1. Executive Summary

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:

1. 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.

2. 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.

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.

1.2. Key takeaways

Creating a Temporally-Aware Knowledge Graph with a Temporal Agent

1. Why make your knowledge graph temporal?
    

   Traditional knowledge graphs treat facts as static, but real-world information evolves constantly. What was true last quarter may be outdated today, risking errors or misinformed decisions if the graph does not capture change over time. Temporal knowledge graphs allow you to precisely answer questions like “What was true on a given date?” or analyse how facts and relationships have shifted, ensuring decisions are always based on the most relevant context.

2. What is a Temporal Agent?
    

   A Temporal Agent is a pipeline component that ingests raw data and produces time-stamped triplets for your knowledge graph. This enables precise time-based querying, timeline construction, trend analysis, and more.

3. How does the pipeline work?
    

   The pipeline starts by semantically chunking your raw documents. These chunks are decomposed into statements ready for our Temporal Agent, which then creates time-aware triplets. An Invalidation Agent can then perform temporal validity checks, spotting and handling any statements that are invalidated by new statements that are incident on the graph.

Multi-Step Retrieval Over a Knowledge Graph

1. Why use multi-step retrieval?
    

   Direct, single-hop queries frequently miss salient facts distributed across a graph's topology. Multi-step (multi-hop) retrieval enables iterative traversal, following relationships and aggregating evidence across several hops. This methodology surfaces complex dependencies and latent connections that would remain hidden with one-shot lookups, providing more comprehensive and nuanced answers to sophisticated queries.

2. Planners
    

   Planners orchestrate the retrieval process. Task-orientated planners decompose queries into concrete, sequential subtasks. Hypothesis-orientated planners, by contrast, propose claims to confirm, refute, or evolve. Choosing the optimal strategy depends on where the problem lies on the spectrum from deterministic reporting (well-defined paths) to exploratory research (open-ended inference).

3. Tool Design Paradigms
    

   Tool design spans a continuum: Fixed tools provide consistent, predictable outputs for specific queries (e.g., a service that always returns today’s weather for San Francisco). At the other end, Free-form tools offer broad flexibility, such as code execution or open-ended data retrieval. Semi-structured tools fall between these extremes, restricting certain actions while allowing tailored flexibility—specialized sub-agents are a typical example. Selecting the appropriate paradigm is a trade-off between control, adaptability, and complexity.

4. Evaluating Retrieval Systems
    

   High-fidelity evaluation hinges on expert-curated "golden" answers, though these are costly and labor-intensive to produce. Automated judgments, such as those from LLMs or tool traces, can be quickly generated to supplement or pre-screen, but may lack the precision of human evaluation. As your system matures, transition towards leveraging real user feedback to measure and optimize retrieval quality in production.

   A proven workflow: Start with synthetic tests, benchmark on your curated human-annotated "golden" dataset, and iteratively refine using live user feedback and ratings.

Prototype to Production

1. Keep the graph lean
    

   Established archival policies and assign numeric relevance scores to each edge (e.g., recency x trust x query-frequency). Automate the archival or sparsification of low-value nodes and edges, ensuring only the most critical and frequently accessed facts remain for rapid retrieval.

2. Parallelize the ingestion pipeline
    

   Transition from a linear document → chunk → extraction → resolution pipeline to a staged, asynchronous architecture. Assign each processing phase its own queue and dedicated worker pool. Apply clustering or network-based batching for invalidation jobs to maximize efficiency. Batch external API requests (e.g., OpenAI) and database writes wherever possible. This design increases throughput, introduces backpressure for reliability, and allows you to scale each pipeline stage independently.

3. Integrate Robust Production Safeguards
    

   Enforce rigorous output validation: standardise temporal fields (e.g., ISO-8601 date formatting), constrain entity types to your controlled vocabulary, and apply lightweight model-based sanity checks for output consistency. Employ structured logging with traceable identifiers and monitor real-time quality and performance metrics in real lime to proactively detect data drift, regressions, or pipeline anomalised before they impact downstream applications.