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feat(meet-kevin): Claude Sonnet 4.6 LLM analyzer (tool-use forcing + prompt cache)

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Viktor Barzin 2026-05-21 19:44:57 +00:00
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"""Claude LLM analyzer for Meet Kevin video transcripts.
Calls Claude Sonnet 4.6 with tool-use forcing to extract structured
MeetKevinAnalysis from a video transcript. Uses prompt caching on the
system block to reduce cost across videos processed within the same
5-minute window.
Public API:
SYSTEM_PROMPT — module-level analyst instructions
compute_cost_usd() — Decimal-precise cost from token counts
LlmCallResult — frozen dataclass returned by analyze()
LlmAnalyzer — async class; .analyze() does the API call
"""
import logging
from dataclasses import dataclass
from datetime import datetime
from decimal import Decimal
from typing import Any
from anthropic import AsyncAnthropic
from shared.schemas.meet_kevin import MeetKevinAnalysis
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Pricing table (USD per 1 000 000 tokens: input, output)
# ---------------------------------------------------------------------------
_PRICING: dict[str, tuple[Decimal, Decimal]] = {
"claude-sonnet-4-6": (Decimal("3"), Decimal("15")),
"claude-opus-4-7": (Decimal("15"), Decimal("75")),
"claude-haiku-4-5-20251001": (Decimal("1"), Decimal("5")),
}
# ---------------------------------------------------------------------------
# System prompt
# ---------------------------------------------------------------------------
SYSTEM_PROMPT = """
You are a professional financial analyst specialising in retail investor sentiment.
Your task is to read the full transcript of a Meet Kevin (Kevin Paffrath) YouTube
video and extract a structured investment analysis from it.
## Your mission
Read the transcript carefully and produce a single, precise call to the
`submit_analysis` tool. Do **not** respond with prose — your entire output must be
that one tool call with all required fields filled in correctly.
## What to extract
### Market outlook
Identify the overall market direction Kevin is expressing: bullish, bearish, neutral,
or mixed. Write a concise `market_outlook_reasoning` (2–4 sentences) that explains
*why* you assigned that direction, grounded in specific statements from the video.
### Macro themes
List the 2–6 highest-level economic or policy themes Kevin discusses (e.g.
"Federal Reserve rate path", "AI capex cycle", "commercial real estate stress",
"dollar strength", "energy transition"). These should be phrase-length labels, not
full sentences.
### Key risks
List the 2–5 principal downside risks Kevin flags. Again, short phrase labels, not
paragraphs. Only include risks Kevin explicitly names or clearly implies — do not
invent risks he did not discuss.
### Summary
Write a ~200-word plain-English summary of the video's investment thesis. Focus on
actionable takeaways and any specific catalysts Kevin mentions. Avoid filler phrases
like "In this video Kevin discusses…" — start directly with the insight.
### Per-ticker mentions (tickers field)
Extract every stock, ETF, or crypto ticker that Kevin makes a substantive statement
about. For each one, fill in the following:
- **symbol** — The uppercase ticker symbol (e.g. "NVDA", "SPY", "BTC"). If Kevin
mentions the company name but not the ticker, infer the ticker from the name (e.g.
"Nvidia" → "NVDA"). Max 6 characters. Only include tickers you are confident about.
- **action** — The clearest action signal you can infer from what Kevin says. Use
exactly one of: `buy`, `sell`, `hold`, `watch`, `avoid`. If Kevin expresses
interest but no clear directional view, use `watch`. If he says he is exiting or
would not touch it, use `sell` or `avoid` respectively. Do not default to `hold`
just because you are unsure — skip the ticker instead.
- **conviction** — A float between 0.0 and 1.0 representing how confident Kevin
sounds. Use 0.8–1.0 for "I'm buying this aggressively / this is my top pick",
0.5–0.7 for a clear directional view with some hedging, 0.2–0.4 for a tentative
or heavily-caveated take. A ticker Kevin mentions only in passing (< 20 words of
commentary) should be **skipped entirely** rather than assigned low conviction.
- **time_horizon** — Pick the closest match from: `intraday`, `days`, `weeks`,
`months`, `long_term`, `unspecified`. If Kevin does not say, use `unspecified`.
- **rationale_quote** — A short verbatim or lightly paraphrased quote (20–80 words)
from the transcript that best justifies the action you assigned. Include enough
context to be meaningful on its own.
