Add model allow-list validation to analysis endpoints

Reject unsupported LLM model identifiers with HTTP 400 on all analysis
endpoints (single, batch, async batch). The SUPPORTED_MODELS list was
already defined for the /models endpoint but not enforced on incoming
requests. This completes the multi-model support feature by adding the
missing server-side validation.

Closes leeworks-agents/SPARC#1013

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.env.example

@@ -35,8 +35,41 @@ JWT_SECRET=your-secure-jwt-secret-change-in-production
 # Defaults to http://localhost:3000,http://localhost:5173 when unset
 # CORS_ORIGINS=https://sparc.example.com,https://app.example.com
 
+# ---- Storage ----
+
+# Backend for patent PDF storage: "local" (default) or "s3"
+STORAGE_BACKEND=local
+
+# S3/MinIO settings (only used when STORAGE_BACKEND=s3)
+# S3_BUCKET=sparc-patents
+# S3_ENDPOINT_URL=http://localhost:9000
+# AWS_ACCESS_KEY_ID=minioadmin
+# AWS_SECRET_ACCESS_KEY=minioadmin
+# To start MinIO locally: docker compose --profile s3 up -d minio
+
+# ---- LLM ----
+
+# LLM model to use via OpenRouter
+# Supported: anthropic/claude-3.5-sonnet, openai/gpt-4o, openai/gpt-4o-mini,
+#            google/gemini-pro-1.5, meta-llama/llama-3.1-70b-instruct
+# MODEL=anthropic/claude-3.5-sonnet
+
 # ---- Cache ----
 
 # When USE_CACHE=true: check database for cached responses before making API calls
 # When USE_CACHE=false: always make fresh API calls (still stores results in database)
 USE_CACHE=true
+
+# SERP API cache TTL in hours (how long cached search results are considered fresh)
+# SERP_CACHE_TTL_HOURS=24
+
+# ---- Logging ----
+
+# Log level: DEBUG, INFO, WARNING, ERROR, CRITICAL
+# LOG_LEVEL=INFO
+
+# ---- Webhooks ----
+
+# Comma-separated list of webhook URLs for job completion and alert notifications
+# Supports generic HTTP POST and Slack/Discord incoming webhooks
+# WEBHOOK_URLS=https://hooks.slack.com/services/XXX,https://example.com/webhook

.gitea/workflows/build.yaml

@@ -33,6 +33,20 @@ jobs:
         run: |
           ruff check SPARC/ tests/
 
+      - name: Install Node.js and check TypeScript types
+        shell: sh
+        run: |
+          apk add --no-cache nodejs npm
+          cd frontend
+          npm ci
+          npm run generate:local
+          if ! git diff --quiet src/api/schema.d.ts; then
+            echo "ERROR: src/api/schema.d.ts is out of date. Run 'npm run generate:local' and commit the result."
+            git diff src/api/schema.d.ts
+            exit 1
+          fi
+          npx tsc --noEmit
+
       - name: Run pytest
         shell: sh
         env:

.gitea/workflows/test.yaml

@@ -34,6 +34,27 @@ jobs:
         run: |
           ruff check SPARC/ tests/
 
+      - name: Install Node.js and frontend dependencies
+        shell: sh
+        run: |
+          apk add --no-cache nodejs npm
+          cd frontend && npm ci
+
+      - name: Verify generated API types are up to date
+        shell: sh
+        run: |
+          cd frontend && npm run generate:local
+          if ! git diff --quiet src/api/schema.d.ts; then
+            echo "ERROR: src/api/schema.d.ts is out of date. Run 'npm run generate:local' and commit the result."
+            git diff src/api/schema.d.ts
+            exit 1
+          fi
+
+      - name: Run TypeScript type check
+        shell: sh
+        run: |
+          cd frontend && npx tsc --noEmit
+
       - name: Run pytest
         shell: sh
         env:

SPARC/analyzer.py

@@ -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
@@ -100,30 +101,29 @@ 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.
 
-        Prerequisite:
-            The patent PDF must already exist at ``patents/{patent_id}.pdf``
-            before calling this method. PDFs are downloaded automatically when
-            using the batch analysis pipeline (``analyze_company`` or the
-            ``/analyze/batch`` API endpoint). For standalone usage, download
-            the PDF manually or call ``SERP.save_patents()`` first.
+        If the patent PDF is not already on disk, this method attempts to
+        download it automatically by looking up the PDF link in the database
+        cache. If the link is not cached either, a ``FileNotFoundError`` is
+        raised with instructions on how to obtain the PDF.
 
         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
 
         Raises:
-          FileNotFoundError: If the patent PDF is not found at the expected path.
+          FileNotFoundError: If the patent PDF cannot be found or downloaded.
         """
         import os
         logger.info("Analyzing patent %s for %s...", patent_id, company_name)
@@ -131,17 +131,29 @@ class CompanyAnalyzer:
         patent_path = f"patents/{patent_id}.pdf"
 
         if not os.path.exists(patent_path):
-            raise FileNotFoundError(
-                f"Patent PDF not found at '{patent_path}'. "
-                f"Download the PDF first using SERP.save_patents() or the batch analysis pipeline."
-            )
+            # Attempt to download the PDF automatically from cached metadata
+            cached = self.db.get_cached_patent(patent_id)
+            pdf_link = cached.get("pdf_link") if cached else None
+
+            if pdf_link:
+                logger.info("PDF not on disk; downloading %s from cached link", patent_id)
+                patent = SERP.save_patents(
+                    Patent(patent_id=patent_id, pdf_link=pdf_link)
+                )
+                patent_path = patent.pdf_path
+            else:
+                raise FileNotFoundError(
+                    f"Patent PDF not found at '{patent_path}' and no download link is "
+                    f"cached for '{patent_id}'. Run a company analysis first to populate "
+                    f"the cache, or call SERP.save_patents() with the patent's PDF link."
+                )
 
         try:
             sections = SERP.parse_patent_pdf(patent_path)
             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
@@ -191,18 +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()
@@ -242,6 +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.
 
@@ -252,6 +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
@@ -263,7 +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

@@ -9,7 +9,7 @@ from typing import Annotated, List
 
 from fastapi import BackgroundTasks, Depends, FastAPI, HTTPException, Query, Request
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 from pydantic import BaseModel, EmailStr, Field
 from slowapi import Limiter
 from slowapi.errors import RateLimitExceeded
@@ -41,6 +41,7 @@ class CompanyAnalysisResponse(BaseModel):
     patent_count: int
     success: bool
     error: str | None = None
+    model: str | None = None
     timestamp: datetime
 
 
@@ -54,6 +55,15 @@ class BatchAnalysisResponse(BaseModel):
     timestamp: datetime
 
 
+class CompanyAnalysisRequest(BaseModel):
+    """Request model for single company analysis with optional model selection."""
+
+    model: str | None = Field(
+        default=None,
+        description="LLM model to use (e.g. 'anthropic/claude-3.5-sonnet', 'openai/gpt-4o'). Defaults to server config.",
+    )
+
+
 class BatchAnalysisRequest(BaseModel):
     """Request model for batch company analysis."""
 
@@ -63,6 +73,10 @@ class BatchAnalysisRequest(BaseModel):
     max_workers: int = Field(
         default=3, ge=1, le=5, description="Max concurrent analyses"
     )
+    model: str | None = Field(
+        default=None,
+        description="LLM model to use for all analyses in this batch. Defaults to server config.",
+    )
 
 
 class JobStatus(BaseModel):
@@ -77,6 +91,13 @@ class JobStatus(BaseModel):
     error: str | None = None
 
 
+class PaginatedJobsResponse(BaseModel):
+    """Paginated response for job listings."""
+
+    items: list["JobStatus"]
+    next_cursor: str | None = None
+
+
 class HealthResponse(BaseModel):
     """Health check response."""
 
@@ -133,6 +154,7 @@ def _convert_result(result: CompanyAnalysisResult) -> CompanyAnalysisResponse:
         patent_count=result.patent_count,
         success=result.success,
         error=result.error,
+        model=result.model,
         timestamp=result.timestamp,
     )
 
@@ -169,6 +191,9 @@ async def lifespan(app: FastAPI):
         import logging
         logging.getLogger(__name__).warning("Marked %d stale jobs as failed on startup", stale)
     _db.close()
+    # Start scheduled analysis if tracked companies are configured
+    from SPARC.scheduler import start_scheduler
+    start_scheduler()
     yield
     # Cleanup
     _analyzer = None
@@ -369,6 +394,60 @@ async def delete_user(
     return {"message": "User deleted"}
 
 
+# ============== Tracked Companies Endpoints ==============
+
+
+class TrackCompanyRequest(BaseModel):
+    """Request to add a company to tracking."""
+
+    company_name: str = Field(..., min_length=1, max_length=255)
+
+
+@app.get("/admin/tracked", tags=["Admin"])
+async def list_tracked_companies(
+    _: UserResponse = Depends(get_current_admin),
+):
+    """List all tracked companies (admin only)."""
+    db = get_db_client()
+    return db.list_tracked_companies()
+
+
+@app.post("/admin/tracked", tags=["Admin"])
+async def add_tracked_company(
+    request: TrackCompanyRequest,
+    _: UserResponse = Depends(get_current_admin),
+):
+    """Add a company to the tracked list (admin only)."""
+    db = get_db_client()
+    result = db.add_tracked_company(request.company_name)
+    if not result:
+        raise HTTPException(status_code=409, detail="Company already tracked")
+    return result
+
+
+@app.delete("/admin/tracked/{company_name}", tags=["Admin"])
+async def remove_tracked_company(
+    company_name: str,
+    _: UserResponse = Depends(get_current_admin),
+):
+    """Remove a company from the tracked list (admin only)."""
+    db = get_db_client()
+    removed = db.remove_tracked_company(company_name)
+    if not removed:
+        raise HTTPException(status_code=404, detail="Company not found in tracking list")
+    return {"message": f"Stopped tracking {company_name}"}
+
+
+@app.get("/admin/alerts", tags=["Admin"])
+async def list_alerts(
+    limit: int = Query(default=50, ge=1, le=200),
+    _: UserResponse = Depends(get_current_admin),
+):
+    """List recent alerts from scheduled analysis (admin only)."""
+    db = get_db_client()
+    return db.list_alerts(limit=limit)
+
+
 # ============== Analytics Endpoint ==============
 
 
@@ -389,6 +468,331 @@ async def get_analytics(
     )
 
