refactor(meet-kevin): switch LLM back to native Anthropic SDK with OAuth bearer
Previous refactor (89f01ad) moved to OpenRouter because no sk-ant-api-* key
was found in Vault. Turns out claude-agent-service-spare-{1,2} hold
sk-ant-oat01-* OAuth tokens (108 chars, scope user:inference, 1-year TTL,
minted via 'claude setup-token' — see memory id=832).
These tokens work with the Anthropic SDK via the auth_token= constructor
argument (routes to Authorization: Bearer ... instead of x-api-key: ...).
They consume the Enterprise Claude subscription quota rather than
per-call billing, so the OpenRouter zero-credit problem goes away.
- llm_analyzer.py: revert OpenAI client to AsyncAnthropic; tool-use API
+ cache_control restored
- config.py: openrouter_api_key -> anthropic_oauth_token; model slug
reverted from anthropic/claude-sonnet-4.5 -> claude-sonnet-4-5
- main.py: AsyncOpenAI -> AsyncAnthropic(auth_token=...), drop OpenRouter
attribution headers
- pyproject: openai>=1.50 -> anthropic>=0.40 in meet_kevin extras
- tests: mocks ported back to messages.create + tool_use blocks
This commit is contained in:
parent
4f4d365652
commit
8a1d03a967
5 changed files with 211 additions and 235 deletions
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@ -20,7 +20,7 @@ news = ["feedparser>=6.0", "praw>=7.7", "asyncpraw>=7.7", "httpx>=0.27"]
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sentiment = ["transformers>=4.38", "torch>=2.2", "ollama>=0.1"]
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trading = ["alpaca-py>=0.21", "pytz>=2024.1", "yfinance>=0.2", "httpx>=0.27"]
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backtester = ["numpy>=1.26", "pandas>=2.2"]
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meet_kevin = ["yt-dlp>=2025.12", "feedparser>=6.0", "openai>=1.50", "httpx>=0.27"]
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meet_kevin = ["yt-dlp>=2025.12", "feedparser>=6.0", "anthropic>=0.40", "httpx>=0.27"]
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dev = ["pytest>=8.0", "pytest-asyncio>=0.23", "pytest-cov>=4.1", "ruff>=0.3", "mypy>=1.8", "httpx>=0.27"]
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[build-system]
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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 = "anthropic/claude-sonnet-4.5"
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meet_kevin_llm_model: str = "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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openrouter_api_key: str = ""
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anthropic_oauth_token: str = ""
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# Runtime settings
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meet_kevin_workdir: str = "/tmp/meet_kevin_captions"
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@ -1,7 +1,7 @@
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"""OpenRouter LLM analyzer for Meet Kevin video transcripts.
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"""Anthropic SDK LLM analyzer for Meet Kevin video transcripts.
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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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Calls Claude Sonnet (via native Anthropic SDK with OAuth bearer token) with
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tool-use forcing to extract 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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@ -10,14 +10,13 @@ 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 openai import AsyncOpenAI
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from anthropic import AsyncAnthropic
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from shared.schemas.meet_kevin import MeetKevinAnalysis
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@ -25,16 +24,16 @@ 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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# Native Anthropic list pricing. With OAuth/Enterprise tokens real billing
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# is via subscription quota, but we still compute notional USD for the
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# daily-cap accounting logic.
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# ---------------------------------------------------------------------------
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_PRICING: dict[str, tuple[Decimal, Decimal]] = {
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"claude-sonnet-4-6": (Decimal("3.10"), Decimal("15.50")),
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"claude-sonnet-4-5": (Decimal("3"), Decimal("15")),
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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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# 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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@ -141,99 +140,96 @@ 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 (OpenAI function-calling format)
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# Tool definition (Anthropic tool-use format)
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# ---------------------------------------------------------------------------
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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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_ANALYSIS_TOOL: dict[str, Any] = {
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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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"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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@ -296,15 +292,15 @@ _MAX_SEGMENTS = 1000
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class LlmAnalyzer:
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"""Calls Claude (via OpenRouter) to extract structured analysis from a video transcript.
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"""Calls Claude (via native Anthropic SDK) to extract structured analysis from a video transcript.
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Args:
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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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client: Configured AsyncAnthropic client with OAuth bearer token.
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model: Model identifier (e.g. "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: AsyncOpenAI, model: str, prompt_version: str) -> None:
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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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@ -331,8 +327,8 @@ 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_calls.
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pydantic.ValidationError: If function arguments fail schema validation.
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ValueError: If the response contains no tool_use block.
