merge: resolve Batch.tsx conflict between model picker and job history

Combine both useQuery hooks (modelsQuery for model selector, jobsQuery for
job history) and pass selectedModel to analyzeBatch while also triggering
jobsQuery.refetch() on successful submission.

.env.example

@@ -47,12 +47,27 @@ STORAGE_BACKEND=local
 # 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

.gitea/workflows/build.yaml

@@ -33,6 +33,14 @@ 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
+          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
@@ -100,12 +101,12 @@ class CompanyAnalyzer:
 
         # Analyze the full portfolio with LLM
         analysis = self.llm_analyzer.analyze_patent_portfolio(
-            patents_data=processed_patents, company_name=company_name
+            patents_data=processed_patents, company_name=company_name, model=model
         )
 
         return analysis
 
-    def analyze_single_patent(self, patent_id: str, company_name: str) -> str:
+    def analyze_single_patent(self, patent_id: str, company_name: str, model: str | None = None) -> str:
         """Analyze a single patent by ID.
 
         If the patent PDF is not already on disk, this method attempts to
@@ -116,6 +117,7 @@ class CompanyAnalyzer:
         Args:
           patent_id: Publication ID of the patent (e.g. "US-11234567-B2")
           company_name: Name of the company (for context)
+          model: Optional LLM model override (e.g. 'openai/gpt-4o')
 
         Returns:
           Analysis of the specific patent's innovation quality
@@ -151,7 +153,7 @@ class CompanyAnalyzer:
             minimized_content = SERP.minimize_patent_for_llm(sections)
 
             analysis = self.llm_analyzer.analyze_patent_content(
-                patent_content=minimized_content, company_name=company_name
+                patent_content=minimized_content, company_name=company_name, model=model
             )
 
             return analysis
@@ -201,18 +203,19 @@ class CompanyAnalyzer:
             logger.warning("Failed to process %s: %s", patent.patent_id, e)
             return None
 
-    def _analyze_company_safe(self, company_name: str) -> CompanyAnalysisResult:
+    def _analyze_company_safe(self, company_name: str, model: str | None = None) -> CompanyAnalysisResult:
         """Internal wrapper that catches exceptions and returns structured result.
 
         Args:
             company_name: Name of the company to analyze
+            model: Optional LLM model override (e.g. 'openai/gpt-4o')
 
         Returns:
             CompanyAnalysisResult with success/failure status
         """
         try:
             # Delegate to analyze_company which handles SERP/patent caching
-            analysis = self.analyze_company(company_name)
+            analysis = self.analyze_company(company_name, model=model)
 
             # Determine patent count from cached SERP query
             query_hash = hashlib.sha256(company_name.lower().encode()).hexdigest()
@@ -252,6 +255,7 @@ class CompanyAnalyzer:
         companies: list[str],
         max_workers: int = 3,
         progress_callback: Callable[[str, int, int], None] | None = None,
+        model: str | None = None,
     ) -> BatchAnalysisResult:
         """Analyze multiple companies' patent portfolios in batch.
 
@@ -262,6 +266,7 @@ class CompanyAnalyzer:
             companies: List of company names to analyze
             max_workers: Maximum concurrent analyses (default 3 to avoid rate limits)
             progress_callback: Optional callback(company_name, completed, total)
+            model: Optional LLM model override (e.g. 'openai/gpt-4o')
 
         Returns:
             BatchAnalysisResult containing all individual results and summary stats
@@ -273,7 +278,7 @@ class CompanyAnalyzer:
 
         with ThreadPoolExecutor(max_workers=max_workers) as executor:
             future_to_company = {
-                executor.submit(self._analyze_company_safe, company): company
+                executor.submit(self._analyze_company_safe, company, model): company
                 for company in companies
             }
 

SPARC/api.py

@@ -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):
@@ -140,6 +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,
     )
 
@@ -453,6 +468,104 @@ 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"},
+]
+
+
+@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 ==============
 
 
@@ -508,6 +621,164 @@ async def export_company_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 ==============
 
