1. Executive Summary

For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to the page URL.

1.1. Purpose and Audience

This notebook provides a hands-on guide for building temporally-aware knowledge graphs and performing multi-hop retrieval directly over those graphs.

It's designed for engineers, architects, and analysts working on temporally-aware knowledge graphs. Whether you’re prototyping, deploying at scale, or exploring new ways to use structured data, you’ll find practical workflows, best practices, and decision frameworks to accelerate your work.

This cookbook presents two hands-on workflows you can use, extend, and deploy right away:

<ol style="margin-left: 1em; line-height: 1.6; padding-left: 0.5em;"> <li style="margin-bottom: 1.2em;"> Temporally-aware knowledge graph (KG) construction A key challenge in developing knowledge-driven AI systems is maintaining a database that stays current and relevant. While much attention is given to boosting retrieval accuracy with techniques like semantic similarity and re-ranking, this guide focuses on a fundamental—yet frequently overlooked—aspect: systematically updating and validating your knowledge base as new data arrives. No matter how advanced your retrieval algorithms are, their effectiveness is limited by the quality and freshness of your database. This cookbook demonstrates how to routinely validate and update knowledge graph entries as new data arrives, helping ensure that your knowledge base remains accurate and up to date. </li> <li style="margin-bottom: 1.2em;"> Multi-hop retrieval using knowledge graphs Learn how to combine OpenAI models (such as o3, o4-mini, GPT-4.1, and GPT-4.1-mini) with structured graph queries via tool calls, enabling the model to traverse your graph in multiple steps across entities and relationships. This method lets your system answer complex, multi-faceted questions that require reasoning over several linked facts, going well beyond what single-hop retrieval can accomplish. </li> </ol>

Inside, you'll discover:

Practical decision frameworks for choosing models and prompting techniques at each stage
Plug-and-play code examples for easy integration into your ML and data pipelines
Links to in-depth resources on OpenAI tool use, fine-tuning, graph backend selection, and more
A clear path from prototype to production, with actionable best practices for scaling and reliability

Note: All benchmarks and recommendations are based on the best available models and practices as of June 2025. As the ecosystem evolves, periodically revisit your approach to stay current with new capabilities and improvements.

gpt-oss

agents_sdk

computer_use_with_daytona

audio

speaker_aware_meeting_intelligence

multimodal

partners

aws

macro_evals_for_agentic_systems

schemaflow_design_guide

temporal_agents_with_knowledge_graphs

1. Executive Summary

1.1. Purpose and Audience

1.2. Key takeaways

本篇目录

computer_use_with_daytona

speaker_aware_meeting_intelligence

aws

macro_evals_for_agentic_systems

schemaflow_design_guide

temporal_agents_with_knowledge_graphs

1. Executive Summary ​

1.1. Purpose and Audience ​

1.2. Key takeaways ​

本篇目录 ​

1. Executive Summary

1.1. Purpose and Audience

1.2. Key takeaways

本篇目录