File size: 37,302 Bytes
b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 745bce3 9e42edc 745bce3 9e42edc b30f068 9e42edc 745bce3 5c47a28 745bce3 5c47a28 9e42edc 745bce3 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 9e42edc b30f068 745bce3 b30f068 745bce3 b30f068 9e42edc 745bce3 b30f068 9e42edc b30f068 9e42edc b30f068 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 | """
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)
|