 
+# ============== Model Selection Endpoints ==============
+
+# Supported models via OpenRouter
+SUPPORTED_MODELS = [
+    {"id": "anthropic/claude-3.5-sonnet", "name": "Claude 3.5 Sonnet", "provider": "Anthropic"},
+    {"id": "openai/gpt-4o", "name": "GPT-4o", "provider": "OpenAI"},
+    {"id": "openai/gpt-4o-mini", "name": "GPT-4o Mini", "provider": "OpenAI"},
+    {"id": "google/gemini-pro-1.5", "name": "Gemini Pro 1.5", "provider": "Google"},
+    {"id": "meta-llama/llama-3.1-70b-instruct", "name": "Llama 3.1 70B", "provider": "Meta"},
+]
+
+_SUPPORTED_MODEL_IDS = {m["id"] for m in SUPPORTED_MODELS}
+
+
+def _validate_model(model: str | None) -> None:
+    """Raise HTTP 400 if *model* is not in the supported allow-list."""
+    if model is not None and model not in _SUPPORTED_MODEL_IDS:
+        raise HTTPException(
+            status_code=400,
+            detail=(
+                f"Unsupported model '{model}'. "
+                f"Supported models: {', '.join(sorted(_SUPPORTED_MODEL_IDS))}"
+            ),
+        )
+
+
+@app.get("/models", tags=["System"])
+async def list_models():
+    """List supported LLM models for analysis.
+
+    Returns the available models that can be passed as the `model` field
+    in analysis requests. The default model is determined by the `MODEL`
+    environment variable on the server.
+    """
+    return {
+        "models": SUPPORTED_MODELS,
+        "default": config.model,
+    }
+
+
+@app.get("/analytics/trends", tags=["Analytics"])
+async def get_analytics_trends(
+    days: int = Query(default=90, ge=7, le=365),
+    _: UserResponse = Depends(get_current_user),
+):
+    """Get trend data for patent analysis over time.
+
+    Returns two datasets:
+    - ``by_month``: analysis count per company per month
+    - ``by_type_over_time``: analysis type distribution per month
+
+    Args:
+        days: Number of days to look back (default 90)
+
+    Returns:
+        Trend data suitable for time-series and distribution charts
+    """
+    db = get_db_client()
+
+    with db.get_conn() as conn:
+        with conn.cursor() as cur:
+            # Analyses per company per month
+            cur.execute(
+                """
+                SELECT
+                    TO_CHAR(timestamp, 'YYYY-MM') AS month,
+                    company_name,
+                    COUNT(*) AS count
+                FROM llm_messages
+                WHERE timestamp >= NOW() - INTERVAL '%s days'
+                    AND is_cached = FALSE
+                    AND company_name IS NOT NULL
+                GROUP BY month, company_name
+                ORDER BY month
+                """,
+                (days,),
+            )
+            by_month_rows = cur.fetchall()
+
+            # Analysis type distribution per month
+            cur.execute(
+                """
+                SELECT
+                    TO_CHAR(timestamp, 'YYYY-MM') AS month,
+                    analysis_type,
+                    COUNT(*) AS count
+                FROM llm_messages
+                WHERE timestamp >= NOW() - INTERVAL '%s days'
+                    AND is_cached = FALSE
+                GROUP BY month, analysis_type
+                ORDER BY month
+                """,
+                (days,),
+            )
+            by_type_rows = cur.fetchall()
+
+    by_month = [
+        {"month": row[0], "company_name": row[1], "count": row[2]}
+        for row in by_month_rows
+    ]
+    by_type_over_time = [
+        {"month": row[0], "analysis_type": row[1], "count": row[2]}
+        for row in by_type_rows
+    ]
+
+    return {
+        "by_month": by_month,
+        "by_type_over_time": by_type_over_time,
+        "period_days": days,
+    }
+
+
+# ============== Export Endpoints ==============
+
+
+@app.get("/export/{company_name}", tags=["Export"])
+async def export_company_csv(
+    company_name: str,
+    _: UserResponse = Depends(get_current_user),
+):
+    """Export analysis results for a company as a CSV file.
+
+    Returns all stored analysis records for the given company, including
+    analysis type, model used, response text, and timestamp.
+
+    Args:
+        company_name: Company name to export results for
+
+    Returns:
+        CSV file download
+    """
+    import csv
+    import io
+
+    db = get_db_client()
+    # Query all non-cached analysis results for this company
+    with db.get_conn() as conn:
+        with conn.cursor() as cur:
+            cur.execute(
+                """
+                SELECT company_name, analysis_type, model, response, timestamp
+                FROM llm_messages
+                WHERE LOWER(company_name) = LOWER(%s) AND is_cached = FALSE
+                ORDER BY timestamp DESC
+                """,
+                (company_name,),
+            )
+            rows = cur.fetchall()
+
+    if not rows:
+        raise HTTPException(status_code=404, detail=f"No analysis results found for '{company_name}'")
+
+    output = io.StringIO()
+    writer = csv.writer(output)
+    writer.writerow(["company_name", "analysis_type", "model", "analysis", "timestamp"])
+    for row in rows:
+        writer.writerow(row)
+
+    output.seek(0)
+    safe_name = company_name.replace(" ", "_").lower()
+    return StreamingResponse(
+        iter([output.getvalue()]),
+        media_type="text/csv",
+        headers={"Content-Disposition": f'attachment; filename="sparc_{safe_name}_export.csv"'},
+    )
+
+
+@app.get("/export/{company_name}/pdf", tags=["Export"])
+async def export_company_pdf(
+    company_name: str,
+    _: UserResponse = Depends(get_current_user),
+):
+    """Export analysis results for a company as a formatted PDF report.
+
+    Returns all stored analysis records for the given company, including
+    analysis type, model used, response text, and timestamp, formatted
+    as a downloadable PDF document.
+
+    Args:
+        company_name: Company name to export results for
+
+    Returns:
+        PDF file download
+    """
+    import io
+    import textwrap
+
+    from reportlab.lib import colors
+    from reportlab.lib.pagesizes import letter
+    from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+    from reportlab.lib.units import inch
+    from reportlab.platypus import (
+        Paragraph,
+        SimpleDocTemplate,
+        Spacer,
+        Table,
+        TableStyle,
+    )
+
+    db = get_db_client()
+    with db.get_conn() as conn:
+        with conn.cursor() as cur:
+            cur.execute(
+                """
+                SELECT company_name, analysis_type, model, response, timestamp
+                FROM llm_messages
+                WHERE LOWER(company_name) = LOWER(%s) AND is_cached = FALSE
+                ORDER BY timestamp DESC
+                """,
+                (company_name,),
+            )
+            rows = cur.fetchall()
+
+    if not rows:
+        raise HTTPException(status_code=404, detail=f"No analysis results found for '{company_name}'")
+
+    buffer = io.BytesIO()
+    doc = SimpleDocTemplate(
+        buffer,
+        pagesize=letter,
+        rightMargin=0.75 * inch,
+        leftMargin=0.75 * inch,
+        topMargin=0.75 * inch,
+        bottomMargin=0.75 * inch,
+    )
+
+    styles = getSampleStyleSheet()
+    title_style = ParagraphStyle(
+        "CustomTitle",
+        parent=styles["Title"],
+        fontSize=20,
+        spaceAfter=6,
+    )
+    subtitle_style = ParagraphStyle(
+        "Subtitle",
+        parent=styles["Normal"],
+        fontSize=11,
+        textColor=colors.grey,
+        spaceAfter=20,
+    )
+    heading_style = ParagraphStyle(
+        "SectionHeading",
+        parent=styles["Heading2"],
+        fontSize=13,
+        spaceBefore=16,
+        spaceAfter=8,
+        textColor=colors.HexColor("#1a1a2e"),
+    )
+    body_style = ParagraphStyle(
+        "BodyText",
+        parent=styles["Normal"],
+        fontSize=9,
+        leading=13,
+        spaceAfter=10,
+    )
+
+    elements = []
+
+    # Title and date
+    display_name = rows[0][0]  # Use the casing from the database
+    analysis_date = datetime.now().strftime("%Y-%m-%d")
+    elements.append(Paragraph(f"SPARC Analysis Report: {display_name}", title_style))
+    elements.append(Paragraph(f"Generated on {analysis_date}", subtitle_style))
+
+    # Summary table
+    summary_data = [
+        ["Total Analyses", str(len(rows))],
+        ["Analysis Types", ", ".join(sorted(set(r[1] for r in rows)))],
+        ["Models Used", ", ".join(sorted(set(r[2] for r in rows)))],
+    ]
+    summary_table = Table(summary_data, colWidths=[2 * inch, 4.5 * inch])
+    summary_table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (0, -1), colors.HexColor("#f0f0f5")),
+                ("FONTNAME", (0, 0), (0, -1), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, -1), 9),
+                ("PADDING", (0, 0), (-1, -1), 6),
+                ("GRID", (0, 0), (-1, -1), 0.5, colors.HexColor("#cccccc")),
+                ("VALIGN", (0, 0), (-1, -1), "TOP"),
+            ]
+        )
+    )
+    elements.append(summary_table)
+    elements.append(Spacer(1, 16))
+
+    # Individual analysis sections
+    for i, row in enumerate(rows, 1):
+        _, analysis_type, model, response, timestamp = row
+        ts_str = timestamp.strftime("%Y-%m-%d %H:%M:%S") if hasattr(timestamp, "strftime") else str(timestamp)
+
+        elements.append(
+            Paragraph(f"Analysis {i}: {analysis_type} (via {model})", heading_style)
+        )
+        elements.append(
+            Paragraph(f"<i>Performed: {ts_str}</i>", body_style)
+        )
+
+        # Wrap long response text into paragraphs, escaping XML special chars
+        safe_response = (
+            response.replace("&", "&amp;")
+            .replace("<", "&lt;")
+            .replace(">", "&gt;")
+        )
+        # Split into manageable paragraphs to avoid overflow
+        for line in safe_response.split("\n"):
+            if line.strip():
+                elements.append(Paragraph(line, body_style))
+            else:
+                elements.append(Spacer(1, 4))
+
+        elements.append(Spacer(1, 10))
+
+    doc.build(elements)
+    buffer.seek(0)
+
+    safe_name = company_name.replace(" ", "_").lower()
+    filename = f"{safe_name}-analysis-{analysis_date}.pdf"
+    return StreamingResponse(
+        iter([buffer.getvalue()]),
+        media_type="application/pdf",
+        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+    )
+
+
 # ============== System Endpoints ==============
 
 
@@ -409,6 +813,7 @@ 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.
@@ -418,17 +823,51 @@ 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
     """
+    _validate_model(model)
     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)
 
 
+@app.get(
+    "/analyze/patent/{patent_id}",
+    tags=["Analysis"],
+)
+async def analyze_single_patent(
+    patent_id: str,
+    company_name: str = Query(description="Company name for analysis context"),
+    _: UserResponse = Depends(get_current_user),
+):
+    """Analyze a single patent by its publication ID.
+
+    If the patent PDF is not already cached locally, the system will attempt
+    to download it automatically from a previously cached link. If no link
+    is available, a 404 error is returned.
+
+    Args:
+        patent_id: Patent publication ID (e.g. "US-11234567-B2")
+        company_name: Company name for analysis context
+
+    Returns:
+        Analysis text for the patent
+    """
+    if not _analyzer:
+        raise HTTPException(status_code=503, detail="Analyzer not initialized")
+
+    try:
+        analysis = _analyzer.analyze_single_patent(patent_id, company_name)
+        return {"patent_id": patent_id, "company_name": company_name, "analysis": analysis}
+    except FileNotFoundError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
 @app.post(
     "/analyze/batch",
     response_model=BatchAnalysisResponse,
@@ -449,12 +888,14 @@ async def analyze_companies_batch(
     Returns:
         Batch results with individual company analyses and summary statistics
     """
+    _validate_model(request.model)
     if not _analyzer:
         raise HTTPException(status_code=503, detail="Analyzer not initialized")
 
     result = _analyzer.analyze_companies(
         companies=request.companies,
         max_workers=request.max_workers,
+        model=request.model,
     )
     return _convert_batch_result(result)
 
@@ -486,7 +927,7 @@ 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
@@ -511,6 +952,7 @@ 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(
@@ -519,8 +961,25 @@ def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
             progress=100,
             result_json=_json.dumps(batch_response.model_dump(), default=str),
         )
+        # Fire webhook notification
+        from SPARC.webhooks import notify_job_completed
+        notify_job_completed(
+            job_id=job_id,
+            status="completed",
+            total_companies=result.total_companies,
+            successful=result.successful,
+            failed=result.failed,
+        )
     except Exception as e:
         db.update_job(job_id, status="failed", error=str(e))
+        from SPARC.webhooks import notify_job_completed
+        notify_job_completed(
+            job_id=job_id,
+            status="failed",
+            total_companies=len(companies),
+            successful=0,
+            failed=len(companies),
+        )
 
 
 @app.post("/analyze/batch/async", response_model=JobStatus, tags=["Analysis"])
@@ -540,6 +999,7 @@ async def analyze_companies_async(
     Returns:
         Job status with job_id for polling
     """
+    _validate_model(request.model)
     global _job_counter
 
     _job_counter += 1
@@ -549,7 +1009,7 @@ 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)
@@ -577,24 +1037,51 @@ async def get_job_status(
     return _job_row_to_status(job_row)
 
 
-@app.get("/jobs", response_model=list[JobStatus], tags=["Jobs"])
+@app.get("/jobs", response_model=PaginatedJobsResponse, tags=["Jobs"])
 async def list_jobs(
     status: Annotated[
         str | None,
         Query(description="Filter by status: pending, running, completed, failed"),
     ] = None,
     limit: Annotated[int, Query(ge=1, le=100)] = 10,
+    cursor: Annotated[
+        str | None,
+        Query(description="Opaque cursor from a previous response's next_cursor field"),
+    ] = None,
     _: UserResponse = Depends(get_current_user),
 ):
-    """List all analysis jobs.
+    """List analysis jobs with cursor-based pagination.
+
+    Pass ``limit`` to control page size. The response includes a ``next_cursor``
+    field; pass it back as the ``cursor`` query parameter to fetch the next page.
+    When ``next_cursor`` is ``null``, there are no more results.
+
+    Existing clients that use only ``limit`` (without ``cursor``) continue to
+    work without modification.
 
     Args:
         status: Optional filter by job status
         limit: Maximum number of jobs to return (default 10, max 100)
+        cursor: Opaque pagination cursor from a previous response
 
     Returns:
-        List of job statuses
+        Paginated list of job statuses
     """
     db = _get_job_db()
-    job_rows = db.list_jobs(status=status, limit=limit)
-    return [_job_row_to_status(row) for row in job_rows]
+    # Fetch one extra to determine if there is a next page
+    job_rows = db.list_jobs(status=status, limit=limit + 1, cursor=cursor)
+
+    has_next = len(job_rows) > limit
+    if has_next:
+        job_rows = job_rows[:limit]
+
+    items = [_job_row_to_status(row) for row in job_rows]
+
+    next_cursor = None
+    if has_next and job_rows:
+        last = job_rows[-1]
+        created = last["created_at"]
+        ts = created.isoformat() if hasattr(created, "isoformat") else str(created)
+        next_cursor = f"{ts}|{last['job_id']}"
+
+    return PaginatedJobsResponse(items=items, next_cursor=next_cursor)

SPARC/config.py

@@ -53,6 +53,13 @@ root_path = os.getenv("ROOT_PATH", "")
 # Used for safety checks (e.g., refusing default JWT secret in production)
 app_env = os.getenv("APP_ENV", "development")
 
+# Storage backend: "local" (default) or "s3" for S3/MinIO object storage
+storage_backend = os.getenv("STORAGE_BACKEND", "local")
+s3_bucket = os.getenv("S3_BUCKET", "sparc-patents")
+s3_endpoint_url = os.getenv("S3_ENDPOINT_URL", "")
+s3_access_key = os.getenv("AWS_ACCESS_KEY_ID", "")
+s3_secret_key = os.getenv("AWS_SECRET_ACCESS_KEY", "")
+
 # CORS allowed origins (comma-separated)
 # Defaults to localhost dev origins when unset
 _cors_origins_raw = os.getenv("CORS_ORIGINS", "")

SPARC/database.py

@@ -192,6 +192,35 @@ class DatabaseClient:
                 ON jobs(status)
             """)
 
