What the Heck Are Hybrid Knowledge Bases? (And Why They Matter for LLM Apps)

#ai #llm #devtools #rag

If you're building with LLMs and trying to give your agents or copilots real context, you've probably used a vector database.

They're great for unstructured data — PDFs, HTML, markdown, text blobs. But what happens when your data isn't just text?

Enter Hybrid Knowledge Bases.

Now available in Griptape Cloud, they let you store and retrieve structured and unstructured data — together — and query them intelligently in your apps.

🧠 So... What Is a Hybrid Knowledge Base?

A Hybrid Knowledge Base compliments a vector store by combining:

🔢 Structured data: things like location, job titles, timestamps, metadata fields
📝 Unstructured data: resumé text, emails, notes, paragraphs, docs

You can use natural language queries or programmatic ones — and get results that combine exact-match filters with vector similarity searches.

🛠️ Example Use Case: Candidate Search

You're building a recruiter assistant. You have:

Structured data: candidate name, location, years of experience
Unstructured data: resumes, LinkedIn profiles, cover letters

With a hybrid knowledge base, your app can answer:

"Which candidates are in New York and have experience in data analysis with Python?"

It will:

Filter by location == New York (structured)
Perform semantic search across profiles and resumes for "data analysis with Python" (unstructured)

📊✅ Combined results. No hacky joins. No second queries. Just clean, LLM-ready responses.

💡 Why It Matters

Most LLM apps fail when the data isn’t flat text.

Real-world knowledge is messy. It’s structured and unstructured.

And most stacks treat those as separate systems.

With Griptape Hybrid Knowledge Bases, you get: