Qdrant PDF Reloader

This skill helps users surgically reload a single PDF document to Qdrant without reloading the entire collection, using the LOADDB/reloadsingle_pdf.py script.

When to Use This Skill

Activate this skill automatically when the user:

• Wants to reload/refresh a specific PDF in Qdrant
• Needs to fix text cleaning issues in a particular document
• Wants to update a document with improved processing
• Needs to regenerate contextual embeddings for one PDF
• Asks to "reload [filename]", "refresh [filename]", or "fix [filename]"
• Uses keywords like "reload PDF", "refresh document", "update single file"

What is Surgical Reload?

A surgical reload is a targeted operation that:

1. Deletes all existing chunks for the specified PDF from Qdrant
2. Re-processes the PDF with current text cleaner and chunking logic
3. Generates fresh contextual embeddings (document context + chunk context)
4. Uploads the new chunks to Qdrant

Benefits:

• Much faster than reloading the entire collection (seconds vs. hours)
• Only affects the target PDF - other documents remain unchanged
• Applies latest fixes (text cleaner, TOC filtering, contextual embeddings)
• No risk of losing other documents' data

How to Use

Step 1: Identify the PDF to Reload

Ask the user which PDF file needs to be reloaded. The filename must match exactly (e.g., document-name.pdf).

Step 2: Verify PDF Exists

Check that the PDF file exists in the scraped_content/raw/pdfs/ directory:

ls scraped_content/raw/pdfs/document-name.pdf

If not found, inform the user and ask them to verify the filename.

Step 3: Run the Reload Script

Execute the surgical reload:

cd LOAD_DB
python reload_single_pdf.py document-name.pdf

The script takes the PDF filename as the only argument.

Step 4: Monitor Progress

The script will output progress through these stages:

1. Deletion: Removing old chunks from Qdrant
2. Processing: Loading PDF, cleaning text, chunking
3. Context Generation: Creating document and chunk contexts
4. Upload: Uploading new chunks with embeddings

Step 5: Report Results

After completion, summarize:

• Number of old chunks deleted
• Number of new chunks uploaded
• Any errors or warnings encountered
• Confirm successful reload

Script Features

Automatic Text Cleaning

The script applies the fixed text cleaner which:

• Normalizes whitespace
• Removes page headers/footers
• Cleans special characters
• Filters out Table of Contents (TOC) chunks

Contextual Embeddings

The script generates contextual metadata:

• Master Context: High-level overview of the entire Texas childcare system
• Document Context: Summary of the specific document (purpose, key topics)
• Chunk Context: Summary of the previous chunk for continuity

These contexts are included in embeddings to improve retrieval accuracy.

Special Handling

Table-heavy PDFs (configured in config.TABLE_PDFS):

• Extracted using Docling (table-aware extraction)
• Tables extracted as markdown and kept as separate chunks
• Narrative text grouped into ~1000 char chunks
• Item-level chunking (no overlap, semantic boundaries)
• Examples: income eligibility tables, evaluation reports

Standard PDFs (PyMuPDF extraction):

• Single-page: Loaded as one chunk to preserve structure
• Multi-page: Split using RecursiveCharacterTextSplitter

- Chunk size: 1000 characters - Overlap: 200 characters

Collection Target

By default, reloads to the contextual collection (tro-child-1 or similar).

Example Workflows

Example 1: Fix Text Cleaning Issue

User: "The income eligibility PDF has garbled text. Can you reload it with the fixed text cleaner?"

Claude: I'll surgically reload that PDF with the updated text cleaner.

[Runs command]
cd LOAD_DB
python reload_single_pdf.py bcy-26-income-eligibility-and-maximum-psoc-twc.pdf

[Script output shows progress]
✓ Deleted 12 old chunks
✓ Loaded 8 pages
✓ Created 10 chunks after filtering (removed 2 TOC chunks)
✓ Generated contextual metadata
✓ Uploaded 10 new chunks

Reload complete! The document has been refreshed with:
- Fixed text cleaning
- TOC filtering
- Fresh contextual embeddings

Old chunks: 12 deleted
New chunks: 10 uploaded

Example 2: Update After PDF Replacement

User: "I replaced the payment rates PDF with a corrected version. Please reload it."

