ci: add pytest and ruff linting to CI, fix all lint errors

- Add test job to build.yaml that runs pytest and ruff before building images
- Add standalone test.yaml workflow for PRs
- Add ruff.toml with E/F/I rules configured
- Fix all ruff lint errors: sort imports, remove unused imports, fix re-exports
- Build jobs now depend on test job passing (needs: test)

Closes leeworks-agents/SPARC#18
Closes leeworks-agents/SPARC#19

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

SPARC/analyzer.py

@@ -5,13 +5,10 @@ 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.llm import LLMAnalyzer
 from SPARC.serp_api import SERP
@@ -55,13 +52,13 @@ 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:
-                logger.info("Using cached SERP results for %s (%d patents)", company_name, len(cached_ids))
+                print(f"Using cached SERP results for {company_name} ({len(cached_ids)} patents)")
                 patents = Patents(patents=[
                     Patent(patent_id=pid, pdf_link="")
                     for pid in cached_ids
                 ])
             else:
-                logger.info("Retrieving patents for %s...", company_name)
+                print(f"Retrieving patents for {company_name}...")
                 patents = SERP.query(company_name)
                 # Cache the SERP results
                 if patents.patents:
@@ -69,13 +66,12 @@ 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}"
 
-        logger.info("Found %d patents. Processing...", len(patents.patents))
+        print(f"Found {len(patents.patents)} patents. Processing...")
 
         # Download, parse, and minimize patents in parallel
         processed_patents = []
@@ -91,12 +87,12 @@ class CompanyAnalyzer:
                     if result:
                         processed_patents.append(result)
                 except Exception as e:
-                    logger.warning("Failed to process %s: %s", patent.patent_id, e)
+                    print(f"Warning: Failed to process {patent.patent_id}: {e}")
 
         if not processed_patents:
             return f"Failed to process any patents for {company_name}"
 
-        logger.info("Analyzing portfolio with LLM...")
+        print("Analyzing portfolio with LLM...")
 
         # Analyze the full portfolio with LLM
         analysis = self.llm_analyzer.analyze_patent_portfolio(
@@ -108,10 +104,12 @@ class CompanyAnalyzer:
     def analyze_single_patent(self, patent_id: str, company_name: str) -> str:
         """Analyze a single patent by ID.
 
-        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.
+        Prerequisite:
+            The patent PDF must already exist at ``patents/{patent_id}.pdf``
+            before calling this method. PDFs are downloaded automatically when
+            using the batch analysis pipeline (``analyze_company`` or the
+            ``/analyze/batch`` API endpoint). For standalone usage, download
+            the PDF manually or call ``SERP.save_patents()`` first.
 
         Args:
           patent_id: Publication ID of the patent (e.g. "US-11234567-B2")
@@ -121,29 +119,16 @@ class CompanyAnalyzer:
           Analysis of the specific patent's innovation quality
 
         Raises:
-          FileNotFoundError: If the patent PDF cannot be found or downloaded.
+          FileNotFoundError: If the patent PDF is not found at the expected path.
         """
         import os
-        logger.info("Analyzing patent %s for %s...", patent_id, 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."
+                f"Patent PDF not found at '{patent_path}'. "
+                f"Download the PDF first using SERP.save_patents() or the batch analysis pipeline."
             )
 
         try:
@@ -198,7 +183,7 @@ class CompanyAnalyzer:
 
             return {"patent_id": patent.patent_id, "content": minimized_content}
         except Exception as e:
-            logger.warning("Failed to process %s: %s", patent.patent_id, e)
+            print(f"Warning: Failed to process {patent.patent_id}: {e}")
             return None
 
     def _analyze_company_safe(self, company_name: str) -> CompanyAnalysisResult:
@@ -269,7 +254,7 @@ class CompanyAnalyzer:
         results: list[CompanyAnalysisResult] = []
         total = len(companies)
 
-        logger.info("Starting batch analysis of %d companies...", total)
+        print(f"Starting batch analysis of {total} companies...")
 
         with ThreadPoolExecutor(max_workers=max_workers) as executor:
             future_to_company = {
@@ -286,8 +271,8 @@ class CompanyAnalyzer:
                     result = future.result()
                     results.append(result)
 
-                    status = "OK" if result.success else "FAIL"
-                    logger.info("[%d/%d] %s %s", completed, total, status, company)
+                    status = "✓" if result.success else "✗"
+                    print(f"[{completed}/{total}] {status} {company}")
 
                     if progress_callback:
                         progress_callback(company, completed, total)
@@ -302,12 +287,12 @@ class CompanyAnalyzer:
                             error=str(e),
                         )
                     )
-                    logger.error("[%d/%d] FAIL %s: %s", completed, total, company, e)
+                    print(f"[{completed}/{total}] ✗ {company}: {e}")
 
         successful = sum(1 for r in results if r.success)
         failed = total - successful
 
