what should I use to search across PDFs and images semantically?

If you want to search across PDFs and images semantically (not just keyword matching), you usually want a multimodal retrieval stack:

Best approach

1. Extract text from PDFs

   • If PDFs have selectable text: use normal text extraction.
   • If they’re scanned: run OCR first.

2. Process images with OCR and/or vision embeddings

   • For text in images: OCR.
   • For general visual similarity or content search: use image embeddings.

3. Store everything in a vector database

   • Chunk PDF text and OCR output.
   • Generate embeddings for both text and images.
   • Put them in a vector DB for semantic search.

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Good model choices

For text/PDF semantic search

• OpenAI text embeddings: strong general-purpose embeddings
• BGE / E5: popular open-source alternatives
• Cohere Embed: also solid

For images

• CLIP / SigLIP: classic image-text embedding models
• OpenAI multimodal embeddings if you want a managed API and unified search
• BLIP / Florence / other vision-language models for richer understanding, depending on your use case

For OCR

• Tesseract: free, but weaker on messy docs
• PaddleOCR: often better for real-world scans
• AWS Textract / Google Document AI / Azure OCR: strong managed options

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Vector databases

• Pinecone: managed, easy scaling
• Weaviate: good multimodal support
• Milvus: powerful open-source
• Qdrant: simple and popular
• FAISS: good if you want local/index-only

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Practical recommendation

If you want the simplest robust setup:

• PDF text: extract text with PyMuPDF / pdfplumber
• Scanned PDFs + images: OCR with PaddleOCR or Textract
• Embeddings:
  • text: OpenAI embeddings or BGE
  • images: CLIP/SigLIP or a multimodal embedding model
• Search backend: Qdrant or Weaviate

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If you want one unified semantic search over both text and images

Look for a shared embedding space:

• CLIP-style models are ideal because both images and text map into the same vector space.
• That makes cross-modal search easier, e.g.:
  • search with text, retrieve images
  • search with image, retrieve text-heavy docs

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Rule of thumb

• If documents are mostly textual: OCR + text embeddings + vector DB
• If you need true cross-modal search: OCR + CLIP-style embeddings + vector DB
• If documents are complex layouts: add document parsing tools like docling, unstructured, or layoutparser

If you want, I can recommend a specific stack for:

• local/open-source only
• best managed/cloud
• highest accuracy
• cheapest setup