427 lines
16 KiB
Python
427 lines
16 KiB
Python
"""Claude LLM analyzer for Meet Kevin video transcripts.
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Calls Claude Sonnet 4.6 with tool-use forcing to extract structured
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MeetKevinAnalysis from a video transcript. Uses prompt caching on the
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system block to reduce cost across videos processed within the same
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5-minute window.
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Public API:
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SYSTEM_PROMPT — module-level analyst instructions
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compute_cost_usd() — Decimal-precise cost from token counts
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LlmCallResult — frozen dataclass returned by analyze()
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LlmAnalyzer — async class; .analyze() does the API call
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"""
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import logging
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from dataclasses import dataclass
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from datetime import datetime
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from decimal import Decimal
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from typing import Any
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from anthropic import AsyncAnthropic
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from shared.schemas.meet_kevin import MeetKevinAnalysis
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Pricing table (USD per 1 000 000 tokens: input, output)
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# ---------------------------------------------------------------------------
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_PRICING: dict[str, tuple[Decimal, Decimal]] = {
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"claude-sonnet-4-6": (Decimal("3"), Decimal("15")),
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"claude-opus-4-7": (Decimal("15"), Decimal("75")),
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"claude-haiku-4-5-20251001": (Decimal("1"), Decimal("5")),
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}
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# ---------------------------------------------------------------------------
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# System prompt
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# ---------------------------------------------------------------------------
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SYSTEM_PROMPT = """
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You are a professional financial analyst specialising in retail investor sentiment.
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Your task is to read the full transcript of a Meet Kevin (Kevin Paffrath) YouTube
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video and extract a structured investment analysis from it.
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## Your mission
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Read the transcript carefully and produce a single, precise call to the
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`submit_analysis` tool. Do **not** respond with prose — your entire output must be
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that one tool call with all required fields filled in correctly.
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## What to extract
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### Market outlook
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Identify the overall market direction Kevin is expressing: bullish, bearish, neutral,
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or mixed. Write a concise `market_outlook_reasoning` (2–4 sentences) that explains
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*why* you assigned that direction, grounded in specific statements from the video.
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### Macro themes
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List the 2–6 highest-level economic or policy themes Kevin discusses (e.g.
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"Federal Reserve rate path", "AI capex cycle", "commercial real estate stress",
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"dollar strength", "energy transition"). These should be phrase-length labels, not
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full sentences.
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### Key risks
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List the 2–5 principal downside risks Kevin flags. Again, short phrase labels, not
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paragraphs. Only include risks Kevin explicitly names or clearly implies — do not
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invent risks he did not discuss.
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### Summary
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Write a ~200-word plain-English summary of the video's investment thesis. Focus on
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actionable takeaways and any specific catalysts Kevin mentions. Avoid filler phrases
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like "In this video Kevin discusses…" — start directly with the insight.
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### Per-ticker mentions (tickers field)
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Extract every stock, ETF, or crypto ticker that Kevin makes a substantive statement
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about. For each one, fill in the following:
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- **symbol** — The uppercase ticker symbol (e.g. "NVDA", "SPY", "BTC"). If Kevin
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mentions the company name but not the ticker, infer the ticker from the name (e.g.
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"Nvidia" → "NVDA"). Max 6 characters. Only include tickers you are confident about.
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- **action** — The clearest action signal you can infer from what Kevin says. Use
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exactly one of: `buy`, `sell`, `hold`, `watch`, `avoid`. If Kevin expresses
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interest but no clear directional view, use `watch`. If he says he is exiting or
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would not touch it, use `sell` or `avoid` respectively. Do not default to `hold`
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just because you are unsure — skip the ticker instead.
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- **conviction** — A float between 0.0 and 1.0 representing how confident Kevin
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sounds. Use 0.8–1.0 for "I'm buying this aggressively / this is my top pick",
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0.5–0.7 for a clear directional view with some hedging, 0.2–0.4 for a tentative
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or heavily-caveated take. A ticker Kevin mentions only in passing (< 20 words of
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commentary) should be **skipped entirely** rather than assigned low conviction.
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- **time_horizon** — Pick the closest match from: `intraday`, `days`, `weeks`,
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`months`, `long_term`, `unspecified`. If Kevin does not say, use `unspecified`.
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- **rationale_quote** — A short verbatim or lightly paraphrased quote (20–80 words)
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from the transcript that best justifies the action you assigned. Include enough
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context to be meaningful on its own.
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- **video_timestamp_seconds** — If the transcript includes segment timestamps (lines
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formatted as `[<N>s] <text>`), set this to the integer second where Kevin first
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makes the substantive statement about this ticker. If no timestamps are available,
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set to null.