-        logger.info("Batch complete: %d succeeded, %d failed", successful, failed)
+        print(f"\nBatch complete: {successful} succeeded, {failed} failed")
 
         return BatchAnalysisResult(
             results=results,
@@ -333,20 +318,20 @@ class CompanyAnalyzer:
         results: list[CompanyAnalysisResult] = []
         total = len(companies)
 
-        logger.info("Starting sequential analysis of %d companies...", total)
+        print(f"Starting sequential analysis of {total} companies...")
 
         for idx, company in enumerate(companies, 1):
-            logger.info("[%d/%d] Analyzing %s...", idx, total, company)
+            print(f"\n[{idx}/{total}] Analyzing {company}...")
             result = self._analyze_company_safe(company)
             results.append(result)
 
-            status = "OK" if result.success else "FAIL"
-            logger.info("[%d/%d] %s %s", idx, total, status, company)
+            status = "✓" if result.success else "✗"
+            print(f"[{idx}/{total}] {status} {company}")
 
         successful = sum(1 for r in results if r.success)
         failed = total - successful
 
-        logger.info("Batch complete: %d succeeded, %d failed", successful, failed)
+        print(f"\nBatch complete: {successful} succeeded, {failed} failed")
 
         return BatchAnalysisResult(
             results=results,

SPARC/api.py

@@ -21,13 +21,11 @@ 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
 
@@ -157,7 +155,6 @@ async def lifespan(app: FastAPI):
     """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
@@ -170,9 +167,8 @@ async def lifespan(app: FastAPI):
         logging.getLogger(__name__).warning("Marked %d stale jobs as failed on startup", stale)
     _db.close()
     yield
-    # Cleanup
+    # Cleanup if needed
     _analyzer = None
-    close_db_client()
 
 
 app = FastAPI(
@@ -429,38 +425,6 @@ async def analyze_company(
     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,

SPARC/auth.py

@@ -146,36 +146,11 @@ 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 the shared pooled database client for auth operations.
-
-    Returns the module-level singleton DatabaseClient. If not yet initialized
-    (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
+    """Get database client for auth operations."""
+    client = DatabaseClient(config.database_url)
+    client.connect()
+    return client
 
 
 async def get_current_user(

SPARC/config.py

@@ -2,20 +2,12 @@
 
 Loads environment variables from .env file for API keys and other secrets.
 """
-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")
 
@@ -39,12 +31,6 @@ 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", "")

SPARC/database.py

@@ -222,6 +222,8 @@ class DatabaseClient:
         Returns:
             Cached message dict if found, None otherwise
         """
+        self.connect()
+
         prompt_hash = self.hash_prompt(prompt)
 
         query = """
@@ -244,8 +246,7 @@ class DatabaseClient:
 
         query += " ORDER BY timestamp DESC LIMIT 1"
 
-        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(query, params)
             result = cursor.fetchone()
             return dict(result) if result else None
@@ -276,10 +277,11 @@ class DatabaseClient:
         Returns:
             The ID of the inserted record
         """
+        self.connect()
+
         prompt_hash = self.hash_prompt(prompt)
 
-        with self.get_conn() as conn:
-            with conn.cursor() as cursor:
+        with self.conn.cursor() as cursor:
             cursor.execute(
                 """
                 INSERT INTO llm_messages
@@ -301,7 +303,7 @@ class DatabaseClient:
             )
 
             message_id = cursor.fetchone()[0]
-            conn.commit()
+            self.conn.commit()
 
             return message_id
 
@@ -323,6 +325,8 @@ class DatabaseClient:
         Returns:
             List of message dictionaries
         """
+        self.connect()
+
         query = "SELECT * FROM llm_messages WHERE 1=1"
         params = []
 
@@ -337,8 +341,7 @@ class DatabaseClient:
         query += " ORDER BY timestamp DESC LIMIT %s OFFSET %s"
         params.extend([limit, offset])
 
-        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(query, params)
             return [dict(row) for row in cursor.fetchall()]
 