+            # Create tracked companies table for scheduled analysis
+            cursor.execute("""
+                CREATE TABLE IF NOT EXISTS tracked_companies (
+                    id SERIAL PRIMARY KEY,
+                    company_name VARCHAR(255) UNIQUE NOT NULL,
+                    last_patent_count INTEGER DEFAULT 0,
+                    last_analysis_at TIMESTAMP,
+                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                )
+            """)
+
+            # Create alerts table for significant changes
+            cursor.execute("""
+                CREATE TABLE IF NOT EXISTS alerts (
+                    id SERIAL PRIMARY KEY,
+                    company_name VARCHAR(255) NOT NULL,
+                    alert_type VARCHAR(50) NOT NULL,
+                    message TEXT NOT NULL,
+                    old_value NUMERIC,
+                    new_value NUMERIC,
+                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                )
+            """)
+
+            cursor.execute("""
+                CREATE INDEX IF NOT EXISTS idx_alerts_company
+                ON alerts(company_name)
+            """)
+
             self.conn.commit()
 
     @staticmethod
@@ -568,20 +597,45 @@ class DatabaseClient:
         self,
         status: Optional[str] = None,
         limit: int = 10,
+        cursor: Optional[str] = None,
     ) -> List[Dict]:
-        """List jobs, optionally filtered by status."""
-        query = "SELECT * FROM jobs"
+        """List jobs with optional status filter and cursor-based pagination.
+
+        Args:
+            status: Optional status filter (pending, running, completed, failed).
+            limit: Maximum number of jobs to return.
+            cursor: Opaque cursor (``created_at|job_id``) from a previous
+                response. When provided, only jobs older than the cursor are
+                returned.
+
+        Returns:
+            List of job dicts ordered by created_at descending.
+        """
+        conditions: list[str] = []
         params: list = []
+
         if status:
-            query += " WHERE status = %s"
+            conditions.append("status = %s")
             params.append(status)
-        query += " ORDER BY created_at DESC LIMIT %s"
+
+        if cursor:
+            try:
+                ts_str, cursor_job_id = cursor.rsplit("|", 1)
+                conditions.append("(created_at, job_id) < (%s, %s)")
+                params.extend([ts_str, cursor_job_id])
+            except ValueError:
+                pass  # Ignore malformed cursors; return from start
+
+        query = "SELECT * FROM jobs"
+        if conditions:
+            query += " WHERE " + " AND ".join(conditions)
+        query += " ORDER BY created_at DESC, job_id DESC LIMIT %s"
         params.append(limit)
 
         with self.get_conn() as conn:
-            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
-                cursor.execute(query, params)
-                return [dict(row) for row in cursor.fetchall()]
+            with conn.cursor(cursor_factory=RealDictCursor) as cur:
+                cur.execute(query, params)
+                return [dict(row) for row in cur.fetchall()]
 
     def mark_stale_jobs_failed(self) -> int:
         """Mark any jobs in 'running' or 'pending' state as 'failed'.
@@ -803,3 +857,81 @@ class DatabaseClient:
             with conn.cursor() as cursor:
                 cursor.execute("SELECT COUNT(*) FROM users")
                 return cursor.fetchone()[0]
+
+    # Tracked Companies Methods
+
+    def add_tracked_company(self, company_name: str) -> Optional[Dict]:
+        """Add a company to the tracking list."""
+        with self.get_conn() as conn:
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+                try:
+                    cursor.execute(
+                        "INSERT INTO tracked_companies (company_name) VALUES (%s) RETURNING *",
+                        (company_name,),
+                    )
+                    row = cursor.fetchone()
+                    conn.commit()
+                    return dict(row) if row else None
+                except Exception:
+                    conn.rollback()
+                    return None
+
+    def remove_tracked_company(self, company_name: str) -> bool:
+        """Remove a company from the tracking list."""
+        with self.get_conn() as conn:
+            with conn.cursor() as cursor:
+                cursor.execute(
+                    "DELETE FROM tracked_companies WHERE LOWER(company_name) = LOWER(%s)",
+                    (company_name,),
+                )
+                conn.commit()
+                return cursor.rowcount > 0
+
+    def list_tracked_companies(self) -> List[Dict]:
+        """List all tracked companies."""
+        with self.get_conn() as conn:
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+                cursor.execute("SELECT * FROM tracked_companies ORDER BY company_name")
+                return [dict(row) for row in cursor.fetchall()]
+
+    def update_tracked_company(
+        self, company_name: str, patent_count: int
+    ) -> None:
+        """Update the last analysis stats for a tracked company."""
+        with self.get_conn() as conn:
+            with conn.cursor() as cursor:
+                cursor.execute(
+                    """UPDATE tracked_companies
+                       SET last_patent_count = %s, last_analysis_at = CURRENT_TIMESTAMP
+                       WHERE LOWER(company_name) = LOWER(%s)""",
+                    (patent_count, company_name),
+                )
+                conn.commit()
+
+    def store_alert(
+        self,
+        company_name: str,
+        alert_type: str,
+        message: str,
+        old_value: float | None = None,
+        new_value: float | None = None,
+    ) -> None:
+        """Record an alert for a significant change."""
+        with self.get_conn() as conn:
+            with conn.cursor() as cursor:
+                cursor.execute(
+                    """INSERT INTO alerts (company_name, alert_type, message, old_value, new_value)
+                       VALUES (%s, %s, %s, %s, %s)""",
+                    (company_name, alert_type, message, old_value, new_value),
+                )
+                conn.commit()
+
+    def list_alerts(self, limit: int = 50) -> List[Dict]:
+        """List recent alerts."""
+        with self.get_conn() as conn:
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+                cursor.execute(
+                    "SELECT * FROM alerts ORDER BY created_at DESC LIMIT %s",
+                    (limit,),
+                )
+                return [dict(row) for row in cursor.fetchall()]

SPARC/llm.py

@@ -40,12 +40,13 @@ class LLMAnalyzer:
         else:
             self.client = None
 
-    def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
+    def analyze_patent_content(self, patent_content: str, company_name: str, model: str | None = None) -> 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
+          model: Optional model override (e.g. "openai/gpt-4o"). Defaults to config.
 
         Returns:
           Analysis text describing innovation quality and potential impact
@@ -63,6 +64,8 @@ Patent Content:
 
 Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
 
+        effective_model = model or self.model
+
         if self.test_mode:
             logger.debug("TEST MODE - Prompt that would be sent to LLM:\n%s", prompt)
             return "[TEST MODE - No API call made]"
@@ -81,7 +84,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
                     response=cached["response"],
                     company_name=company_name,
                     analysis_type="single_patent",
-                    model=self.model,
+                    model=effective_model,
                     metadata={
                         "patent_content_length": len(patent_content),
                         "cache_hit": True,
@@ -94,7 +97,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
         # Call API if no cache hit and client is available
         if self.client:
             response = self.client.chat.completions.create(
-                model=self.model,
+                model=effective_model,
                 max_tokens=1024,
                 messages=[{"role": "user", "content": prompt}],
             )
@@ -106,7 +109,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
                 response=response_text,
                 company_name=company_name,
                 analysis_type="single_patent",
-                model=self.model,
+                model=effective_model,
                 metadata={"patent_content_length": len(patent_content)},
                 token_usage={
                     "prompt_tokens": response.usage.prompt_tokens,
@@ -124,13 +127,13 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
             response=placeholder,
             company_name=company_name,
             analysis_type="single_patent",
-            model=self.model,
+            model=effective_model,
             metadata={"patent_content_length": len(patent_content), "pending": True}
         )
         return placeholder
 
     def analyze_patent_portfolio(
-        self, patents_data: list[Dict[str, str]], company_name: str
+        self, patents_data: list[Dict[str, str]], company_name: str, model: str | None = None
     ) -> str:
         """Analyze multiple patents to estimate overall company performance.
 
@@ -165,13 +168,16 @@ Patent Portfolio:
 
 Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the company's innovation strength and performance outlook."""
 
+        effective_model = model or self.model
+
         if self.test_mode:
             logger.debug("TEST MODE - Portfolio prompt:\n%s", prompt)
             return "[TEST MODE]"
 
         metadata = {
             "patent_count": len(patents_data),
-            "patent_ids": [p['patent_id'] for p in patents_data]
+            "patent_ids": [p['patent_id'] for p in patents_data],
+            "model": effective_model,
         }
 
         # Check cache first
@@ -188,7 +194,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
                     response=cached["response"],
                     company_name=company_name,
                     analysis_type="portfolio",
-                    model=self.model,
+                    model=effective_model,
                     metadata={
                         **metadata,
                         "cache_hit": True,
@@ -202,7 +208,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
         if self.client:
             try:
                 response = self.client.chat.completions.create(
-                    model=self.model,
+                    model=effective_model,
                     max_tokens=2048,
                     messages=[{"role": "user", "content": prompt}],
                 )
@@ -215,7 +221,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
                     response=response_text,
                     company_name=company_name,
                     analysis_type="portfolio",
-                    model=self.model,
+                    model=effective_model,
                     metadata=metadata,
                     token_usage={
                         "prompt_tokens": response.usage.prompt_tokens,
@@ -235,7 +241,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
             response=placeholder,
             company_name=company_name,
             analysis_type="portfolio",
-            model=self.model,
+            model=effective_model,
             metadata={**metadata, "pending": True}
         )
         return placeholder

SPARC/scheduler.py

@@ -0,0 +1,109 @@
+"""Scheduled patent analysis for tracked companies.
+
+Uses APScheduler to periodically re-analyze tracked companies and
+detect significant changes in patent counts.
+"""
+
+import logging
+import os
+
+from SPARC import config
+from SPARC.analyzer import CompanyAnalyzer
+from SPARC.database import DatabaseClient
+
+logger = logging.getLogger(__name__)
+
+# Configurable via environment variable (in hours, default 24)
+SCHEDULE_INTERVAL_HOURS = int(os.getenv("SCHEDULE_INTERVAL_HOURS", "24"))
+
+# Patent count change threshold (percentage) to trigger an alert
+CHANGE_THRESHOLD_PERCENT = int(os.getenv("CHANGE_THRESHOLD_PERCENT", "20"))
+
+
+def run_scheduled_analysis() -> None:
+    """Re-analyze all tracked companies and check for significant changes."""
+    db = DatabaseClient(config.database_url)
+    db.connect()
+    db.initialize_schema()
+
+    tracked = db.list_tracked_companies()
+    if not tracked:
+        logger.info("No tracked companies configured; skipping scheduled analysis")
+        return
+
+    logger.info("Running scheduled analysis for %d tracked companies", len(tracked))
+
+    analyzer = CompanyAnalyzer(db_client=db)
+
+    for company_row in tracked:
+        name = company_row["company_name"]
+        old_count = company_row.get("last_patent_count", 0) or 0
+
+        try:
+            result = analyzer._analyze_company_safe(name)
+
+            if result.success:
+                new_count = result.patent_count
+
+                # Update tracking record
+                db.update_tracked_company(name, new_count)
+
+                # Check for significant change
+                if old_count > 0:
+                    delta_pct = abs(new_count - old_count) / old_count * 100
+                    if delta_pct >= CHANGE_THRESHOLD_PERCENT:
+                        direction = "increased" if new_count > old_count else "decreased"
+                        message = (
+                            f"Patent count for {name} {direction} by {delta_pct:.0f}% "
+                            f"({old_count} -> {new_count})"
+                        )
+                        logger.warning("ALERT: %s", message)
+                        db.store_alert(
+                            company_name=name,
+                            alert_type="patent_count_change",
+                            message=message,
+                            old_value=old_count,
+                            new_value=new_count,
+                        )
+                elif new_count > 0:
+                    # First analysis -- record baseline
+                    logger.info("Baseline for %s: %d patents", name, new_count)
+            else:
+                logger.warning("Scheduled analysis failed for %s: %s", name, result.error)
+
+        except Exception as e:
+            logger.error("Error analyzing tracked company %s: %s", name, e)
+
+    db.close()
+    logger.info("Scheduled analysis complete")
+
+
+def start_scheduler() -> None:
+    """Start the APScheduler background scheduler.
+
+    Safe to call at application startup. If apscheduler is not installed,
+    the function logs a warning and returns without starting anything.
+    """
+    try:
+        from apscheduler.schedulers.background import BackgroundScheduler
+    except ImportError:
+        logger.warning(
+            "apscheduler not installed; scheduled analysis disabled. "
+            "Install with: pip install apscheduler"
+        )
+        return
+
+    scheduler = BackgroundScheduler()
+    scheduler.add_job(
+        run_scheduled_analysis,
+        "interval",
+        hours=SCHEDULE_INTERVAL_HOURS,
+        id="scheduled_patent_analysis",
+        replace_existing=True,
+    )
+    scheduler.start()
+    logger.info(
+        "Scheduled patent analysis started (every %d hours, threshold %d%%)",
+        SCHEDULE_INTERVAL_HOURS,
+        CHANGE_THRESHOLD_PERCENT,
+    )

SPARC/serp_api.py

@@ -1,4 +1,5 @@
-import os
+import io
+import logging
 import re
 from datetime import datetime, timedelta
 from typing import Dict
@@ -8,8 +9,21 @@ import requests
 import serpapi
 
 from SPARC import config
+from SPARC.storage import StorageBackend, get_storage_backend
 from SPARC.types import Patent, Patents
 
+logger = logging.getLogger(__name__)
+
+# Module-level storage instance (lazy-initialized)
+_storage: StorageBackend | None = None
+
+
+def _get_storage() -> StorageBackend:
+    global _storage
+    if _storage is None:
+        _storage = get_storage_backend()
+    return _storage
+
 
 class SERP:
   def query(company: str, days_back: int = None) -> Patents:
@@ -44,6 +58,7 @@ class SERP:
       "tbs": date_filter,
       "api_key": config.api_key,
     }
+    logger.info("Querying Google Patents for '%s' (last %d days)", company, days_back)
     search = serpapi.search(params)
     # Convert results to Patent objects, skipping any without PDF links
     patent_ids = []
@@ -52,13 +67,16 @@ class SERP:
         pdf_link = patent.get("pdf")
         if pdf_link:
             patent_ids.append(Patent(patent_id=patent["publication_number"], pdf_link=pdf_link, summary=None))
-        # Patents without PDF links are skipped (see docstring for details)
+        else:
+            logger.debug("Skipping patent %s (no PDF link)", patent.get("publication_number", "unknown"))
 
+    logger.info("Found %d patents with PDF links for '%s'", len(patent_ids), company)
     return Patents(patents=patent_ids)
 
   def save_patents(patent: Patent) -> Patent:
-    """
-    Save the patent PDF to the patents folder, skipping download if already cached.
+    """Save the patent PDF to storage, skipping download if already cached.
+
+    Uses the configured storage backend (local filesystem or S3).
 
