""" CDMS Label Search Tool Search for pesticide product labels from the CDMS database with full citations """ import os from typing import Dict, Any, Optional from pathlib import Path import sys # Add project root to path project_root = Path(__file__).parent.parent.parent sys.path.insert(0, str(project_root)) from src.cdms.rag_search import CDMSRAGSearch def _offline_index_enabled() -> bool: """Legacy env flag, retained as a hallucination guard elsewhere. Still read by llm_response_generator to refuse ungrounded Tavily-only summaries. It no longer gates retrieval mode here — see _live_fallback_enabled. """ return os.environ.get("CDMS_OFFLINE_INDEX", "1") == "1" def _live_fallback_enabled() -> bool: """Auto-mode: on an index miss, live-fetch the label and cache it for the session. On (default) the tool serves from the committed index when the label is present and only reaches out to CDMS (Tavily + download + re-embed) when it isn't — caching the result in-process so repeat asks are fast. Set CDMS_LIVE_FALLBACK=0 to force index-only (e.g. a keyless deploy or to cap live-fetch cost). """ return os.environ.get("CDMS_LIVE_FALLBACK", "1") == "1" class CDMSLabelTool: """ Tool for searching CDMS pesticide labels Auto-mode (default): serve from the committed Qdrant index when the label is already indexed; otherwise fetch it live via Tavily (cdms.net domain filter), download + index it, and answer — caching it in the running process so later asks for the same label are fast. With no Tavily key (or CDMS_LIVE_FALLBACK=0) it degrades to index-only and abstains for un-indexed products. """ def __init__(self, offline: bool = None): """Initialize the CDMS label search tool. offline: None -> auto-mode (index first, live-fetch on miss when a Tavily key is available; see _live_fallback_enabled). True -> force index-only (never live-fetch). False -> allow live fallback. Retained for callers/tests that still pass it explicitly. """ # force_index_only: never reach out to CDMS, answer only from the index. if offline is None: self.force_index_only = not _live_fallback_enabled() else: self.force_index_only = bool(offline) self.tool_name = "cdms_label_search" self.description = "Search for pesticide product labels and safety data sheets from the CDMS database" # The index searcher is always available (offline or not). self.rag_search = CDMSRAGSearch() self.client = None self.pdf_downloader = None self.document_loader = None # live_available gates the fetch-on-miss path. Building the Tavily stack # needs a key; if it's absent (or the import fails), we simply stay # index-only and abstain on a miss rather than crashing the request. self.live_available = False if not self.force_index_only: try: from src.api_clients.tavily_client import TavilyAPIClient from src.cdms.pdf_downloader import CDMSPDFDownloader from src.cdms.document_loader import DocumentLoader from src.config.paths import PDF_DIR # Both the searcher and the loader default to the process-wide # shared vector store (get_shared_vector_store), so search AND # indexing use the SAME single embedded-Qdrant client — no second # client to lock-fail, and safe across concurrent requests. self.client = TavilyAPIClient() self.pdf_downloader = CDMSPDFDownloader() self.document_loader = DocumentLoader(pdf_folder=str(PDF_DIR)) self.live_available = True except Exception as e: print(f"⚠️ Live fetch unavailable ({e}); serving from the offline index only.") self.live_available = False def search( self, product_name: str, active_ingredient: Optional[str] = None, max_results: int = 5 ) -> Dict[str, Any]: """ Search for CDMS pesticide labels Args: product_name: Product or brand name (e.g., "Roundup", "Sevin") active_ingredient: Optional active ingredient (e.g., "glyphosate") max_results: Maximum number of label results to return (1-5) Returns: Dict with: - success: bool - product_name: str - active_ingredient: str or None - summary: str (AI-generated summary) - labels: List[Dict] with: - title: str (label name) - url: str (direct PDF link) - snippet: str (preview text) - relevance: float (0-1) - label_count: int - citations: str (formatted citation text) """ # Perform search via Tavily client raw_results = self.client.search_cdms_labels( product_name=product_name, active_ingredient=active_ingredient, max_results=max_results ) if not raw_results.get("success"): return { "success": False, "error": raw_results.get("error", "Search failed"), "product_name": product_name, "labels": [], "label_count": 0 } # Format results with full