forked from 0xWheatyz/SPARC
feat: add multi-model support for per-analysis LLM selection
Allow users to choose the LLM model on a per-analysis basis. The model field is optional in both single and batch analysis requests, defaulting to the server-configured MODEL env var. The model used is recorded in the analysis result and database. - Add model parameter to LLMAnalyzer.analyze_patent_content and analyze_patent_portfolio - Add model field to CompanyAnalysisResult and API response - Add model field to BatchAnalysisRequest - Add GET /models endpoint listing supported models and the default - Store model in llm_messages metadata for attribution Closes leeworks-agents/SPARC#37 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
agent-company
+18
-12
@@ -40,12 +40,13 @@ class LLMAnalyzer:
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else:
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self.client = None
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def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
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def analyze_patent_content(self, patent_content: str, company_name: str, model: str | None = None) -> 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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model: Optional model override (e.g. "openai/gpt-4o"). Defaults to config.
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Returns:
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Analysis text describing innovation quality and potential impact
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@@ -63,6 +64,8 @@ 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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effective_model = model or self.model
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if self.test_mode:
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logger.debug("TEST MODE - Prompt that would be sent to LLM:\n%s", prompt)
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return "[TEST MODE - No API call made]"
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@@ -81,7 +84,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
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response=cached["response"],
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company_name=company_name,
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analysis_type="single_patent",
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model=self.model,
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model=effective_model,
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metadata={
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"patent_content_length": len(patent_content),
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"cache_hit": True,
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@@ -94,7 +97,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
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# Call API if no cache hit and client is available
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if self.client:
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response = self.client.chat.completions.create(
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model=self.model,
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model=effective_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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@@ -106,7 +109,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
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response=response_text,
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company_name=company_name,
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analysis_type="single_patent",
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model=self.model,
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model=effective_model,
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metadata={"patent_content_length": len(patent_content)},
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token_usage={
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"prompt_tokens": response.usage.prompt_tokens,
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@@ -124,13 +127,13 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
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response=placeholder,
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company_name=company_name,
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analysis_type="single_patent",
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model=self.model,
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model=effective_model,
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metadata={"patent_content_length": len(patent_content), "pending": True}
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)
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return placeholder
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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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self, patents_data: list[Dict[str, str]], company_name: str, model: str | None = None
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) -> str:
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"""Analyze multiple patents to estimate overall company performance.
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@@ -165,13 +168,16 @@ Patent Portfolio:
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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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effective_model = model or self.model
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if self.test_mode:
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logger.debug("TEST MODE - Portfolio prompt:\n%s", prompt)
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return "[TEST MODE]"
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metadata = {
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"patent_count": len(patents_data),
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"patent_ids": [p['patent_id'] for p in patents_data]
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"patent_ids": [p['patent_id'] for p in patents_data],
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"model": effective_model,
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}
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# Check cache first
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@@ -188,7 +194,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
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response=cached["response"],
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company_name=company_name,
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analysis_type="portfolio",
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model=self.model,
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model=effective_model,
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metadata={
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**metadata,
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"cache_hit": True,
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@@ -202,7 +208,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
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if self.client:
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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model=effective_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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@@ -215,7 +221,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
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response=response_text,
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company_name=company_name,
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analysis_type="portfolio",
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model=self.model,
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model=effective_model,
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metadata=metadata,
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token_usage={
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"prompt_tokens": response.usage.prompt_tokens,
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@@ -235,7 +241,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
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response=placeholder,
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company_name=company_name,
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analysis_type="portfolio",
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model=self.model,
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model=effective_model,
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metadata={**metadata, "pending": True}
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)
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return placeholder
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