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## Rules for ticker inclusion
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1. **Skip tickers mentioned only in passing.** Kevin often references tickers as
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examples or comparisons without making any recommendation. If he says fewer than
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~20 words about a ticker with no clear directional signal, omit it from `tickers`.
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2. **Do not duplicate tickers.** If Kevin mentions the same ticker multiple times,
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merge the signals into a single entry that represents his overall view from the
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video. Use the timestamp of the *first* substantive mention.
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3. **Symbols only, no company names.** The `symbol` field must be a ticker, not a
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company name. "Nvidia" is wrong; "NVDA" is correct.
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4. **Conviction scores are comparative.** Calibrate them relative to each other
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within the video — Kevin's "top conviction" pick in a video might be 0.85, while
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a hedged mention is 0.45.
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## Quality checklist (review before calling submit_analysis)
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- [ ] `market_outlook_direction` is one of: bullish, neutral, bearish, mixed
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- [ ] `macro_themes` has 2–6 items, each a concise phrase
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- [ ] `key_risks` has 2–5 items, each a concise phrase
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- [ ] `summary` is approximately 200 words
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- [ ] Every ticker in `tickers` has a clear actionable signal (no "I'm not sure")
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- [ ] Tickers mentioned only in passing are omitted
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- [ ] `conviction` values are floats in [0.0, 1.0]
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- [ ] `time_horizon` is one of the six allowed values
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- [ ] `rationale_quote` is grounded in something Kevin actually said
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- [ ] You are calling `submit_analysis` exactly once with all required fields
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Now read the transcript provided in the user message and call `submit_analysis`.
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""".strip()
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# ---------------------------------------------------------------------------
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# Tool definition (JSON Schema mirroring MeetKevinAnalysis)
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# ---------------------------------------------------------------------------
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_ANALYSIS_TOOL: dict[str, Any] = {
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"name": "submit_analysis",
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"description": (
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"Submit a structured analysis of a Meet Kevin video transcript. "
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"Call this exactly once with all fields filled in."
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),
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"input_schema": {
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"type": "object",
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"required": [
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"market_outlook_direction",
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"market_outlook_reasoning",
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"macro_themes",
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"key_risks",
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"summary",
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"tickers",
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],
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"properties": {
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"market_outlook_direction": {
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"type": "string",
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"enum": ["bullish", "neutral", "bearish", "mixed"],
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"description": "Overall market sentiment direction",
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},
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"market_outlook_reasoning": {
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"type": "string",
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"description": "2-4 sentence explanation of the market outlook direction",
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},
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"macro_themes": {
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"type": "array",
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"items": {"type": "string"},
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"description": "2-6 high-level macro economic themes discussed",
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},
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"key_risks": {
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"type": "array",
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"items": {"type": "string"},
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"description": "2-5 principal downside risks Kevin mentions",
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},
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"summary": {
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"type": "string",
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"description": "~200-word plain-English investment thesis summary",
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},
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"tickers": {
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"type": "array",
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"description": "Per-ticker mentions with action and conviction",
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"items": {
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"type": "object",
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"required": [
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"symbol",
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"action",
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"conviction",
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"time_horizon",
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"rationale_quote",
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"video_timestamp_seconds",
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],
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"properties": {
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"symbol": {
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"type": "string",
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"description": "Uppercase ticker symbol (1-6 chars)",
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},
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"action": {
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"type": "string",
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"enum": ["buy", "sell", "hold", "watch", "avoid"],
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"description": "Recommendation action",
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},
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"conviction": {
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"type": "number",
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"minimum": 0.0,
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"maximum": 1.0,
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"description": "Confidence in recommendation (0.0-1.0)",
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},
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"time_horizon": {
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"type": "string",
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"enum": [
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"intraday",
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"days",
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"weeks",
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"months",
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"long_term",
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"unspecified",
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],
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"description": "Time horizon for the recommendation",
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},
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"rationale_quote": {
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"type": "string",
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"description": "Short verbatim or paraphrased quote from video",
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},
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"video_timestamp_seconds": {
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"type": ["integer", "null"],
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"description": "Timestamp in seconds for deep-link target",
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},
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},
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},
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},
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},
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},
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}
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# ---------------------------------------------------------------------------
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# Public helpers
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# ---------------------------------------------------------------------------
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def compute_cost_usd(model: str, input_tokens: int, output_tokens: int) -> Decimal:
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"""Compute LLM call cost in USD using pinned per-model pricing.
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Args:
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model: Model identifier string (must be a key in _PRICING).
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input_tokens: Number of input/prompt tokens consumed.
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output_tokens: Number of output/completion tokens generated.
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Returns:
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Cost as a Decimal. Returns Decimal("0") for unknown models (logs warning).