- **video_timestamp_seconds** — If the transcript includes segment timestamps (lines
formatted as `[<N>s] <text>`), set this to the integer second where Kevin first
makes the substantive statement about this ticker. If no timestamps are available,
set to null.
## Rules for ticker inclusion
1. **Skip tickers mentioned only in passing.** Kevin often references tickers as
examples or comparisons without making any recommendation. If he says fewer than
~20 words about a ticker with no clear directional signal, omit it from `tickers`.
2. **Do not duplicate tickers.** If Kevin mentions the same ticker multiple times,
merge the signals into a single entry that represents his overall view from the
video. Use the timestamp of the *first* substantive mention.
3. **Symbols only, no company names.** The `symbol` field must be a ticker, not a
company name. "Nvidia" is wrong; "NVDA" is correct.
4. **Conviction scores are comparative.** Calibrate them relative to each other
within the video — Kevin's "top conviction" pick in a video might be 0.85, while
a hedged mention is 0.45.
## Quality checklist (review before calling submit_analysis)
- [ ] `market_outlook_direction` is one of: bullish, neutral, bearish, mixed
- [ ] `macro_themes` has 2–6 items, each a concise phrase
- [ ] `key_risks` has 2–5 items, each a concise phrase
- [ ] `summary` is approximately 200 words
- [ ] Every ticker in `tickers` has a clear actionable signal (no "I'm not sure")
- [ ] Tickers mentioned only in passing are omitted
- [ ] `conviction` values are floats in [0.0, 1.0]
- [ ] `time_horizon` is one of the six allowed values
- [ ] `rationale_quote` is grounded in something Kevin actually said
- [ ] You are calling `submit_analysis` exactly once with all required fields
Now read the transcript provided in the user message and call `submit_analysis`.
""".strip()
# ---------------------------------------------------------------------------
# Tool definition (JSON Schema mirroring MeetKevinAnalysis)
# ---------------------------------------------------------------------------
_ANALYSIS_TOOL: dict[str, Any] = {
"name": "submit_analysis",
"description": (
"Submit a structured analysis of a Meet Kevin video transcript. "
"Call this exactly once with all fields filled in."
),
"input_schema": {
"type": "object",
"required": [
"market_outlook_direction",
"market_outlook_reasoning",
"macro_themes",
"key_risks",
"summary",
"tickers",
],
"properties": {
"market_outlook_direction": {
"type": "string",
"enum": ["bullish", "neutral", "bearish", "mixed"],
"description": "Overall market sentiment direction",
},
"market_outlook_reasoning": {
"type": "string",
"description": "2-4 sentence explanation of the market outlook direction",
},
"macro_themes": {
"type": "array",
"items": {"type": "string"},
"description": "2-6 high-level macro economic themes discussed",
},
"key_risks": {
"type": "array",
"items": {"type": "string"},
"description": "2-5 principal downside risks Kevin mentions",
},
"summary": {
"type": "string",
"description": "~200-word plain-English investment thesis summary",
},
"tickers": {
"type": "array",
"description": "Per-ticker mentions with action and conviction",
"items": {
"type": "object",
"required": [
"symbol",
"action",
"conviction",
"time_horizon",
"rationale_quote",
"video_timestamp_seconds",
],
"properties": {
"symbol": {
"type": "string",
"description": "Uppercase ticker symbol (1-6 chars)",
},
"action": {
"type": "string",
"enum": ["buy", "sell", "hold", "watch", "avoid"],
"description": "Recommendation action",
},
"conviction": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0,
"description": "Confidence in recommendation (0.0-1.0)",
},
"time_horizon": {
"type": "string",
"enum": [
"intraday",
"days",
"weeks",
"months",
"long_term",
"unspecified",
],
"description": "Time horizon for the recommendation",
},
"rationale_quote": {
"type": "string",
"description": "Short verbatim or paraphrased quote from video",
},
"video_timestamp_seconds": {
"type": ["integer", "null"],
"description": "Timestamp in seconds for deep-link target",
},
},
},
},
},
},
}
# ---------------------------------------------------------------------------
# Public helpers
# ---------------------------------------------------------------------------
def compute_cost_usd(model: str, input_tokens: int, output_tokens: int) -> Decimal:
"""Compute LLM call cost in USD using pinned per-model pricing.
Args:
model: Model identifier string (must be a key in _PRICING).
input_tokens: Number of input/prompt tokens consumed.
output_tokens: Number of output/completion tokens generated.
