.env.example

@@ -1,21 +1,75 @@
 # SPARC Configuration
 
+# ---- Application Environment ----
+# Set to "production" or "staging" in deployed environments.
+# The API will refuse to start with the default JWT secret unless APP_ENV=development.
+APP_ENV=development
+
+# ---- API Keys ----
+
 # SerpAPI key for patent search
 API_KEY=your_serpapi_key_here
 
 # OpenRouter API key for LLM analysis
 OPENROUTER_API_KEY=your_openrouter_key_here
 
-# Database configuration
+# ---- Database ----
-# All messages are stored in the database for persistence and caching
-DATABASE_URL=postgresql://postgres:postgres@localhost:5432/sparc
 
-# Cache configuration
+# PostgreSQL credentials (used by docker-compose)
-# When USE_CACHE=true: check database for cached responses before making API calls
+POSTGRES_USER=postgres
-# When USE_CACHE=false: always make fresh API calls (still stores results in database)
+POSTGRES_PASSWORD=change-me-to-a-secure-password
-# Default: true
+POSTGRES_DB=sparc
-USE_CACHE=true
 
-# JWT Secret for authentication
+# Full database URL (must match the credentials above)
+DATABASE_URL=postgresql://postgres:change-me-to-a-secure-password@localhost:5432/sparc
+
+# ---- Authentication ----
+
+# JWT Secret for signing tokens
 # IMPORTANT: Change this to a secure random string in production
 JWT_SECRET=your-secure-jwt-secret-change-in-production
+
+# ---- CORS ----
+
+# Comma-separated list of allowed origins for CORS
+# 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

@@ -9,7 +9,57 @@ on:
   workflow_dispatch:
 
 jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Install system dependencies
+        shell: sh
+        run: |
+          apk add --no-cache git python3 py3-pip gcc musl-dev libpq-dev python3-dev
+
+      - name: Checkout code
+        shell: sh
+        run: |
+          git clone http://gitea.gitea.svc.cluster.local/${{ gitea.repository }}.git .
+          git checkout ${{ gitea.sha }}
+
+      - name: Install Python dependencies
+        shell: sh
+        run: |
+          pip3 install --break-system-packages -r requirements.txt ruff
+
+      - name: Run ruff linter
+        shell: sh
+        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:
+          DATABASE_URL: "sqlite://"
+          API_KEY: "test-key"
+          OPENROUTER_API_KEY: "test-key"
+          JWT_SECRET: "test-secret-for-ci"
+          APP_ENV: "development"
+        run: |
+          python3 -m pytest tests/ -v --tb=short -x
+
   build-api:
+    needs: test
     runs-on: ubuntu-latest
     steps:
       - name: Install dependencies
@@ -81,6 +131,7 @@ jobs:
           echo "API image available at ${{ steps.tags.outputs.IMAGE_TAG }}"
 
   build-frontend:
+    needs: test
     runs-on: ubuntu-latest
     steps:
       - name: Install dependencies

.gitea/workflows/test.yaml

@@ -0,0 +1,67 @@
+name: Test and Lint
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Install system dependencies
+        shell: sh
+        run: |
+          apk add --no-cache git python3 py3-pip gcc musl-dev libpq-dev python3-dev
+
+      - name: Checkout code
+        shell: sh
+        run: |
+          git clone http://gitea.gitea.svc.cluster.local/${{ gitea.repository }}.git .
+          git checkout ${{ gitea.sha }}
+
+      - name: Install Python dependencies
+        shell: sh
+        run: |
+          pip3 install --break-system-packages -r requirements.txt ruff
+
+      - name: Run ruff linter
+        shell: sh
+        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:
+          DATABASE_URL: "sqlite://"
+          API_KEY: "test-key"
+          OPENROUTER_API_KEY: "test-key"
+          JWT_SECRET: "test-secret-for-ci"
+          APP_ENV: "development"
+        run: |
+          python3 -m pytest tests/ -v --tb=short -x

README.md

@@ -54,6 +54,21 @@ docker-compose up -d
 # - API Docs: http://localhost:8000/docs
 ```
 
+#### Patent PDF Storage
+
+The API stores downloaded patent PDFs in a `patents/` directory. In Docker,
+this is mounted as a bind mount (`./patents:/app/patents`) so that PDFs persist
+across container restarts.
+
+If you deploy to a different environment, ensure the `patents/` directory is a
+persistent volume. Without it, PDFs will be re-downloaded on every analysis.
+
+```yaml
+# docker-compose.yml excerpt
+volumes:
+  - ./patents:/app/patents
+```
+
 ### NixOS
 
 ```bash

ROADMAP.md

@@ -0,0 +1,122 @@
+# SPARC Roadmap
+
+Semiconductor Patent & Analytics Report Core -- development priorities.
+
+## Current State
+
+SPARC is a patent analysis platform with a working end-to-end pipeline:
+Python/FastAPI backend, React/TypeScript frontend, PostgreSQL for persistence
+and caching, Docker Compose for local development, and Gitea Actions CI/CD for
+image builds. Core features (patent retrieval via SerpAPI, PDF parsing, LLM
+analysis via OpenRouter/Claude, batch processing, JWT authentication, analytics
+dashboard) are all implemented and functional.
+
+---
+
+## P1 -- High Priority
+
+These items address correctness, security, and reliability gaps that should be
+resolved before broader production use.
+
+### Security hardening
+
+- **Rotate default JWT secret.** `auth.py` ships a fallback
+  `sparc-secret-key-change-in-production` that will be used if `JWT_SECRET` is
+  unset. Add a startup check that refuses to start with the default secret in
+  non-development environments.
+- **CORS allow-origins are hardcoded.** `api.py` only permits
+  `localhost:3000` and `localhost:5173`. Make the allowed origins configurable
+  via environment variable so the dashboard works when deployed behind a real
+  domain.
+- **Database credentials in docker-compose.yml.** The compose file embeds
+  `postgres:postgres` in plain text. Reference a `.env` file or Docker secrets
+  instead.
+
+### Error handling and resilience
+
+- **`get_db_client()` in `auth.py` creates a new `DatabaseClient` on every
+  call.** This bypasses the connection pool and can exhaust database
+  connections under load. Refactor to share a single pooled client.
+- **`_jobs` dict is in-memory only.** Job state is lost on API restart. Persist
+  job status in PostgreSQL or Redis so async batch results survive restarts.
+- **No rate limiting on auth endpoints.** `/auth/login` and `/auth/register`
+  are unprotected against brute-force or abuse. Add rate limiting middleware.
+
+### Test coverage for auth and admin
+
+- The existing API tests (`tests/test_api.py`) bypass authentication entirely.
+  Add tests that exercise the JWT flow: registration, login, protected-route
+  access, token refresh, and admin-only endpoints.
+
+---
+
+## P2 -- Medium Priority
+
+Improvements to usability, performance, and developer experience.
+
+### Backend
+
+- **Add structured logging.** Replace `print()` calls throughout `analyzer.py`,
+  `serp_api.py`, and `llm.py` with Python `logging` so log levels and
+  formatting are consistent.
+- **Make LLM model configurable.** `llm.py` hardcodes
+  `anthropic/claude-3.5-sonnet`. Accept a `MODEL` environment variable to allow
+  switching models without code changes.
+- **SERP cache TTL is hardcoded to 24 hours.** Expose `SERP_CACHE_TTL_HOURS`
+  as an environment variable in `config.py`.
+- **Patent PDF storage.** PDFs are saved to a local `patents/` directory. For
+  containerized deployments, consider object storage (S3/MinIO) or at minimum
+  document the volume mount requirement more prominently.
+- **`analyze_single_patent` assumes local file path.** The method constructs
+  `patents/{patent_id}.pdf` and reads from disk, but does not download the PDF
+  first. Either integrate the download step or document the prerequisite.
+- **`Patent.patent_id` typed as `int` in `types.py` but used as `str`
+  everywhere.** Fix the type annotation to `str`.
+
+### Frontend
+
+- **No loading/error states on several pages.** The Batch and Analytics pages
+  would benefit from skeleton loaders and user-friendly error messages.
+- **No dark mode.** Tailwind is configured but no dark variant is applied.
+- **Missing `package-lock.json` or `pnpm-lock.yaml`.** The frontend has no
+  lockfile committed, leading to non-reproducible builds.
+
+### CI/CD
+
+- **No test stage in the Gitea Actions workflow.** `build.yaml` builds and
+  pushes images but never runs `pytest`. Add a test job that gates the build.
+- **No linting or type checking.** Add `ruff` (Python) and `tsc --noEmit`
+  (TypeScript) to CI.
+
+---
+
+## P3 -- Nice to Have
+
+Lower-urgency enhancements and future features.
+
+- **Export analysis reports.** Allow users to download analysis results as PDF
+  or CSV from the dashboard.
+- **Comparison view.** Side-by-side comparison of two companies' patent
+  portfolios.
+- **Scheduled/recurring analysis.** Periodically re-analyze tracked companies
+  and alert on significant changes.
+- **Webhook/notification support.** Send alerts (Slack, Discord, email) when
+  batch jobs complete or when a company's innovation score changes
+  significantly.
+- **Multi-model support.** Let users choose between LLM providers per analysis
+  (e.g., GPT-4o, Gemini, Claude) and compare outputs.
+- **Patent trend charts.** Visualize patent filing frequency and technology
+  category distribution over time in the Analytics page.
+- **API pagination.** The `/analyze/batch` and `/jobs` endpoints could benefit
+  from cursor-based pagination for large result sets.
+- **OpenAPI client generation.** Auto-generate the TypeScript API client from
+  the FastAPI OpenAPI spec to keep frontend types in sync.
+
+---
+
+## Infrastructure and Deployment
+
+Kubernetes manifests, Helm charts, and cluster-level concerns (MetalLB,
+storage, FluxCD sync) are tracked in the
+[Talos](https://10.0.1.10/leeworks-agents/Talos) repository. File
+infrastructure-related issues there, not here.