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"""
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pricing = _PRICING.get(model)
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if pricing is None:
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logger.warning("compute_cost_usd: unknown model %r — returning zero cost", model)
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return Decimal("0")
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price_per_m_input, price_per_m_output = pricing
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million = Decimal("1000000")
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cost = (
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Decimal(input_tokens) / million * price_per_m_input
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+ Decimal(output_tokens) / million * price_per_m_output
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)
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return cost.quantize(Decimal("0.0001"))
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# ---------------------------------------------------------------------------
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# Result dataclass
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True)
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class LlmCallResult:
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"""Immutable result of one LLM analyze() call."""
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analysis: MeetKevinAnalysis
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raw_response: dict
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prompt_tokens: int
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completion_tokens: int
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cost_usd: Decimal
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# ---------------------------------------------------------------------------
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# Analyzer class
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# ---------------------------------------------------------------------------
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_MAX_SEGMENTS = 1000
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class LlmAnalyzer:
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"""Calls Claude to extract structured analysis from a video transcript.
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Args:
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client: Configured AsyncAnthropic client.
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model: Model identifier (e.g. "claude-sonnet-4-6").
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prompt_version: Prompt version string stored in kevin_analyses.
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"""
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def __init__(self, client: AsyncAnthropic, model: str, prompt_version: str) -> None:
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self._client = client
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self._model = model
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self._prompt_version = prompt_version
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async def analyze(
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self,
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*,
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title: str,
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description: str,
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published_at: datetime,
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transcript_text: str,
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transcript_segments: list[dict],
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) -> LlmCallResult:
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"""Run Claude analysis on a transcript and return a structured result.
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Args:
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title: Video title.
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description: Video description (may be empty).
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published_at: UTC publication timestamp.
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transcript_text: Full concatenated transcript text.
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transcript_segments: List of {start, end, text} dicts.
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Returns:
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LlmCallResult with parsed MeetKevinAnalysis and token accounting.
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Raises:
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ValueError: If the response contains no tool_use block.
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pydantic.ValidationError: If tool_use input fails schema validation.
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"""
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user_msg = self._build_user_message(
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title=title,
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description=description,
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published_at=published_at,
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transcript_text=transcript_text,
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transcript_segments=transcript_segments,
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)
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response = await self._client.messages.create(
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model=self._model,
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max_tokens=4096,
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system=[
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{
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"type": "text",
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"text": SYSTEM_PROMPT,
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"cache_control": {"type": "ephemeral"},
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}
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],
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tools=[_ANALYSIS_TOOL],
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tool_choice={"type": "tool", "name": "submit_analysis"},
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messages=[{"role": "user", "content": user_msg}],
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)
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# Find the first tool_use block
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tool_block = next(
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(b for b in response.content if b.type == "tool_use"),
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None,
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)
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if tool_block is None:
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raise ValueError(
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f"Claude response contained no tool_use block "
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f"(stop_reason={response.stop_reason!r})"
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)
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analysis = MeetKevinAnalysis.model_validate(tool_block.input)
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prompt_tokens: int = response.usage.input_tokens
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completion_tokens: int = response.usage.output_tokens
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cost_usd = compute_cost_usd(self._model, prompt_tokens, completion_tokens)
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raw_response: dict = {
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"stop_reason": response.stop_reason,
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"tool_name": tool_block.name,
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"tool_input": tool_block.input,
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"usage": {
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"input_tokens": prompt_tokens,
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"output_tokens": completion_tokens,
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},
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}
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return LlmCallResult(
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analysis=analysis,
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raw_response=raw_response,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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cost_usd=cost_usd,
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)
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# ------------------------------------------------------------------
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# Private helpers
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# ------------------------------------------------------------------
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def _build_user_message(
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self,
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*,
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title: str,
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description: str,
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published_at: datetime,
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transcript_text: str,
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transcript_segments: list[dict],
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) -> str:
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"""Build the user-turn message for the API call."""
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parts: list[str] = [
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f"Title: {title}",
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f"Published: {published_at.strftime('%Y-%m-%d %H:%M UTC')}",
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]
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if description:
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parts.append(f"Description: {description}")
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parts.append("") # blank line before transcript
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if transcript_segments:
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# Prefer timestamped segments (up to _MAX_SEGMENTS)
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segment_lines = [
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f"[{int(seg.get('start', 0))}s] {seg.get('text', '').strip()}"
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for seg in transcript_segments[:_MAX_SEGMENTS]
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]
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parts.append("Transcript (with timestamps):")
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parts.extend(segment_lines)
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elif transcript_text:
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parts.append("Transcript:")
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parts.append(transcript_text)
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else:
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parts.append("Transcript: (no transcript available)")
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return "\n".join(parts)
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