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feat: add model picker to Analysis and Batch pages with full backend wiring

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Thread the optional model parameter through the entire analysis pipeline:
- analyzer.py: analyze_company, _analyze_company_safe, analyze_companies,
  and analyze_single_patent now accept and forward model override
- api.py: single company endpoint accepts model query param; batch and
  async batch endpoints pass request.model through to the analyzer
- client.ts: analyzeCompany, analyzeBatch, analyzeBatchAsync accept model;
  add listModels() to fetch available models from GET /models
- Analysis.tsx: add model selector dropdown that loads from /models API
- Batch.tsx: add model selector alongside the workers slider

Users can now pick a specific LLM (GPT-4o, Claude 3.5, Gemini, etc.)
per analysis request, or leave it on the server default.

Closes leeworks-agents/SPARC#351

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
agent-company
2026-03-27 16:13:00 +00:00
parent 514e274fdb
commit 223d5f7e5d
5 changed files with 137 additions and 43 deletions
Download Patch File Download Diff File Expand all files Collapse all files
SPARC/analyzer.py
+12 -7
View File
@@ -33,7 +33,7 @@ class CompanyAnalyzer:
self.db.connect()
self.db.initialize_schema()
def analyze_company(self, company_name: str, patents: "Patents | None" = None) -> str:
def analyze_company(self, company_name: str, patents: "Patents | None" = None, model: str | None = None) -> str:
"""Analyze a company's performance based on their patent portfolio.
This is the main entry point that orchestrates the full pipeline:
@@ -46,6 +46,7 @@ class CompanyAnalyzer:
Args:
company_name: Name of the company to analyze
patents: Optional pre-fetched Patents result to avoid duplicate API calls
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
Comprehensive analysis of company's innovation and performance outlook
@@ -100,12 +101,12 @@ class CompanyAnalyzer:
# Analyze the full portfolio with LLM
analysis = self.llm_analyzer.analyze_patent_portfolio(
patents_data=processed_patents, company_name=company_name
patents_data=processed_patents, company_name=company_name, model=model
)
return analysis
def analyze_single_patent(self, patent_id: str, company_name: str) -> str:
def analyze_single_patent(self, patent_id: str, company_name: str, model: str | None = None) -> str:
"""Analyze a single patent by ID.
If the patent PDF is not already on disk, this method attempts to
@@ -116,6 +117,7 @@ class CompanyAnalyzer:
Args:
patent_id: Publication ID of the patent (e.g. "US-11234567-B2")
company_name: Name of the company (for context)
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
Analysis of the specific patent's innovation quality
@@ -151,7 +153,7 @@ class CompanyAnalyzer:
minimized_content = SERP.minimize_patent_for_llm(sections)
analysis = self.llm_analyzer.analyze_patent_content(
patent_content=minimized_content, company_name=company_name
patent_content=minimized_content, company_name=company_name, model=model
)
return analysis
@@ -201,18 +203,19 @@ class CompanyAnalyzer:
logger.warning("Failed to process %s: %s", patent.patent_id, e)
return None
def _analyze_company_safe(self, company_name: str) -> CompanyAnalysisResult:
def _analyze_company_safe(self, company_name: str, model: str | None = None) -> CompanyAnalysisResult:
"""Internal wrapper that catches exceptions and returns structured result.
Args:
company_name: Name of the company to analyze
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
CompanyAnalysisResult with success/failure status
"""
try:
# Delegate to analyze_company which handles SERP/patent caching
analysis = self.analyze_company(company_name)
analysis = self.analyze_company(company_name, model=model)
# Determine patent count from cached SERP query
query_hash = hashlib.sha256(company_name.lower().encode()).hexdigest()
@@ -252,6 +255,7 @@ class CompanyAnalyzer:
companies: list[str],
max_workers: int = 3,
progress_callback: Callable[[str, int, int], None] | None = None,
model: str | None = None,
) -> BatchAnalysisResult:
"""Analyze multiple companies' patent portfolios in batch.
@@ -262,6 +266,7 @@ class CompanyAnalyzer:
companies: List of company names to analyze
max_workers: Maximum concurrent analyses (default 3 to avoid rate limits)
progress_callback: Optional callback(company_name, completed, total)
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
BatchAnalysisResult containing all individual results and summary stats
@@ -273,7 +278,7 @@ class CompanyAnalyzer:
with ThreadPoolExecutor(max_workers=max_workers) as executor:
future_to_company = {
executor.submit(self._analyze_company_safe, company): company
executor.submit(self._analyze_company_safe, company, model): company
for company in companies
}
SPARC/api.py
+7 -3
View File
@@ -799,6 +799,7 @@ async def health_check():
)
async def analyze_company(
company_name: str,
model: str | None = Query(default=None, description="LLM model to use (e.g. 'openai/gpt-4o'). Defaults to server config."),
_: UserResponse = Depends(get_current_user),
):
"""Analyze a single company's patent portfolio.
@@ -808,6 +809,7 @@ async def analyze_company(
Args:
company_name: Name of the company to analyze (e.g., "nvidia", "intel")
model: Optional LLM model override
Returns:
Analysis results including patent count, AI insights, and success status
@@ -815,7 +817,7 @@ async def analyze_company(
if not _analyzer:
raise HTTPException(status_code=503, detail="Analyzer not initialized")
result = _analyzer._analyze_company_safe(company_name)
result = _analyzer._analyze_company_safe(company_name, model=model)
return _convert_result(result)
@@ -877,6 +879,7 @@ async def analyze_companies_batch(
result = _analyzer.analyze_companies(
companies=request.companies,
max_workers=request.max_workers,
model=request.model,
)
return _convert_batch_result(result)
@@ -908,7 +911,7 @@ def _job_row_to_status(row: dict) -> JobStatus:
)
def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
def _run_batch_job(job_id: str, companies: list[str], max_workers: int, model: str | None = None):
"""Background task for batch analysis."""
import json as _json
global _analyzer
@@ -933,6 +936,7 @@ def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
companies=companies,
max_workers=max_workers,
progress_callback=progress_callback,
model=model,
)
batch_response = _convert_batch_result(result)
db.update_job(
@@ -988,7 +992,7 @@ async def analyze_companies_async(
job_row = db.create_job(job_id=job_id, total_companies=len(request.companies))
background_tasks.add_task(
_run_batch_job, job_id, request.companies, request.max_workers
_run_batch_job, job_id, request.companies, request.max_workers, request.model
)
return _job_row_to_status(job_row)