SPARC/__init__.py

@@ -1,3 +1,4 @@
-from .types import Patents, Patent
+from .types import Patent as Patent
+from .types import Patents as Patents
 
-all = ["Patents", "Patent"]
+__all__ = ["Patents", "Patent"]

SPARC/analyzer.py

@@ -5,14 +5,17 @@ to provide company performance estimation based on patent portfolios.
 """
 
 import hashlib
+import logging
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Callable
 
 from SPARC import config
+
+logger = logging.getLogger(__name__)
 from SPARC.database import DatabaseClient
-from SPARC.serp_api import SERP
 from SPARC.llm import LLMAnalyzer
-from SPARC.types import Patent, Patents, CompanyAnalysisResult, BatchAnalysisResult
+from SPARC.serp_api import SERP
+from SPARC.types import BatchAnalysisResult, CompanyAnalysisResult, Patent, Patents
 
 
 class CompanyAnalyzer:
@@ -30,7 +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:
@@ -43,6 +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
@@ -52,13 +56,13 @@ class CompanyAnalyzer:
             query_hash = hashlib.sha256(company_name.lower().encode()).hexdigest()
             cached_ids = self.db.get_cached_serp_query(query_hash)
             if cached_ids is not None:
-                print(f"Using cached SERP results for {company_name} ({len(cached_ids)} patents)")
+                logger.info("Using cached SERP results for %s (%d patents)", company_name, len(cached_ids))
                 patents = Patents(patents=[
                     Patent(patent_id=pid, pdf_link="")
                     for pid in cached_ids
                 ])
             else:
-                print(f"Retrieving patents for {company_name}...")
+                logger.info("Retrieving patents for %s...", company_name)
                 patents = SERP.query(company_name)
                 # Cache the SERP results
                 if patents.patents:
@@ -66,12 +70,13 @@ class CompanyAnalyzer:
                         company_name=company_name,
                         query_hash=query_hash,
                         patent_ids=[p.patent_id for p in patents.patents],
+                        ttl_hours=config.serp_cache_ttl_hours,
                     )
 
         if not patents.patents:
             return f"No patents found for {company_name}"
 
-        print(f"Found {len(patents.patents)} patents. Processing...")
+        logger.info("Found %d patents. Processing...", len(patents.patents))
 
         # Download, parse, and minimize patents in parallel
         processed_patents = []
@@ -87,48 +92,74 @@ class CompanyAnalyzer:
                     if result:
                         processed_patents.append(result)
                 except Exception as e:
-                    print(f"Warning: Failed to process {patent.patent_id}: {e}")
+                    logger.warning("Failed to process %s: %s", patent.patent_id, e)
 
         if not processed_patents:
             return f"Failed to process any patents for {company_name}"
 
-        print(f"Analyzing portfolio with LLM...")
+        logger.info("Analyzing portfolio with LLM...")
 
         # 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.
 
-        Useful for focused analysis of specific innovations.
+        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
+          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 cannot be found or downloaded.
         """
-        # Note: This simplified version assumes the patent PDF is already downloaded
+        import os
-        # A more complete implementation would support direct patent ID lookup
+        logger.info("Analyzing patent %s for %s...", patent_id, company_name)
-        print(f"Analyzing patent {patent_id} for {company_name}...")
 
         patent_path = f"patents/{patent_id}.pdf"
 
+        if not os.path.exists(patent_path):
+            # 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
 
+        except FileNotFoundError:
+            raise
         except Exception as e:
             return f"Failed to analyze patent {patent_id}: {e}"
 
@@ -169,21 +200,22 @@ class CompanyAnalyzer:
 
             return {"patent_id": patent.patent_id, "content": minimized_content}
         except Exception as e:
-            print(f"Warning: Failed to process {patent.patent_id}: {e}")
+            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()
@@ -223,6 +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.
 
@@ -233,6 +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
@@ -240,11 +274,11 @@ class CompanyAnalyzer:
         results: list[CompanyAnalysisResult] = []
         total = len(companies)
 
-        print(f"Starting batch analysis of {total} companies...")
+        logger.info("Starting batch analysis of %d companies...", total)
 
         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
             }
 
@@ -257,8 +291,8 @@ class CompanyAnalyzer:
                     result = future.result()
                     results.append(result)
 
-                    status = "✓" if result.success else "✗"
+                    status = "OK" if result.success else "FAIL"
-                    print(f"[{completed}/{total}] {status} {company}")
+                    logger.info("[%d/%d] %s %s", completed, total, status, company)
 
                     if progress_callback:
                         progress_callback(company, completed, total)
@@ -273,12 +307,12 @@ class CompanyAnalyzer:
                             error=str(e),
                         )
                     )
-                    print(f"[{completed}/{total}] ✗ {company}: {e}")
+                    logger.error("[%d/%d] FAIL %s: %s", completed, total, company, e)
 
         successful = sum(1 for r in results if r.success)
         failed = total - successful
 
-        print(f"\nBatch complete: {successful} succeeded, {failed} failed")
+        logger.info("Batch complete: %d succeeded, %d failed", successful, failed)
 
         return BatchAnalysisResult(
             results=results,
@@ -304,20 +338,20 @@ class CompanyAnalyzer:
         results: list[CompanyAnalysisResult] = []
         total = len(companies)
 
-        print(f"Starting sequential analysis of {total} companies...")
+        logger.info("Starting sequential analysis of %d companies...", total)
 
         for idx, company in enumerate(companies, 1):
-            print(f"\n[{idx}/{total}] Analyzing {company}...")
+            logger.info("[%d/%d] Analyzing %s...", idx, total, company)
             result = self._analyze_company_safe(company)
             results.append(result)
 
-            status = "✓" if result.success else "✗"
+            status = "OK" if result.success else "FAIL"
-            print(f"[{idx}/{total}] {status} {company}")
+            logger.info("[%d/%d] %s %s", idx, total, status, company)
 
         successful = sum(1 for r in results if r.success)
         failed = total - successful
 
-        print(f"\nBatch complete: {successful} succeeded, {failed} failed")
+        logger.info("Batch complete: %d succeeded, %d failed", successful, failed)
 
         return BatchAnalysisResult(
             results=results,

SPARC/api.py

@@ -7,20 +7,27 @@ from contextlib import asynccontextmanager
 from datetime import datetime
 from typing import Annotated, List
 
-from fastapi import BackgroundTasks, Depends, FastAPI, HTTPException, Query
+from fastapi import BackgroundTasks, Depends, FastAPI, HTTPException, Query, Request
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, StreamingResponse
 from pydantic import BaseModel, EmailStr, Field
+from slowapi import Limiter
+from slowapi.errors import RateLimitExceeded
+from slowapi.util import get_remote_address
 
 from SPARC import config
 from SPARC.analyzer import CompanyAnalyzer
 from SPARC.auth import (
     TokenResponse,
     UserResponse,
+    check_jwt_secret,
+    close_db_client,
     create_tokens,
     decode_token,
     get_current_admin,
     get_current_user,
     get_db_client,
+    init_db_client,
 )
 from SPARC.types import BatchAnalysisResult, CompanyAnalysisResult
 
@@ -34,6 +41,7 @@ class CompanyAnalysisResponse(BaseModel):
     patent_count: int
     success: bool
     error: str | None = None
+    model: str | None = None
     timestamp: datetime
 
 
@@ -47,6 +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."""
 
@@ -56,6 +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):
@@ -70,6 +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."""
 
@@ -114,8 +142,7 @@ class AnalyticsResponse(BaseModel):
     period_days: int
 
 
-# In-memory job storage (for demo; production would use Redis/DB)
+# Job counter for generating unique IDs (the actual state is in PostgreSQL)
-_jobs: dict[str, JobStatus] = {}
 _job_counter = 0
 
 
@@ -127,6 +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,
     )
 
@@ -148,12 +176,28 @@ _analyzer: CompanyAnalyzer | None = None
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    """Initialize resources on startup."""
+    """Initialize resources on startup, clean up on shutdown."""
     global _analyzer
+    check_jwt_secret()
+    init_db_client()
     _analyzer = CompanyAnalyzer()
+    # Mark any jobs that were running/pending before the restart as failed
+    from SPARC.database import DatabaseClient
+    _db = DatabaseClient(config.database_url)
+    _db.connect()
+    _db.initialize_schema()
+    stale = _db.mark_stale_jobs_failed()
+    if stale:
+        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 if needed
+    # Cleanup
     _analyzer = None
+    close_db_client()
 
 
 app = FastAPI(
@@ -164,10 +208,26 @@ app = FastAPI(
     root_path=config.root_path,
 )
 
+# Rate limiter (in-memory storage, suitable for single-instance deployments)
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+
+
+@app.exception_handler(RateLimitExceeded)
+async def rate_limit_handler(request: Request, exc: RateLimitExceeded):
+    """Return 429 with Retry-After header when rate limit is exceeded."""
+    retry_after = getattr(exc, "retry_after", 60)
+    return JSONResponse(
+        status_code=429,
+        content={"detail": "Rate limit exceeded. Please try again later."},
+        headers={"Retry-After": str(retry_after)},
+    )
+
+
 # Add CORS middleware for React frontend
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["http://localhost:3000", "http://localhost:5173"],
+    allow_origins=config.cors_origins,
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
@@ -178,7 +238,8 @@ app.add_middleware(
 
 
 @app.post("/auth/register", response_model=UserResponse, tags=["Auth"])
-async def register(request: RegisterRequest):
+@limiter.limit("5/minute")
+async def register(request: Request, body: RegisterRequest):
     """Register a new user.
 