     Args:
       patent: Patent object
@@ -66,35 +84,51 @@ class SERP:
     Returns:
       Patent object with updated PDF path
     """
-    pdf_path = f"patents/{patent.patent_id}.pdf"
-    os.makedirs("patents", exist_ok=True)
+    storage = _get_storage()
+    key = f"{patent.patent_id}.pdf"
 
-    if not (os.path.exists(pdf_path) and os.path.getsize(pdf_path) > 0):
+    if not storage.exists(key):
+      logger.info("Downloading PDF for %s", patent.patent_id)
       response = requests.get(patent.pdf_link)
-      with open(pdf_path, "wb") as f:
-        f.write(response.content)
+      storage.write(key, response.content)
+      logger.debug("Saved %d bytes for %s", len(response.content), patent.patent_id)
+    else:
+      logger.debug("Using cached PDF for %s", patent.patent_id)
 
-    patent.pdf_path = pdf_path
+    patent.pdf_path = storage.path_for(key)
     return patent
 
   def parse_patent_pdf(pdf_path: str) -> Dict:
     """Extract structured sections from patent PDF.
 
     Extracts all major sections from a patent PDF including abstract,
-    claims, summary, and detailed description.
+    claims, summary, and detailed description. Supports both local file
+    paths and S3 URIs (s3://bucket/key).
 
     Args:
-      pdf_path: Path to the patent PDF file
+      pdf_path: Local path or S3 URI to the patent PDF file
 
     Returns:
       Dictionary containing all extracted sections
     """
+    logger.debug("Parsing patent PDF: %s", pdf_path)
 
-    with pdfplumber.open(pdf_path) as pdf:
+    if pdf_path.startswith("s3://"):
+      # Read from S3 via storage backend
+      storage = _get_storage()
+      # Extract key from "s3://bucket/key"
+      key = pdf_path.split("/", 3)[-1]
+      data = storage.read(key)
+      pdf_file: io.BytesIO | str = io.BytesIO(data)
+    else:
+      pdf_file = pdf_path
+
+    with pdfplumber.open(pdf_file) as pdf:
       # Extract all text
       full_text = ""
       for page in pdf.pages:
         full_text += page.extract_text() + "\n"
+      logger.debug("Extracted text from %d pages (%d chars)", len(pdf.pages), len(full_text))
 
     # Define section patterns (common in patents)
     sections = {

SPARC/storage.py

@@ -0,0 +1,171 @@
+"""Patent PDF storage abstraction.
+
+Provides a unified interface for reading and writing patent PDF files,
+with pluggable backends for local filesystem and S3-compatible object
+storage (e.g., MinIO, AWS S3).
+"""
+
+import logging
+import os
+from abc import ABC, abstractmethod
+
+from SPARC import config
+
+logger = logging.getLogger(__name__)
+
+
+class StorageBackend(ABC):
+    """Abstract base class for patent PDF storage."""
+
+    @abstractmethod
+    def read(self, key: str) -> bytes:
+        """Read a file by key.
+
+        Args:
+            key: Storage key (e.g., "US-12345678-B2.pdf")
+
+        Returns:
+            File contents as bytes.
+
+        Raises:
+            FileNotFoundError: If the file does not exist.
+        """
+
+    @abstractmethod
+    def write(self, key: str, data: bytes) -> None:
+        """Write data to storage.
+
+        Args:
+            key: Storage key (e.g., "US-12345678-B2.pdf")
+            data: File contents as bytes.
+        """
+
+    @abstractmethod
+    def exists(self, key: str) -> bool:
+        """Check if a file exists in storage.
+
+        Args:
+            key: Storage key.
+
+        Returns:
+            True if the file exists and has non-zero size.
+        """
+
+    @abstractmethod
+    def path_for(self, key: str) -> str:
+        """Return a path or URI suitable for downstream consumers.
+
+        For local storage this is a filesystem path; for S3 it is the
+        object key (callers that need a local file should use read()
+        and write to a temporary location).
+        """
+
+
+class LocalStorageBackend(StorageBackend):
+    """Store patent PDFs on the local filesystem under a directory."""
+
+    def __init__(self, base_dir: str = "patents"):
+        self.base_dir = base_dir
+        os.makedirs(self.base_dir, exist_ok=True)
+
+    def _full_path(self, key: str) -> str:
+        return os.path.join(self.base_dir, key)
+
+    def read(self, key: str) -> bytes:
+        path = self._full_path(key)
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"File not found: {path}")
+        with open(path, "rb") as f:
+            return f.read()
+
+    def write(self, key: str, data: bytes) -> None:
+        path = self._full_path(key)
+        os.makedirs(os.path.dirname(path) or self.base_dir, exist_ok=True)
+        with open(path, "wb") as f:
+            f.write(data)
+        logger.debug("Wrote %d bytes to %s", len(data), path)
+
+    def exists(self, key: str) -> bool:
+        path = self._full_path(key)
+        return os.path.exists(path) and os.path.getsize(path) > 0
+
+    def path_for(self, key: str) -> str:
+        return self._full_path(key)
+
+
+class S3StorageBackend(StorageBackend):
+    """Store patent PDFs in an S3-compatible bucket."""
+
+    def __init__(
+        self,
+        bucket: str,
+        endpoint_url: str = "",
+        access_key: str = "",
+        secret_key: str = "",
+    ):
+        import boto3
+
+        kwargs: dict = {}
+        if endpoint_url:
+            kwargs["endpoint_url"] = endpoint_url
+        if access_key and secret_key:
+            kwargs["aws_access_key_id"] = access_key
+            kwargs["aws_secret_access_key"] = secret_key
+
+        self.s3 = boto3.client("s3", **kwargs)
+        self.bucket = bucket
+
+        # Ensure bucket exists (useful for MinIO local dev)
+        try:
+            self.s3.head_bucket(Bucket=self.bucket)
+        except Exception:
+            try:
+                self.s3.create_bucket(Bucket=self.bucket)
+                logger.info("Created S3 bucket: %s", self.bucket)
+            except Exception as e:
+                logger.warning("Could not create bucket %s: %s", self.bucket, e)
+
+    def read(self, key: str) -> bytes:
+        try:
+            response = self.s3.get_object(Bucket=self.bucket, Key=key)
+            return response["Body"].read()
+        except self.s3.exceptions.NoSuchKey:
+            raise FileNotFoundError(f"S3 object not found: s3://{self.bucket}/{key}")
+        except Exception as e:
+            if "NoSuchKey" in str(e) or "404" in str(e):
+                raise FileNotFoundError(f"S3 object not found: s3://{self.bucket}/{key}")
+            raise
+
+    def write(self, key: str, data: bytes) -> None:
+        self.s3.put_object(
+            Bucket=self.bucket,
+            Key=key,
+            Body=data,
+            ContentType="application/pdf",
+        )
+        logger.debug("Wrote %d bytes to s3://%s/%s", len(data), self.bucket, key)
+
+    def exists(self, key: str) -> bool:
+        try:
+            response = self.s3.head_object(Bucket=self.bucket, Key=key)
+            return response["ContentLength"] > 0
+        except Exception:
+            return False
+
+    def path_for(self, key: str) -> str:
+        return f"s3://{self.bucket}/{key}"
+
+
+def get_storage_backend() -> StorageBackend:
+    """Factory: return the configured storage backend instance."""
+    backend = config.storage_backend.lower()
+    if backend == "s3":
+        logger.info("Using S3 storage backend (bucket=%s)", config.s3_bucket)
+        return S3StorageBackend(
+            bucket=config.s3_bucket,
+            endpoint_url=config.s3_endpoint_url,
+            access_key=config.s3_access_key,
+            secret_key=config.s3_secret_key,
+        )
+    logger.info("Using local storage backend")
+    return LocalStorageBackend()

SPARC/types.py

@@ -24,6 +24,7 @@ class CompanyAnalysisResult:
     patent_count: int
     success: bool
     error: str | None = None
+    model: str | None = None
     timestamp: datetime = field(default_factory=datetime.now)
 
 

SPARC/webhooks.py

@@ -0,0 +1,139 @@
+"""Webhook notifications for job completion and alert events.
+
+Sends JSON payloads to configured webhook URLs with retry logic.
+Supports generic HTTP POST and Slack-compatible text payloads.
+"""
+
+import logging
+import os
+import time
+from datetime import datetime
+from typing import Any
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+# Comma-separated list of webhook URLs (env var based config)
+_WEBHOOK_URLS_RAW = os.getenv("WEBHOOK_URLS", "")
+WEBHOOK_URLS: list[str] = [
+    url.strip() for url in _WEBHOOK_URLS_RAW.split(",") if url.strip()
+]
+
+MAX_RETRIES = 3
+BACKOFF_BASE = 2  # seconds
+
+
+def _is_slack_url(url: str) -> bool:
+    """Check if a URL looks like a Slack incoming webhook."""
+    return "hooks.slack.com" in url or "discord.com/api/webhooks" in url
+
+
+def _build_payload(event_type: str, data: dict[str, Any], slack: bool = False) -> dict:
+    """Build the webhook payload.
+
+    Args:
+        event_type: Type of event (e.g., "job_completed", "alert")
+        data: Event-specific data
+        slack: If True, wrap in Slack-compatible ``text`` format
+
+    Returns:
+        JSON-serializable payload dict
+    """
+    payload = {
+        "event": event_type,
+        "timestamp": datetime.utcnow().isoformat() + "Z",
+        **data,
+    }
+
+    if slack:
+        # Build a human-readable summary for Slack/Discord
+        lines = [f"*[SPARC] {event_type}*"]
+        for key, value in data.items():
+            lines.append(f"  {key}: {value}")
+        return {"text": "\n".join(lines)}
+
+    return payload
+
+
+def _send_with_retry(url: str, payload: dict) -> bool:
+    """Send a POST request with exponential backoff retry.
+
+    Args:
+        url: Webhook URL
+        payload: JSON payload to send
+
+    Returns:
+        True if delivered successfully, False after all retries exhausted
+    """
+    for attempt in range(1, MAX_RETRIES + 1):
+        try:
+            response = requests.post(url, json=payload, timeout=10)
+            if response.status_code < 300:
+                logger.debug("Webhook delivered to %s (attempt %d)", url, attempt)
+                return True
+            logger.warning(
+                "Webhook %s returned %d (attempt %d/%d)",
+                url, response.status_code, attempt, MAX_RETRIES,
+            )
+        except requests.RequestException as e:
+            logger.warning(
+                "Webhook delivery failed for %s (attempt %d/%d): %s",
+                url, attempt, MAX_RETRIES, e,
+            )
+
+        if attempt < MAX_RETRIES:
+            wait = BACKOFF_BASE ** attempt
+            time.sleep(wait)
+
+    logger.error("Webhook permanently failed for %s after %d attempts", url, MAX_RETRIES)
+    return False
+
+
+def notify(event_type: str, data: dict[str, Any]) -> None:
+    """Fire all configured webhooks for an event.
+
+    Safe to call even when no webhooks are configured (returns immediately).
+
+    Args:
+        event_type: Event identifier (e.g., "job_completed", "patent_alert")
+        data: Event data to include in the payload
+    """
+    if not WEBHOOK_URLS:
+        return
+
+    for url in WEBHOOK_URLS:
+        slack = _is_slack_url(url)
+        payload = _build_payload(event_type, data, slack=slack)
+        _send_with_retry(url, payload)
+
+
+def notify_job_completed(
+    job_id: str,
+    status: str,
+    total_companies: int,
+    successful: int,
+    failed: int,
+) -> None:
+    """Send notification when a batch job completes."""
+    notify("job_completed", {
+        "job_id": job_id,
+        "status": status,
+        "total_companies": total_companies,
+        "successful": successful,
+        "failed": failed,
+        "summary": f"Batch job {job_id}: {successful}/{total_companies} succeeded",
+    })
+
+
+def notify_alert(
+    company_name: str,
+    alert_type: str,
+    message: str,
+) -> None:
+    """Send notification for a tracked company alert."""
+    notify("patent_alert", {
+        "company_name": company_name,
+        "alert_type": alert_type,
+        "message": message,
+    })

docker-compose.yml

@@ -52,6 +52,29 @@ services:
       - ./patents:/app/patents
     restart: unless-stopped
 