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pydantic.ValidationError: If tool 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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@ -342,34 +338,39 @@ class LlmAnalyzer:
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transcript_segments=transcript_segments,
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)
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response = await self._client.chat.completions.create(
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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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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_msg},
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system=[
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{"type": "text", "text": SYSTEM_PROMPT, "cache_control": {"type": "ephemeral"}}
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],
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tools=[_ANALYSIS_TOOL_OPENAI],
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tool_choice={"type": "function", "function": {"name": "submit_analysis"}},
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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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message = response.choices[0].message
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if not message.tool_calls:
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# Find the first tool_use block in the response
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tool_use_block = None
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for block in response.content:
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if block.type == "tool_use":
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tool_use_block = block
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break
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if tool_use_block is None:
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raise ValueError(
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"LLM response contained no tool_calls (expected submit_analysis function call)"
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"LLM response contained no tool_use block (expected submit_analysis call)"
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)
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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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tool_input: dict = tool_use_block.input
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analysis = MeetKevinAnalysis.model_validate(tool_input)
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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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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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"finish_reason": response.choices[0].finish_reason,
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"tool_name": tool_call.function.name,
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"stop_reason": response.stop_reason,
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"tool_name": tool_use_block.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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@ -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 openai import AsyncOpenAI
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from anthropic import AsyncAnthropic
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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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@ -179,14 +179,9 @@ async def run() -> None:
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# Database
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engine, session_factory = create_db(config)
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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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# Anthropic client + LLM analyzer (OAuth bearer token)
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client = AsyncAnthropic(
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auth_token=config.anthropic_oauth_token,
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)
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analyzer = LlmAnalyzer(
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client=client,
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@ -1,9 +1,8 @@
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"""Tests for the OpenRouter LLM analyzer (Task 7).
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"""Tests for the Anthropic SDK LLM analyzer (Task 7).
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Tests use MagicMock/AsyncMock to avoid real API calls.
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"""
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import json
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from datetime import datetime, timezone
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from decimal import Decimal
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from unittest.mock import AsyncMock, MagicMock
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@ -28,23 +27,17 @@ from shared.schemas.meet_kevin import (
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# Test helpers
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# ---------------------------------------------------------------------------
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def _make_openai_response(tool_args: dict, in_tokens: int = 5000, out_tokens: int = 800):
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"""Mock an OpenAI ChatCompletion response with one tool_call."""
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tool_call = MagicMock()
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tool_call.function = MagicMock()
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tool_call.function.name = "submit_analysis"
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tool_call.function.arguments = json.dumps(tool_args)
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msg = MagicMock()
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msg.tool_calls = [tool_call]
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choice = MagicMock()
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choice.message = msg
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choice.finish_reason = "tool_calls"
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def _make_anthropic_response(tool_input: dict, in_tokens: int = 5000, out_tokens: int = 800):
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"""Mock an Anthropic Messages response with one tool_use block."""
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block = MagicMock()
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block.type = "tool_use"
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block.name = "submit_analysis"
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block.input = tool_input
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resp = MagicMock()
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resp.choices = [choice]
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resp.usage = MagicMock(prompt_tokens=in_tokens, completion_tokens=out_tokens)
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resp.content = [block]
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resp.usage = MagicMock(input_tokens=in_tokens, output_tokens=out_tokens)
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resp.stop_reason = "tool_use"
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return resp
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@ -70,15 +63,13 @@ def _valid_analysis_input() -> dict:
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def _make_client(response=None):
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"""Return a mocked AsyncOpenAI client with chat.completions.create wired up."""
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"""Return a mocked AsyncAnthropic client with messages.create wired up."""
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mock_create = AsyncMock(return_value=response)
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mock_completions = MagicMock()
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mock_completions.create = mock_create
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mock_chat = MagicMock()
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mock_chat.completions = mock_completions
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mock_messages = MagicMock()
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mock_messages.create = mock_create
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client = MagicMock()
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client.chat = mock_chat
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client.messages = mock_messages
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return client, mock_create
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@ -90,16 +81,16 @@ def _make_client(response=None):
|
|||
class TestComputeCostUsd:
|
||||
"""Verify monetary cost calculations using Decimal arithmetic."""
|
||||
|
||||
def test_sonnet_45_openrouter_pricing(self):
|
||||
"""anthropic/claude-sonnet-4.5: $3.10/M input + $15.50/M output."""
|
||||
# 1M input + 1M output = $3.10 + $15.50 = $18.60
|
||||
result = compute_cost_usd("anthropic/claude-sonnet-4.5", 1_000_000, 1_000_000)
|
||||
assert result == Decimal("18.6000")
|
||||
def test_sonnet_45_native_pricing(self):
|
||||
"""claude-sonnet-4-5: $3/M input + $15/M output = $18/M total."""