     The first registered user automatically becomes an admin.
@@ -190,8 +251,8 @@ async def register(request: RegisterRequest):
     role = "admin" if user_count == 0 else "user"
 
     user = db.create_user(
-        email=request.email,
+        email=body.email,
-        password=request.password,
+        password=body.password,
         role=role,
     )
 
@@ -210,11 +271,12 @@ async def register(request: RegisterRequest):
 
 
 @app.post("/auth/login", response_model=TokenResponse, tags=["Auth"])
-async def login(request: LoginRequest):
+@limiter.limit("10/minute")
+async def login(request: Request, body: LoginRequest):
     """Authenticate user and return JWT tokens."""
     db = get_db_client()
 
-    user = db.authenticate_user(request.email, request.password)
+    user = db.authenticate_user(body.email, body.password)
 
     if not user:
         raise HTTPException(
@@ -332,6 +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 ==============
 
 
@@ -352,6 +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 ==============
 
 
@@ -372,6 +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.
@@ -381,17 +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,
@@ -412,43 +888,98 @@ 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)
 
 
-def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
+def _get_job_db() -> "DatabaseClient":
+    """Get a DatabaseClient for job persistence."""
+    from SPARC.database import DatabaseClient
+    db = DatabaseClient(config.database_url)
+    return db
+
+
+def _job_row_to_status(row: dict) -> JobStatus:
+    """Convert a database job row to a JobStatus model."""
+    import json as _json
+    result = None
+    if row.get("result_json"):
+        result_data = row["result_json"]
+        if isinstance(result_data, str):
+            result_data = _json.loads(result_data)
+        result = BatchAnalysisResponse(**result_data)
+    return JobStatus(
+        job_id=row["job_id"],
+        status=row["status"],
+        progress=row["progress"],
+        total_companies=row["total_companies"],
+        completed_companies=row["completed_companies"],
+        result=result,
+        error=row.get("error"),
+    )
+
+
+def _run_batch_job(job_id: str, companies: list[str], max_workers: int, model: str | None = None):
     """Background task for batch analysis."""
-    global _jobs, _analyzer
+    import json as _json
+    global _analyzer
+
+    db = _get_job_db()
 
     if not _analyzer:
-        _jobs[job_id].status = "failed"
+        db.update_job(job_id, status="failed", error="Analyzer not initialized")
-        _jobs[job_id].error = "Analyzer not initialized"
         return
 
-    _jobs[job_id].status = "running"
+    db.update_job(job_id, status="running")
 
     def progress_callback(company: str, completed: int, total: int):
-        _jobs[job_id].completed_companies = completed
+        db.update_job(
-        _jobs[job_id].progress = int((completed / total) * 100)
+            job_id,
+            completed_companies=completed,
+            progress=int((completed / total) * 100),
+        )
 
     try:
         result = _analyzer.analyze_companies(
             companies=companies,
             max_workers=max_workers,
             progress_callback=progress_callback,
+            model=model,
+        )
+        batch_response = _convert_batch_result(result)
+        db.update_job(
+            job_id,
+            status="completed",
+            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,
         )
-        _jobs[job_id].status = "completed"
-        _jobs[job_id].progress = 100
-        _jobs[job_id].result = _convert_batch_result(result)
     except Exception as e:
-        _jobs[job_id].status = "failed"
+        db.update_job(job_id, status="failed", error=str(e))
-        _jobs[job_id].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"])
@@ -468,24 +999,20 @@ async def analyze_companies_async(
     Returns:
         Job status with job_id for polling
     """
+    _validate_model(request.model)
     global _job_counter
 
     _job_counter += 1
     job_id = f"job_{_job_counter}_{datetime.now().strftime('%Y%m%d%H%M%S')}"
 
-    _jobs[job_id] = JobStatus(
+    db = _get_job_db()
-        job_id=job_id,
+    job_row = db.create_job(job_id=job_id, total_companies=len(request.companies))
-        status="pending",
-        progress=0,
-        total_companies=len(request.companies),
-        completed_companies=0,
-    )
 
     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 _jobs[job_id]
+    return _job_row_to_status(job_row)
 
 
 @app.get("/jobs/{job_id}", response_model=JobStatus, tags=["Jobs"])
@@ -501,36 +1028,60 @@ async def get_job_status(
     Returns:
         Current job status including progress and results when complete
     """
-    if job_id not in _jobs:
+    db = _get_job_db()
+    job_row = db.get_job(job_id)
+
+    if not job_row:
         raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
 
-    return _jobs[job_id]
+    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
     """
-    jobs = list(_jobs.values())
+    db = _get_job_db()
+    # Fetch one extra to determine if there is a next page
+    job_rows = db.list_jobs(status=status, limit=limit + 1, cursor=cursor)
 
-    if status:
+    has_next = len(job_rows) > limit
-        jobs = [j for j in jobs if j.status == status]
+    if has_next:
+        job_rows = job_rows[:limit]
 
-    # Return most recent first
+    items = [_job_row_to_status(row) for row in job_rows]
-    jobs.sort(key=lambda j: j.job_id, reverse=True)
 
-    return jobs[:limit]
+    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/auth.py

@@ -13,11 +13,25 @@ from SPARC import config
 from SPARC.database import DatabaseClient
 
 # JWT Configuration
-JWT_SECRET = os.getenv("JWT_SECRET", "sparc-secret-key-change-in-production")
+_DEFAULT_JWT_SECRET = "sparc-secret-key-change-in-production"
+JWT_SECRET = os.getenv("JWT_SECRET", _DEFAULT_JWT_SECRET)
 JWT_ALGORITHM = "HS256"
 ACCESS_TOKEN_EXPIRE_MINUTES = 30
 REFRESH_TOKEN_EXPIRE_DAYS = 7
 
+
+def check_jwt_secret() -> None:
+    """Refuse to start with the default JWT secret in non-development environments.
+
+    Raises:
+        RuntimeError: If JWT_SECRET is the default value and APP_ENV is not 'development'.
+    """
+    if JWT_SECRET == _DEFAULT_JWT_SECRET and config.app_env != "development":
+        raise RuntimeError(
+            f"FATAL: JWT_SECRET is set to the default value and APP_ENV={config.app_env!r}. "
+            "Set a secure JWT_SECRET environment variable before running in non-development environments."
+        )
+
 security = HTTPBearer()
 
 
@@ -132,11 +146,36 @@ def decode_token(token: str) -> Optional[TokenPayload]:
         return None
 
 
+# Shared database client singleton, initialized at startup via init_db_client()
+_db_client: DatabaseClient | None = None
+
+
+def init_db_client() -> None:
+    """Initialize the shared database client. Call once at app startup."""
+    global _db_client
+    _db_client = DatabaseClient(config.database_url)
+    _db_client.connect()
+
+
+def close_db_client() -> None:
+    """Close the shared database client. Call at app shutdown."""
+    global _db_client
+    if _db_client:
+        _db_client.close()
+        _db_client = None
+
+
 def get_db_client() -> DatabaseClient:
-    """Get database client for auth operations."""
+    """Get the shared pooled database client for auth operations.
-    client = DatabaseClient(config.database_url)
+
-    client.connect()
+    Returns the module-level singleton DatabaseClient. If not yet initialized
-    return client
+    (e.g., during tests), creates a new instance as a fallback.
+    """
+    global _db_client
+    if _db_client is None:
+        _db_client = DatabaseClient(config.database_url)
+        _db_client.connect()
+    return _db_client
 
 
 async def get_current_user(

SPARC/config.py

@@ -2,11 +2,20 @@
 
 Loads environment variables from .env file for API keys and other secrets.
 """
-from dotenv import load_dotenv
+import logging
 import os
 
+from dotenv import load_dotenv
+
 load_dotenv()
 
+# Logging configuration
+log_level = os.getenv("LOG_LEVEL", "INFO").upper()
+logging.basicConfig(
+    level=getattr(logging, log_level, logging.INFO),
+    format="%(asctime)s %(levelname)s %(name)s %(message)s",
+)
+
 # SerpAPI key for patent search
 api_key = os.getenv("API_KEY")
 
@@ -30,6 +39,32 @@ use_database = os.getenv("USE_DATABASE", "false").lower() in ("true", "1", "yes"
 patent_search_days = int(os.getenv("PATENT_SEARCH_DAYS", "90"))
 patent_thread_workers = int(os.getenv("PATENT_THREAD_WORKERS", "5"))
 
+# LLM model to use via OpenRouter (e.g. "anthropic/claude-3.5-sonnet", "openai/gpt-4o")
+model = os.getenv("MODEL", "anthropic/claude-3.5-sonnet")
+
+# SERP cache TTL in hours (how long cached search results are considered fresh)
+serp_cache_ttl_hours = int(os.getenv("SERP_CACHE_TTL_HOURS", "24"))
+
 # Root path for running behind a reverse proxy (e.g., "/api" when served at /api/)
 # This ensures OpenAPI docs work correctly when accessed via the proxy
 root_path = os.getenv("ROOT_PATH", "")
+
+# Application environment: "development", "staging", or "production"
+# 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", "")
+cors_origins: list[str] = (
+    [o.strip() for o in _cors_origins_raw.split(",") if o.strip()]
+    if _cors_origins_raw
+    else ["http://localhost:3000", "http://localhost:5173"]
+)

SPARC/database.py

@@ -1,14 +1,15 @@
 """Database client for storing and retrieving LLM messages and user authentication."""
 
 import contextlib
-import psycopg2
-from psycopg2.pool import ThreadedConnectionPool
-from psycopg2.extras import RealDictCursor
-from typing import Dict, List, Optional
-from datetime import datetime, timedelta
-import json
 import hashlib
+import json
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional
+
 import bcrypt
+import psycopg2
+from psycopg2.extras import RealDictCursor
+from psycopg2.pool import ThreadedConnectionPool
 
 
 class DatabaseClient:
@@ -171,6 +172,55 @@ class DatabaseClient:
                 ON serp_queries(query_hash)
             """)
 
+            # Create jobs table for persisting async batch job state
+            cursor.execute("""
+                CREATE TABLE IF NOT EXISTS jobs (
+                    job_id VARCHAR(128) PRIMARY KEY,
+                    status VARCHAR(20) NOT NULL DEFAULT 'pending',
+                    progress INTEGER NOT NULL DEFAULT 0,
+                    total_companies INTEGER NOT NULL DEFAULT 0,
+                    completed_companies INTEGER NOT NULL DEFAULT 0,
+                    result_json JSONB,
+                    error TEXT,
+                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                )
+            """)
+
+            cursor.execute("""
+                CREATE INDEX IF NOT EXISTS idx_jobs_status
+                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
@@ -201,8 +251,6 @@ class DatabaseClient:
         Returns:
             Cached message dict if found, None otherwise
         """
-        self.connect()
-
         prompt_hash = self.hash_prompt(prompt)
 
         query = """
@@ -225,7 +273,8 @@ class DatabaseClient:
 
         query += " ORDER BY timestamp DESC LIMIT 1"
 
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        with self.get_conn() as conn:
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
                 cursor.execute(query, params)
                 result = cursor.fetchone()
                 return dict(result) if result else None
@@ -256,11 +305,10 @@ class DatabaseClient:
         Returns:
             The ID of the inserted record
         """
-        self.connect()
-
         prompt_hash = self.hash_prompt(prompt)
 