+  # Optional: MinIO for S3-compatible local object storage
+  # Enable by setting STORAGE_BACKEND=s3 in .env
+  minio:
+    image: minio/minio:latest
+    container_name: sparc-minio
+    command: server /data --console-address ":9001"
+    environment:
+      MINIO_ROOT_USER: ${AWS_ACCESS_KEY_ID:-minioadmin}
+      MINIO_ROOT_PASSWORD: ${AWS_SECRET_ACCESS_KEY:-minioadmin}
+    ports:
+      - "9000:9000"
+      - "9001:9001"
+    volumes:
+      - minio_data:/data
+    healthcheck:
+      test: ["CMD", "mc", "ready", "local"]
+      interval: 10s
+      timeout: 5s
+      retries: 3
+    restart: unless-stopped
+    profiles:
+      - s3
+
   dashboard:
     build: ./frontend
     container_name: sparc-dashboard
@@ -63,3 +86,4 @@ services:
 
 volumes:
   postgres_data:
+  minio_data:

frontend/package-lock.json

@@ -10,7 +10,7 @@
       "dependencies": {
         "@tanstack/react-query": "^5.51.0",
         "axios": "^1.7.2",
-        "lucide-react": "^0.400.0",
+        "lucide-react": "^1.7.0",
         "react": "^18.3.1",
         "react-dom": "^18.3.1",
         "react-router-dom": "^6.24.0",
@@ -3452,9 +3452,9 @@
       }
     },
     "node_modules/lucide-react": {
-      "version": "0.400.0",
-      "resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-0.400.0.tgz",
-      "integrity": "sha512-rpp7pFHh3Xd93KHixNgB0SqThMHpYNzsGUu69UaQbSZ75Q/J3m5t6EhKyMT3m4w2WOxmJ2mY0tD3vebnXqQryQ==",
+      "version": "1.7.0",
+      "resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-1.7.0.tgz",
+      "integrity": "sha512-yI7BeItCLZJTXikmK4KNUGCKoGzSvbKlfCvw44bU4fXAL6v3gYS4uHD1jzsLkfwODYwI6Drw5Tu9Z5ulDe0TSg==",
       "license": "ISC",
       "peerDependencies": {
         "react": "^16.5.1 || ^17.0.0 || ^18.0.0 || ^19.0.0"

frontend/package.json

@@ -7,12 +7,15 @@
     "dev": "vite",
     "build": "tsc -b && vite build",
     "lint": "eslint .",
+    "generate": "openapi-typescript http://localhost:8000/api/openapi.json -o src/api/schema.d.ts",
+    "generate:local": "openapi-typescript src/api/openapi.json -o src/api/schema.d.ts",
+    "typecheck": "tsc --noEmit",
     "preview": "vite preview"
   },
   "dependencies": {
     "@tanstack/react-query": "^5.51.0",
     "axios": "^1.7.2",
-    "lucide-react": "^0.400.0",
+    "lucide-react": "^1.7.0",
     "react": "^18.3.1",
     "react-dom": "^18.3.1",
     "react-router-dom": "^6.24.0",
@@ -30,6 +33,7 @@
     "globals": "^15.8.0",
     "postcss": "^8.4.39",
     "tailwindcss": "^3.4.4",
+    "openapi-typescript": "^7.0.0",
     "typescript": "~5.5.3",
     "typescript-eslint": "^8.0.0",
     "vite": "^5.3.3"

frontend/src/App.tsx

@@ -11,6 +11,7 @@ import { Batch } from './pages/Batch';
 import { AnalyticsPage } from './pages/Analytics';
 import { About } from './pages/About';
 import { AdminUsers } from './pages/AdminUsers';
+import { Compare } from './pages/Compare';
 
 const queryClient = new QueryClient({
   defaultOptions: {
@@ -43,6 +44,7 @@ function App() {
               <Route path="/analysis" element={<Analysis />} />
               <Route path="/batch" element={<Batch />} />
               <Route path="/analytics" element={<AnalyticsPage />} />
+              <Route path="/compare" element={<Compare />} />
               <Route path="/about" element={<About />} />
 
               {/* Admin routes */}

frontend/src/api/client.ts

@@ -89,29 +89,53 @@ export const authApi = {
   },
 };
 
+// Model types
+export interface ModelInfo {
+  id: string;
+  name: string;
+  provider: string;
+}
+
+export interface ModelsResponse {
+  models: ModelInfo[];
+  default: string;
+}
+
 // Analysis API
 export const analysisApi = {
-  analyzeCompany: async (companyName: string): Promise<CompanyAnalysis> => {
-    const response = await api.get<CompanyAnalysis>(`/analyze/${encodeURIComponent(companyName)}`);
+  analyzeCompany: async (companyName: string, model?: string): Promise<CompanyAnalysis> => {
+    const params = new URLSearchParams();
+    if (model) params.append('model', model);
+    const qs = params.toString();
+    const response = await api.get<CompanyAnalysis>(
+      `/analyze/${encodeURIComponent(companyName)}${qs ? `?${qs}` : ''}`
+    );
     return response.data;
   },
 
-  analyzeBatch: async (companies: string[], maxWorkers = 3): Promise<BatchAnalysisResult> => {
+  analyzeBatch: async (companies: string[], maxWorkers = 3, model?: string): Promise<BatchAnalysisResult> => {
     const response = await api.post<BatchAnalysisResult>('/analyze/batch', {
       companies,
       max_workers: maxWorkers,
+      ...(model ? { model } : {}),
     });
     return response.data;
   },
 
-  analyzeBatchAsync: async (companies: string[], maxWorkers = 3): Promise<JobStatus> => {
+  analyzeBatchAsync: async (companies: string[], maxWorkers = 3, model?: string): Promise<JobStatus> => {
     const response = await api.post<JobStatus>('/analyze/batch/async', {
       companies,
       max_workers: maxWorkers,
+      ...(model ? { model } : {}),
     });
     return response.data;
   },
 
+  listModels: async (): Promise<ModelsResponse> => {
+    const response = await api.get<ModelsResponse>('/models');
+    return response.data;
+  },
+
   getJobStatus: async (jobId: string): Promise<JobStatus> => {
     const response = await api.get<JobStatus>(`/jobs/${jobId}`);
     return response.data;
@@ -126,12 +150,55 @@ export const analysisApi = {
   },
 };
 
+// Export API
+export const exportApi = {
+  exportCsv: async (companyName: string): Promise<void> => {
+    const response = await api.get(`/export/${encodeURIComponent(companyName)}`, {
+      responseType: 'blob',
+    });
+    const url = window.URL.createObjectURL(new Blob([response.data]));
+    const link = document.createElement('a');
+    link.href = url;
+    link.setAttribute('download', `sparc_${companyName.toLowerCase().replace(/\s+/g, '_')}_export.csv`);
+    document.body.appendChild(link);
+    link.click();
+    link.remove();
+    window.URL.revokeObjectURL(url);
+  },
+  exportPdf: async (companyName: string): Promise<void> => {
+    const response = await api.get(`/export/${encodeURIComponent(companyName)}/pdf`, {
+      responseType: 'blob',
+    });
+    const safeName = companyName.toLowerCase().replace(/\s+/g, '_');
+    const date = new Date().toISOString().split('T')[0];
+    const url = window.URL.createObjectURL(new Blob([response.data], { type: 'application/pdf' }));
+    const link = document.createElement('a');
+    link.href = url;
+    link.setAttribute('download', `${safeName}-analysis-${date}.pdf`);
+    document.body.appendChild(link);
+    link.click();
+    link.remove();
+    window.URL.revokeObjectURL(url);
+  },
+};
+
 // Analytics API
+export interface TrendData {
+  by_month: Array<{ month: string; company_name: string; count: number }>;
+  by_type_over_time: Array<{ month: string; analysis_type: string; count: number }>;
+  period_days: number;
+}
+
 export const analyticsApi = {
   getAnalytics: async (days = 30): Promise<Analytics> => {
     const response = await api.get<Analytics>(`/analytics?days=${days}`);
     return response.data;
   },
+
+  getTrends: async (days = 90): Promise<TrendData> => {
+    const response = await api.get<TrendData>(`/analytics/trends?days=${days}`);
+    return response.data;
+  },
 };
 
