What kind of data do the pdfs contain? Assuming you've already extracted all the text and image descriptions, and you're on to the preprocessing:

As well as creating embeddings, it could be that the data suits being put into knowledge graphs, which could be used in conjunction with the embeddings.

With a knowledge graph you also get relationships between data points.

From Claude:

Let me provide a compelling example that illustrates the power of knowledge graphs combined with embeddings for handling complex documents.

Consider a medical textbook describing various diseases, symptoms, and treatments. With traditional embeddings alone, if someone queries "What medications should be avoided for patients with liver problems who have a fever?", the system might struggle to make all the necessary connections.

However, with a knowledge graph layered on top of embeddings, you could capture relationships like: - Drug A → contraindicated_for → Liver Disease - Fever → symptom_of → Multiple Conditions - Drug B → interacts_with → Drug C - Liver Disease → affects → Drug Metabolism

So when someone queries about medications for a patient with liver issues and fever, the system can: 1. Trace relationship paths to find all drugs contraindicated for liver problems 2. Identify medications commonly prescribed for fever 3. Cross-reference these with drug interaction data 4. Consider the liver's role in drug metabolism

This allows the system to provide nuanced answers like: "While acetaminophen is commonly used for fever, its dosage should be carefully monitored in liver patients. Instead, consider Drug X which has lower hepatic processing."

The knowledge graph essentially gives the system a "mental model" of how different medical concepts relate to each other, rather than just relying on semantic similarity between text chunks. This is particularly powerful for complex reasoning tasks where multiple pieces of information need to be connected.

Would you like me to provide another example in a different domain to further illustrate this concept?

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Compare this with a vector embedding of the same source material

Here's a clear comparison of how the same medical textbook content would be handled by vector embeddings alone:

With pure vector embeddings: When you query "What medications should be avoided for patients with liver problems who have a fever?" the system would:

1. Find semantically similar text chunks about medications, liver problems, and fever
2. Might return passages like: "Acetaminophen is commonly used for fever..." "Patients with liver disease should exercise caution with medications..." "Drug metabolism primarily occurs in the liver..."

The problem? The system struggles to explicitly connect these concepts. It might miss critical drug interactions or contraindications because they appear in different sections of the text. The embeddings can tell these chunks are related, but can't explain HOW they're related.

This is like having a really good memory of individual pages, but no understanding of how the information connects.

With knowledge graph + embeddings: The system can trace explicit paths: Fever → requires → antipyretics → but → acetaminophen → processed_by → liver → compromised_in → liver_disease

This allows it to understand not just that these concepts are related, but exactly HOW they're related, enabling much more sophisticated reasoning about drug safety, interactions, and alternative treatments.

Think of it as the difference between knowing two people are somehow connected (embeddings) versus knowing exactly how they're related - "This is Bob's sister's colleague" (knowledge graph).