-        with self.conn.cursor() as cursor:
+        with self.get_conn() as conn:
+            with conn.cursor() as cursor:
                 cursor.execute(
                     """
                     INSERT INTO llm_messages
@@ -282,7 +330,7 @@ class DatabaseClient:
                 )
 
                 message_id = cursor.fetchone()[0]
-            self.conn.commit()
+            conn.commit()
 
             return message_id
 
@@ -304,8 +352,6 @@ class DatabaseClient:
         Returns:
             List of message dictionaries
         """
-        self.connect()
-
         query = "SELECT * FROM llm_messages WHERE 1=1"
         params = []
 
@@ -320,7 +366,8 @@ class DatabaseClient:
         query += " ORDER BY timestamp DESC LIMIT %s OFFSET %s"
         params.extend([limit, offset])
 
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        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()]
 
@@ -333,9 +380,8 @@ class DatabaseClient:
         Returns:
             Dictionary with analytics data
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
                 # Total messages
                 cursor.execute(
                     """
@@ -462,6 +508,156 @@ class DatabaseClient:
                 )
             conn.commit()
 
+    # Job Persistence Methods
+
+    def create_job(
+        self,
+        job_id: str,
+        total_companies: int,
+    ) -> Dict:
+        """Create a new job record.
+
+        Args:
+            job_id: Unique job identifier
+            total_companies: Number of companies in the batch
+
+        Returns:
+            Job dict
+        """
+        with self.get_conn() as conn:
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+                cursor.execute(
+                    """
+                    INSERT INTO jobs (job_id, status, progress, total_companies, completed_companies)
+                    VALUES (%s, 'pending', 0, %s, 0)
+                    RETURNING *
+                    """,
+                    (job_id, total_companies),
+                )
+                job = cursor.fetchone()
+            conn.commit()
+            return dict(job)
+
+    def update_job(
+        self,
+        job_id: str,
+        status: Optional[str] = None,
+        progress: Optional[int] = None,
+        completed_companies: Optional[int] = None,
+        result_json: Optional[str] = None,
+        error: Optional[str] = None,
+    ) -> Optional[Dict]:
+        """Update a job's state.
+
+        Only non-None fields are updated.
+        """
+        updates = []
+        params = []
+        if status is not None:
+            updates.append("status = %s")
+            params.append(status)
+        if progress is not None:
+            updates.append("progress = %s")
+            params.append(progress)
+        if completed_companies is not None:
+            updates.append("completed_companies = %s")
+            params.append(completed_companies)
+        if result_json is not None:
+            updates.append("result_json = %s")
+            params.append(result_json)
+        if error is not None:
+            updates.append("error = %s")
+            params.append(error)
+
+        if not updates:
+            return self.get_job(job_id)
+
+        updates.append("updated_at = CURRENT_TIMESTAMP")
+        params.append(job_id)
+
+        with self.get_conn() as conn:
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+                cursor.execute(
+                    f"UPDATE jobs SET {', '.join(updates)} WHERE job_id = %s RETURNING *",
+                    params,
+                )
+                job = cursor.fetchone()
+            conn.commit()
+            return dict(job) if job else None
+
+    def get_job(self, job_id: str) -> Optional[Dict]:
+        """Get a job by ID."""
+        with self.get_conn() as conn:
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+                cursor.execute("SELECT * FROM jobs WHERE job_id = %s", (job_id,))
+                job = cursor.fetchone()
+                return dict(job) if job else None
+
+    def list_jobs(
+        self,
+        status: Optional[str] = None,
+        limit: int = 10,
+        cursor: Optional[str] = None,
+    ) -> List[Dict]:
+        """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:
+            conditions.append("status = %s")
+            params.append(status)
+
+        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 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'.
+
+        Called at startup to clean up jobs that were interrupted by a restart.
+
+        Returns:
+            Number of jobs marked as failed.
+        """
+        with self.get_conn() as conn:
+            with conn.cursor() as cursor:
+                cursor.execute(
+                    """
+                    UPDATE jobs SET status = 'failed', error = 'Interrupted by server restart',
+                    updated_at = CURRENT_TIMESTAMP
+                    WHERE status IN ('running', 'pending')
+                    """
+                )
+                count = cursor.rowcount
+            conn.commit()
+            return count
+
     # User Authentication Methods
 
     @staticmethod
@@ -505,12 +701,11 @@ class DatabaseClient:
         Returns:
             Created user dict or None if email exists
         """
-        self.connect()
-
         password_hash = self.hash_password(password)
 
         try:
-            with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
+            with self.get_conn() as conn:
+                with conn.cursor(cursor_factory=RealDictCursor) as cursor:
                     cursor.execute(
                         """
                         INSERT INTO users (email, password_hash, role)
@@ -520,10 +715,9 @@ class DatabaseClient:
                         (email, password_hash, role),
                     )
                     user = cursor.fetchone()
-                self.conn.commit()
+                conn.commit()
                 return dict(user) if user else None
         except psycopg2.errors.UniqueViolation:
-            self.conn.rollback()
             return None
 
     def authenticate_user(self, email: str, password: str) -> Optional[Dict]:
@@ -536,9 +730,8 @@ class DatabaseClient:
         Returns:
             User dict if authenticated, None otherwise
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
                 cursor.execute(
                     "SELECT * FROM users WHERE email = %s",
                     (email,),
@@ -563,9 +756,8 @@ class DatabaseClient:
         Returns:
             User dict or None
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
                 cursor.execute(
                     "SELECT id, email, role, created_at FROM users WHERE id = %s",
                     (user_id,),
@@ -582,9 +774,8 @@ class DatabaseClient:
         Returns:
             User dict or None
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
                 cursor.execute(
                     "SELECT id, email, role, created_at FROM users WHERE email = %s",
                     (email,),
@@ -602,9 +793,8 @@ class DatabaseClient:
         Returns:
             List of user dicts
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
                 cursor.execute(
                     """
                     SELECT id, email, role, created_at
@@ -626,9 +816,8 @@ class DatabaseClient:
         Returns:
             Updated user dict or None
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
-        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
                 cursor.execute(
                     """
                     UPDATE users
@@ -639,7 +828,7 @@ class DatabaseClient:
                     (role, user_id),
                 )
                 user = cursor.fetchone()
-            self.conn.commit()
+            conn.commit()
             return dict(user) if user else None
 
     def delete_user(self, user_id: int) -> bool:
@@ -651,12 +840,11 @@ class DatabaseClient:
         Returns:
             True if deleted
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor() as cursor:
-        with self.conn.cursor() as cursor:
                 cursor.execute("DELETE FROM users WHERE id = %s", (user_id,))
                 deleted = cursor.rowcount > 0
-            self.conn.commit()
+            conn.commit()
             return deleted
 
     def get_user_count(self) -> int:
@@ -665,8 +853,85 @@ class DatabaseClient:
         Returns:
             Number of users
         """
-        self.connect()
+        with self.get_conn() as conn:
-
+            with conn.cursor() as cursor:
-        with self.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

@@ -1,9 +1,14 @@
 """LLM integration for patent analysis using OpenRouter."""
 
+import logging
+from typing import Dict
+
 from openai import OpenAI
+
 from SPARC import config
 from SPARC.database import DatabaseClient
-from typing import Dict
+
+logger = logging.getLogger(__name__)
 
 
 class LLMAnalyzer:
@@ -20,7 +25,7 @@ class LLMAnalyzer:
         """
         self.test_mode = test_mode
         self.use_cache = use_cache if use_cache is not None else config.use_cache
-        self.model = "anthropic/claude-3.5-sonnet"
+        self.model = config.model
 
         # Always initialize database client for storage and caching
         self.db_client = DatabaseClient(config.database_url)
@@ -35,12 +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
@@ -58,12 +64,10 @@ 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:
-            print("=" * 80)
+            logger.debug("TEST MODE - Prompt that would be sent to LLM:\n%s", prompt)
-            print("TEST MODE - Prompt that would be sent to LLM:")
-            print("=" * 80)
-            print(prompt)
-            print("=" * 80)
             return "[TEST MODE - No API call made]"
 
         # Check cache first
@@ -80,7 +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,
@@ -93,7 +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}],
             )
@@ -105,7 +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,
@@ -123,13 +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.
 
@@ -164,13 +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:
-            print(prompt)
+            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
@@ -187,7 +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,
@@ -201,7 +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}],
                 )
@@ -214,7 +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,
@@ -234,7 +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

@@ -1,12 +1,29 @@
-import os
+import io
-import serpapi
+import logging
-from SPARC import config
 import re
-import pdfplumber  # pip install pdfplumber
-import requests
 from datetime import datetime, timedelta
 from typing import Dict
-from SPARC.types import Patents, Patent
+
+import pdfplumber  # pip install pdfplumber
+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:
@@ -41,6 +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 = []
@@ -49,13 +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 storage, skipping download if already cached.
-    Save the patent PDF to the patents folder, skipping download if already cached.
+
+    Uses the configured storage backend (local filesystem or S3).
 
     Args:
       patent: Patent object
@@ -63,35 +84,51 @@ class SERP:
     Returns:
       Patent object with updated PDF path
     """
-    pdf_path = f"patents/{patent.patent_id}.pdf"
+    storage = _get_storage()
-    os.makedirs("patents", exist_ok=True)
+    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:
+      storage.write(key, response.content)
-        f.write(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

@@ -4,7 +4,7 @@ from datetime import datetime
 
 @dataclass
 class Patent:
-    patent_id: int
+    patent_id: str
     pdf_link: str
     pdf_path: str | None = None
     summary: dict | None = None
@@ -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