 // Admin API

frontend/src/api/schema.d.ts

@@ -0,0 +1,975 @@
+/**
+ * This file was auto-generated by openapi-typescript.
+ * Do not make direct changes to the file.
+ */
+
+export interface paths {
+    "/auth/register": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Register
+         * @description Register a new user.
+         *
+         *     The first registered user automatically becomes an admin.
+         */
+        post: operations["register_auth_register_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/auth/login": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Login
+         * @description Authenticate user and return JWT tokens.
+         */
+        post: operations["login_auth_login_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/auth/refresh": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Refresh Token
+         * @description Refresh access token using refresh token.
+         */
+        post: operations["refresh_token_auth_refresh_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/auth/me": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        /**
+         * Get Me
+         * @description Get current authenticated user.
+         */
+        get: operations["get_me_auth_me_get"];
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/admin/users": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        /**
+         * List Users
+         * @description List all users (admin only).
+         */
+        get: operations["list_users_admin_users_get"];
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/admin/users/{user_id}/role": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        /**
+         * Update User Role
+         * @description Update a user's role (admin only).
+         */
+        patch: operations["update_user_role_admin_users__user_id__role_patch"];
+        trace?: never;
+    };
+    "/admin/users/{user_id}": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        post?: never;
+        /**
+         * Delete User
+         * @description Delete a user (admin only).
+         */
+        delete: operations["delete_user_admin_users__user_id__delete"];
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/analytics": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        /**
+         * Get Analytics
+         * @description Get analytics data (authenticated users only).
+         */
+        get: operations["get_analytics_analytics_get"];
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/health": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        /**
+         * Health Check
+         * @description Check API health status.
+         */
+        get: operations["health_check_health_get"];
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/analyze/{company_name}": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        /**
+         * Analyze Company
+         * @description Analyze a single company's patent portfolio.
+         *
+         *     This endpoint retrieves recent patents for the specified company,
+         *     parses them, and uses AI to generate a comprehensive analysis.
+         *
+         *     Args:
+         *         company_name: Name of the company to analyze (e.g., "nvidia", "intel")
+         *
+         *     Returns:
+         *         Analysis results including patent count, AI insights, and success status
+         */
+        get: operations["analyze_company_analyze__company_name__get"];
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/analyze/batch": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Analyze Companies Batch
+         * @description Analyze multiple companies' patent portfolios.
+         *
+         *     Processes companies concurrently for improved performance.
+         *     Limited to 20 companies per request.
+         *
+         *     Args:
+         *         request: List of company names and optional worker count
+         *
+         *     Returns:
+         *         Batch results with individual company analyses and summary statistics
+         */
+        post: operations["analyze_companies_batch_analyze_batch_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/analyze/batch/async": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Analyze Companies Async
+         * @description Start an asynchronous batch analysis job.
+         *
+         *     Returns immediately with a job ID that can be used to poll for status.
+         *     Useful for large batch analyses that may take a long time.
+         *
+         *     Args:
+         *         request: List of company names and optional worker count
+         *
+         *     Returns:
+         *         Job status with job_id for polling
+         */
+        post: operations["analyze_companies_async_analyze_batch_async_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/jobs/{job_id}": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        /**
+         * Get Job Status
+         * @description Get the status of a background analysis job.
+         *
+         *     Args:
+         *         job_id: The job ID returned from the async batch endpoint
+         *
+         *     Returns:
+         *         Current job status including progress and results when complete
+         */
+        get: operations["get_job_status_jobs__job_id__get"];
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+    "/jobs": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        /**
+         * List Jobs
+         * @description List all analysis jobs.
+         *
+         *     Args:
+         *         status: Optional filter by job status
+         *         limit: Maximum number of jobs to return (default 10, max 100)
+         *
+         *     Returns:
+         *         List of job statuses
+         */
+        get: operations["list_jobs_jobs_get"];
+        put?: never;
+        post?: never;
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
+}
+export type webhooks = Record<string, never>;
+export interface components {
+    schemas: {
+        /**
+         * AnalyticsResponse
+         * @description Analytics response model.
+         */
+        AnalyticsResponse: {
+            /** Total Messages */
+            total_messages: number;
+            /** By Company */
+            by_company: {
+                [key: string]: unknown;
+            }[];
+            /** By Type */
+            by_type: {
+                [key: string]: unknown;
+            }[];
+            /** Period Days */
+            period_days: number;
+        };
+        /**
+         * BatchAnalysisRequest
+         * @description Request model for batch company analysis.
+         */
+        BatchAnalysisRequest: {
+            /**
+             * Companies
+             * @description List of company names to analyze
+             */
+            companies: string[];
+            /**
+             * Max Workers
+             * @description Max concurrent analyses
+             * @default 3
+             */
+            max_workers: number;
+        };
+        /**
+         * BatchAnalysisResponse
+         * @description Response model for batch company analysis.
+         */
+        BatchAnalysisResponse: {
+            /** Results */
+            results: components["schemas"]["CompanyAnalysisResponse"][];
+            /** Total Companies */
+            total_companies: number;
+            /** Successful */
+            successful: number;
+            /** Failed */
+            failed: number;
+            /**
+             * Timestamp
+             * Format: date-time
+             */
+            timestamp: string;
+        };
+        /**
+         * CompanyAnalysisResponse
+         * @description Response model for single company analysis.
+         */
+        CompanyAnalysisResponse: {
+            /** Company Name */
+            company_name: string;
+            /** Analysis */
+            analysis: string;
+            /** Patent Count */
+            patent_count: number;
+            /** Success */
+            success: boolean;
+            /** Error */
+            error?: string | null;
+            /**
+             * Timestamp
+             * Format: date-time
+             */
+            timestamp: string;
+        };
+        /** HTTPValidationError */
+        HTTPValidationError: {
+            /** Detail */
+            detail?: components["schemas"]["ValidationError"][];
+        };
+        /**
+         * HealthResponse
+         * @description Health check response.
+         */
+        HealthResponse: {
+            /** Status */
+            status: string;
+            /** Version */
+            version: string;
+            /**
+             * Timestamp
+             * Format: date-time
+             */
+            timestamp: string;
+        };
+        /**
+         * JobStatus
+         * @description Status of a background analysis job.
+         */
+        JobStatus: {
+            /** Job Id */
+            job_id: string;
+            /** Status */
+            status: string;
+            /** Progress */
+            progress: number;
+            /** Total Companies */
+            total_companies: number;
+            /** Completed Companies */
+            completed_companies: number;
+            result?: components["schemas"]["BatchAnalysisResponse"] | null;
+            /** Error */
+            error?: string | null;
+        };
+        /**
+         * LoginRequest
+         * @description User login request.
+         */
+        LoginRequest: {
+            /**
+             * Email
+             * Format: email
+             */
+            email: string;
+            /** Password */
+            password: string;
+        };
+        /**
+         * RefreshRequest
+         * @description Token refresh request.
+         */
+        RefreshRequest: {
+            /** Refresh Token */
+            refresh_token: string;
+        };
+        /**
+         * RegisterRequest
+         * @description User registration request.
+         */
+        RegisterRequest: {
+            /**
+             * Email
+             * Format: email
+             */
+            email: string;
+            /**
+             * Password
+             * @description Password (min 8 characters)
+             */
+            password: string;
+        };
+        /**
+         * TokenResponse
+         * @description Token response model.
+         */
+        TokenResponse: {
+            /** Access Token */
+            access_token: string;
+            /** Refresh Token */
+            refresh_token: string;
+            /**
+             * Token Type
+             * @default bearer
+             */
+            token_type: string;
+        };
+        /**
+         * UpdateRoleRequest
+         * @description Update user role request.
+         */
+        UpdateRoleRequest: {
+            /** Role */
+            role: string;
+        };
+        /**
+         * UserResponse
+         * @description User response model.
+         */
+        UserResponse: {
+            /** Id */
+            id: number;
+            /** Email */
+            email: string;
+            /** Role */
+            role: string;
+            /**
+             * Created At
+             * Format: date-time
+             */
+            created_at: string;
+        };
+        /** ValidationError */
+        ValidationError: {
+            /** Location */
+            loc: (string | number)[];
+            /** Message */
+            msg: string;
+            /** Error Type */
+            type: string;
+            /** Input */
+            input?: unknown;
+            /** Context */
+            ctx?: Record<string, never>;
+        };
+    };
+    responses: never;
+    parameters: never;
+    requestBodies: never;
+    headers: never;
+    pathItems: never;
+}
+export type $defs = Record<string, never>;
+export interface operations {
+    register_auth_register_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody: {
+            content: {
+                "application/json": components["schemas"]["RegisterRequest"];
+            };
+        };
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["UserResponse"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    login_auth_login_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody: {
+            content: {
+                "application/json": components["schemas"]["LoginRequest"];
+            };
+        };
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["TokenResponse"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    refresh_token_auth_refresh_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody: {
+            content: {
+                "application/json": components["schemas"]["RefreshRequest"];
+            };
+        };
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["TokenResponse"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    get_me_auth_me_get: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["UserResponse"];
+                };
+            };
+        };
+    };
+    list_users_admin_users_get: {
+        parameters: {
+            query?: {
+                limit?: number;
+                offset?: number;
+            };
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["UserResponse"][];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    update_user_role_admin_users__user_id__role_patch: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path: {
+                user_id: number;
+            };
+            cookie?: never;
+        };
+        requestBody: {
+            content: {
+                "application/json": components["schemas"]["UpdateRoleRequest"];
+            };
+        };
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["UserResponse"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    delete_user_admin_users__user_id__delete: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path: {
+                user_id: number;
+            };
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": unknown;
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    get_analytics_analytics_get: {
+        parameters: {
+            query?: {
+                days?: number;
+            };
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["AnalyticsResponse"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    health_check_health_get: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HealthResponse"];
+                };
+            };
+        };
+    };
+    analyze_company_analyze__company_name__get: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path: {
+                company_name: string;
+            };
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["CompanyAnalysisResponse"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    analyze_companies_batch_analyze_batch_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody: {
+            content: {
+                "application/json": components["schemas"]["BatchAnalysisRequest"];
+            };
+        };
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["BatchAnalysisResponse"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    analyze_companies_async_analyze_batch_async_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody: {
+            content: {
+                "application/json": components["schemas"]["BatchAnalysisRequest"];
+            };
+        };
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["JobStatus"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    get_job_status_jobs__job_id__get: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path: {
+                job_id: string;
+            };
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["JobStatus"];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+    list_jobs_jobs_get: {
+        parameters: {
+            query?: {
+                /** @description Filter by status: pending, running, completed, failed */
+                status?: string | null;
+                limit?: number;
+            };
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["JobStatus"][];
+                };
+            };
+            /** @description Validation Error */
+            422: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": components["schemas"]["HTTPValidationError"];
+                };
+            };
+        };
+    };
+}

frontend/src/components/Layout.tsx

@@ -1,7 +1,7 @@
 import { Outlet, NavLink, useNavigate } from 'react-router-dom';
 import { useAuth } from '../context/AuthContext';
 import { useTheme } from '../context/ThemeContext';
-import { Search, Layers, BarChart3, Info, Users, LogOut, Sun, Moon } from 'lucide-react';
+import { Search, Layers, BarChart3, Info, Users, LogOut, GitCompareArrows, Sun, Moon } from 'lucide-react';
 
 export function Layout() {
   const { user, isAdmin, logout } = useAuth();
@@ -17,6 +17,7 @@ export function Layout() {
     { to: '/analysis', icon: Search, label: 'Analysis' },
     { to: '/batch', icon: Layers, label: 'Batch' },
     { to: '/analytics', icon: BarChart3, label: 'Analytics' },
+    { to: '/compare', icon: GitCompareArrows, label: 'Compare' },
     { to: '/about', icon: Info, label: 'About' },
   ];
 

frontend/src/pages/Analysis.tsx

@@ -1,15 +1,21 @@
 import { useState } from 'react';
-import { useMutation } from '@tanstack/react-query';
-import { analysisApi } from '../api/client';
-import { Search, CheckCircle, AlertCircle, Clock, FileText } from 'lucide-react';
+import { useMutation, useQuery } from '@tanstack/react-query';
+import { analysisApi, exportApi } from '../api/client';
+import { Search, CheckCircle, AlertCircle, Clock, FileText, Download, ChevronDown } from 'lucide-react';
 import type { CompanyAnalysis } from '../types';
 
 export function Analysis() {
   const [companyName, setCompanyName] = useState('');
+  const [selectedModel, setSelectedModel] = useState('');
   const [result, setResult] = useState<CompanyAnalysis | null>(null);
 
+  const modelsQuery = useQuery({
+    queryKey: ['models'],
+    queryFn: () => analysisApi.listModels(),
+  });
+
   const mutation = useMutation({
-    mutationFn: (name: string) => analysisApi.analyzeCompany(name),
+    mutationFn: (name: string) => analysisApi.analyzeCompany(name, selectedModel || undefined),
     onSuccess: (data) => setResult(data),
   });
 
@@ -33,31 +39,57 @@ export function Analysis() {
       </div>
 
       {/* Search Form */}
-      <form onSubmit={handleSubmit} className="flex gap-4">
-        <div className="flex-1 relative">
-          <Search className="absolute left-4 top-1/2 -translate-y-1/2 text-text-secondary" size={18} />
-          <input
-            type="text"
-            value={companyName}
-            onChange={(e) => setCompanyName(e.target.value)}
-            placeholder="Enter company name (e.g., nvidia, intel, amd)"
-            className="w-full bg-bg-card/80 border border-primary/30 rounded-xl pl-12 pr-4 py-3 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
-          />
+      <form onSubmit={handleSubmit} className="space-y-4">
+        <div className="flex gap-4">
+          <div className="flex-1 relative">
+            <Search className="absolute left-4 top-1/2 -translate-y-1/2 text-text-secondary" size={18} />
+            <input
+              type="text"
+              value={companyName}
+              onChange={(e) => setCompanyName(e.target.value)}
+              placeholder="Enter company name (e.g., nvidia, intel, amd)"
+              className="w-full bg-bg-card/80 border border-primary/30 rounded-xl pl-12 pr-4 py-3 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
+            />
+          </div>
+          <button
+            type="submit"
+            disabled={mutation.isPending || !companyName.trim()}
+            className="bg-gradient-to-r from-primary to-primary-dark text-white font-semibold py-3 px-6 rounded-xl hover:shadow-lg hover:shadow-primary/30 transition-all disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2"
+          >
+            {mutation.isPending ? (
+              <div className="animate-spin rounded-full h-5 w-5 border-t-2 border-b-2 border-white"></div>
+            ) : (
+              <>
+                <Search size={18} />
+                Analyze
+              </>
+            )}
+          </button>
+        </div>
+
+        {/* Model Selector */}
+        <div className="flex items-center gap-3">
+          <label className="text-sm font-medium text-text-secondary whitespace-nowrap">
+            LLM Model
+          </label>
+          <div className="relative flex-1 max-w-xs">
+            <select
+              value={selectedModel}
+              onChange={(e) => setSelectedModel(e.target.value)}
+              className="w-full appearance-none bg-bg-card/80 border border-primary/30 rounded-lg pl-3 pr-8 py-2 text-sm text-text-primary focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all cursor-pointer"
+            >
+              <option value="">
+                {modelsQuery.data ? `Default (${modelsQuery.data.default})` : 'Default'}
+              </option>
+              {modelsQuery.data?.models.map((m) => (
+                <option key={m.id} value={m.id}>
+                  {m.name} ({m.provider})
+                </option>
+              ))}
+            </select>
+            <ChevronDown className="absolute right-2 top-1/2 -translate-y-1/2 text-text-secondary pointer-events-none" size={16} />
+          </div>
         </div>
-        <button
-          type="submit"
-          disabled={mutation.isPending || !companyName.trim()}
-          className="bg-gradient-to-r from-primary to-primary-dark text-white font-semibold py-3 px-6 rounded-xl hover:shadow-lg hover:shadow-primary/30 transition-all disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2"
-        >
-          {mutation.isPending ? (
-            <div className="animate-spin rounded-full h-5 w-5 border-t-2 border-b-2 border-white"></div>
-          ) : (
-            <>
-              <Search size={18} />
-              Analyze
-            </>
-          )}
-        </button>
       </form>
 
       {/* Error */}
@@ -106,9 +138,27 @@ export function Analysis() {
           {/* Analysis Content */}
           {result.success && result.analysis && (
             <div className="bg-bg-card/60 backdrop-blur-lg border border-primary/15 rounded-2xl p-6">
-              <h3 className="text-lg font-semibold text-text-primary border-b-2 border-primary/30 pb-2 mb-4">
-                AI Analysis Results
-              </h3>
+              <div className="flex items-center justify-between border-b-2 border-primary/30 pb-2 mb-4">
+                <h3 className="text-lg font-semibold text-text-primary">
+                  AI Analysis Results
+                </h3>
+                <div className="flex items-center gap-2">
+                  <button
+                    onClick={() => exportApi.exportCsv(result.company_name)}
+                    className="flex items-center gap-2 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-3 py-1.5 rounded-lg transition-colors"
+                  >
+                    <Download size={14} />
+                    Export CSV
+                  </button>
+                  <button
+                    onClick={() => exportApi.exportPdf(result.company_name)}
+                    className="flex items-center gap-2 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-3 py-1.5 rounded-lg transition-colors"
+                  >
+                    <FileText size={14} />
+                    Export PDF
+                  </button>
+                </div>
+              </div>
               <div className="prose prose-invert max-w-none">
                 <div className="text-text-primary whitespace-pre-wrap leading-relaxed">
                   {result.analysis}

frontend/src/pages/Analytics.tsx

@@ -2,22 +2,50 @@ import { useState } from 'react';
 import { useQuery } from '@tanstack/react-query';
 import { analyticsApi } from '../api/client';
 import { AlertCircle, Database } from 'lucide-react';
-import { PieChart, Pie, Cell, BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Legend } from 'recharts';
+import { PieChart, Pie, Cell, BarChart, Bar, LineChart, Line, XAxis, YAxis, Tooltip, ResponsiveContainer, Legend } from 'recharts';
 
 const COLORS = ['#6366f1', '#0ea5e9', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6', '#ec4899', '#14b8a6'];
 
 export function AnalyticsPage() {
   const [days, setDays] = useState(30);
 
-  const { data, isLoading, isError } = useQuery({
+  const { data, isLoading, isError, refetch } = useQuery({
     queryKey: ['analytics', days],
     queryFn: () => analyticsApi.getAnalytics(days),
   });
 
