174 lines
6.4 KiB
Plaintext
174 lines
6.4 KiB
Plaintext
<DIRECTIONS>
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How can I make this run just print the text instead of making a call to an LLM? I am looking to test before wasting any credits.
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</DIRECTIONS>
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<Context>
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You receive a selection in neovim that you need to replace with new code.
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The selection's contents may contain notes, incorporate the notes every time if there are some.
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consider the context of the selection and what you are suppose to be implementing
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<SELECTION_LOCATION>
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range(point(11,4),point(56,1))
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</SELECTION_LOCATION>
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<SELECTION_CONTENT>
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def __init__(self, api_key: str | None = None):
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"""Initialize the LLM analyzer.
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Args:
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api_key: Anthropic API key. If None, will attempt to load from config.
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"""
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if config.anthropic_api_key:
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self.client = Anthropic(api_key=api_key or config.anthropic_api_key)
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self.model = "claude-3-5-sonnet-20241022"
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def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
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"""Analyze patent content to estimate company innovation and performance.
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Args:
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patent_content: Minimized patent text (abstract, claims, summary)
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company_name: Name of the company for context
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Returns:
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Analysis text describing innovation quality and potential impact
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"""
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prompt = f"""You are a patent analyst evaluating {company_name}'s innovation strategy.
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Analyze the following patent content and provide insights on:
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1. Innovation quality and novelty
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2. Technical complexity and defensibility
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3. Market potential and commercial viability
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4. Strategic positioning relative to industry trends
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Patent Content:
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{patent_content}
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Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
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if self.client:
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message = self.client.messages.create(
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model=self.model,
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max_tokens=1024,
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messages=[{"role": "user", "content": prompt}],
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)
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else:
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with open(f"AI_Prompts/{company_name}", "w") as f:
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f.write(prompt)
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return True
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return message.content[0].text
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</SELECTION_CONTENT>
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<FILE_CONTAINING_SELECTION>
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"""LLM integration for patent analysis using Anthropic's Claude."""
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from anthropic import Anthropic
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from SPARC import config
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from typing import Dict
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class LLMAnalyzer:
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"""Handles LLM-based analysis of patent content."""
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def __init__(self, api_key: str | None = None):
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"""Initialize the LLM analyzer.
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Args:
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api_key: Anthropic API key. If None, will attempt to load from config.
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"""
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if config.anthropic_api_key:
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self.client = Anthropic(api_key=api_key or config.anthropic_api_key)
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self.model = "claude-3-5-sonnet-20241022"
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def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
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"""Analyze patent content to estimate company innovation and performance.
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Args:
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patent_content: Minimized patent text (abstract, claims, summary)
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company_name: Name of the company for context
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Returns:
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Analysis text describing innovation quality and potential impact
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"""
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prompt = f"""You are a patent analyst evaluating {company_name}'s innovation strategy.
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Analyze the following patent content and provide insights on:
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1. Innovation quality and novelty
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2. Technical complexity and defensibility
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3. Market potential and commercial viability
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4. Strategic positioning relative to industry trends
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Patent Content:
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{patent_content}
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Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
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if self.client:
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message = self.client.messages.create(
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model=self.model,
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max_tokens=1024,
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messages=[{"role": "user", "content": prompt}],
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)
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else:
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with open(f"AI_Prompts/{company_name}", "w") as f:
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f.write(prompt)
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return True
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return message.content[0].text
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def analyze_patent_portfolio(
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self, patents_data: list[Dict[str, str]], company_name: str
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) -> str:
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"""Analyze multiple patents to estimate overall company performance.
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Args:
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patents_data: List of dicts, each containing 'patent_id' and 'content'
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company_name: Name of the company being analyzed
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Returns:
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Comprehensive analysis of company's innovation trajectory and outlook
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"""
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# Combine all patent summaries
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portfolio_summary = []
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for idx, patent in enumerate(patents_data, 1):
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portfolio_summary.append(
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f"Patent {idx} ({patent['patent_id']}):\n{patent['content']}"
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)
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combined_content = "\n\n---\n\n".join(portfolio_summary)
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prompt = f"""You are analyzing {company_name}'s patent portfolio to estimate their future performance and innovation trajectory.
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You have {len(patents_data)} recent patents to analyze. Evaluate the portfolio holistically:
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1. Innovation Trends: What technology areas are they focusing on?
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2. Strategic Direction: What does this reveal about their business strategy?
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3. Competitive Position: How defensible are these innovations?
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4. Market Outlook: What market opportunities do these patents target?
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5. Performance Forecast: Based on this innovation activity, what's your assessment of their likely performance?
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Patent Portfolio:
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{combined_content}
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Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the company's innovation strength and performance outlook."""
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message = self.client.messages.create(
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model=self.model,
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max_tokens=2048,
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messages=[{"role": "user", "content": prompt}],
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)
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return message.content[0].text
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</FILE_CONTAINING_SELECTION>
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</Context>
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<MustObey>
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NEVER alter any file other than TEMP_FILE.
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never provide the requested changes as conversational output. Return only the code.
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ONLY provide requested changes by writing the change to TEMP_FILE
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never attempt to read TEMP_FILE.
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It is purely for output.
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Previous contents, which may not exist, can be written over without worry
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After writing TEMP_FILE once you should be done. Be done and end the session.
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</MustObey>
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<TEMP_FILE>/home/l-wyatt/Documents/side-work/SPARC/tmp/99-7861</TEMP_FILE> |