Do I need embeddings for a RAG system over PDFs?

Usually, yes — if you want a practical RAG system over PDFs, you almost always use embeddings.

Why embeddings are useful

PDFs are typically split into chunks, and embeddings let you:

• Search by meaning, not just exact keywords
• Retrieve the most relevant chunks even when the user phrasing differs from the PDF wording
• Scale to lots of documents efficiently with a vector database

Typical RAG flow for PDFs

1. Extract text from PDFs
2. Chunk the text into smaller sections
3. Create embeddings for each chunk
4. Store them in a vector index/database
5. For a user query:
   • embed the query
   • find the nearest chunks
   • pass those chunks to the LLM to answer

When you might not need embeddings

You may skip embeddings if:

• Your PDFs are very small and you can just stuff all text into the prompt
• You only need exact keyword search rather than semantic search
• You’re using a hybrid setup where keyword search is enough
• You have a specialized retrieval method like structured metadata lookup

Alternatives

• Keyword search / BM25: simpler, good for exact terms
• Hybrid search: combines keyword + embeddings, often best in practice
• No retrieval: if the docs are tiny

Bottom line

For most real-world PDF RAG systems, embeddings are strongly recommended and are the standard approach.
If you want, I can also sketch a minimal PDF RAG architecture with or without embeddings.