+  const trendsQuery = useQuery({
+    queryKey: ['analytics-trends', days],
+    queryFn: () => analyticsApi.getTrends(days),
+  });
+
   if (isLoading) {
     return (
-      <div className="flex items-center justify-center min-h-[400px]">
-        <div className="animate-spin rounded-full h-12 w-12 border-t-2 border-b-2 border-primary"></div>
+      <div className="space-y-6">
+        <div>
+          <h2 className="text-xl font-semibold text-text-primary border-b-2 border-primary/30 pb-2 mb-2">
+            Analytics Dashboard
+          </h2>
+          <p className="text-text-secondary">Loading analytics data...</p>
+        </div>
+        {/* Skeleton cards */}
+        <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
+          {[1, 2, 3].map((i) => (
+            <div key={i} className="bg-gradient-to-br from-primary/10 to-secondary/10 border border-primary/20 rounded-xl p-5 text-center animate-pulse">
+              <div className="h-9 w-16 bg-primary/20 rounded mx-auto mb-2" />
+              <div className="h-4 w-24 bg-primary/10 rounded mx-auto" />
+            </div>
+          ))}
+        </div>
+        {/* Skeleton charts */}
+        <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
+          {[1, 2].map((i) => (
+            <div key={i} className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6 animate-pulse">
+              <div className="h-5 w-40 bg-primary/20 rounded mb-4" />
+              <div className="h-[300px] bg-primary/5 rounded" />
+            </div>
+          ))}
+        </div>
       </div>
     );
   }
@@ -33,15 +61,18 @@ export function AnalyticsPage() {
         <div className="bg-gradient-to-br from-primary/10 to-secondary/5 border border-primary/20 rounded-xl p-6">
           <div className="flex items-center gap-3 text-warning mb-2">
             <Database size={24} />
-            <span className="font-semibold">Database Not Connected</span>
+            <span className="font-semibold">Unable to Load Analytics</span>
           </div>
           <p className="text-text-secondary">
-            Set <code className="bg-bg-card px-2 py-1 rounded">USE_DATABASE=true</code> in your .env file to enable analytics tracking.
+            Could not connect to the analytics database. Ensure PostgreSQL is running and
+            <code className="bg-bg-card px-2 py-1 rounded mx-1">DATABASE_URL</code> is configured correctly.
           </p>
-        </div>
-        <div className="flex items-center gap-2 bg-secondary/10 border border-secondary/20 text-secondary rounded-xl px-4 py-3">
-          <AlertCircle size={18} />
-          <span>Analytics features require storing analysis results in PostgreSQL for historical tracking.</span>
+          <button
+            onClick={() => refetch()}
+            className="mt-3 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-4 py-2 rounded-lg transition-colors"
+          >
+            Retry
+          </button>
         </div>
       </div>
     );
@@ -163,6 +194,114 @@ export function AnalyticsPage() {
           </div>
         )}
       </div>
+
+      {/* Trend Charts */}
+      {trendsQuery.data && (
+        <div className="space-y-6">
+          <h3 className="text-lg font-semibold text-text-primary border-b-2 border-primary/30 pb-2">
+            Trends Over Time
+          </h3>
+
+          <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
+            {/* Patent count over time per company (line chart) */}
+            {trendsQuery.data.by_month.length > 0 && (() => {
+              // Pivot data: each month as a row, companies as columns
+              const companies = [...new Set(trendsQuery.data!.by_month.map(d => d.company_name))];
+              const months = [...new Set(trendsQuery.data!.by_month.map(d => d.month))].sort();
+              const pivoted = months.map(month => {
+                const row: Record<string, string | number> = { month };
+                for (const c of companies) {
+                  const entry = trendsQuery.data!.by_month.find(d => d.month === month && d.company_name === c);
+                  row[c] = entry?.count || 0;
+                }
+                return row;
+              });
+
+              return (
+                <div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6">
+                  <h4 className="text-md font-semibold text-text-primary mb-4">Analyses per Company Over Time</h4>
+                  <ResponsiveContainer width="100%" height={300}>
+                    <LineChart data={pivoted}>
+                      <XAxis dataKey="month" stroke="#94a3b8" fontSize={12} />
+                      <YAxis stroke="#94a3b8" fontSize={12} />
+                      <Tooltip
+                        contentStyle={{
+                          backgroundColor: '#1e293b',
+                          border: '1px solid rgba(99, 102, 241, 0.3)',
+                          borderRadius: '8px',
+                        }}
+                        labelStyle={{ color: '#f8fafc' }}
+                      />
+                      <Legend />
+                      {companies.map((company, idx) => (
+                        <Line
+                          key={company}
+                          type="monotone"
+                          dataKey={company}
+                          stroke={COLORS[idx % COLORS.length]}
+                          strokeWidth={2}
+                          dot={{ r: 4 }}
+                          name={company.toUpperCase()}
+                        />
+                      ))}
+                    </LineChart>
+                  </ResponsiveContainer>
+                </div>
+              );
+            })()}
+
+            {/* Analysis type distribution over time (stacked bar) */}
+            {trendsQuery.data.by_type_over_time.length > 0 && (() => {
+              const types = [...new Set(trendsQuery.data!.by_type_over_time.map(d => d.analysis_type))];
+              const months = [...new Set(trendsQuery.data!.by_type_over_time.map(d => d.month))].sort();
+              const pivoted = months.map(month => {
+                const row: Record<string, string | number> = { month };
+                for (const t of types) {
+                  const entry = trendsQuery.data!.by_type_over_time.find(d => d.month === month && d.analysis_type === t);
+                  row[t] = entry?.count || 0;
+                }
+                return row;
+              });
+
+              return (
+                <div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6">
+                  <h4 className="text-md font-semibold text-text-primary mb-4">Analysis Types Over Time</h4>
+                  <ResponsiveContainer width="100%" height={300}>
+                    <BarChart data={pivoted}>
+                      <XAxis dataKey="month" stroke="#94a3b8" fontSize={12} />
+                      <YAxis stroke="#94a3b8" fontSize={12} />
+                      <Tooltip
+                        contentStyle={{
+                          backgroundColor: '#1e293b',
+                          border: '1px solid rgba(99, 102, 241, 0.3)',
+                          borderRadius: '8px',
+                        }}
+                        labelStyle={{ color: '#f8fafc' }}
+                      />
+                      <Legend />
+                      {types.map((type, idx) => (
+                        <Bar
+                          key={type}
+                          dataKey={type}
+                          stackId="types"
+                          fill={COLORS[idx % COLORS.length]}
+                          name={type}
+                        />
+                      ))}
+                    </BarChart>
+                  </ResponsiveContainer>
+                </div>
+              );
+            })()}
+          </div>
+
+          {trendsQuery.data.by_month.length === 0 && (
+            <div className="text-text-secondary text-center py-8">
+              No trend data available yet. Run analyses over multiple days to see trends.
+            </div>
+          )}
+        </div>
+      )}
     </div>
   );
 }

frontend/src/pages/Batch.tsx

@@ -1,20 +1,34 @@
 import { useState } from 'react';
-import { useMutation } from '@tanstack/react-query';
+import { useMutation, useQuery } from '@tanstack/react-query';
 import { analysisApi } from '../api/client';
-import { Rocket, CheckCircle, AlertCircle, ChevronDown, ChevronUp } from 'lucide-react';
+import { Rocket, CheckCircle, AlertCircle, ChevronDown, ChevronUp, RefreshCw, Inbox } from 'lucide-react';
 import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts';
 import type { BatchAnalysisResult } from '../types';
 
 export function Batch() {
   const [companiesInput, setCompaniesInput] = useState('');
   const [maxWorkers, setMaxWorkers] = useState(3);
+  const [selectedModel, setSelectedModel] = useState('');
   const [result, setResult] = useState<BatchAnalysisResult | null>(null);
   const [expandedItems, setExpandedItems] = useState<Set<string>>(new Set());
 
+  const modelsQuery = useQuery({
+    queryKey: ['models'],
+    queryFn: () => analysisApi.listModels(),
+  });
+
+  const jobsQuery = useQuery({
+    queryKey: ['jobs'],
+    queryFn: () => analysisApi.listJobs(undefined, 20),
+  });
+
   const mutation = useMutation({
     mutationFn: ({ companies, workers }: { companies: string[]; workers: number }) =>
-      analysisApi.analyzeBatch(companies, workers),
-    onSuccess: (data) => setResult(data),
+      analysisApi.analyzeBatch(companies, workers, selectedModel || undefined),
+    onSuccess: (data) => {
+      setResult(data);
+      jobsQuery.refetch();
+    },
   });
 
   const handleSubmit = (e: React.FormEvent) => {
@@ -85,6 +99,29 @@ export function Batch() {
             <div className="text-center text-text-primary font-semibold">{maxWorkers}</div>
           </div>
 
+          <div>
+            <label className="block text-sm font-medium text-text-secondary mb-2">
+              LLM Model
+            </label>
+            <div className="relative">
+              <select
+                value={selectedModel}
+                onChange={(e) => setSelectedModel(e.target.value)}
+                className="w-full appearance-none bg-bg-card/80 border border-primary/30 rounded-lg pl-3 pr-8 py-2 text-sm text-text-primary focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all cursor-pointer"
+              >
+                <option value="">
+                  {modelsQuery.data ? `Default (${modelsQuery.data.default})` : 'Default'}
+                </option>
+                {modelsQuery.data?.models.map((m) => (
+                  <option key={m.id} value={m.id}>
+                    {m.name} ({m.provider})
+                  </option>
+                ))}
+              </select>
+              <ChevronDown className="absolute right-2 top-1/2 -translate-y-1/2 text-text-secondary pointer-events-none" size={16} />
+            </div>
+          </div>
+
           <button
             type="submit"
             disabled={mutation.isPending || !companiesInput.trim()}
@@ -114,9 +151,38 @@ export function Batch() {
 
       {/* Error */}
       {mutation.isError && (
-        <div className="flex items-center gap-2 bg-error/10 border border-error/20 text-error rounded-xl px-4 py-3">
-          <AlertCircle size={18} />
-          <span>Batch analysis failed. Please try again.</span>
+        <div className="bg-error/10 border border-error/20 rounded-xl px-4 py-3">
+          <div className="flex items-center gap-2 text-error">
+            <AlertCircle size={18} />
+            <span className="font-semibold">Batch analysis failed</span>
+          </div>
+          <p className="text-text-secondary text-sm mt-1 ml-7">
+            {mutation.error instanceof Error ? mutation.error.message : 'An unexpected error occurred.'}
+            {' '}Check your connection and try again.
+          </p>
+          <div className="ml-7 mt-2 flex items-center gap-3">
+            <button
+              onClick={() => {
+                const companies = companiesInput
+                  .split(/[,\n]/)
+                  .map((c) => c.trim())
+                  .filter((c) => c.length > 0);
+                if (companies.length > 0) {
+                  mutation.mutate({ companies, workers: maxWorkers });
+                }
+              }}
+              className="text-sm text-primary hover:text-primary-dark underline flex items-center gap-1"
+            >
+              <RefreshCw size={14} />
+              Retry
+            </button>
+            <button
+              onClick={() => mutation.reset()}
+              className="text-sm text-text-secondary hover:text-text-primary underline"
+            >
+              Dismiss
+            </button>
+          </div>
         </div>
       )}
 