Claude: I'll reload the payment rates PDF with the new version.

[Runs command]
cd LOAD_DB
python reload_single_pdf.py bcy25-child-care-provider-payment-rates-twc.pdf

✓ Surgical reload complete!
Deleted: 5 old chunks (single-page table)
Uploaded: 5 new chunks

The updated PDF is now in Qdrant with fresh contextual embeddings.

Example 3: Regenerate Contextual Embeddings

User: "Can you regenerate the contextual embeddings for the state plan document?"

Claude: I'll reload the state plan to regenerate contextual metadata.

cd LOAD_DB
python reload_single_pdf.py tx-ccdf-state-plan-ffy2025-2027-approved.pdf

This will:
1. Delete existing chunks
2. Re-process the PDF
3. Generate new document and chunk contexts
4. Upload with fresh embeddings

[After completion]
✓ Reloaded with new contextual embeddings:
- Document context: [summary of document]
- Chunk contexts: Generated for all 87 chunks

Example 4: Fix Single-Page Table

User: "The PSOC chart isn't being retrieved correctly. It's a single-page table PDF."

Claude: Single-page PDFs are loaded as one chunk to preserve table structure. Let me reload it.

cd LOAD_DB
python reload_single_pdf.py bcy2025-psoc-chart-twc.pdf

✓ Single-page PDF: loading as 1 chunk (3,245 characters)
✓ Uploaded 1 chunk with full table content

The table is now loaded as a single coherent chunk, which should improve retrieval.

When to Use vs. Full Reload

Use Surgical Reload When:

• ✅ Fixing a specific document
• ✅ One or a few PDFs need updating
• ✅ Quick fix needed (seconds/minutes)
• ✅ Other documents are correct and shouldn't be touched

Use Full Reload When:

• ❌ Major changes to chunking logic or embeddings model
• ❌ Many/most documents need updating
• ❌ Collection structure changed
• ❌ Starting fresh with new documents

Rule of thumb: If updating < 10 PDFs, use surgical reload. If updating > 10 PDFs or making system-wide changes, use full reload.

Technical Details

File Location

• Script: LOADDB/reloadsingle_pdf.py
• PDF Directory: scraped_content/raw/pdfs/
• Collection: QDRANTCOLLECTIONNAME_CONTEXTUAL from config

Architecture

The reload script uses a modular architecture:

• Extractors (LOAD_DB/extractors/): Factory pattern for PyMuPDF vs. Docling selection
• Shared Utilities (LOAD_DB/shared/): Common processing and upload logic
• PyMuPDFExtractor: Fast text extraction for standard PDFs
• DoclingExtractor: Table-aware extraction with item-level chunking for table-heavy PDFs

Dependencies

The script requires:

• LangChain (PyMuPDFLoader, RecursiveCharacterTextSplitter)
• OpenAI embeddings (text-embedding-3-small)
• Qdrant client
• GROQ API (for contextual metadata generation)
• Docling (for table-aware PDFs in config.TABLE_PDFS)
• Local modules:

- extractors - Factory pattern and extractor classes - shared - Processing utilities and upload logic - contextualprocessor - Context generation - textcleaner - Text cleaning and TOC detection - prompts - Master context template

Process Flow

1. DELETE PHASE
   └─ Scroll through collection
   └─ Find chunks where filename == pdf_filename
   └─ Delete all matching chunks