citation info labels = [] for result in raw_results.get("results", []): label = { "title": result.get("title", "No title"), "url": result.get("url", ""), "snippet": result.get("content", "")[:300], # First 300 chars "relevance": result.get("score", 0.0) } labels.append(label) # Generate formatted citations citations = self._format_citations(labels) # Build response return { "success": True, "product_name": product_name, "active_ingredient": active_ingredient, "summary": raw_results.get("answer", ""), "labels": labels, "label_count": len(labels), "citations": citations, "query_used": raw_results.get("query", ""), "search_metadata": raw_results.get("search_metadata", {}), "raw_tavily_results": raw_results # Keep for PDF extraction } def _format_citations(self, labels: list) -> str: """ Format label results as citation text Args: labels: List of label results Returns: Formatted citation string """ if not labels: return "No citations available." citation_parts = ["**Sources:**\n"] for i, label in enumerate(labels, 1): citation_parts.append( f"{i}. **{label['title']}**\n" f" - URL: {label['url']}\n" f" - Relevance: {label['relevance']:.2f}\n" ) return "\n".join(citation_parts) def format_response_for_user(self, result: Dict[str, Any]) -> str: """ Format search results for user-friendly display Args: result: Search result from search() Returns: Formatted string for display to user """ if not result.get("success"): return f"❌ Could not find labels: {result.get('error', 'Unknown error')}" product = result.get("product_name", "Unknown product") ingredient = result.get("active_ingredient") summary = result.get("summary", "") labels = result.get("labels", []) # Build response response_parts = [] # Header if ingredient: response_parts.append(f"**CDMS Labels for {product} ({ingredient})**\n") else: response_parts.append(f"**CDMS Labels for {product}**\n") # Summary if summary: response_parts.append(f"**Summary:** {summary}\n") # Labels found response_parts.append(f"**Found {len(labels)} label(s):**\n") for i, label in enumerate(labels, 1): response_parts.append( f"{i}. **{label['title']}**\n" f" 📄 Download: {label['url']}\n" f" 📝 Preview: {label['snippet'][:150]}...\n" ) # Citations response_parts.append(f"\n{result.get('citations', '')}") return "\n".join(response_parts) def download_pdfs(self, tavily_results: Dict[str, Any], product_name: str) -> Dict[str, Any]: """ Download PDFs from Tavily search results Args: tavily_results: Results from search() method (includes raw_tavily_results) product_name: Product name for filename Returns: Dict with: - success: bool - downloaded_pdfs: List[Dict] with filepath, filename, cached status - pdf_count: int - errors: List[str] (any download errors) """ # Extract PDF URLs from Tavily results raw_results = tavily_results.get("raw_tavily_results", tavily_results) pdf_urls = self.pdf_downloader.extract_pdf_urls(raw_results) if not pdf_urls: print(f"⚠️ No PDF URLs found in Tavily results for {product_name}") # Also check labels directly labels = tavily_results.get("labels", []) for label in labels: url = label.get("url", "") if url and (url.lower().endswith('.pdf') or 'pdf' in url.lower()): pdf_urls.append(url) print(f" Found PDF URL in labels: {url}") if not pdf_urls: return { "success": False, "error": "No PDF URLs found in search results", "downloaded_pdfs": [], "pdf_count": 0 } print(f"📥 Found {len(pdf_urls)} PDF URL(s) to download for {product_name}") # Download top 3 PDFs downloaded_pdfs = [] errors = [] for i, url in enumerate(pdf_urls[:3], 1): # Top 3 print(f" Downloading PDF {i}/{min(len(pdf_urls), 3)}: {url[:60]}...") result = self.pdf_downloader.download_pdf(url, product_name) if result.get("success"): cached_status = "cached" if result.get("cached") else "downloaded" print(f" ✅ {cached_status}: {result.get('filename')}") downloaded_pdfs.append({ "filepath": result["filepath"], "filename": result["filename"], "cached": result["cached"], "url": result["url"], "url_hash": result["url_hash"] }) else: error_msg = result.get('error', 'Unknown error') print(f" ❌ Failed: {error_msg}") errors.append(f"Failed to download {url}: {error_msg}") if downloaded_pdfs: print(f"✅ Successfully downloaded {len(downloaded_pdfs)} PDF(s)") else: print("❌ No PDFs were downloaded") return { "success": len(downloaded_pdfs) > 0, "downloaded_pdfs": downloaded_pdfs, "pdf_count": len(downloaded_pdfs), "errors": errors if errors else None } def _is_pdf_indexed(self, pdf_path: str) -> bool: """ Check if PDF is already indexed in Qdrant Args: pdf_path: Path to PDF file Returns: True if PDF is indexed, False otherwise """ try: from src.cdms.schema import Document, DatabaseManager db_manager = DatabaseManager() session = db_manager.get_session() try: pdf_path_obj = Path(pdf_path) doc_id = Document.generate_id(str(pdf_path_obj)) existing_doc = session.query(Document).filter_by(id=doc_id).first() if existing_doc and existing_doc.processed == 1: return True return False finally: session.close() except Exception: return False def search_with_rag( self, product_name: str, user_question: str, active_ingredient: Optional[str] = None, on_step=None, ) -> Dict[str, Any]: """ Auto-mode RAG pipeline: index-first, live-fetch (Tavily → Download → Process → Index → RAG Search) only on an index miss, then cache. Args: product_name: Product name (e.g., "Roundup") user_question: User's question (e.g., "What's the application rate?") active_ingredient: Optional active ingredient on_step: Optional callback(str) invoked at each stage, so the UI can surface the live pipeline (and show that a first-time label fetch is what's taking the extra time). Returns: Dict with: - success: bool - product_name: str - rag_chunks: List[Dict] with content, page_number, score - pdfs_downloaded: int - pdfs_indexed: int - total_chunks_found: int - source: "index" | "live" (where the answer came from) """ def _step(msg: str) -> None: if on_step: try: on_step(msg) except Exception: pass # never let step reporting break the pipeline # Step A: try the committed/local index first (fast path). Chunks already # carry pdf_url from the payload for a real Sources link. _step(f"Searching indexed labels for “{product_name}”…") rag_chunks = self.rag_search.search( query=user_question, product_name=product_name, limit=5, score_threshold=0.4 ) # Served from the index, OR nothing to fall back to (no live stack / # forced index-only) — return the index result (which may abstain). if rag_chunks or not self.live_available: if rag_chunks: _step("Found matching label pages in the index.") elif self.force_index_only: _step("Label not in the index (index-only mode).") else: _step("Label not in the index and live fetch is unavailable.") return { "success": True, "product_name": product_name, "rag_chunks": rag_chunks, "pdfs_downloaded": 0, "pdfs_indexed": 0, "total_chunks_found": len(rag_chunks), "offline_index": True, "source": "index", } # Step B: index miss + live fetch available — fetch, index, re-search, # and cache the label in-process for subsequent requests. _step(f"Not indexed yet — fetching “{product_name}” from CDMS…") # Step 1: Tavily search for PDF URLs print(f"🔍 Step 1: Searching Tavily for '{product_name}' PDFs...") tavily_result = self.search( product_name=product_name, active_ingredient=active_ingredient, max_results=3 ) if not tavily_result.get("success"): error_msg = tavily_result.get("error", "Tavily search failed") print(f"❌ Tavily search failed: {error_msg}") return { "success": False, "error": error_msg, "product_name": product_name } labels_found = tavily_result.get("label_count", 0) print(f"✅ Tavily search successful: Found {labels_found} label(s)") # Step 2: Download PDFs _step("Downloading the label PDF from CDMS…") print(f"📥 Step 2: Downloading PDFs for '{product_name}'...") download_result = self.download_pdfs(tavily_result, product_name) if not download_result.get("success"): error_msg = download_result.get("error", "PDF download failed") print(f"❌ PDF download failed: {error_msg}") return { "success": False, "error": error_msg, "product_name": product_name } downloaded_pdfs = download_result.get("downloaded_pdfs", []) # Step 3: Process and index PDFs (if not already indexed) _step("Processing & indexing the label (first-time only)…") pdfs_indexed = 0 for pdf_info in downloaded_pdfs: pdf_path = pdf_info["filepath"] pdf_url = pdf_info.get("url", "") # PHASE 1 FIX: Get URL from download info # Check if already indexed if not self._is_pdf_indexed(pdf_path): # Process and index with PDF URL try: index_result = self.document_loader.load_pdf( pdf_path, force_reprocess=False, pdf_url=pdf_url # PHASE 1 FIX: Pass PDF URL to store in metadata ) if index_result.get("success"): pdfs_indexed += 1 except Exception as e: print(f"⚠️ Warning: Could not index {pdf_path}: {e}") # Step 4: RAG search _step("Reading the freshly indexed label…") rag_chunks = self.rag_search.search( query=user_question, product_name=product_name, limit=5, score_threshold=0.4 ) # PHASE 1 FIX: Create multiple mapping strategies for PDF URL matching # Strategy 