refactor(meet-kevin): switch LLM analyzer to OpenRouter via OpenAI SDK
User's Vault has openrouter_api_key but no direct sk-ant-* Anthropic key. OpenRouter passes through Claude Sonnet 4.6 (~3% markup over Anthropic list pricing) and matches the existing gpt_mini_endpoint pattern used by recruiter-responder. - Replace anthropic.AsyncAnthropic with openai.AsyncOpenAI + base_url - Convert Anthropic tool-use API to OpenAI function-calling - System prompt unchanged (analyst instructions are model-agnostic) - Drop cache_control (not in OpenAI API); revisit later if cost matters - Model slug: anthropic/claude-sonnet-4.5 (OpenRouter's current Claude tier) - Pricing: $3.10/M input, $15.50/M output (OpenRouter pass-through) - Config field anthropic_api_key -> openrouter_api_key - pyproject extras: anthropic>=0.40 -> openai>=1.50 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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5 changed files with 244 additions and 216 deletions
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@ -18,12 +18,12 @@ class MeetKevinWatcherConfig(BaseConfig):
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# LLM analysis settings
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meet_kevin_max_llm_retries: int = 3
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meet_kevin_llm_model: str = "claude-sonnet-4-6"
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meet_kevin_llm_model: str = "anthropic/claude-sonnet-4.5"
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meet_kevin_prompt_version: str = "v1"
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meet_kevin_daily_cost_cap_usd: float = 5.0
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# API credentials
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anthropic_api_key: str = ""
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openrouter_api_key: str = ""
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# Runtime settings
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meet_kevin_workdir: str = "/tmp/meet_kevin_captions"
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@ -1,9 +1,7 @@
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"""Claude LLM analyzer for Meet Kevin video transcripts.
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"""OpenRouter 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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Calls Claude Sonnet (via OpenRouter) with function-calling forcing to extract
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structured MeetKevinAnalysis from a video transcript.
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Public API:
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SYSTEM_PROMPT — module-level analyst instructions
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@ -12,13 +10,14 @@ Public API:
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LlmAnalyzer — async class; .analyze() does the API call
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"""
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import json
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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 openai import AsyncOpenAI
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from shared.schemas.meet_kevin import MeetKevinAnalysis
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@ -26,12 +25,16 @@ 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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# OpenRouter pass-through pricing (~3% markup over Anthropic list)
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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-sonnet-4-6": (Decimal("3.10"), Decimal("15.50")),
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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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# OpenRouter model slugs
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"anthropic/claude-sonnet-4.5": (Decimal("3.10"), Decimal("15.50")),
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"anthropic/claude-sonnet-4.6": (Decimal("3.10"), Decimal("15.50")),
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}
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# ---------------------------------------------------------------------------
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@ -138,97 +141,99 @@ 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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# Tool definition (OpenAI function-calling format)
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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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_ANALYSIS_TOOL_OPENAI: dict[str, Any] = {
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"type": "function",
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"function": {
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"name": "submit_analysis",
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"description": (
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"Submit the structured analysis of one Meet Kevin video. Call this exactly once."
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),
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"parameters": {
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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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@ -291,15 +296,15 @@ _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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"""Calls Claude (via OpenRouter) 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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client: Configured AsyncOpenAI client pointed at OpenRouter.
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model: Model identifier (e.g. "anthropic/claude-sonnet-4.5").
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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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def __init__(self, client: AsyncOpenAI, 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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@ -313,7 +318,7 @@ class LlmAnalyzer:
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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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"""Run LLM 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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@ -326,8 +331,8 @@ class LlmAnalyzer:
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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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ValueError: If the response contains no tool_calls.
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pydantic.ValidationError: If function arguments fail 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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@ -337,42 +342,35 @@ class LlmAnalyzer:
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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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response = await self._client.chat.completions.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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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_msg},
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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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tools=[_ANALYSIS_TOOL_OPENAI],
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tool_choice={"type": "function", "function": {"name": "submit_analysis"}},
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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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message = response.choices[0].message
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if not message.tool_calls:
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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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"LLM response contained no tool_calls (expected submit_analysis function call)"
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)
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analysis = MeetKevinAnalysis.model_validate(tool_block.input)
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tool_call = message.tool_calls[0]
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tool_input = json.loads(tool_call.function.arguments)
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analysis = MeetKevinAnalysis.model_validate(tool_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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prompt_tokens: int = response.usage.prompt_tokens
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completion_tokens: int = response.usage.completion_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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"finish_reason": response.choices[0].finish_reason,
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"tool_name": tool_call.function.name,
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"tool_input": tool_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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@ -16,7 +16,7 @@ from datetime import timezone
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from decimal import Decimal
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import httpx
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from anthropic import AsyncAnthropic
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from openai import AsyncOpenAI
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from sqlalchemy import select
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from sqlalchemy.dialects.postgresql import insert as pg_insert
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@ -179,10 +179,17 @@ async def run() -> None:
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# Database
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engine, session_factory = create_db(config)
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# Anthropic client + LLM analyzer
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anthropic = AsyncAnthropic(api_key=config.anthropic_api_key)
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# OpenRouter client + LLM analyzer
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client = AsyncOpenAI(
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api_key=config.openrouter_api_key,
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base_url="https://openrouter.ai/api/v1",
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default_headers={
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"HTTP-Referer": "https://trading.viktorbarzin.me",
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"X-Title": "trading-bot meet-kevin",
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},
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)
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analyzer = LlmAnalyzer(
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client=anthropic,
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client=client,
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model=config.meet_kevin_llm_model,
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prompt_version=config.meet_kevin_prompt_version,
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)
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@ -241,7 +248,7 @@ async def run() -> None:
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except asyncio.TimeoutError:
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pass # Normal timeout — loop again
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finally:
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await anthropic.close()
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await client.close()
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await engine.dispose()
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logger.info("meet-kevin-watcher stopped gracefully")
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