2. EXTRACTION PHASE (via Factory Pattern)
   ├─ Check if PDF in config.TABLE_PDFS
   ├─ If YES: Use DoclingExtractor
   │   ├─ Convert PDF with Docling
   │   ├─ Extract tables as markdown
   │   ├─ Group items by page and sort by y-position
   │   └─ Create item-level chunks (tables + narrative)
   └─ If NO: Use PyMuPDFExtractor
       └─ Load PDF with PyMuPDFLoader (standard extraction)

3. PROCESS PHASE (via shared utilities)
   └─ Clean text on each page (clean_documents)
   └─ Enrich metadata (enrich_metadata)
   └─ Split into chunks if multi-page (text_splitter)
   └─ Filter out TOC chunks (filter_toc_chunks)
   └─ Add chunk metadata (add_chunk_metadata)

4. CONTEXT PHASE (Contextual Mode)
   └─ Generate document context from first 2000 chars
   └─ Generate chunk context for each chunk (uses previous chunk)
   └─ Add master context, document context, chunk context to metadata

5. UPLOAD PHASE (via shared uploader)
   └─ Generate OpenAI embeddings from enriched text
   └─ Create Qdrant points with embeddings + metadata
   └─ Store only original content in page_content
   └─ Upload in batches (100 per batch)

Metadata Added to Chunks

Each uploaded chunk includes:

{
    'text': chunk.page_content,
    'filename': pdf_filename,
    'content_type': 'pdf',
    'page': page_number,
    'total_pages': total_page_count,
    'chunk_index': chunk_number,
    'total_chunks': total_chunk_count,
    'chunk_type': 'table' | 'narrative',  # Docling only
    'extractor': 'docling' | 'pymupdf',
    'has_context': True,
    'master_context': master_context_text,
    'document_context': document_summary,
    'chunk_context': previous_chunk_summary,
    'source_url': url (if available from metadata.json)
}

Error Handling

PDF Not Found

If the PDF doesn't exist in scraped_content/raw/pdfs/:

• Verify the filename (exact match, case-sensitive)
• Check if the PDF was scraped/downloaded
• Suggest running the scraper if needed

Deletion Errors

If deletion fails:

• Check Qdrant connection
• Verify API credentials
• Check if chunks actually exist (use retrievechunksby_filename.py)

Processing Errors

If PDF processing fails:

• Verify PDF is not corrupted
• Check if PDF is readable (try opening manually)
• Review error logs for specifics

Upload Errors

If upload fails:

• Check OpenAI API key (for embeddings)
• Verify Qdrant connection
• Check if collection exists

Advanced Usage

Verify Before Reloading

Use the qdrant-chunk-retriever skill to check existing chunks before reloading.

Verify After Reloading

After reloading, use the qdrant-chunk-retriever skill to confirm:

• Chunk count matches expectations
• Text is cleaned properly
• Context fields are populated
• Extractor type is correct (docling vs pymupdf)
• Chunk types are set (for Docling PDFs)

Compare Before/After

1. Use qdrant-file-exporter skill to export chunks before reload
2. Reload the PDF:

cd LOAD_DB
python reload_single_pdf.py doc.pdf

1. Use qdrant-file-exporter skill again to export chunks after reload
2. Compare the exported files to see what changed

Notes

• Contextual mode is default: All reloads use contextual embeddings
• Chunk IDs are deterministic: Based on hash of (filename, chunk_index, page)
• Idempotent operation: Running multiple times produces same result
• No undo: Once chunks are deleted, they're gone (reload completes the operation)
• Collection must exist: Script doesn't create collections, only updates existing ones

Related Tools

• Skills:

- qdrant-chunk-retriever: Verify chunks before/after reload - qdrant-doc-deleter: Delete without reloading (if you just want to remove) - qdrant-file-exporter: Export all chunks from a PDF to text file

• Scripts:

- LOADDB/loadpdfqdrant.py: Full collection reload (all PDFs) - LOADDB/verify_qdrant.py: Verify collection statistics

• Shared Modules:

- LOADDB/extractors/: PDF extraction with factory pattern - LOADDB/shared/: Common processing and upload utilities