Returns:
Cost as a Decimal. Returns Decimal("0") for unknown models (logs warning).
"""
pricing = _PRICING.get(model)
if pricing is None:
logger.warning("compute_cost_usd: unknown model %r — returning zero cost", model)
return Decimal("0")
price_per_m_input, price_per_m_output = pricing
million = Decimal("1000000")
cost = (
Decimal(input_tokens) / million * price_per_m_input
+ Decimal(output_tokens) / million * price_per_m_output
)
return cost.quantize(Decimal("0.0001"))
# ---------------------------------------------------------------------------
# Result dataclass
# ---------------------------------------------------------------------------
@dataclass(frozen=True)
class LlmCallResult:
"""Immutable result of one LLM analyze() call."""
analysis: MeetKevinAnalysis
raw_response: dict
prompt_tokens: int
completion_tokens: int
cost_usd: Decimal
# ---------------------------------------------------------------------------
# Analyzer class
# ---------------------------------------------------------------------------
_MAX_SEGMENTS = 1000
class LlmAnalyzer:
"""Calls Claude to extract structured analysis from a video transcript.
Args:
client: Configured AsyncAnthropic client.
model: Model identifier (e.g. "claude-sonnet-4-6").
prompt_version: Prompt version string stored in kevin_analyses.
"""
def __init__(self, client: AsyncAnthropic, model: str, prompt_version: str) -> None:
self._client = client
self._model = model
self._prompt_version = prompt_version
async def analyze(
self,
*,
title: str,
description: str,
published_at: datetime,
transcript_text: str,
transcript_segments: list[dict],
) -> LlmCallResult:
"""Run Claude analysis on a transcript and return a structured result.
Args:
title: Video title.
description: Video description (may be empty).
published_at: UTC publication timestamp.
transcript_text: Full concatenated transcript text.
transcript_segments: List of {start, end, text} dicts.
Returns:
LlmCallResult with parsed MeetKevinAnalysis and token accounting.
Raises:
ValueError: If the response contains no tool_use block.
pydantic.ValidationError: If tool_use input fails schema validation.
"""
user_msg = self._build_user_message(
title=title,
description=description,
published_at=published_at,
transcript_text=transcript_text,
transcript_segments=transcript_segments,
)
response = await self._client.messages.create(
model=self._model,
max_tokens=4096,
system=[
{
"type": "text",
"text": SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"},
}
],
tools=[_ANALYSIS_TOOL],
tool_choice={"type": "tool", "name": "submit_analysis"},
messages=[{"role": "user", "content": user_msg}],
)
# Find the first tool_use block
tool_block = next(
(b for b in response.content if b.type == "tool_use"),
None,
)
if tool_block is None:
raise ValueError(
f"Claude response contained no tool_use block "
f"(stop_reason={response.stop_reason!r})"
)
analysis = MeetKevinAnalysis.model_validate(tool_block.input)
prompt_tokens: int = response.usage.input_tokens
completion_tokens: int = response.usage.output_tokens
cost_usd = compute_cost_usd(self._model, prompt_tokens, completion_tokens)
raw_response: dict = {
"stop_reason": response.stop_reason,
"tool_name": tool_block.name,
"tool_input": tool_block.input,
"usage": {
"input_tokens": prompt_tokens,
"output_tokens": completion_tokens,
},
}
return LlmCallResult(
analysis=analysis,
raw_response=raw_response,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
cost_usd=cost_usd,
)
# ------------------------------------------------------------------
# Private helpers
# ------------------------------------------------------------------
def _build_user_message(
self,
*,
title: str,
description: str,
published_at: datetime,
transcript_text: str,
transcript_segments: list[dict],
) -> str:
"""Build the user-turn message for the API call."""
parts: list[str] = [
f"Title: {title}",
f"Published: {published_at.strftime('%Y-%m-%d %H:%M UTC')}",
]
if description:
parts.append(f"Description: {description}")
parts.append("") # blank line before transcript
if transcript_segments:
# Prefer timestamped segments (up to _MAX_SEGMENTS)
segment_lines = [
f"[{int(seg.get('start', 0))}s] {seg.get('text', '').strip()}"
for seg in transcript_segments[:_MAX_SEGMENTS]
]
parts.append("Transcript (with timestamps):")
parts.extend(segment_lines)
elif transcript_text:
parts.append("Transcript:")
parts.append(transcript_text)
else:
parts.append("Transcript: (no transcript available)")
return "\n".join(parts)