@@ -3,15 +3,15 @@ services:
     image: postgres:16-alpine
     container_name: sparc-postgres
     environment:
-      POSTGRES_USER: postgres
+      POSTGRES_USER: ${POSTGRES_USER}
-      POSTGRES_PASSWORD: postgres
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
-      POSTGRES_DB: sparc
+      POSTGRES_DB: ${POSTGRES_DB}
     ports:
       - "5432:5432"
     volumes:
       - postgres_data:/var/lib/postgresql/data
     healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U postgres"]
+      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER}"]
       interval: 5s
       timeout: 5s
       retries: 5
@@ -22,7 +22,7 @@ services:
     container_name: sparc-init-db
     command: python scripts/init_database.py
     environment:
-      DATABASE_URL: postgresql://postgres:postgres@postgres:5432/sparc
+      DATABASE_URL: postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@postgres:5432/${POSTGRES_DB}
     depends_on:
       postgres:
         condition: service_healthy
@@ -35,9 +35,11 @@ services:
     environment:
       API_KEY: ${API_KEY}
       OPENROUTER_API_KEY: ${OPENROUTER_API_KEY}
-      DATABASE_URL: postgresql://postgres:postgres@postgres:5432/sparc
+      DATABASE_URL: postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@postgres:5432/${POSTGRES_DB}
       USE_CACHE: "true"
       JWT_SECRET: ${JWT_SECRET:-sparc-secret-key-change-in-production}
+      CORS_ORIGINS: ${CORS_ORIGINS:-}
+      APP_ENV: ${APP_ENV:-development}
       ROOT_PATH: /api
     ports:
       - "8000:8000"
@@ -47,9 +49,32 @@ services:
       init-db:
         condition: service_completed_successfully
     volumes:
-      - ./patents:/app/patents
+      - patent_data:/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
@@ -61,3 +86,5 @@ services:
 
 volumes:
   postgres_data:
+  patent_data:
+  minio_data:

docs/DEPLOYMENT.md

@@ -276,7 +276,7 @@ The `docker-compose.yml` includes all services needed for production:
 |---------|-----------|------|-------------|
 | `postgres` | sparc-postgres | 5432 | PostgreSQL database |
 | `init-db` | sparc-init-db | - | One-time database initialization (seeds admin user) |
-| `api` | sparc-api | 8000 | FastAPI REST API with JWT auth |
+| `api` | sparc-api | 8000 | FastAPI REST API with JWT auth (patent PDFs stored in `patent_data` volume) |
 | `dashboard` | sparc-dashboard | 8080 | React TypeScript web UI |
 
 ### Common Docker Compose Commands
@@ -307,6 +307,81 @@ docker-compose restart api
 
 ---
 
+## Patent PDF Storage
+
+The SPARC API downloads patent PDFs during analysis and stores them at `/app/patents` inside the container. These files are used for subsequent single-patent analysis requests and as a local cache to avoid re-downloading. If this directory is not persisted, all downloaded PDFs are lost when the container is recreated.
+
+### Docker Compose (default)
+
+The default `docker-compose.yml` declares a named volume called `patent_data` that is mounted at `/app/patents`:
+
+```yaml
+# In the api service:
+volumes:
+  - patent_data:/app/patents
+
+# At the top-level volumes section:
+volumes:
+  patent_data:
+```
+
+This means PDFs survive `docker compose down` and `docker compose up` cycles. To remove patent data intentionally, run:
+
+```bash
+docker compose down -v   # WARNING: also removes postgres_data
+# or selectively:
+docker volume rm sparc_patent_data
+```
+
+If you prefer a bind mount (e.g., for easy host-side access during development), replace the volume with:
+
+```yaml
+volumes:
+  - ./patents:/app/patents
+```
+
+### Kubernetes
+
+For Kubernetes deployments, create a PersistentVolumeClaim and mount it into the API pod:
+
+```yaml
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: sparc-patent-data
+spec:
+  accessModes:
+    - ReadWriteOnce
+  resources:
+    requests:
+      storage: 5Gi
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: sparc-api
+spec:
+  template:
+    spec:
+      containers:
+        - name: api
+          volumeMounts:
+            - name: patent-data
+              mountPath: /app/patents
+      volumes:
+        - name: patent-data
+          persistentVolumeClaim:
+            claimName: sparc-patent-data
+```
+
+Adjust the storage size based on expected patent volume. Each patent PDF is typically 1-5 MB.
+
+### S3 Object Storage (alternative)
+
+For production deployments that need shared or highly durable storage, set `STORAGE_BACKEND=s3` in your `.env` file. This stores patent PDFs in an S3-compatible bucket (AWS S3 or MinIO) instead of the local filesystem, eliminating the need for a persistent volume. See the S3/MinIO section in `.env.example` for configuration details.
+
+---
+
 ## Troubleshooting
 
 ### Database Connection Issues

frontend/index.html

@@ -7,6 +7,15 @@
     <title>SPARC Dashboard</title>
   </head>
   <body>
+    <script>
+      // Prevent FOUC: apply saved theme before first render
+      (function() {
+        var theme = localStorage.getItem('theme');
+        if (theme === 'dark' || (!theme && window.matchMedia('(prefers-color-scheme: dark)').matches)) {
+          document.documentElement.classList.add('dark');
+        }
+      })();
+    </script>
     <div id="root"></div>
     <script type="module" src="/src/main.tsx"></script>
   </body>

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

@@ -1,6 +1,7 @@
 import { BrowserRouter, Routes, Route, Navigate } from 'react-router-dom';
 import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
 import { AuthProvider } from './context/AuthContext';
+import { ThemeProvider } from './context/ThemeContext';
 import { Layout } from './components/Layout';
 import { ProtectedRoute } from './components/ProtectedRoute';
 import { Login } from './pages/Login';
@@ -10,6 +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: {
@@ -22,6 +24,7 @@ const queryClient = new QueryClient({
 
 function App() {
   return (
+    <ThemeProvider>
     <QueryClientProvider client={queryClient}>
       <AuthProvider>
         <BrowserRouter>
@@ -41,6 +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 */}
@@ -61,6 +65,7 @@ function App() {
         </BrowserRouter>
       </AuthProvider>
     </QueryClientProvider>
+    </ThemeProvider>
   );
 }
 

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> => {
+  analyzeCompany: async (companyName: string, model?: string): Promise<CompanyAnalysis> => {
-    const response = await api.get<CompanyAnalysis>(`/analyze/${encodeURIComponent(companyName)}`);
+    const params = new URLSearchParams();
+    if (model) params.append('model', model);
+    const qs = params.toString();
+    const response = await api.get<CompanyAnalysis>(
+      `/analyze/${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

@@ -1,9 +1,11 @@
 import { Outlet, NavLink, useNavigate } from 'react-router-dom';
 import { useAuth } from '../context/AuthContext';
-import { Search, Layers, BarChart3, Info, Users, LogOut } from 'lucide-react';
+import { useTheme } from '../context/ThemeContext';
+import { Search, Layers, BarChart3, Info, Users, LogOut, GitCompareArrows, Sun, Moon } from 'lucide-react';
 
 export function Layout() {
   const { user, isAdmin, logout } = useAuth();
+  const { theme, toggleTheme } = useTheme();
   const navigate = useNavigate();
 
   const handleLogout = () => {
@@ -15,6 +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' },
   ];
 
@@ -23,7 +26,7 @@ export function Layout() {
   }
 
   return (
-    <div className="min-h-screen bg-gradient-to-br from-bg-dark to-indigo-950">
+    <div className="min-h-screen bg-gradient-to-br from-bg-dark to-slate-100 dark:to-indigo-950">
       {/* Header */}
       <header className="bg-bg-card/80 backdrop-blur-lg border-b border-primary/20">
         <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8">
@@ -63,6 +66,13 @@ export function Layout() {
 
             {/* User menu */}
             <div className="flex items-center gap-4">
+              <button
+                onClick={toggleTheme}
+                className="p-2 rounded-lg text-text-secondary hover:text-text-primary hover:bg-bg-card-hover transition-all"
+                aria-label={theme === 'dark' ? 'Switch to light mode' : 'Switch to dark mode'}
+              >
+                {theme === 'dark' ? <Sun size={18} /> : <Moon size={18} />}
+              </button>
               <div className="text-right hidden sm:block">
                 <div className="text-sm font-medium text-text-primary">{user?.email}</div>
                 <div className="text-xs text-text-secondary capitalize">{user?.role}</div>

frontend/src/components/ProtectedRoute.tsx

@@ -12,7 +12,7 @@ export function ProtectedRoute({ children, requireAdmin = false }: ProtectedRout
 
   if (isLoading) {
     return (
-      <div className="min-h-screen bg-gradient-to-br from-bg-dark to-indigo-950 flex items-center justify-center">
+      <div className="min-h-screen bg-gradient-to-br from-bg-dark to-slate-100 dark:to-indigo-950 flex items-center justify-center">
         <div className="animate-spin rounded-full h-12 w-12 border-t-2 border-b-2 border-primary"></div>
       </div>
     );

frontend/src/context/ThemeContext.tsx

@@ -0,0 +1,48 @@
+import { createContext, useContext, useEffect, useState } from 'react';
+
+type Theme = 'light' | 'dark';
+
+interface ThemeContextType {
+  theme: Theme;
+  toggleTheme: () => void;
+}
+
+const ThemeContext = createContext<ThemeContextType | undefined>(undefined);
+
+function getInitialTheme(): Theme {
+  const stored = localStorage.getItem('theme');
+  if (stored === 'light' || stored === 'dark') return stored;
+  return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
+}
+
+export function ThemeProvider({ children }: { children: React.ReactNode }) {
+  const [theme, setTheme] = useState<Theme>(getInitialTheme);
+
+  useEffect(() => {
+    const root = document.documentElement;
+    if (theme === 'dark') {
+      root.classList.add('dark');
+    } else {
+      root.classList.remove('dark');
+    }
+    localStorage.setItem('theme', theme);
+  }, [theme]);
+
+  const toggleTheme = () => {
+    setTheme((prev) => (prev === 'dark' ? 'light' : 'dark'));
+  };
+
+  return (
+    <ThemeContext.Provider value={{ theme, toggleTheme }}>
+      {children}
+    </ThemeContext.Provider>
+  );
+}
+
+export function useTheme() {
+  const context = useContext(ThemeContext);
+  if (!context) {
+    throw new Error('useTheme must be used within a ThemeProvider');
+  }
+  return context;
+}

frontend/src/context/useChartTheme.ts

@@ -0,0 +1,41 @@
+import { useTheme } from './ThemeContext';
+
+/**
+ * Returns theme-aware color values for recharts components.
+ *
+ * Recharts accepts only raw color strings (not CSS variables),
+ * so this hook bridges the Tailwind/CSS-variable theme system
+ * to the imperative recharts API.
+ */
+export function useChartTheme() {
+  const { theme } = useTheme();
+  const isDark = theme === 'dark';
+
+  return {
+    /** Axis tick and grid line stroke color */
+    axisStroke: isDark ? '#94a3b8' : '#64748b',
+    /** Tooltip container background */
+    tooltipBg: isDark ? '#1e293b' : '#ffffff',
+    /** Tooltip container border */
+    tooltipBorder: isDark
+      ? '1px solid rgba(99, 102, 241, 0.3)'
+      : '1px solid rgba(99, 102, 241, 0.2)',
+    /** Tooltip label text color */
+    tooltipLabelColor: isDark ? '#f8fafc' : '#0f172a',
+    /** Tooltip item text color */
+    tooltipItemColor: isDark ? '#e2e8f0' : '#334155',
+    /** Convenience: full contentStyle object for recharts Tooltip */
+    tooltipContentStyle: {
+      backgroundColor: isDark ? '#1e293b' : '#ffffff',
+      border: isDark
+        ? '1px solid rgba(99, 102, 241, 0.3)'
+        : '1px solid rgba(99, 102, 241, 0.2)',
+      borderRadius: '8px',
+      color: isDark ? '#f8fafc' : '#0f172a',
+    },
+    /** Convenience: labelStyle for recharts Tooltip */
+    tooltipLabelStyle: {
+      color: isDark ? '#f8fafc' : '#0f172a',
+    },
+  };
+}

frontend/src/index.css

@@ -2,6 +2,26 @@
 @tailwind components;
 @tailwind utilities;
 