1: Filename to URL mapping (for backwards compatibility) filename_to_url = {} # Strategy 2: URL hash to URL mapping (most reliable) url_hash_to_url = {} for pdf_info in downloaded_pdfs: filename = pdf_info.get("filename", "") url = pdf_info.get("url", "") url_hash = pdf_info.get("url_hash", "") if filename and url: filename_to_url[filename] = url if url_hash and url: url_hash_to_url[url_hash] = url # Strategy 4: Create mapping from Tavily labels (fallback for PDFs not downloaded) tavily_urls = {} tavily_labels = tavily_result.get("labels", []) for label in tavily_labels: url = label.get("url", "") if url and url.lower().endswith('.pdf'): # Use URL as key (for direct matching) tavily_urls[url] = url # Also try to match by extracting identifier from URL # CDMS URLs often have format: .../ldat/mp50B003.pdf if '/ldat/' in url: url_id = url.split('/ldat/')[-1].replace('.pdf', '') tavily_urls[url_id] = url # PHASE 1 FIX: Enhanced URL matching with multiple fallback strategies chunks_with_url = 0 chunks_without_url = 0 for chunk in rag_chunks: # Strategy 1: Check if URL already in chunk (from Qdrant metadata - preferred) if chunk.get("pdf_url"): chunks_with_url += 1 continue # Already has URL from metadata source_file = chunk.get("source_file", "") document_id = chunk.get("document_id", "") chunk_url_hash = chunk.get("url_hash", "") # Strategy 2: Match by URL hash (most reliable - from Qdrant metadata) if chunk_url_hash and chunk_url_hash in url_hash_to_url: chunk["pdf_url"] = url_hash_to_url[chunk_url_hash] chunks_with_url += 1 continue # Strategy 3: Match by document_id (reliable - from Qdrant metadata) # Document IDs are generated from filepath, so we can match by checking downloaded PDFs if document_id: for pdf_info in downloaded_pdfs: # Generate document ID from filepath to match from src.cdms.schema import Document pdf_doc_id = Document.generate_id(pdf_info["filepath"]) if pdf_doc_id == document_id: chunk["pdf_url"] = pdf_info.get("url", "") if chunk["pdf_url"]: chunks_with_url += 1 break if chunk.get("pdf_url"): continue # Strategy 4: Match by exact filename if source_file in filename_to_url: chunk["pdf_url"] = filename_to_url[source_file] chunks_with_url += 1 continue # Strategy 5: Match by source_file partial match (handle sanitized filenames) # Try to find URL by matching product name in filename product_lower = product_name.lower() matched = False for filename, url in filename_to_url.items(): if product_lower in filename.lower(): chunk["pdf_url"] = url chunks_with_url += 1 matched = True break if matched: continue # Strategy 6: Fallback to Tavily labels (if no match found) if not chunk.get("pdf_url"): # Try to match by checking if any Tavily URL matches # This is a last resort - use first available Tavily URL if tavily_urls: # Use the first Tavily URL as fallback chunk["pdf_url"] = list(tavily_urls.values())[0] chunks_with_url += 1 else: chunks_without_url += 1 print(f"⚠️ Warning: Could not find PDF URL for chunk from {source_file} (document_id: {document_id})") # Log URL matching results if chunks_without_url > 0: print(f"⚠️ Warning: {chunks_without_url} chunk(s) missing PDF URLs") print(f"✅ PDF URL matching: {chunks_with_url}/{len(rag_chunks)} chunks have URLs") # Step 5: Return results _step("Writing the answer…") return { "success": True, "product_name": product_name, "rag_chunks": rag_chunks, "pdfs_downloaded": len(downloaded_pdfs), "pdfs_indexed": pdfs_indexed, "total_chunks_found": len(rag_chunks), "source": "live", "tavily_results": tavily_result, "download_info": download_result, "pdf_urls": list(filename_to_url.values()), # All PDF URLs "tavily_labels": tavily_labels # Include Tavily labels with URLs } def execute_cdms_label_tool(question: str, conversation_context: list = None, offline: bool = None, on_step=None) -> Dict: """ Execute CDMS label search tool This is the interface for the tool executor. Extracts product name and active ingredient from the question and searches CDMS. Uses conversation context for follow-up questions. Args: question: User's natural language question conversation_context: Optional list of previous messages for context Returns: Dict with: { "success": True/False, "tool": "cdms_label", "data": {...search results with citations...