ontologies for AI & ML
LangChain
When it comes to structured data, most LangChain apps face the same challenges: unreliable access, inconsistent outputs, and a lack of business context.
With Timbr ontologies, LangChain apps can use any LLM to reason over structured data with the same clarity and accuracy they bring to natural language, reliably, contextually, and at scale.
Timbr LangChain SDK
The Timbr LangChain SDK makes it easy to build enterprise-grade LLM applications that query governed, structured data with accuracy and control.
By connecting LangChain agents directly to Timbr’s SQL knowledge graph, the SDK enables users to implement LLM work-flows to understand and navigate complex relationships, hierarchies, and metrics, without hallucinating or breaking governance.
Timbr Helps Solve Challenges in LangChain + Structured Data
| Challenge | Impact | How Timbr Helps |
|---|---|---|
| LLMs hallucinate SQL over raw schemas | Inaccurate answers, broken agents | Timbr exposes meaningful concepts, not tables, simplifying query generation |
| Lack of consistent metric definitions | Conflicting outputs from different prompts | Semantic layer provides reusable, governed metrics for consistent responses |
| Limited understanding of relationships | Agents struggle with JOINs and filters | Timbr ontologies model relationships explicitly, no JOINs needed |
| Data governance is bypassed | Risk of unauthorized or non-compliant access | Timbr enforces access control and lineage even through LLM interfaces |
Ontologies + LangChain:
A Powerful Combination
Timbr brings structured meaning into LangChain workflows:
- Multi-LLM Support: Integrates with the user’s LLM of choice, providing flexibility in choosing the appropriate model for specific use cases.
- Replace Table Names: Replace table names and joins with intuitive concepts and relationships.
- Generate High-Accuracy SQL: Generate high-accuracy SQL by grounding prompts in a governed ontology.
- Access Governed Metrics: Access governed metrics defined and validated by your data team.
- Improve Interpretability: Improve interpretability with transparent business logic and lineage.
- Enable Self-Service RAG: Enable self-service RAG for both structured and unstructured data.
Timbr transforms LangChain from a prototype engine into a production-grade platform for enterprise AI.
Why LangChain Needs Timbr
| Without Timbr | With Timbr |
|---|---|
| Agents guess SQL over complex schemas | Agents generate accurate SQL over defined concepts |
| Business logic lives in prompt engineering | Logic is reused from governed models |
| Hard to ensure consistent, trusted outputs | Responses are grounded in standardized metrics |
| Risky, open-ended data access | Fine-grained governance is enforced automatically |
Use Cases
Natural Language to SQL (NL2SQL)
Enable accurate, explainable NL2SQL over enterprise data using ontology-aware prompts.
Chatbots with Structured Context
Augment responses with governed data pulled in real time from your semantic layer.
Enterprise Q&A with Governance
Ensure AI-generated answers are always based on consistent filters and metrics.
Agent Workflows with Semantic Reasoning
Let agents explore relationships, hierarchies, and rules baked into the data model.
