406 lines
16 KiB
Python
406 lines
16 KiB
Python
"""Anthropic Claude summarization service."""
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import asyncio
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import json
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import time
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import sys
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import os
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from typing import Dict, List, Optional
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import re
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from anthropic import AsyncAnthropic
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# Add library path to import BaseAIService
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lib_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../lib'))
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if lib_path not in sys.path:
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sys.path.insert(0, lib_path)
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try:
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from ai_assistant_lib.services.ai.base_ai_service import BaseAIService, AIModelConfig, AIRequest, AIResponse
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except ImportError:
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# Fallback to old implementation if library not available
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from .ai_service import AIService as BaseAIService
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# Create dummy classes for compatibility
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class AIModelConfig:
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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setattr(self, k, v)
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class AIRequest:
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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setattr(self, k, v)
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class AIResponse:
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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setattr(self, k, v)
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from .ai_service import SummaryRequest, SummaryResult, SummaryLength
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from ..core.exceptions import AIServiceError, ErrorCode
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class AnthropicSummarizer(BaseAIService):
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"""Anthropic Claude-based summarization service."""
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def __init__(self, api_key: str, model: str = "claude-3-5-haiku-20241022"):
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"""Initialize Anthropic summarizer.
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Args:
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api_key: Anthropic API key
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model: Model to use (default: claude-3-5-haiku for cost efficiency)
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"""
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config = AIModelConfig(
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model_name=model,
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temperature=0.3,
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max_tokens=8192,
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timeout_seconds=120,
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max_retries=3,
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backoff_factor=2.0
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)
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# Initialize BaseAIService
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super().__init__(
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name="anthropic-summarizer",
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api_key=api_key,
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default_config=config
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)
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# Cost per 1K tokens (as of 2025) - Claude 3.5 Haiku
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self.input_cost_per_1k = 0.00025 # $0.25 per 1M input tokens
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self.output_cost_per_1k = 0.00125 # $1.25 per 1M output tokens
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# Token limits for Claude models
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self.max_tokens_input = 200000 # 200k context window
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self.max_tokens_output = 8192 # Max output tokens
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async def _create_client(self):
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"""Create the Anthropic client."""
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return AsyncAnthropic(api_key=self.api_key)
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async def _make_prediction(self, request: AIRequest) -> AIResponse:
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"""Make prediction using Anthropic Claude."""
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try:
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response = await self._client.messages.create(
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model=request.model_config.model_name,
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max_tokens=request.model_config.max_tokens or self.max_tokens_output,
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temperature=request.model_config.temperature,
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messages=[{"role": "user", "content": request.prompt}]
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)
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response_text = response.content[0].text
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return AIResponse(
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request_id=request.request_id,
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content=response_text,
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model_name=request.model_config.model_name,
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usage={
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"input_tokens": response.usage.input_tokens,
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"output_tokens": response.usage.output_tokens,
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"total_tokens": response.usage.input_tokens + response.usage.output_tokens
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}
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)
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except Exception as e:
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from ai_assistant_lib.core.exceptions import AIServiceError as LibAIServiceError
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raise LibAIServiceError(
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service_name=self.name,
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operation="_make_prediction",
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details={
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"error": str(e),
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"model": request.model_config.model_name
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}
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) from e
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async def generate_summary(self, request: SummaryRequest) -> SummaryResult:
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"""Generate structured summary using Anthropic Claude."""
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# Handle very long transcripts with chunking
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estimated_tokens = self.get_token_count(request.transcript)
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if estimated_tokens > 150000: # Leave room for prompt and response
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return await self._generate_chunked_summary(request)
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prompt = self._build_summary_prompt(request)
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try:
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# Create model config for this request
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model_config = AIModelConfig(
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model_name=self.default_config.model_name,
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temperature=0.3,
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max_tokens=self._get_max_tokens(request.length),
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timeout_seconds=self.default_config.timeout_seconds
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)
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# Use BaseAIService predict method with retry, rate limiting, etc.
