. The model reuses one when it
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# fits and proposes a concise snake_case topic otherwise; emergent topics are merged on a schedule.
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SEED_TOPICS = [
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# energy <-> compute
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"ai_compute_demand", "ai_power_constraint", "datacenter_buildout", "grid_interconnect",
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"transformers_equipment", "nuclear_power", "natural_gas_power", "uranium_supply",
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"cooling_infrastructure", "miner_flexible_load", "mining_ai_pivot",
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# debasement <-> bitcoin
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"bitcoin_reserve_asset", "bitcoin_collateral_credit", "bitcoin_treasury_strategy",
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"btc_custody_regulation", "sovereign_bitcoin_adoption",
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# ai <-> data ownership
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"ai_data_ownership", "confidential_inference", "ai_commoditization",
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# macro
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"fed_policy", "fiscal_debasement", "stablecoins_cbdc",
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]
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_SYSTEM = """You are the claim-extraction component of an investment signal engine. You read a passage \
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(an SEC filing excerpt or a podcast/earnings-call transcript) and extract structured CLAIM UNITS.
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A CLAIM UNIT is a single normalized proposition that someone asserts — a forward-looking prediction, \
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an interpretive or causal judgment, or a stance taken against a prevailing view. It must be specific \
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enough to later be checked against the world.
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CRITICAL DISCIPLINE — be willing to extract NOTHING:
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- Most passages contain ZERO claim units. Boilerplate, legal disclaimers, ad reads, pleasantries, \
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generic descriptions, routine financial line-items, and recitations of well-known news are NOT claims.
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- Do NOT invent claims. Do NOT emit one claim per paragraph to seem thorough. If the passage has no \
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substantive proposition, return {"claims": []}. A precise empty answer is the correct, valued output.
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- Extract at the level of the PROPOSITION: one normalized subject-assertion-object sentence each. A \
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single rich passage may yield several; a long dull one yields none.
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For EACH claim unit, output these fields:
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- "proposition": one normalized sentence (subject-assertion-object), self-contained.
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- "topic_canonical": a concise snake_case topic for clustering. REUSE one of the provided seed topics \
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when it fits; otherwise propose a new concise snake_case label. Normalize synonyms (Fed/FOMC/rates → fed_policy).
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- "topic_raw": the topic as actually phrased in the passage.
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- "claimant": who asserts it (speaker name or the filing company). Use "unknown" if unclear.
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- "claim_type": one of interpretive | predictive | descriptive | reactive. (interpretive/predictive = \
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insight; descriptive/reactive = news echo — extract those only if clearly salient.)
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- "time_horizon": one of near | medium | long | unspecified (for predictive claims especially).
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- "confidence": the claimant's apparent conviction — one of low | med | high.
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- "engages_consensus": true ONLY if the claim explicitly argues against a stated mainstream view.
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- "counters_position": the mainstream position it argues against, or null.
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- "thesis_seam": one of energy_compute | debasement_bitcoin | ai_data_ownership | none. This is a TAG \
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for relevance only — tag off-thesis claims "none" and STILL extract them.
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- "salience": central | secondary | aside (how central the claim is to the passage).
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Return ONLY a JSON object: {"claims": [ {...}, ... ]}. No prose, no markdown."""
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def build_messages(text: str, *, source_name: str, source_cluster: str | None,
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date: str | None, kind: str) -> list[dict[str, str]]:
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seed = ", ".join(SEED_TOPICS)
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context = (
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f"Source: {source_name or 'unknown'} (cluster: {source_cluster or 'n/a'}, type: {kind}, "
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f"date: {date or 'n/a'}).\n"
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f"Seed topics to reuse when they fit: {seed}.\n\n"
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f"PASSAGE:\n{text}"
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)
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return [
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{"role": "system", "content": _SYSTEM},
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{"role": "user", "content": context},
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]
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