tests: testing modes have been added in an attempt to tune without wasting tokens.

.gitignore

@@ -2,4 +2,5 @@
 .pyenv
 __pycache__
 .venv
-patents
+patents
+tmp/

SPARC/llm.py

@@ -8,14 +8,20 @@ from typing import Dict
 class LLMAnalyzer:
     """Handles LLM-based analysis of patent content."""
 
-    def __init__(self, api_key: str | None = None):
+    def __init__(self, api_key: str | None = None, test_mode: bool = False):
         """Initialize the LLM analyzer.
 
         Args:
           api_key: Anthropic API key. If None, will attempt to load from config.
+          test_mode: If True, print prompts instead of making API calls
         """
-        self.client = Anthropic(api_key=api_key or config.anthropic_api_key)
+        self.test_mode = test_mode
-        self.model = "claude-3-5-sonnet-20241022"
+
+        if config.anthropic_api_key and not test_mode:
+            self.client = Anthropic(api_key=api_key or config.anthropic_api_key)
+            self.model = "claude-3-5-sonnet-20241022"
+        else:
+            self.client = None
 
     def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
         """Analyze patent content to estimate company innovation and performance.
@@ -40,14 +46,22 @@ Patent Content:
 
 Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
 
-        message = self.client.messages.create(
+        if self.test_mode:
-            model=self.model,
+            print("=" * 80)
-            max_tokens=1024,
+            print("TEST MODE - Prompt that would be sent to LLM:")
-            messages=[{"role": "user", "content": prompt}],
+            print("=" * 80)
-        )
+            print(prompt)
-
+            print("=" * 80)
-        return message.content[0].text
+            return "[TEST MODE - No API call made]"
 
+        if self.client:
+            message = self.client.messages.create(
+                model=self.model,
+                max_tokens=1024,
+                messages=[{"role": "user", "content": prompt}],
+            )
+            return message.content[0].text
+       
     def analyze_patent_portfolio(
         self, patents_data: list[Dict[str, str]], company_name: str
     ) -> str:
@@ -84,10 +98,18 @@ Patent Portfolio:
 
 Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the company's innovation strength and performance outlook."""
 
-        message = self.client.messages.create(
+        if self.test_mode:
-            model=self.model,
+            print(prompt)
-            max_tokens=2048,
+            return "[TEST MODE]"
-            messages=[{"role": "user", "content": prompt}],
-        )
 
-        return message.content[0].text
+        try:
+            message = self.client.messages.create(
+                model=self.model,
+                max_tokens=2048,
+                messages=[{"role": "user", "content": prompt}],
+            )
+
+            return message.content[0].text
+        except AttributeError:
+            return prompt
+       

tmp/99-4277

@@ -0,0 +1,17 @@
+
+        if self.test_mode:
+            print(prompt)
+            return "[TEST MODE]"
+
+        if not self.client:
+            raise ValueError("LLM client not initialized. Please provide a valid API key.")
+
+        message = self.client.messages.create(
+            model=self.model,
+            max_tokens=2048,
+            messages=[{"role": "user", "content": prompt}],
+        )
+
+        return message.content[0].text
+
+       