@@ -218,6 +284,123 @@ export function Batch() {
           </div>
         </div>
       )}
+
+      {/* Job History */}
+      <div>
+        <h3 className="text-lg font-semibold text-text-primary border-b-2 border-primary/30 pb-2 mb-4">
+          Job History
+        </h3>
+
+        {/* Loading skeleton */}
+        {jobsQuery.isLoading && (
+          <div className="space-y-3">
+            {[...Array(3)].map((_, i) => (
+              <div
+                key={i}
+                className="bg-bg-card/60 border border-primary/15 rounded-xl p-4 animate-pulse"
+              >
+                <div className="flex items-center justify-between">
+                  <div className="flex items-center gap-3">
+                    <div className="h-5 w-5 rounded-full bg-primary/20" />
+                    <div className="h-4 w-32 rounded bg-primary/20" />
+                    <div className="h-4 w-20 rounded bg-primary/10" />
+                  </div>
+                  <div className="h-6 w-20 rounded-full bg-primary/15" />
+                </div>
+                <div className="mt-3 flex gap-4">
+                  <div className="h-3 w-24 rounded bg-primary/10" />
+                  <div className="h-3 w-16 rounded bg-primary/10" />
+                </div>
+              </div>
+            ))}
+          </div>
+        )}
+
+        {/* Job history error */}
+        {jobsQuery.isError && (
+          <div className="bg-error/10 border border-error/20 rounded-xl px-4 py-3">
+            <div className="flex items-center gap-2 text-error">
+              <AlertCircle size={18} />
+              <span className="font-semibold">Failed to load job history</span>
+            </div>
+            <p className="text-text-secondary text-sm mt-1 ml-7">
+              {jobsQuery.error instanceof Error ? jobsQuery.error.message : 'Could not retrieve past jobs.'}
+            </p>
+            <button
+              onClick={() => jobsQuery.refetch()}
+              className="ml-7 mt-2 text-sm text-primary hover:text-primary-dark underline flex items-center gap-1"
+            >
+              <RefreshCw size={14} />
+              Retry
+            </button>
+          </div>
+        )}
+
+        {/* Empty state */}
+        {jobsQuery.isSuccess && jobsQuery.data.length === 0 && !result && (
+          <div className="bg-bg-card/60 border border-primary/15 border-dashed rounded-xl p-8 text-center">
+            <Inbox className="mx-auto text-text-secondary/40 mb-3" size={40} />
+            <p className="text-text-secondary font-medium">No batch jobs yet</p>
+            <p className="text-text-secondary/70 text-sm mt-1">
+              Submit a batch analysis above to get started. Your job history will appear here.
+            </p>
+          </div>
+        )}
+
+        {/* Job list */}
+        {jobsQuery.isSuccess && jobsQuery.data.length > 0 && (
+          <div className="space-y-3">
+            {jobsQuery.data.map((job) => (
+              <div
+                key={job.job_id}
+                className="bg-bg-card/60 border border-primary/15 rounded-xl p-4"
+              >
+                <div className="flex items-center justify-between">
+                  <div className="flex items-center gap-3">
+                    {job.status === 'completed' && <CheckCircle className="text-success" size={18} />}
+                    {job.status === 'failed' && <AlertCircle className="text-error" size={18} />}
+                    {(job.status === 'pending' || job.status === 'running') && (
+                      <div className="animate-spin rounded-full h-[18px] w-[18px] border-t-2 border-b-2 border-secondary" />
+                    )}
+                    <span className="font-mono text-sm text-text-primary">{job.job_id.slice(0, 8)}</span>
+                    <span className="text-text-secondary text-sm">
+                      {job.total_companies} {job.total_companies === 1 ? 'company' : 'companies'}
+                    </span>
+                  </div>
+                  <span
+                    className={`text-xs font-semibold px-2.5 py-1 rounded-full ${
+                      job.status === 'completed'
+                        ? 'bg-success/15 text-success'
+                        : job.status === 'failed'
+                          ? 'bg-error/15 text-error'
+                          : 'bg-secondary/15 text-secondary'
+                    }`}
+                  >
+                    {job.status}
+                  </span>
+                </div>
+                {(job.status === 'running' || job.status === 'pending') && job.total_companies > 0 && (
+                  <div className="mt-3">
+                    <div className="flex items-center justify-between text-xs text-text-secondary mb-1">
+                      <span>Progress</span>
+                      <span>{job.completed_companies}/{job.total_companies}</span>
+                    </div>
+                    <div className="h-1.5 bg-bg-dark rounded-full overflow-hidden">
+                      <div
+                        className="h-full bg-gradient-to-r from-primary to-secondary rounded-full transition-all duration-300"
+                        style={{ width: `${(job.completed_companies / job.total_companies) * 100}%` }}
+                      />
+                    </div>
+                  </div>
+                )}
+                {job.status === 'failed' && job.error && (
+                  <p className="mt-2 text-sm text-error/80">{job.error}</p>
+                )}
+              </div>
+            ))}
+          </div>
+        )}
+      </div>
     </div>
   );
 }

frontend/src/pages/Compare.tsx

@@ -0,0 +1,161 @@
+import { useState } from 'react';
+import { useSearchParams } from 'react-router-dom';
+import { useQuery } from '@tanstack/react-query';
+import { analysisApi } from '../api/client';
+import { GitCompareArrows, AlertCircle, FileText, Clock } from 'lucide-react';
+import type { CompanyAnalysis } from '../types';
+
+function CompanyPanel({ data, isLoading, isError }: { data?: CompanyAnalysis; isLoading: boolean; isError: boolean }) {
+  if (isLoading) {
+    return (
+      <div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6 animate-pulse">
+        <div className="h-6 w-32 bg-primary/20 rounded mb-4" />
+        <div className="space-y-3">
+          <div className="h-4 bg-primary/10 rounded w-full" />
+          <div className="h-4 bg-primary/10 rounded w-3/4" />
+          <div className="h-4 bg-primary/10 rounded w-5/6" />
+        </div>
+      </div>
+    );
+  }
+
+  if (isError) {
+    return (
+      <div className="bg-error/10 border border-error/20 rounded-2xl p-6">
+        <div className="flex items-center gap-2 text-error">
+          <AlertCircle size={18} />
+          <span>Failed to load analysis. Check the company name and try again.</span>
+        </div>
+      </div>
+    );
+  }
+
+  if (!data) return null;
+
+  return (
+    <div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6 space-y-4">
+      <h3 className="text-lg font-bold text-text-primary border-b-2 border-primary/30 pb-2">
+        {data.company_name.toUpperCase()}
+      </h3>
+
+      <div className="grid grid-cols-2 gap-3">
+        <div className="bg-primary/10 rounded-lg p-3 text-center">
+          <FileText className="mx-auto mb-1 text-primary" size={18} />
+          <div className="text-xl font-bold text-text-primary">{data.patent_count}</div>
+          <div className="text-xs text-text-secondary uppercase">Patents</div>
+        </div>
+        <div className="bg-primary/10 rounded-lg p-3 text-center">
+          <Clock className="mx-auto mb-1 text-primary" size={18} />
+          <div className="text-sm font-medium text-text-primary">
+            {new Date(data.timestamp).toLocaleDateString()}
+          </div>
+          <div className="text-xs text-text-secondary uppercase">Analyzed</div>
+        </div>
+      </div>
+
+      {data.success && data.analysis ? (
+        <div className="text-text-primary whitespace-pre-wrap leading-relaxed text-sm">
+          {data.analysis}
+        </div>
+      ) : (
+        <div className="text-error text-sm">{data.error || 'Analysis not available'}</div>
+      )}
+    </div>
+  );
+}
+
+export function Compare() {
+  const [searchParams, setSearchParams] = useSearchParams();
+  const [companyA, setCompanyA] = useState(searchParams.get('a') || '');
+  const [companyB, setCompanyB] = useState(searchParams.get('b') || '');
+
+  const queryA = searchParams.get('a') || '';
+  const queryB = searchParams.get('b') || '';
+
+  const resultA = useQuery({
+    queryKey: ['analyze', queryA],
+    queryFn: () => analysisApi.analyzeCompany(queryA),
+    enabled: !!queryA,
+  });
+
+  const resultB = useQuery({
+    queryKey: ['analyze', queryB],
+    queryFn: () => analysisApi.analyzeCompany(queryB),
+    enabled: !!queryB,
+  });
+
+  const handleCompare = (e: React.FormEvent) => {
+    e.preventDefault();
+    const a = companyA.trim();
+    const b = companyB.trim();
+    if (a && b) {
+      setSearchParams({ a, b });
+    }
+  };
+
+  return (
+    <div className="space-y-6">
+      {/* Header */}
+      <div>
+        <h2 className="text-xl font-semibold text-text-primary border-b-2 border-primary/30 pb-2 mb-2">
+          Portfolio Comparison
+        </h2>
+        <p className="text-text-secondary">
+          Compare patent portfolios of two companies side by side.
+        </p>
+      </div>
+
+      {/* Input Form */}
+      <form onSubmit={handleCompare} className="flex flex-col sm:flex-row gap-3 items-end">
+        <div className="flex-1">
+          <label className="block text-sm font-medium text-text-secondary mb-1">Company A</label>
+          <input
+            type="text"
+            value={companyA}
+            onChange={(e) => setCompanyA(e.target.value)}
+            placeholder="e.g. nvidia"
+            className="w-full bg-bg-card/80 border border-primary/30 rounded-xl px-4 py-2.5 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
+          />
+        </div>
+        <div className="flex-1">
+          <label className="block text-sm font-medium text-text-secondary mb-1">Company B</label>
+          <input
+            type="text"
+            value={companyB}
+            onChange={(e) => setCompanyB(e.target.value)}
+            placeholder="e.g. intel"
+            className="w-full bg-bg-card/80 border border-primary/30 rounded-xl px-4 py-2.5 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
+          />
+        </div>
+        <button
+          type="submit"
+          disabled={!companyA.trim() || !companyB.trim() || resultA.isLoading || resultB.isLoading}
+          className="bg-gradient-to-r from-primary to-primary-dark text-white font-semibold py-2.5 px-6 rounded-xl hover:shadow-lg hover:shadow-primary/30 transition-all disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2"
+        >
+          <GitCompareArrows size={18} />
+          Compare
+        </button>
+      </form>
+
+      {/* Comparison Panels */}
+      {(queryA || queryB) && (
+        <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
+          {queryA && (
+            <CompanyPanel
+              data={resultA.data}
+              isLoading={resultA.isLoading}
+              isError={resultA.isError}
+            />
+          )}
+          {queryB && (
+            <CompanyPanel
+              data={resultB.data}
+              isLoading={resultB.isLoading}
+              isError={resultB.isError}
+            />
+          )}
+        </div>
+      )}
+    </div>
+  );
+}

frontend/src/types/index.ts

@@ -1,46 +1,32 @@
-export interface User {
-  id: number;
-  email: string;
-  role: 'admin' | 'user';
-  created_at: string;
-}
+/**
+ * Application types derived from the auto-generated OpenAPI schema.
+ *
+ * Run `npm run generate:local` (or `npm run generate` with the API running)
+ * to regenerate `src/api/schema.d.ts` from the backend OpenAPI spec.
+ *
+ * These aliases keep the rest of the codebase stable while the source of
+ * truth lives in the generated file.
+ */
 
-export interface TokenResponse {
-  access_token: string;
-  refresh_token: string;
-  token_type: string;
-}
+import type { components } from '../api/schema';
 
-export interface CompanyAnalysis {
-  company_name: string;
-  analysis: string;
-  patent_count: number;
-  success: boolean;
-  error: string | null;
-  timestamp: string;
-}
-
-export interface BatchAnalysisResult {
-  results: CompanyAnalysis[];
-  total_companies: number;
-  successful: number;
-  failed: number;
-  timestamp: string;
-}
-
-export interface JobStatus {
-  job_id: string;
-  status: 'pending' | 'running' | 'completed' | 'failed';
-  progress: number;
-  total_companies: number;
-  completed_companies: number;
-  result: BatchAnalysisResult | null;
-  error: string | null;
-}
-
-export interface Analytics {
-  total_messages: number;
+// Re-export schema types under the names the rest of the app expects.
+export type User = components['schemas']['UserResponse'];
+export type TokenResponse = components['schemas']['TokenResponse'];
+export type CompanyAnalysis = components['schemas']['CompanyAnalysisResponse'];
+export type BatchAnalysisResult = components['schemas']['BatchAnalysisResponse'];
+export type JobStatus = components['schemas']['JobStatus'];
+export type Analytics = Omit<components['schemas']['AnalyticsResponse'], 'by_company' | 'by_type'> & {
   by_company: Array<{ company_name: string; count: number }>;
   by_type: Array<{ analysis_type: string; count: number }>;
-  period_days: number;
-}
+};
+
+// Additional generated types that may be useful elsewhere.
+export type RegisterRequest = components['schemas']['RegisterRequest'];
+export type LoginRequest = components['schemas']['LoginRequest'];
+export type RefreshRequest = components['schemas']['RefreshRequest'];
+export type UpdateRoleRequest = components['schemas']['UpdateRoleRequest'];
+export type HealthResponse = components['schemas']['HealthResponse'];
+export type BatchAnalysisRequest = components['schemas']['BatchAnalysisRequest'];
+export type ValidationError = components['schemas']['ValidationError'];
+export type HTTPValidationError = components['schemas']['HTTPValidationError'];

requirements.txt

@@ -15,3 +15,6 @@ pandas
 bcrypt
 PyJWT
 slowapi
+apscheduler
+boto3
+reportlab

tests/test_api.py

@@ -182,3 +182,47 @@ class TestJobEndpoints:
         """Test listing jobs with status filter."""
         response = client.get("/jobs?status=completed")
         assert response.status_code == 200
+
+
+class TestModelValidation:
+    """Test that unsupported model identifiers are rejected."""
+
+    def test_analyze_rejects_unsupported_model(self, client, mock_analyzer):
+        """GET /analyze/{company} with unsupported model returns 400."""
+        response = client.get("/analyze/nvidia?model=fake/nonexistent-model")
+        assert response.status_code == 400
+        assert "Unsupported model" in response.json()["detail"]
+
+    def test_analyze_accepts_supported_model(self, client, mock_analyzer):
+        """GET /analyze/{company} with a supported model succeeds."""
+        mock_result = CompanyAnalysisResult(
+            company_name="nvidia",
+            analysis="test",
+            patent_count=1,
+            success=True,
+            timestamp=datetime.now(),
+            model="anthropic/claude-3.5-sonnet",
+        )
+        mock_analyzer._analyze_company_safe.return_value = mock_result
+
+        response = client.get("/analyze/nvidia?model=anthropic/claude-3.5-sonnet")
+        assert response.status_code == 200
+
+    def test_batch_rejects_unsupported_model(self, client, mock_analyzer):
+        """POST /analyze/batch with unsupported model returns 400."""
+        response = client.post(
+            "/analyze/batch",
+            json={"companies": ["nvidia"], "model": "fake/nonexistent-model"},
+        )
+        assert response.status_code == 400
+        assert "Unsupported model" in response.json()["detail"]
+
+    def test_list_models_returns_supported(self, client):
+        """GET /models returns the allow-list."""
+        response = client.get("/models")
+        assert response.status_code == 200
+        data = response.json()
+        assert "models" in data
+        assert "default" in data
+        assert len(data["models"]) > 0
+        assert all("id" in m and "name" in m and "provider" in m for m in data["models"])