|
||||
# 1M input + 1M output = $3 + $15 = $18
|
||||
result = compute_cost_usd("claude-sonnet-4-5", 1_000_000, 1_000_000)
|
||||
assert result == Decimal("18.0000")
|
||||
|
||||
def test_sonnet_46_legacy_slug(self):
|
||||
"""claude-sonnet-4-6 (legacy slug) is also priced at $3.10/$15.50."""
|
||||
def test_sonnet_46_native_pricing(self):
|
||||
"""claude-sonnet-4-6: same pricing as 4-5 ($3/$15)."""
|
||||
result = compute_cost_usd("claude-sonnet-4-6", 1_000_000, 1_000_000)
|
||||
assert result == Decimal("18.6000")
|
||||
assert result == Decimal("18.0000")
|
||||
|
||||
def test_opus_47_pricing(self):
|
||||
"""claude-opus-4-7: $15/M input + $75/M output."""
|
||||
|
|
@ -118,21 +109,21 @@ class TestComputeCostUsd:
|
|||
|
||||
def test_zero_tokens(self):
|
||||
"""Zero tokens produce zero cost."""
|
||||
result = compute_cost_usd("anthropic/claude-sonnet-4.5", 0, 0)
|
||||
result = compute_cost_usd("claude-sonnet-4-5", 0, 0)
|
||||
assert result == Decimal("0")
|
||||
|
||||
def test_result_is_decimal(self):
|
||||
"""Return type is always Decimal, not float."""
|
||||
result = compute_cost_usd("anthropic/claude-sonnet-4.5", 5000, 800)
|
||||
result = compute_cost_usd("claude-sonnet-4-5", 5000, 800)
|
||||
assert isinstance(result, Decimal)
|
||||
|
||||
def test_small_realistic_call(self):
|
||||
"""Realistic 10K input + 1K output token call (Sonnet 4.5 via OpenRouter)."""
|
||||
# input: 10000/1_000_000 * 3.10 = 0.03100
|
||||
# output: 1000/1_000_000 * 15.50 = 0.01550
|
||||
# total: 0.04650
|
||||
result = compute_cost_usd("anthropic/claude-sonnet-4.5", 10_000, 1_000)
|
||||
assert result == Decimal("0.0465")
|
||||
"""Realistic 10K input + 1K output token call (Sonnet 4.5 native)."""
|
||||
# input: 10000/1_000_000 * 3 = 0.03000
|
||||
# output: 1000/1_000_000 * 15 = 0.01500
|
||||
# total: 0.04500
|
||||
result = compute_cost_usd("claude-sonnet-4-5", 10_000, 1_000)
|
||||
assert result == Decimal("0.0450")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
|
@ -178,7 +169,7 @@ class TestLlmCallResult:
|
|||
analysis = MeetKevinAnalysis(**_valid_analysis_input())
|
||||
result = LlmCallResult(
|
||||
analysis=analysis,
|
||||
raw_response={"finish_reason": "tool_calls"},
|
||||
raw_response={"stop_reason": "tool_use"},
|
||||
prompt_tokens=5000,
|
||||
completion_tokens=800,
|
||||
cost_usd=Decimal("0.027"),
|
||||
|
|
@ -192,13 +183,13 @@ class TestLlmCallResult:
|
|||
cost = Decimal("0.027")
|
||||
result = LlmCallResult(
|
||||
analysis=analysis,
|
||||
raw_response={"finish_reason": "tool_calls"},
|
||||
raw_response={"stop_reason": "tool_use"},
|
||||
prompt_tokens=5000,
|
||||
completion_tokens=800,
|
||||
cost_usd=cost,
|
||||
)
|
||||
assert result.analysis is analysis
|
||||
assert result.raw_response == {"finish_reason": "tool_calls"}
|
||||
assert result.raw_response == {"stop_reason": "tool_use"}
|
||||
assert result.prompt_tokens == 5000
|
||||
assert result.completion_tokens == 800
|
||||
assert result.cost_usd == cost
|
||||
|
|
@ -216,10 +207,10 @@ class TestLlmAnalyzerHappyPath:
|
|||
async def test_returns_llm_call_result(self):
|
||||
"""analyze() returns an LlmCallResult with parsed MeetKevinAnalysis."""