}, "error": "error message" if failed } """ try: # Parameter extraction. # # Product recognition is driven by the ProductCatalog (what we have # actually indexed) instead of a fixed six-name keyword list. The old # hard-coded list -- ["roundup", "sevin", "2,4-d", "glyphosate", # "carbaryl", "atrazine"] -- failed to recognise every other product in # the index (Dauntless, Kozami, Megalodon, ...). When it failed, the # pipeline ran a global vector search that surfaced the dominant product # (Roundup here, "Trust" in the deployed data) for unrelated questions. from src.cdms.product_catalog import get_catalog catalog = get_catalog() product_name = None active_ingredient = None question_lower = question.lower() # 1) Resolve against products we can actually answer for. product_name = catalog.resolve(question) # 2) If not in the current question, carry it over from the conversation # (handles follow-ups like "what about its safety?"). if not product_name and conversation_context: for msg in reversed(conversation_context): # most recent first resolved = catalog.resolve(msg.get("content", "")) if resolved: product_name = resolved break # If still no product found, try to extract from phrases like "label for X" or "X label" if not product_name: if "label for" in question_lower: parts = question_lower.split("label for") if len(parts) > 1: product_name = parts[1].strip().split()[0] if parts[1].strip() else None elif "label" in question_lower: parts = question_lower.split("label") if parts[0].strip(): words = parts[0].strip().split() if words: product_name = words[-1] # If no product found, check if this is a pesticide-related question # If it is, we'll try CDMS anyway (might find something), otherwise it will fallback is_pesticide_related = any( kw in question_lower for kw in [ "pesticide", "herbicide", "insecticide", "fungicide", "label", "application rate", "safety", "mixing", "chemical", "cdms" ] ) # PHASE 2 FIX: Be more flexible - if tool matcher selected CDMS, trust it and try to search # Extract any potential product name from the question itself if not product_name: # Try to extract product name from common patterns # Pattern: "X label", "label for X", "X pesticide", etc. words = question_lower.split() # Look for word before "label" if "label" in words: label_idx = words.index("label") if label_idx > 0: # Take words before "label" as potential product name (could be multiple words) # Example: "Actagro 10% Boron label" -> "Actagro 10% Boron" potential_product_parts = [] for i in range(label_idx - 1, -1, -1): # Go backwards from label word = words[i] if word in ["the", "a", "an", "find", "get", "show", "search", "for", "of"]: break potential_product_parts.insert(0, word) if len(potential_product_parts) >= 4: # Limit to 4 words max break if potential_product_parts: product_name = " ".join(potential_product_parts) # Look for word before "pesticide", "herbicide", etc. if not product_name: for term in ["pesticide", "herbicide", "insecticide", "fungicide"]: if term in words: term_idx = words.index(term) if term_idx > 0: # Take words before the term potential_product_parts = [] for i in range(term_idx - 1, -1, -1): word = words[i] if word in ["the", "a", "an", "find", "get", "show", "search", "for", "of"]: break potential_product_parts.insert(0, word) if len(potential_product_parts) >= 4: break if potential_product_parts: product_name = " ".join(potential_product_parts) break # If still no product but pesticide-related, extract the product token robustly. if not product_name: if is_pesticide_related: # Preferred: the noun right after "for"/"of"/"about" # ("application rate for atrazine" -> "atrazine"). Otherwise drop # question words AND pesticide-domain filler and keep the remaining # content word(s). The old filter kept "is"/"application"/"rate", so # "What is the application rate for atrazine?" became the bogus product # "is application rate atrazine" -> live fetch then abstained on it. import re as _re _STOP = { "what", "whats", "how", "tell", "me", "about", "find", "get", "show", "give", "search", "for", "the", "a", "an", "is", "are", "was", "were", "do", "does", "did", "can", "could", "will", "would", "should", "i", "my", "need", "want", "know", "of", "on", "in", "at", "to", "and", "or", "this", "that", "it", "its", "please", "label", "labels", "pesticide", "herbicide", "insecticide", "fungicide", "application", "rate", "rates", "apply", "applied", "safety", "mixing", "mix", "interval", "use", "using", "chemical", "information", "info", "product", "amount", "dose", "dosage", "much", } toks = [t for t in (_re.sub(r"[^\w%.