+/* Light mode (default) */
+:root {
+  --color-bg-dark: #f1f5f9;
+  --color-bg-card: #ffffff;
+  --color-bg-card-hover: #e2e8f0;
+  --color-text-primary: #0f172a;
+  --color-text-secondary: #475569;
+  --color-border: #cbd5e1;
+}
+
+/* Dark mode */
+.dark {
+  --color-bg-dark: #0f172a;
+  --color-bg-card: #1e293b;
+  --color-bg-card-hover: #334155;
+  --color-text-primary: #f8fafc;
+  --color-text-secondary: #94a3b8;
+  --color-border: #334155;
+}
+
 body {
   font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
   -webkit-font-smoothing: antialiased;
@@ -15,7 +35,7 @@ body {
 }
 
 ::-webkit-scrollbar-track {
-  background: #1e293b;
+  background: var(--color-bg-card);
 }
 
 ::-webkit-scrollbar-thumb {
@@ -30,5 +50,5 @@ body {
 /* Selection */
 ::selection {
   background: rgba(99, 102, 241, 0.3);
-  color: #f8fafc;
+  color: var(--color-text-primary);
 }

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 } from '../api/client';
+import { analysisApi, exportApi } from '../api/client';
-import { Search, CheckCircle, AlertCircle, Clock, FileText } 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 */}
@@ -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">
+              <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 +2,52 @@ 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';
+import { useChartTheme } from '../context/useChartTheme';
 
 const COLORS = ['#6366f1', '#0ea5e9', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6', '#ec4899', '#14b8a6'];
 
 export function AnalyticsPage() {
   const [days, setDays] = useState(30);
+  const chartTheme = useChartTheme();
 
-  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="space-y-6">
-        <div className="animate-spin rounded-full h-12 w-12 border-t-2 border-b-2 border-primary"></div>
+        <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 +63,18 @@ 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>
+          <button
-        <div className="flex items-center gap-2 bg-secondary/10 border border-secondary/20 text-secondary rounded-xl px-4 py-3">
+            onClick={() => refetch()}
-          <AlertCircle size={18} />
+            className="mt-3 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-4 py-2 rounded-lg transition-colors"
-          <span>Analytics features require storing analysis results in PostgreSQL for historical tracking.</span>
+          >
+            Retry
+          </button>
         </div>
       </div>
     );
@@ -129,11 +162,7 @@ export function AnalyticsPage() {
                   ))}
                 </Pie>
                 <Tooltip
-                  contentStyle={{
+                  contentStyle={chartTheme.tooltipContentStyle}
-                    backgroundColor: '#1e293b',
-                    border: '1px solid rgba(99, 102, 241, 0.3)',
-                    borderRadius: '8px',
-                  }}
                 />
                 <Legend />
               </PieChart>
@@ -147,15 +176,11 @@ export function AnalyticsPage() {
             <h3 className="text-lg font-semibold text-text-primary mb-4">Analysis Types</h3>
             <ResponsiveContainer width="100%" height={300}>
               <BarChart data={typeData}>
-                <XAxis dataKey="name" stroke="#94a3b8" fontSize={12} />
+                <XAxis dataKey="name" stroke={chartTheme.axisStroke} fontSize={12} />
-                <YAxis stroke="#94a3b8" fontSize={12} />
+                <YAxis stroke={chartTheme.axisStroke} fontSize={12} />
                 <Tooltip
-                  contentStyle={{
+                  contentStyle={chartTheme.tooltipContentStyle}
-                    backgroundColor: '#1e293b',
+                  labelStyle={chartTheme.tooltipLabelStyle}
-                    border: '1px solid rgba(99, 102, 241, 0.3)',
-                    borderRadius: '8px',
-                  }}
-                  labelStyle={{ color: '#f8fafc' }}
                 />
                 <Bar dataKey="count" fill="#6366f1" radius={[4, 4, 0, 0]} />
               </BarChart>
@@ -163,6 +188,106 @@ 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={chartTheme.axisStroke} fontSize={12} />
+                      <YAxis stroke={chartTheme.axisStroke} fontSize={12} />
+                      <Tooltip
+                        contentStyle={chartTheme.tooltipContentStyle}
+                        labelStyle={chartTheme.tooltipLabelStyle}
+                      />
+                      <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={chartTheme.axisStroke} fontSize={12} />
+                      <YAxis stroke={chartTheme.axisStroke} fontSize={12} />
+                      <Tooltip
+                        contentStyle={chartTheme.tooltipContentStyle}
+                        labelStyle={chartTheme.tooltipLabelStyle}
+                      />
+                      <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 +1,37 @@
 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 { useChartTheme } from '../context/useChartTheme';
 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 chartTheme = useChartTheme();
+
+  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),
+      analysisApi.analyzeBatch(companies, workers, selectedModel || undefined),
-    onSuccess: (data) => setResult(data),
+    onSuccess: (data) => {
+      setResult(data);
+      jobsQuery.refetch();
+    },
   });
 
   const handleSubmit = (e: React.FormEvent) => {
@@ -85,6 +102,29 @@ 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 +154,38 @@ 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">
+        <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>Batch analysis failed. Please try again.</span>
+            <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>
       )}
 
@@ -144,15 +213,11 @@ export function Batch() {
             <div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6">
               <ResponsiveContainer width="100%" height={300}>
                 <BarChart data={chartData}>
-                  <XAxis dataKey="name" stroke="#94a3b8" fontSize={12} />
+                  <XAxis dataKey="name" stroke={chartTheme.axisStroke} fontSize={12} />
-                  <YAxis stroke="#94a3b8" fontSize={12} />
+                  <YAxis stroke={chartTheme.axisStroke} fontSize={12} />
                   <Tooltip
-                    contentStyle={{
+                    contentStyle={chartTheme.tooltipContentStyle}
-                      backgroundColor: '#1e293b',
+                    labelStyle={chartTheme.tooltipLabelStyle}
-                      border: '1px solid rgba(99, 102, 241, 0.3)',
-                      borderRadius: '8px',
-                    }}
-                    labelStyle={{ color: '#f8fafc' }}
                   />
                   <Bar dataKey="patents" radius={[4, 4, 0, 0]}>
                     {chartData.map((entry, index) => (
@@ -218,6 +283,123 @@ 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/pages/Login.tsx

@@ -31,7 +31,7 @@ export function Login() {
   };
 
   return (
-    <div className="min-h-screen bg-gradient-to-br from-bg-dark to-indigo-950 flex items-center justify-center px-4">
+    <div className="min-h-screen bg-gradient-to-br from-bg-dark to-slate-100 dark:to-indigo-950 flex items-center justify-center px-4">
       <div className="w-full max-w-md">
         {/* Brand */}
         <div className="text-center mb-8">

frontend/src/pages/Register.tsx

@@ -40,7 +40,7 @@ export function Register() {
   };
 
   return (
-    <div className="min-h-screen bg-gradient-to-br from-bg-dark to-indigo-950 flex items-center justify-center px-4">
+    <div className="min-h-screen bg-gradient-to-br from-bg-dark to-slate-100 dark:to-indigo-950 flex items-center justify-center px-4">
       <div className="w-full max-w-md">
         {/* Brand */}
         <div className="text-center mb-8">

frontend/src/types/index.ts

@@ -1,46 +1,32 @@
-export interface User {
+/**
-  id: number;
+ * Application types derived from the auto-generated OpenAPI schema.
-  email: string;
+ *
-  role: 'admin' | 'user';
+ * Run `npm run generate:local` (or `npm run generate` with the API running)
-  created_at: string;
+ * 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 {
+import type { components } from '../api/schema';
-  access_token: string;
-  refresh_token: string;
-  token_type: string;
-}
 
-export interface CompanyAnalysis {
+// Re-export schema types under the names the rest of the app expects.
-  company_name: string;
+export type User = components['schemas']['UserResponse'];
-  analysis: string;
+export type TokenResponse = components['schemas']['TokenResponse'];
-  patent_count: number;
+export type CompanyAnalysis = components['schemas']['CompanyAnalysisResponse'];
-  success: boolean;
+export type BatchAnalysisResult = components['schemas']['BatchAnalysisResponse'];
-  error: string | null;
+export type JobStatus = components['schemas']['JobStatus'];
-  timestamp: string;
+export type Analytics = Omit<components['schemas']['AnalyticsResponse'], 'by_company' | 'by_type'> & {
-}
-
-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;
   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'];

frontend/tailwind.config.js

@@ -4,6 +4,7 @@ export default {
     "./index.html",
     "./src/**/*.{js,ts,jsx,tsx}",
   ],
+  darkMode: 'class',
   theme: {
     extend: {
       colors: {
@@ -16,15 +17,15 @@ export default {
         warning: '#f59e0b',
         error: '#ef4444',
         bg: {
-          dark: '#0f172a',
+          dark: 'var(--color-bg-dark)',
-          card: '#1e293b',
+          card: 'var(--color-bg-card)',
-          'card-hover': '#334155',
+          'card-hover': 'var(--color-bg-card-hover)',
         },
         text: {
-          primary: '#f8fafc',
+          primary: 'var(--color-text-primary)',
-          secondary: '#94a3b8',
+          secondary: 'var(--color-text-secondary)',
         },
-        border: '#334155',
+        border: 'var(--color-border)',
       },
     },
   },

requirements.txt

@@ -14,3 +14,7 @@ numpy
 pandas
 bcrypt
 PyJWT
+slowapi
+apscheduler
+boto3
+reportlab

ruff.toml

@@ -0,0 +1,8 @@
+[lint]
+select = ["E", "F", "I"]
+ignore = [
+    "E501",  # line too long (handled by formatter)
+]
+
+[lint.per-file-ignores]
+"tests/*" = ["E402", "F841"]  # allow import not at top of file, unused vars (mocks) in tests

scripts/init_database.py

@@ -40,6 +40,9 @@ def main():
         print("\nTables created:")
         print("  - llm_messages: Stores all LLM prompts and responses")
         print("  - users: Stores user accounts")
+        print("  - jobs: Stores async batch job state")
+        print("  - patents: Patent PDF cache")
+        print("  - serp_queries: SERP query result cache")
         print("\nIndexes created:")
         print("  - idx_messages_timestamp: For time-based queries")
         print("  - idx_messages_company: For company-specific queries")

tests/test_analyzer.py

@@ -1,9 +1,11 @@
 """Tests for the high-level company analyzer orchestration."""
 