|
||||
tool_input = _valid_analysis_input()
|
||||
resp = _make_openai_response(tool_input, in_tokens=5000, out_tokens=800)
|
||||
resp = _make_anthropic_response(tool_input, in_tokens=5000, out_tokens=800)
|
||||
client, mock_create = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
result = await analyzer.analyze(
|
||||
title="Market Update",
|
||||
description="Kevin covers the latest market trends.",
|
||||
|
|
@ -240,10 +231,10 @@ class TestLlmAnalyzerHappyPath:
|
|||
async def test_analysis_fields_parsed_correctly(self):
|
||||
"""Parsed MeetKevinAnalysis has correct field values from tool input."""
|
||||
tool_input = _valid_analysis_input()
|
||||
resp = _make_openai_response(tool_input)
|
||||
resp = _make_anthropic_response(tool_input)
|
||||
client, _ = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
result = await analyzer.analyze(
|
||||
title="Test Video",
|
||||
description="Description",
|
||||
|
|
@ -265,10 +256,10 @@ class TestLlmAnalyzerHappyPath:
|
|||
@pytest.mark.asyncio
|
||||
async def test_cost_usd_is_positive(self):
|
||||
"""cost_usd is calculated and positive for a valid token count."""
|
||||
resp = _make_openai_response(_valid_analysis_input(), in_tokens=10_000, out_tokens=1_000)
|
||||
resp = _make_anthropic_response(_valid_analysis_input(), in_tokens=10_000, out_tokens=1_000)
|
||||
client, _ = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
result = await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -281,11 +272,11 @@ class TestLlmAnalyzerHappyPath:
|
|||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_called_with_tool_choice_forcing(self):
|
||||
"""chat.completions.create is called with tool_choice forcing submit_analysis."""
|
||||
resp = _make_openai_response(_valid_analysis_input())
|
||||
"""messages.create is called with tool_choice forcing submit_analysis."""
|
||||
resp = _make_anthropic_response(_valid_analysis_input())
|
||||
client, mock_create = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -296,15 +287,15 @@ class TestLlmAnalyzerHappyPath:
|
|||
|
||||
mock_create.assert_called_once()
|
||||
kwargs = mock_create.call_args.kwargs
|
||||
assert kwargs["tool_choice"] == {"type": "function", "function": {"name": "submit_analysis"}}
|
||||
assert kwargs["tool_choice"] == {"type": "tool", "name": "submit_analysis"}
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_called_with_system_prompt_in_messages(self):
|
||||
"""System prompt is passed as a system role message in the messages list."""
|
||||
resp = _make_openai_response(_valid_analysis_input())
|
||||
async def test_api_called_with_system_prompt_in_system_param(self):
|
||||
"""System prompt is passed as the system parameter (list with cache_control)."""
|
||||
resp = _make_anthropic_response(_valid_analysis_input())
|
||||
client, mock_create = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -314,18 +305,19 @@ class TestLlmAnalyzerHappyPath:
|
|||
)
|
||||
|
||||
kwargs = mock_create.call_args.kwargs
|
||||
messages = kwargs["messages"]
|
||||
assert isinstance(messages, list)
|
||||
assert messages[0]["role"] == "system"
|
||||
assert SYSTEM_PROMPT in messages[0]["content"]
|
||||
system = kwargs["system"]
|
||||
assert isinstance(system, list)
|
||||
assert system[0]["type"] == "text"
|
||||
assert SYSTEM_PROMPT in system[0]["text"]
|
||||
assert system[0]["cache_control"] == {"type": "ephemeral"}
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_called_with_correct_model(self):
|
||||
"""chat.completions.create is called with the model passed to LlmAnalyzer."""
|
||||
resp = _make_openai_response(_valid_analysis_input())
|
||||
"""messages.create is called with the model passed to LlmAnalyzer."""
|
||||
resp = _make_anthropic_response(_valid_analysis_input())
|
||||
client, mock_create = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -335,15 +327,15 @@ class TestLlmAnalyzerHappyPath:
|
|||
)
|
||||
|
||||
kwargs = mock_create.call_args.kwargs
|
||||
assert kwargs["model"] == "anthropic/claude-sonnet-4.5"
|
||||
assert kwargs["model"] == "claude-sonnet-4-5"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_api_called_with_submit_analysis_tool(self):
|
||||
"""Tool definition includes function name 'submit_analysis'."""
|
||||
resp = _make_openai_response(_valid_analysis_input())
|
||||
"""Tool definition includes name 'submit_analysis' with input_schema."""
|
||||
resp = _make_anthropic_response(_valid_analysis_input())
|
||||
client, mock_create = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -355,17 +347,17 @@ class TestLlmAnalyzerHappyPath:
|
|||
kwargs = mock_create.call_args.kwargs
|
||||
tools = kwargs["tools"]
|
||||
assert any(
|
||||
t.get("type") == "function" and t.get("function", {}).get("name") == "submit_analysis"
|
||||
t.get("name") == "submit_analysis" and "input_schema" in t
|
||||
for t in tools
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_raw_response_is_captured(self):
|
||||
"""raw_response in LlmCallResult holds serializable dict."""