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response = await self.predict(
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prompt=prompt,
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model_config=model_config
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)
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# Extract JSON from response
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result_data = self._extract_json_from_response(response.content)
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# Calculate costs
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input_tokens = response.usage.get("input_tokens", 0)
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output_tokens = response.usage.get("output_tokens", 0)
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input_cost = (input_tokens / 1000) * self.input_cost_per_1k
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output_cost = (output_tokens / 1000) * self.output_cost_per_1k
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total_cost = input_cost + output_cost
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return SummaryResult(
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summary=result_data.get("summary", ""),
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key_points=result_data.get("key_points", []),
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main_themes=result_data.get("main_themes", []),
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actionable_insights=result_data.get("actionable_insights", []),
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confidence_score=result_data.get("confidence_score", 0.85),
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processing_metadata={
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"model": response.model_name,
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"processing_time_seconds": response.processing_time_ms / 1000 if response.processing_time_ms else 0,
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"input_tokens": input_tokens,
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"output_tokens": output_tokens,
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"total_tokens": input_tokens + output_tokens,
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"chunks_processed": 1
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},
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cost_data={
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"input_cost_usd": input_cost,
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"output_cost_usd": output_cost,
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"total_cost_usd": total_cost,
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"cost_per_summary": total_cost
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}
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)
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except Exception as e:
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raise AIServiceError(
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message=f"Anthropic summarization failed: {str(e)}",
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error_code=ErrorCode.AI_SERVICE_ERROR,
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details={
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"model": self.default_config.model_name,
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"transcript_length": len(request.transcript),
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"error_type": type(e).__name__
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}
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)
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def _extract_json_from_response(self, response_text: str) -> dict:
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"""Extract JSON from Claude's response which may include additional text."""
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try:
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# First try direct JSON parsing
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return json.loads(response_text)
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except json.JSONDecodeError:
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# Look for JSON block in the response
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json_match = re.search(r'\{.*\}', response_text, re.DOTALL)
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if json_match:
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try:
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return json.loads(json_match.group())
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except json.JSONDecodeError:
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pass
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# Fallback: create structure from response text
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return self._parse_structured_response(response_text)
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def _parse_structured_response(self, response_text: str) -> dict:
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"""Parse structured response when JSON parsing fails."""
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# This is a fallback parser for when Claude doesn't return pure JSON
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lines = response_text.split('\n')
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summary = ""
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key_points = []
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main_themes = []
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actionable_insights = []
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confidence_score = 0.85
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current_section = None
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for line in lines:
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line = line.strip()
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if not line:
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continue
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# Detect sections
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if "summary" in line.lower() and ":" in line:
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current_section = "summary"
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if ":" in line:
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summary = line.split(":", 1)[1].strip()
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continue
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elif "key points" in line.lower() or "key_points" in line.lower():
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current_section = "key_points"
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continue
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elif "main themes" in line.lower() or "main_themes" in line.lower():
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current_section = "main_themes"
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continue
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elif "actionable insights" in line.lower() or "actionable_insights" in line.lower():
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current_section = "actionable_insights"
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continue
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elif "confidence" in line.lower():
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# Extract confidence score
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numbers = re.findall(r'0?\.\d+|\d+', line)
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if numbers:
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confidence_score = float(numbers[0])
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continue
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# Add content to appropriate section
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if current_section == "summary" and summary == "":
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summary = line
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elif current_section == "key_points" and line.startswith(('-', '•', '*')):
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key_points.append(line[1:].strip())
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elif current_section == "main_themes" and line.startswith(('-', '•', '*')):
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main_themes.append(line[1:].strip())
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elif current_section == "actionable_insights" and line.startswith(('-', '•', '*')):
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actionable_insights.append(line[1:].strip())
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return {
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"summary": summary,
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"key_points": key_points,
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"main_themes": main_themes,
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"actionable_insights": actionable_insights,
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"confidence_score": confidence_score
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}
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def _build_summary_prompt(self, request: SummaryRequest) -> str:
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"""Build optimized prompt for Claude summary generation."""
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length_instructions = {
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SummaryLength.BRIEF: "Generate a concise summary in 100-200 words",
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SummaryLength.STANDARD: "Generate a comprehensive summary in 300-500 words",
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SummaryLength.DETAILED: "Generate a detailed summary in 500-800 words"
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}
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focus_instruction = ""
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if request.focus_areas:
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focus_instruction = f"\nPay special attention to these areas: {', '.join(request.focus_areas)}"
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return f"""
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Analyze this YouTube video transcript and provide a structured summary in JSON format.
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{length_instructions[request.length]}.