+from unittest.mock import MagicMock, Mock
+
 import pytest
-from unittest.mock import Mock, patch, call, MagicMock
+
 from SPARC.analyzer import CompanyAnalyzer
-from SPARC.types import Patent, Patents, CompanyAnalysisResult, BatchAnalysisResult
+from SPARC.types import BatchAnalysisResult, Patent, Patents
 
 
 @pytest.fixture(autouse=True)
@@ -24,7 +26,7 @@ class TestCompanyAnalyzer:
         """Test analyzer initialization with API key."""
         mock_llm = mocker.patch("SPARC.analyzer.LLMAnalyzer")
 
-        analyzer = CompanyAnalyzer(openrouter_api_key="test-key")
+        _analyzer = CompanyAnalyzer(openrouter_api_key="test-key")  # noqa: F841
 
         mock_llm.assert_called_once_with(api_key="test-key")
 

tests/test_api.py

@@ -1,12 +1,13 @@
 """Tests for FastAPI web service endpoints."""
 
-import pytest
 from datetime import datetime
-from unittest.mock import Mock, patch
+from unittest.mock import Mock
+
+import pytest
 from fastapi.testclient import TestClient
 
-from SPARC.api import app, _analyzer, _jobs
+from SPARC.api import app
-from SPARC.types import CompanyAnalysisResult, BatchAnalysisResult
+from SPARC.types import BatchAnalysisResult, CompanyAnalysisResult
 
 
 @pytest.fixture
@@ -181,3 +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"])

tests/test_auth.py

@@ -0,0 +1,302 @@
+"""Tests for JWT authentication flow: register, login, protected routes, refresh, admin access."""
+
+from datetime import datetime, timezone
+from unittest.mock import MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+
+from SPARC.api import app
+from SPARC.auth import create_access_token, create_refresh_token
+
+
+@pytest.fixture
+def client():
+    """Create test client."""
+    return TestClient(app)
+
+
+@pytest.fixture(autouse=True)
+def mock_db(monkeypatch):
+    """Mock the database client used by auth endpoints.
+
+    Returns a MagicMock with all DB methods pre-configured.
+    """
+    db = MagicMock()
+
+    # Default: no users exist
+    db.get_user_count.return_value = 0
+    db.get_user_by_id.return_value = None
+    db.get_user_by_email.return_value = None
+    db.authenticate_user.return_value = None
+    db.create_user.return_value = None
+    db.get_all_users.return_value = []
+    db.update_user_role.return_value = None
+    db.delete_user.return_value = False
+
+    with patch("SPARC.api.get_db_client", return_value=db), \
+         patch("SPARC.auth.get_db_client", return_value=db):
+        yield db
+
+
+def _make_admin_user():
+    return {
+        "id": 1,
+        "email": "admin@test.com",
+        "role": "admin",
+        "created_at": datetime(2025, 1, 1, tzinfo=timezone.utc),
+    }
+
+
+def _make_regular_user():
+    return {
+        "id": 2,
+        "email": "user@test.com",
+        "role": "user",
+        "created_at": datetime(2025, 1, 1, tzinfo=timezone.utc),
+    }
+
+
+def _auth_header(user_dict):
+    """Create an Authorization header with a valid access token for the given user."""
+    token = create_access_token(user_dict["id"], user_dict["email"], user_dict["role"])
+    return {"Authorization": f"Bearer {token}"}
+
+
+class TestRegister:
+    """POST /auth/register"""
+
+    def test_register_first_user_becomes_admin(self, client, mock_db):
+        """First registered user should get admin role."""
+        mock_db.get_user_count.return_value = 0
+        mock_db.create_user.return_value = {
+            "id": 1,
+            "email": "admin@test.com",
+            "role": "admin",
+            "created_at": datetime(2025, 1, 1, tzinfo=timezone.utc),
+        }
+
+        response = client.post(
+            "/auth/register",
+            json={"email": "admin@test.com", "password": "securepass123"},
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["email"] == "admin@test.com"
+        assert data["role"] == "admin"
+        mock_db.create_user.assert_called_once_with(
+            email="admin@test.com", password="securepass123", role="admin"
+        )
+
+    def test_register_subsequent_user_gets_user_role(self, client, mock_db):
+        """Non-first user should get regular user role."""
+        mock_db.get_user_count.return_value = 1
+        mock_db.create_user.return_value = _make_regular_user()
+
+        response = client.post(
+            "/auth/register",
+            json={"email": "user@test.com", "password": "securepass123"},
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["role"] == "user"
+
+    def test_register_duplicate_email_returns_400(self, client, mock_db):
+        """Registering with an existing email should return 400."""
+        mock_db.get_user_count.return_value = 1
+        mock_db.create_user.return_value = None  # indicates duplicate
+
+        response = client.post(
+            "/auth/register",
+            json={"email": "existing@test.com", "password": "securepass123"},
+        )
+
+        assert response.status_code == 400
+        assert "already registered" in response.json()["detail"].lower()
+
+
+class TestLogin:
+    """POST /auth/login"""
+
+    def test_login_valid_credentials_returns_tokens(self, client, mock_db):
+        """Valid credentials should return access and refresh tokens."""
+        user = _make_regular_user()
+        mock_db.authenticate_user.return_value = user
+
+        response = client.post(
+            "/auth/login",
+            json={"email": "user@test.com", "password": "correctpassword"},
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert "access_token" in data
+        assert "refresh_token" in data
+        assert data["token_type"] == "bearer"
+
+    def test_login_invalid_credentials_returns_401(self, client, mock_db):
+        """Invalid credentials should return 401."""
+        mock_db.authenticate_user.return_value = None
+
+        response = client.post(
+            "/auth/login",
+            json={"email": "user@test.com", "password": "wrongpassword"},
+        )
+
+        assert response.status_code == 401
+        assert "invalid" in response.json()["detail"].lower()
+
+
+class TestGetMe:
+    """GET /auth/me"""
+
+    def test_valid_access_token_returns_user(self, client, mock_db):
+        """A valid access token should return the user's data."""
+        user = _make_regular_user()
+        mock_db.get_user_by_id.return_value = user
+
+        response = client.get("/auth/me", headers=_auth_header(user))
+
+        assert response.status_code == 200
+        data = response.json()
+        assert data["email"] == "user@test.com"
+        assert data["id"] == 2
+
+    def test_missing_token_returns_401(self, client):
+        """No token should return 401 (403 from HTTPBearer)."""
+        response = client.get("/auth/me")
+        assert response.status_code in (401, 403)
+
+    def test_expired_token_returns_401(self, client, mock_db):
+        """An expired token should return 401."""
+        # Create a token that has already expired
+        from datetime import timedelta
+
+        import jwt as pyjwt
+        from SPARC.auth import JWT_ALGORITHM, JWT_SECRET
+
+        payload = {
+            "sub": "1",
+            "email": "user@test.com",
+            "role": "user",
+            "exp": datetime.now(timezone.utc) - timedelta(hours=1),
+            "type": "access",
+        }
+        expired_token = pyjwt.encode(payload, JWT_SECRET, algorithm=JWT_ALGORITHM)
+
+        response = client.get(
+            "/auth/me", headers={"Authorization": f"Bearer {expired_token}"}
+        )
+        assert response.status_code == 401
+
+    def test_refresh_token_as_access_returns_401(self, client, mock_db):
+        """Using a refresh token as an access token should return 401."""
+        user = _make_regular_user()
+        refresh_token = create_refresh_token(user["id"], user["email"], user["role"])
+
+        response = client.get(
+            "/auth/me", headers={"Authorization": f"Bearer {refresh_token}"}
+        )
+        assert response.status_code == 401
+
+
+class TestRefreshToken:
+    """POST /auth/refresh"""
+
+    def test_valid_refresh_token_returns_new_tokens(self, client, mock_db):
+        """A valid refresh token should issue new access and refresh tokens."""
+        user = _make_regular_user()
+        mock_db.get_user_by_id.return_value = user
+        refresh = create_refresh_token(user["id"], user["email"], user["role"])
+
+        response = client.post(
+            "/auth/refresh", json={"refresh_token": refresh}
+        )
+
+        assert response.status_code == 200
+        data = response.json()
+        assert "access_token" in data
+        assert "refresh_token" in data
+
+    def test_invalid_refresh_token_returns_401(self, client, mock_db):
+        """An invalid refresh token should return 401."""
+        response = client.post(
+            "/auth/refresh", json={"refresh_token": "invalid-token-string"}
+        )
+        assert response.status_code == 401
+
+    def test_access_token_as_refresh_returns_401(self, client, mock_db):
+        """Using an access token as a refresh token should return 401."""
+        user = _make_regular_user()
+        access = create_access_token(user["id"], user["email"], user["role"])
+
+        response = client.post(
+            "/auth/refresh", json={"refresh_token": access}
+        )
+        assert response.status_code == 401
+
+
+class TestAdminUsers:
+    """GET /admin/users and PATCH /admin/users/{id}/role"""
+
+    def test_admin_can_list_users(self, client, mock_db):
+        """Admin token should allow listing users."""
+        admin = _make_admin_user()
+        mock_db.get_user_by_id.return_value = admin
+        mock_db.get_all_users.return_value = [admin, _make_regular_user()]
+
+        response = client.get("/admin/users", headers=_auth_header(admin))
+
+        assert response.status_code == 200
+        data = response.json()
+        assert len(data) == 2
+
+    def test_regular_user_cannot_list_users(self, client, mock_db):
+        """Regular user token should be rejected with 403."""
+        user = _make_regular_user()
+        mock_db.get_user_by_id.return_value = user
+
+        response = client.get("/admin/users", headers=_auth_header(user))
+
+        assert response.status_code == 403
+
+    def test_no_token_cannot_list_users(self, client):
+        """No token should be rejected."""
+        response = client.get("/admin/users")
+        assert response.status_code in (401, 403)
+
+    def test_admin_can_change_user_role(self, client, mock_db):
+        """Admin should be able to change another user's role."""
+        admin = _make_admin_user()
+        mock_db.get_user_by_id.return_value = admin
+        mock_db.update_user_role.return_value = {
+            "id": 2,
+            "email": "user@test.com",
+            "role": "admin",
+            "created_at": datetime(2025, 1, 1, tzinfo=timezone.utc),
+        }
+
+        response = client.patch(
+            "/admin/users/2/role",
+            json={"role": "admin"},
+            headers=_auth_header(admin),
+        )
+
+        assert response.status_code == 200
+        assert response.json()["role"] == "admin"
+
+    def test_admin_cannot_change_own_role(self, client, mock_db):
+        """Admin should not be able to change their own role."""
+        admin = _make_admin_user()
+        mock_db.get_user_by_id.return_value = admin
+
+        response = client.patch(
+            "/admin/users/1/role",
+            json={"role": "user"},
+            headers=_auth_header(admin),
+        )
+
+        assert response.status_code == 400
+        assert "own role" in response.json()["detail"].lower()