tmp/99-4277-prompt

@@ -0,0 +1,160 @@
+<DIRECTIONS>
+How can I rewrite this to not fail when client is a None type?
+</DIRECTIONS>
+<Context>
+You receive a selection in neovim that you need to replace with new code.
+The selection's contents may contain notes, incorporate the notes every time if there are some.
+consider the context of the selection and what you are suppose to be implementing
+<SELECTION_LOCATION>
+range(point(100,1),point(115,8))
+</SELECTION_LOCATION>
+<SELECTION_CONTENT>
+
+        if self.test_mode:
+            print(prompt)
+            return "[TEST MODE]"
+
+
+        else: 
+            message = self.client.messages.create(
+                model=self.model,
+                max_tokens=2048,
+                messages=[{"role": "user", "content": prompt}],
+            )
+
+            return message.content[0].text
+
+       
+</SELECTION_CONTENT>
+<FILE_CONTAINING_SELECTION>
+"""LLM integration for patent analysis using Anthropic's Claude."""
+
+from anthropic import Anthropic
+from SPARC import config
+from typing import Dict
+
+
+class LLMAnalyzer:
+    """Handles LLM-based analysis of patent content."""
+
+    def __init__(self, api_key: str | None = None, test_mode: bool = False):
+        """Initialize the LLM analyzer.
+
+        Args:
+          api_key: Anthropic API key. If None, will attempt to load from config.
+          test_mode: If True, print prompts instead of making API calls
+        """
+        self.test_mode = test_mode
+
+        if config.anthropic_api_key and not test_mode:
+            self.client = Anthropic(api_key=api_key or config.anthropic_api_key)
+            self.model = "claude-3-5-sonnet-20241022"
+        else:
+            self.client = None
+
+    def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
+        """Analyze patent content to estimate company innovation and performance.
+
+        Args:
+          patent_content: Minimized patent text (abstract, claims, summary)
+          company_name: Name of the company for context
+
+        Returns:
+          Analysis text describing innovation quality and potential impact
+        """
+        prompt = f"""You are a patent analyst evaluating {company_name}'s innovation strategy.
+
+Analyze the following patent content and provide insights on:
+1. Innovation quality and novelty
+2. Technical complexity and defensibility
+3. Market potential and commercial viability
+4. Strategic positioning relative to industry trends
+
+Patent Content:
+{patent_content}
+
+Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
+
+        if self.test_mode:
+            print("=" * 80)
+            print("TEST MODE - Prompt that would be sent to LLM:")
+            print("=" * 80)
+            print(prompt)
+            print("=" * 80)
+            return "[TEST MODE - No API call made]"
+
+        if self.client:
+            message = self.client.messages.create(
+                model=self.model,
+                max_tokens=1024,
+                messages=[{"role": "user", "content": prompt}],
+            )
+            return message.content[0].text
+       
+    def analyze_patent_portfolio(
+        self, patents_data: list[Dict[str, str]], company_name: str
+    ) -> str:
+        """Analyze multiple patents to estimate overall company performance.
+
+        Args:
+          patents_data: List of dicts, each containing 'patent_id' and 'content'
+          company_name: Name of the company being analyzed
+
+        Returns:
+          Comprehensive analysis of company's innovation trajectory and outlook
+        """
+        # Combine all patent summaries
+        portfolio_summary = []
+        for idx, patent in enumerate(patents_data, 1):
+            portfolio_summary.append(
+                f"Patent {idx} ({patent['patent_id']}):\n{patent['content']}"
+            )
+
+        combined_content = "\n\n---\n\n".join(portfolio_summary)
+
+        prompt = f"""You are analyzing {company_name}'s patent portfolio to estimate their future performance and innovation trajectory.
+
+You have {len(patents_data)} recent patents to analyze. Evaluate the portfolio holistically:
+
+1. Innovation Trends: What technology areas are they focusing on?
+2. Strategic Direction: What does this reveal about their business strategy?
+3. Competitive Position: How defensible are these innovations?
+4. Market Outlook: What market opportunities do these patents target?
+5. Performance Forecast: Based on this innovation activity, what's your assessment of their likely performance?
+
+Patent Portfolio:
+{combined_content}
+
+Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the company's innovation strength and performance outlook."""
+
+        if self.test_mode:
+            print(prompt)
+            return "[TEST MODE]"
+
+
+        else: 
+            message = self.client.messages.create(
+                model=self.model,
+                max_tokens=2048,
+                messages=[{"role": "user", "content": prompt}],
+            )
+
+            return message.content[0].text
+
+       
+</FILE_CONTAINING_SELECTION>
+
+</Context>
+
+<MustObey>
+NEVER alter any file other than TEMP_FILE.
+never provide the requested changes as conversational output. Return only the code.
+ONLY provide requested changes by writing the change to TEMP_FILE
+
+never attempt to read TEMP_FILE.
+It is purely for output.
+Previous contents, which may not exist, can be written over without worry
+After writing TEMP_FILE once you should be done.  Be done and end the session.
+
+</MustObey>
+<TEMP_FILE>/home/l-wyatt/Documents/side-work/SPARC/tmp/99-4277</TEMP_FILE>

tmp/99-7861

@@ -0,0 +1,56 @@
+    def __init__(self, api_key: str | None = None, test_mode: bool = False):
+        """Initialize the LLM analyzer.
+
+        Args:
+          api_key: Anthropic API key. If None, will attempt to load from config.
+          test_mode: If True, print prompts instead of making API calls
+        """
+        self.test_mode = test_mode
+        if config.anthropic_api_key and not test_mode:
+            self.client = Anthropic(api_key=api_key or config.anthropic_api_key)
+            self.model = "claude-3-5-sonnet-20241022"
+        else:
+            self.client = None
+
+    def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
+        """Analyze patent content to estimate company innovation and performance.
+
+        Args:
+          patent_content: Minimized patent text (abstract, claims, summary)
+          company_name: Name of the company for context
+
+        Returns:
+          Analysis text describing innovation quality and potential impact
+        """
+        prompt = f"""You are a patent analyst evaluating {company_name}'s innovation strategy.
+
+Analyze the following patent content and provide insights on:
+1. Innovation quality and novelty
+2. Technical complexity and defensibility
+3. Market potential and commercial viability
+4. Strategic positioning relative to industry trends
+
+Patent Content:
+{patent_content}
+
+Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
+
+        if self.test_mode:
+            print("=" * 80)
+            print("TEST MODE - Prompt that would be sent to LLM:")
+            print("=" * 80)
+            print(prompt)
+            print("=" * 80)
+            return "[TEST MODE - No API call made]"
+
+        if self.client:
+            message = self.client.messages.create(
+                model=self.model,
+                max_tokens=1024,
+                messages=[{"role": "user", "content": prompt}],
+            )
+            return message.content[0].text
+        else:
+            with open(f"AI_Prompts/{company_name}", "w") as f:
+                f.write(prompt)
+            return True

tmp/99-7861-prompt

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