ClemsAI Now Supports Google’s Open Knowledge Format (OKF): Faster, More Accurate AI Knowledge Retrieval
Artificial intelligence is only as reliable as the knowledge it retrieves. To make responses faster, more accurate, and more consistent, ClemsAI now supports Google’s Open Knowledge Format (OKF)—the open, vendor-neutral knowledge standard introduced by Google Cloud in June 2026.
Rather than replacing Retrieval-Augmented Generation (RAG), OKF works alongside ClemsAI’s existing Vector RAG engine, allowing each technology to handle the tasks it performs best.
What Is Google’s Open Knowledge Format (OKF)?
Traditional Retrieval-Augmented Generation (RAG) retrieves information by searching semantically similar document chunks. This works exceptionally well for unstructured content such as PDFs, reports, research papers, and manuals. However, vector search is probabilistic, meaning the same factual question can sometimes retrieve slightly different supporting context.
Google’s Open Knowledge Format (OKF) takes a different approach. Instead of relying on embeddings, knowledge is organised as linked Markdown files with YAML metadata, creating a structured knowledge graph where concepts are explicitly connected.
How OKF Improves AI Knowledge Retrieval
Instead of searching for similar paragraphs, ClemsAI follows explicit relationships between concepts, enabling deterministic and fully traceable answers for factual questions.
I: Vector RAG vs OKF Comparison Table
How ClemsAI Combines OKF and Vector RAG
Every user question first passes through ClemsAI’s intelligent retrieval router.
Questions Routed to OKF
- Definitions
- Business metrics
- Policies
- Procedures
- Formulas
- Reference information
These queries are answered directly from the structured knowledge graph.
Questions Routed to Vector RAG
- Document summaries
- Uploaded PDFs
- Reports
- Research papers
- Open-ended analysis
These continue to use semantic vector search.
Hybrid Retrieval
Retrieval Routing Table
┌─────────────────────────────────────────────────────────────────────┐
│ USER QUERY ("What is churn rate?") │
└─────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ ROUTER / QUERY HANDLER │
│ (app/main/routes.py - /ask) │
└─────────────────────────────────────────────────────────────────────┘
│
┌───────────────┴───────────────┐
▼ ▼
┌─────────────────────┐ ┌─────────────────────┐
│ OKF ENGINE │ │ VECTOR RAG │
│ (Structured) │ │ (Unstructured) │
└─────────────────────┘ └─────────────────────┘
│ │
▼ ▼
┌─────────────────────┐ ┌─────────────────────┐
│ OKF Knowledge │ │ Vector Store │
│ Bundle (.md files) │ │ (FAISS + JSON) │
└─────────────────────┘ └─────────────────────┘
│ │
└───────────────┬───────────────┘
▼
┌─────────────────────────────────┐
│ LLM (DeepSeek/OpenAI) │
│ Generates Final Answer │
└─────────────────────────────────┘
Why Open Knowledge Format Makes ClemsAI Faster
Some questions require both systems. ClemsAI combines structured knowledge from OKF with contextual information retrieved through Vector RAG to produce a single grounded response.
Because OKF stores concepts as a lightweight knowledge graph instead of searching a large vector index, factual lookups require no embedding generation or similarity search.
Performance Advantages
- Faster factual retrieval
- Consistent responses
- Lower compute cost
- Source traceability
- Reduced retrieval latency
Vector RAG remains the preferred engine for analysing large collections of unstructured documents.
Performance Comparison Table
Benefits of Google’s Open Knowledge Format in ClemsAI
Users immediately benefit from:
- Faster answers for definitions, formulas, and policies.
- Consistent responses to factual questions.
- Automatic routing between structured and unstructured knowledge.
- Explainable answers with traceable sources.
- Vendor-neutral knowledge stored as version-controlled Markdown.
Why This Matters for Enterprise AI
Modern enterprise AI should not rely exclusively on vector search or knowledge graphs. The strongest systems intelligently combine both.
By integrating Google’s Open Knowledge Format (OKF) with Vector RAG, ClemsAI delivers deterministic retrieval for structured knowledge while preserving powerful semantic search across enterprise documents. The result is faster, more reliable, and more explainable AI for organisations that depend on trustworthy knowledge retrieval.
As the OKF knowledge base grows, ClemsAI will continue providing increasingly accurate, transparent, and enterprise-ready AI responses—without changing how users interact with the platform.
Frequently Asked Questions (FAQ)
Does ClemsAI support the latest AI retrieval methods?
Yes. ClemsAI continuously adopts modern AI retrieval technologies. It now supports Google’s Open Knowledge Format (OKF) alongside Vector Retrieval-Augmented Generation (RAG), enabling intelligent routing between structured knowledge and semantic document search to deliver faster, more accurate responses.
Does ClemsAI use Retrieval-Augmented Generation (RAG)?
Yes. Vector RAG remains a core component of ClemsAI. It retrieves relevant information from uploaded documents such as PDFs, Word files, reports, manuals, and research papers using semantic similarity search before generating responses.
What is Google’s Open Knowledge Format (OKF)?
Google’s Open Knowledge Format (OKF) is an open, vendor-neutral specification for organising structured knowledge. Instead of relying on vector embeddings, OKF stores information as linked Markdown files with YAML metadata, making knowledge easier to manage, trace, version-control, and retrieve accurately.
Why does ClemsAI use both OKF and Vector RAG?
Each retrieval method solves a different problem. OKF excels at retrieving structured facts such as definitions, policies, metrics, and formulas, while Vector RAG is ideal for analysing unstructured content like reports, contracts, and research documents. Combining both allows ClemsAI to deliver the most appropriate answer for every query.
How does ClemsAI decide which retrieval method to use?
ClemsAI automatically analyses every user query through an intelligent routing engine. Factual questions are directed to the OKF knowledge graph, document-based questions are answered using Vector RAG, and complex queries may combine both retrieval methods to produce a single grounded response.
Does ClemsAI support hybrid retrieval?
Yes. ClemsAI supports hybrid retrieval by combining structured knowledge from Google’s Open Knowledge Format with semantic retrieval from Vector RAG. This enables accurate factual responses while preserving deep contextual understanding of enterprise documents.
Can organisations upload their own documents to ClemsAI?
Yes. Organisations can upload documents such as PDFs, Microsoft Word files, policies, reports, manuals, and knowledge-base articles. ClemsAI indexes this content using Vector RAG while structured organisational knowledge can be maintained through OKF.
Is ClemsAI suitable for enterprise knowledge management?
Yes. ClemsAI is designed for enterprise environments that require accurate, explainable, and traceable AI responses. Its combination of OKF, Vector RAG, intelligent routing, and source-grounded retrieval helps organisations reduce hallucinations and improve knowledge accessibility.
Does ClemsAI reduce AI hallucinations?
Yes. ClemsAI reduces hallucinations by grounding responses in trusted organisational knowledge. Structured information is retrieved from the OKF knowledge graph, while unstructured information comes from Vector RAG, ensuring responses are based on authoritative sources rather than model memory alone.
Why is ClemsAI different from traditional AI chatbots?
Unlike traditional chatbots that rely primarily on an LLM’s internal knowledge, ClemsAI retrieves information from trusted enterprise sources before generating answers. By combining Google’s Open Knowledge Format, Vector RAG, and intelligent retrieval routing, ClemsAI delivers responses that are faster, more accurate, consistent, and explainable.