@@ -351,8 +354,9 @@ class DatabaseClient:
         Returns:
             Dictionary with analytics data
         """
-        with self.get_conn() as conn:
-            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        self.connect()
+
+        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
             # Total messages
             cursor.execute(
                 """
@@ -647,11 +651,12 @@ class DatabaseClient:
         Returns:
             Created user dict or None if email exists
         """
+        self.connect()
+
         password_hash = self.hash_password(password)
 
         try:
-            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(
                     """
                     INSERT INTO users (email, password_hash, role)
@@ -661,9 +666,10 @@ class DatabaseClient:
                     (email, password_hash, role),
                 )
                 user = cursor.fetchone()
-                conn.commit()
+                self.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]:
@@ -676,8 +682,9 @@ class DatabaseClient:
         Returns:
             User dict if authenticated, None otherwise
         """
-        with self.get_conn() as conn:
-            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        self.connect()
+
+        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
             cursor.execute(
                 "SELECT * FROM users WHERE email = %s",
                 (email,),
@@ -702,8 +709,9 @@ class DatabaseClient:
         Returns:
             User dict or None
         """
-        with self.get_conn() as conn:
-            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        self.connect()
+
+        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
             cursor.execute(
                 "SELECT id, email, role, created_at FROM users WHERE id = %s",
                 (user_id,),
@@ -720,8 +728,9 @@ class DatabaseClient:
         Returns:
             User dict or None
         """
-        with self.get_conn() as conn:
-            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        self.connect()
+
+        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
             cursor.execute(
                 "SELECT id, email, role, created_at FROM users WHERE email = %s",
                 (email,),
@@ -739,8 +748,9 @@ class DatabaseClient:
         Returns:
             List of user dicts
         """
-        with self.get_conn() as conn:
-            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        self.connect()
+
+        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
             cursor.execute(
                 """
                 SELECT id, email, role, created_at
@@ -762,8 +772,9 @@ class DatabaseClient:
         Returns:
             Updated user dict or None
         """
-        with self.get_conn() as conn:
-            with conn.cursor(cursor_factory=RealDictCursor) as cursor:
+        self.connect()
+
+        with self.conn.cursor(cursor_factory=RealDictCursor) as cursor:
             cursor.execute(
                 """
                 UPDATE users
@@ -774,7 +785,7 @@ class DatabaseClient:
                 (role, user_id),
             )
             user = cursor.fetchone()
-            conn.commit()
+            self.conn.commit()
             return dict(user) if user else None
 
     def delete_user(self, user_id: int) -> bool:
@@ -786,11 +797,12 @@ class DatabaseClient:
         Returns:
             True if deleted
         """
-        with self.get_conn() as conn:
-            with conn.cursor() as cursor:
+        self.connect()
+
+        with self.conn.cursor() as cursor:
             cursor.execute("DELETE FROM users WHERE id = %s", (user_id,))
             deleted = cursor.rowcount > 0
-            conn.commit()
+            self.conn.commit()
             return deleted
 
     def get_user_count(self) -> int:
@@ -799,7 +811,8 @@ class DatabaseClient:
         Returns:
             Number of users
         """
-        with self.get_conn() as conn:
-            with conn.cursor() as cursor:
+        self.connect()
+
+        with self.conn.cursor() as cursor:
             cursor.execute("SELECT COUNT(*) FROM users")
             return cursor.fetchone()[0]

SPARC/llm.py

@@ -1,6 +1,5 @@
 """LLM integration for patent analysis using OpenRouter."""
 
-import logging
 from typing import Dict
 
 from openai import OpenAI
@@ -8,8 +7,6 @@ from openai import OpenAI
 from SPARC import config
 from SPARC.database import DatabaseClient
 
-logger = logging.getLogger(__name__)
-
 
 class LLMAnalyzer:
     """Handles LLM-based analysis of patent content."""
@@ -25,7 +22,7 @@ class LLMAnalyzer:
         """
         self.test_mode = test_mode
         self.use_cache = use_cache if use_cache is not None else config.use_cache
-        self.model = config.model
+        self.model = "anthropic/claude-3.5-sonnet"
 
         # Always initialize database client for storage and caching
         self.db_client = DatabaseClient(config.database_url)
@@ -64,7 +61,11 @@ Patent Content:
 Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
 
         if self.test_mode:
-            logger.debug("TEST MODE - Prompt that would be sent to LLM:\n%s", prompt)
+            print("=" * 80)
+            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
@@ -166,7 +167,7 @@ Patent Portfolio:
 Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the company's innovation strength and performance outlook."""
 
         if self.test_mode:
-            logger.debug("TEST MODE - Portfolio prompt:\n%s", prompt)
+            print(prompt)
             return "[TEST MODE]"
 
         metadata = {

SPARC/types.py

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