 
@@ -528,6 +799,7 @@ async def health_check():
 )
 async def analyze_company(
     company_name: str,
+    model: str | None = Query(default=None, description="LLM model to use (e.g. 'openai/gpt-4o'). Defaults to server config."),
     _: UserResponse = Depends(get_current_user),
 ):
     """Analyze a single company's patent portfolio.
@@ -537,6 +809,7 @@ async def analyze_company(
 
     Args:
         company_name: Name of the company to analyze (e.g., "nvidia", "intel")
+        model: Optional LLM model override
 
     Returns:
         Analysis results including patent count, AI insights, and success status
@@ -544,7 +817,7 @@ async def analyze_company(
     if not _analyzer:
         raise HTTPException(status_code=503, detail="Analyzer not initialized")
 
-    result = _analyzer._analyze_company_safe(company_name)
+    result = _analyzer._analyze_company_safe(company_name, model=model)
     return _convert_result(result)
 
 
@@ -606,6 +879,7 @@ async def analyze_companies_batch(
     result = _analyzer.analyze_companies(
         companies=request.companies,
         max_workers=request.max_workers,
+        model=request.model,
     )
     return _convert_batch_result(result)
 
@@ -637,7 +911,7 @@ def _job_row_to_status(row: dict) -> JobStatus:
     )
 
 
-def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
+def _run_batch_job(job_id: str, companies: list[str], max_workers: int, model: str | None = None):
     """Background task for batch analysis."""
     import json as _json
     global _analyzer
@@ -662,6 +936,7 @@ def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
             companies=companies,
             max_workers=max_workers,
             progress_callback=progress_callback,
+            model=model,
         )
         batch_response = _convert_batch_result(result)
         db.update_job(
@@ -717,7 +992,7 @@ async def analyze_companies_async(
     job_row = db.create_job(job_id=job_id, total_companies=len(request.companies))
 
     background_tasks.add_task(
-        _run_batch_job, job_id, request.companies, request.max_workers
+        _run_batch_job, job_id, request.companies, request.max_workers, request.model
     )
 
     return _job_row_to_status(job_row)

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/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)
 
 

frontend/package.json

@@ -7,6 +7,8 @@
     "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"
   },
@@ -31,6 +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/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/${jobId}`);
     return response.data;
@@ -141,14 +165,40 @@ export const exportApi = {
     link.remove();
     window.URL.revokeObjectURL(url);
   },
+  exportPdf: async (companyName: string): Promise<void> => {
+    const response = await api.get(`/export/${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/pages/Analysis.tsx

@@ -1,15 +1,21 @@
 import { useState } from 'react';
-import { useMutation } from '@tanstack/react-query';
+import { useMutation, useQuery } from '@tanstack/react-query';
 import { analysisApi, exportApi } from '../api/client';
-import { Search, CheckCircle, AlertCircle, Clock, FileText, Download } from 'lucide-react';
+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),
   });
 
@@ -33,7 +39,8 @@ export function Analysis() {
       </div>
 
       {/* Search Form */}
-      <form onSubmit={handleSubmit} className="flex gap-4">
+      <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
@@ -58,6 +65,31 @@ export function Analysis() {
               </>
             )}
           </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>
       </form>
 
       {/* Error */}
@@ -110,6 +142,7 @@ export function Analysis() {
                 <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"
@@ -117,6 +150,14 @@ export function Analysis() {
                     <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">

frontend/src/pages/Analytics.tsx

@@ -2,7 +2,7 @@ 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'];
 
@@ -14,6 +14,11 @@ export function AnalyticsPage() {
     queryFn: () => analyticsApi.getAnalytics(days),
   });
 
+  const trendsQuery = useQuery({
+    queryKey: ['analytics-trends', days],
+    queryFn: () => analyticsApi.getTrends(days),
+  });
+
   if (isLoading) {
     return (
       <div className="space-y-6">
@@ -189,6 +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()}
@@ -123,13 +160,30 @@ export function Batch() {
             {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="ml-7 mt-2 text-sm text-primary hover:text-primary-dark underline"
+              className="text-sm text-text-secondary hover:text-text-primary underline"
             >
               Dismiss
             </button>
           </div>
+        </div>
       )}
 
       {/* Results */}
@@ -230,6 +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>
   );
 }

requirements.txt

@@ -17,3 +17,4 @@ PyJWT
 slowapi
 apscheduler
 boto3
+reportlab