-]", "", w) for w in question.split()) if t] lower = [t.lower() for t in toks] anchors = [i for i, lo in enumerate(lower) if lo in ("for", "of", "about")] cand = [] if anchors: cand = [toks[i] for i in range(max(anchors) + 1, len(toks)) if lower[i] not in _STOP] if not cand: # no for/of/about, or nothing useful after it cand = [t for t, lo in zip(toks, lower) if lo not in _STOP] product_name = " ".join(cand[:4]) if cand else "pesticide" else: # Not pesticide-related and no product - return error (will trigger fallback) return { "success": False, "tool": "cdms_label", "error": "Could not identify the pesticide product name. Please specify a product (e.g., 'Find Roundup label')", "should_fallback": True # Flag for fallback } # Create tool (offline=None -> auto-mode: index first, live-fetch on miss # when a Tavily key is configured; True -> force index-only). tool = CDMSLabelTool(offline=offline) # Enhance question with context if this is a follow-up enhanced_question = question # Detect follow-up question types followup_keywords = { "safety": ["safety", "safe", "precaution", "hazard", "danger", "toxic", "poison", "warning", "protective"], "application": ["application", "apply", "rate", "dosage", "amount", "how much", "when to apply"], "mixing": ["mix", "mixing", "dilute", "dilution", "solution", "concentrate", "ratio"], "reentry": ["re-entry", "reentry", "rei", "when can i", "how long", "wait", "interval"], "storage": ["store", "storage", "keep", "shelf life", "expiration"], "crops": ["crop", "crops", "use on", "for", "suitable", "compatible"] } detected_type = None for ftype, fkeywords in followup_keywords.items(): if any(kw in question_lower for kw in fkeywords): detected_type = ftype break # If this looks like a follow-up question, enhance with context is_followup = ( conversation_context and ( # No product name in current question but has context (not product_name or product_name == "pesticide") or # Question is vague/short len(question.split()) <= 5 or # Detected follow-up type detected_type is not None or # Common follow-up phrases any(phrase in question_lower for phrase in [ "what about", "how about", "tell me more", "and", "also", "what's the" ]) ) ) if is_followup: # Find the most recent product mentioned in the conversation, using # the data-driven catalog. (The previous code iterated an undefined # `keywords` list -> NameError, silently breaking every follow-up.) context_product = None for msg in reversed(conversation_context): resolved = catalog.resolve(msg.get("content", "")) if resolved: context_product = resolved break # If we found a product in context, use it if context_product and (not product_name or product_name == "pesticide"): product_name = context_product # Enhance question with product context and follow-up type if product_name and product_name != "pesticide": if detected_type: # Add specific context based on follow-up type enhanced_question = f"{question} for {product_name} {detected_type}" else: enhanced_question = f"{question} about {product_name}" elif detected_type: # Add follow-up type context enhanced_question = f"{question} {detected_type}" # Auto-mode RAG pipeline: index first, live-fetch + cache on a miss. result = tool.search_with_rag( product_name=product_name, user_question=enhanced_question, # Pass enhanced question for RAG search active_ingredient=active_ingredient, on_step=on_step, ) if not result.get("success"): return { "success": False, "tool": "cdms_label", "error": result.get("error", "CDMS RAG search failed") } # Return successful result with RAG chunks and page citations return { "success": True, "tool": "cdms_label", "data": result } except Exception as e: return { "success": False, "tool": "cdms_label", "error": f"Unexpected error: {str(e)}" } # Test the tool if __name__ == "__main__": print("=" * 80) print("Testing CDMS Label Tool with Citations") print("=" * 80) tool = CDMSLabelTool() # Test 1: Roundup (common product) print("\nTEST 1: Search for Roundup labels") print("-" * 80) result = tool.search( product_name="Roundup", active_ingredient="glyphosate", max_results=3 ) # Show formatted output print(tool.format_response_for_user(result)) # Test 2: Sevin (another common product) print("\n" + "=" * 80) print("TEST 2: Search for Sevin labels") print("-" * 80) result = tool.search( product_name="Sevin", active_ingredient="carbaryl", max_results=3 ) print(tool.format_response_for_user(result)) # Test 3: Just product name (no ingredient) print("\n" + "=" * 80) print("TEST 3: Search with product name only") print("-" * 80) result = tool.search( product_name="2,4-D", max_results=3 ) print(tool.format_response_for_user(result)) print("\n" + "=" * 80) print("✅ All tests complete!") print("=" * 80)