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Please respond with a valid JSON object in this exact format:
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{{
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"summary": "Main summary text here",
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"key_points": ["Point 1", "Point 2", "Point 3"],
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"main_themes": ["Theme 1", "Theme 2", "Theme 3"],
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"actionable_insights": ["Insight 1", "Insight 2"],
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"confidence_score": 0.95
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}}
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Guidelines:
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- Extract 3-7 key points that capture the most important information
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- Identify 2-4 main themes or topics discussed
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- Provide 2-5 actionable insights that viewers can apply
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- Assign a confidence score (0.0-1.0) based on transcript quality and coherence
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- Use clear, engaging language that's accessible to a general audience
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- Focus on value and practical takeaways{focus_instruction}
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Transcript:
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{request.transcript}
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"""
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async def _generate_chunked_summary(self, request: SummaryRequest) -> SummaryResult:
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"""Handle very long transcripts using map-reduce approach."""
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# Split transcript into manageable chunks
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chunks = self._split_transcript_intelligently(request.transcript)
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# Generate summary for each chunk
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chunk_summaries = []
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total_cost = 0.0
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total_tokens = 0
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for i, chunk in enumerate(chunks):
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chunk_request = SummaryRequest(
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transcript=chunk,
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length=SummaryLength.BRIEF, # Brief summaries for chunks
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focus_areas=request.focus_areas,
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language=request.language
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)
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chunk_result = await self.generate_summary(chunk_request)
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chunk_summaries.append(chunk_result.summary)
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total_cost += chunk_result.cost_data["total_cost_usd"]
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total_tokens += chunk_result.processing_metadata["total_tokens"]
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# Add delay to respect rate limits
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await asyncio.sleep(0.1)
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# Combine chunk summaries into final summary
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combined_transcript = "\n\n".join([
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f"Section {i+1} Summary: {summary}"
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for i, summary in enumerate(chunk_summaries)
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])
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final_request = SummaryRequest(
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transcript=combined_transcript,
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length=request.length,
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focus_areas=request.focus_areas,
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language=request.language
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)
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final_result = await self.generate_summary(final_request)
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# Update metadata to reflect chunked processing
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final_result.processing_metadata.update({
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"chunks_processed": len(chunks),
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"total_tokens": total_tokens + final_result.processing_metadata["total_tokens"],
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"chunking_strategy": "intelligent_content_boundaries"
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})
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final_result.cost_data["total_cost_usd"] = total_cost + final_result.cost_data["total_cost_usd"]
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return final_result
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def _split_transcript_intelligently(self, transcript: str, max_tokens: int = 120000) -> List[str]:
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"""Split transcript at natural boundaries while respecting token limits."""
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# Split by paragraphs first, then sentences if needed
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paragraphs = transcript.split('\n\n')
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chunks = []
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current_chunk = []
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current_tokens = 0
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for paragraph in paragraphs:
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paragraph_tokens = self.get_token_count(paragraph)
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# If single paragraph exceeds limit, split by sentences
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if paragraph_tokens > max_tokens:
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sentences = paragraph.split('. ')
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for sentence in sentences:
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sentence_tokens = self.get_token_count(sentence)
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if current_tokens + sentence_tokens > max_tokens and current_chunk:
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chunks.append(' '.join(current_chunk))
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current_chunk = [sentence]
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current_tokens = sentence_tokens
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else:
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current_chunk.append(sentence)
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current_tokens += sentence_tokens
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else:
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if current_tokens + paragraph_tokens > max_tokens and current_chunk:
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chunks.append('\n\n'.join(current_chunk))
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current_chunk = [paragraph]
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current_tokens = paragraph_tokens
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else:
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current_chunk.append(paragraph)
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current_tokens += paragraph_tokens
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# Add final chunk
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if current_chunk:
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chunks.append('\n\n'.join(current_chunk))
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return chunks
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def _get_max_tokens(self, length: SummaryLength) -> int:
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"""Get max output tokens based on summary length."""
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return {
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SummaryLength.BRIEF: 400,
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SummaryLength.STANDARD: 800,
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SummaryLength.DETAILED: 1500
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}[length]
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def estimate_cost(self, transcript: str, length: SummaryLength) -> float:
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"""Estimate cost for summarizing transcript."""
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input_tokens = self.get_token_count(transcript)
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output_tokens = self._get_max_tokens(length)
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input_cost = (input_tokens / 1000) * self.input_cost_per_1k
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output_cost = (output_tokens / 1000) * self.output_cost_per_1k
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return input_cost + output_cost
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def get_token_count(self, text: str) -> int:
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"""Estimate token count for Anthropic model (roughly 4 chars per token)."""
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# Anthropic uses a similar tokenization to OpenAI, roughly 4 characters per token
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return len(text) // 4 |