tests/test_llm.py

@@ -1,7 +1,9 @@
 """Tests for LLM analysis functionality."""
 
+from unittest.mock import Mock
+
 import pytest
-from unittest.mock import Mock, MagicMock, patch
+
 from SPARC.llm import LLMAnalyzer
 
 

tests/test_rate_limit.py

@@ -0,0 +1,97 @@
+"""Tests for rate limiting on auth endpoints."""
+
+import pytest
+from unittest.mock import Mock, patch, MagicMock
+from fastapi.testclient import TestClient
+
+from SPARC.api import app
+
+
+@pytest.fixture
+def client():
+    """Create test client with rate limiter enabled."""
+    return TestClient(app)
+
+
+@pytest.fixture(autouse=True)
+def reset_limiter():
+    """Reset rate limiter storage between tests."""
+    from SPARC.api import limiter
+    limiter.reset()
+    yield
+
+
+class TestRateLimiting:
+    """Test rate limiting on login and register endpoints."""
+
+    @patch("SPARC.api.get_db_client")
+    def test_login_allows_requests_under_limit(self, mock_db_client, client):
+        """Login endpoint allows requests under the rate limit."""
+        mock_db = MagicMock()
+        mock_db.authenticate_user.return_value = None
+        mock_db_client.return_value = mock_db
+
+        # Should allow at least a few requests
+        for _ in range(5):
+            response = client.post(
+                "/auth/login",
+                json={"email": "test@example.com", "password": "password123"},
+            )
+            # 401 is expected (invalid credentials), not 429
+            assert response.status_code == 401
+
+    @patch("SPARC.api.get_db_client")
+    def test_login_rate_limited_after_threshold(self, mock_db_client, client):
+        """Login endpoint returns 429 after exceeding rate limit."""
+        mock_db = MagicMock()
+        mock_db.authenticate_user.return_value = None
+        mock_db_client.return_value = mock_db
+
+        # Send more than the limit (10/minute)
+        statuses = []
+        for _ in range(15):
+            response = client.post(
+                "/auth/login",
+                json={"email": "test@example.com", "password": "password123"},
+            )
+            statuses.append(response.status_code)
+
+        # At least one should be 429
+        assert 429 in statuses, f"Expected 429 in statuses but got: {set(statuses)}"
+
+    @patch("SPARC.api.get_db_client")
+    def test_register_rate_limited_after_threshold(self, mock_db_client, client):
+        """Register endpoint returns 429 after exceeding rate limit."""
+        mock_db = MagicMock()
+        mock_db.get_user_count.return_value = 1
+        mock_db.create_user.return_value = None  # triggers 400 (email exists)
+        mock_db_client.return_value = mock_db
+
+        # Send more than the limit (5/minute)
+        statuses = []
+        for _ in range(10):
+            response = client.post(
+                "/auth/register",
+                json={"email": "test@example.com", "password": "password123"},
+            )
+            statuses.append(response.status_code)
+
+        # At least one should be 429
+        assert 429 in statuses, f"Expected 429 in statuses but got: {set(statuses)}"
+
+    @patch("SPARC.api.get_db_client")
+    def test_rate_limit_returns_retry_after_header(self, mock_db_client, client):
+        """Rate limited responses include a Retry-After header."""
+        mock_db = MagicMock()
+        mock_db.authenticate_user.return_value = None
+        mock_db_client.return_value = mock_db
+
+        # Exhaust the limit
+        for _ in range(15):
+            response = client.post(
+                "/auth/login",
+                json={"email": "test@example.com", "password": "password123"},
+            )
+            if response.status_code == 429:
+                assert "Retry-After" in response.headers
+                break

tests/test_security.py

@@ -0,0 +1,116 @@
+"""Tests for security hardening: JWT secret startup check, CORS config, credential handling."""
+
+import os
+from unittest.mock import patch
+
+import pytest
+
+
+class TestJWTSecretStartupCheck:
+    """Test the startup guard that refuses default JWT secret in non-dev environments."""
+
+    def test_default_secret_in_production_raises(self):
+        """Starting with default secret and APP_ENV=production must raise RuntimeError."""
+        with patch.dict(os.environ, {"APP_ENV": "production"}):
+            # Reload config to pick up the new APP_ENV
+            import importlib
+            import SPARC.config
+            importlib.reload(SPARC.config)
+
+            from SPARC.auth import _DEFAULT_JWT_SECRET, check_jwt_secret
+            # Patch JWT_SECRET to the default
+            with patch("SPARC.auth.JWT_SECRET", _DEFAULT_JWT_SECRET):
+                with pytest.raises(RuntimeError, match="FATAL.*JWT_SECRET"):
+                    check_jwt_secret()
+
+            # Restore config
+            with patch.dict(os.environ, {"APP_ENV": "development"}):
+                importlib.reload(SPARC.config)
+
+    def test_default_secret_in_development_succeeds(self):
+        """Starting with default secret and APP_ENV=development must not raise."""
+        with patch.dict(os.environ, {"APP_ENV": "development"}):
+            import importlib
+            import SPARC.config
+            importlib.reload(SPARC.config)
+
+            from SPARC.auth import _DEFAULT_JWT_SECRET, check_jwt_secret
+            with patch("SPARC.auth.JWT_SECRET", _DEFAULT_JWT_SECRET):
+                # Should not raise
+                check_jwt_secret()
+
+            # Restore
+            importlib.reload(SPARC.config)
+
+    def test_custom_secret_in_production_succeeds(self):
+        """Starting with a custom secret in production must not raise."""
+        with patch.dict(os.environ, {"APP_ENV": "production"}):
+            import importlib
+            import SPARC.config
+            importlib.reload(SPARC.config)
+
+            from SPARC.auth import check_jwt_secret
+            with patch("SPARC.auth.JWT_SECRET", "my-secure-random-secret-abc123"):
+                # Should not raise
+                check_jwt_secret()
+
+            with patch.dict(os.environ, {"APP_ENV": "development"}):
+                importlib.reload(SPARC.config)
+
+    def test_default_secret_unset_env_succeeds(self):
+        """When APP_ENV is unset (defaults to development), default secret is allowed."""
+        with patch.dict(os.environ, {}, clear=False):
+            # Remove APP_ENV if present
+            env = os.environ.copy()
+            env.pop("APP_ENV", None)
+            with patch.dict(os.environ, env, clear=True):
+                import importlib
+                import SPARC.config
+                importlib.reload(SPARC.config)
+
+                from SPARC.auth import _DEFAULT_JWT_SECRET, check_jwt_secret
+                with patch("SPARC.auth.JWT_SECRET", _DEFAULT_JWT_SECRET):
+                    # Should not raise (defaults to development)
+                    check_jwt_secret()
+
+                with patch.dict(os.environ, {"APP_ENV": "development"}):
+                    importlib.reload(SPARC.config)
+
+
+class TestCORSConfig:
+    """Test that CORS origins are configurable via environment variable."""
+
+    def test_default_cors_origins(self):
+        """When CORS_ORIGINS is unset, defaults to localhost origins."""
+        with patch.dict(os.environ, {"CORS_ORIGINS": ""}):
+            import importlib
+            import SPARC.config
+            importlib.reload(SPARC.config)
+            assert SPARC.config.cors_origins == [
+                "http://localhost:3000",
+                "http://localhost:5173",
+            ]
+
+    def test_custom_cors_origins(self):
+        """Setting CORS_ORIGINS configures allowed origins."""
+        with patch.dict(os.environ, {"CORS_ORIGINS": "https://sparc.example.com,https://app.example.com"}):
+            import importlib
+            import SPARC.config
+            importlib.reload(SPARC.config)
+            assert SPARC.config.cors_origins == [
+                "https://sparc.example.com",
+                "https://app.example.com",
+            ]
+            # Restore
+            with patch.dict(os.environ, {"CORS_ORIGINS": ""}):
+                importlib.reload(SPARC.config)
+
+    def test_single_cors_origin(self):
+        """A single origin without comma works correctly."""
+        with patch.dict(os.environ, {"CORS_ORIGINS": "https://sparc.example.com"}):
+            import importlib
+            import SPARC.config
+            importlib.reload(SPARC.config)
+            assert SPARC.config.cors_origins == ["https://sparc.example.com"]
+            with patch.dict(os.environ, {"CORS_ORIGINS": ""}):
+                importlib.reload(SPARC.config)

tests/test_serp_api.py

@@ -1,9 +1,8 @@
 """Tests for SERP API patent retrieval and parsing functionality."""
 
-import os
-import pytest
-from unittest.mock import patch, Mock
 from datetime import datetime, timedelta
+from unittest.mock import Mock
+
 from SPARC.serp_api import SERP
 from SPARC.types import Patent