|
||||
resp = _make_openai_response(_valid_analysis_input())
|
||||
resp = _make_anthropic_response(_valid_analysis_input())
|
||||
client, _ = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
result = await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -379,7 +371,7 @@ class TestLlmAnalyzerHappyPath:
|
|||
@pytest.mark.asyncio
|
||||
async def test_transcript_segments_included_in_user_message(self):
|
||||
"""User message contains timestamped segment lines from transcript_segments."""
|
||||
resp = _make_openai_response(_valid_analysis_input())
|
||||
resp = _make_anthropic_response(_valid_analysis_input())
|
||||
client, mock_create = _make_client(resp)
|
||||
|
||||
segments = [
|
||||
|
|
@ -387,7 +379,7 @@ class TestLlmAnalyzerHappyPath:
|
|||
{"start": 5.0, "end": 10.0, "text": "Let's talk stocks."},
|
||||
]
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -397,8 +389,8 @@ class TestLlmAnalyzerHappyPath:
|
|||
)
|
||||
|
||||
kwargs = mock_create.call_args.kwargs
|
||||
# user message is the second entry in messages list
|
||||
user_content = kwargs["messages"][1]["content"]
|
||||
# user message is in the messages list
|
||||
user_content = kwargs["messages"][0]["content"]
|
||||
assert "Hello world." in user_content
|
||||
assert "Let's talk stocks." in user_content
|
||||
|
||||
|
|
@ -412,23 +404,17 @@ class TestLlmAnalyzerFailurePaths:
|
|||
"""Failure path tests."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_no_tool_calls_raises_value_error(self):
|
||||
"""If response message has no tool_calls, raises ValueError."""
|
||||
msg = MagicMock()
|
||||
msg.tool_calls = None
|
||||
|
||||
choice = MagicMock()
|
||||
choice.message = msg
|
||||
choice.finish_reason = "stop"
|
||||
|
||||
async def test_no_tool_use_block_raises_value_error(self):
|
||||
"""If response has no tool_use block, raises ValueError containing 'tool_use'."""
|
||||
resp = MagicMock()
|
||||
resp.choices = [choice]
|
||||
resp.usage = MagicMock(prompt_tokens=5000, completion_tokens=800)
|
||||
resp.content = [MagicMock(type="text")]
|
||||
resp.usage = MagicMock(input_tokens=5000, output_tokens=800)
|
||||
resp.stop_reason = "end_turn"
|
||||
|
||||
client, _ = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
with pytest.raises(ValueError):
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
with pytest.raises(ValueError, match="tool_use"):
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
description="",
|
||||
|
|
@ -438,22 +424,16 @@ class TestLlmAnalyzerFailurePaths:
|
|||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_empty_tool_calls_raises_value_error(self):
|
||||
"""If response message has empty tool_calls list, raises ValueError."""
|
||||
msg = MagicMock()
|
||||
msg.tool_calls = []
|
||||
|
||||
choice = MagicMock()
|
||||
choice.message = msg
|
||||
choice.finish_reason = "stop"
|
||||
|
||||
async def test_empty_content_raises_value_error(self):
|
||||
"""If response.content is empty, raises ValueError."""
|
||||
resp = MagicMock()
|
||||
resp.choices = [choice]
|
||||
resp.usage = MagicMock(prompt_tokens=5000, completion_tokens=800)
|
||||
resp.content = []
|
||||
resp.usage = MagicMock(input_tokens=5000, output_tokens=800)
|
||||
resp.stop_reason = "end_turn"
|
||||
|
||||
client, _ = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
with pytest.raises(ValueError):
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
|
|
@ -469,10 +449,10 @@ class TestLlmAnalyzerFailurePaths:
|
|||
bad_input = _valid_analysis_input()
|
||||
bad_input["market_outlook_direction"] = "extremely_bullish" # not a valid enum
|
||||
|
||||
resp = _make_openai_response(bad_input)
|
||||
resp = _make_anthropic_response(bad_input)
|
||||
client, _ = _make_client(resp)
|
||||
|
||||
analyzer = LlmAnalyzer(client=client, model="anthropic/claude-sonnet-4.5", prompt_version="v1")
|
||||
analyzer = LlmAnalyzer(client=client, model="claude-sonnet-4-5", prompt_version="v1")
|
||||
with pytest.raises(Exception): # pydantic ValidationError or ValueError
|
||||
await analyzer.analyze(
|
||||
title="Test",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue