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:
- A unified query layer
- Tight integration with agents, workflows, and pipelines
- Real-time, semantic + structured retrieval
Read more
Hybrid Knowledge Bases are now available in Griptape Cloud.
🙋♂️ What Would You Build?
Got a use case that blends structured and unstructured data?
Want to give your agents actual intelligence without cobbling multiple tools together?
