Beyond Similarity: Introducing Graph-Aware Embeddings for Even Smarter Retrieval

While many retrieval systems pair graphs with embeddings, typically the embeddings remain blind to structure—unaware of how ideas relate, evolve, or depend on one another.

But what if the embeddings themselves could carry that structural context, instead of staying isolated from it? That's exactly the innovation we've been chasing here at cognee, and we're excited to finally pull back the curtain on our proprietary graph-aware embeddings.

We've been quietly refining this tech over the past few months, blending embeddings with graph intelligence to integrate semantic representation with structural signals from knowledge graphs. This proprietary capability now powers more precise, relevant, structured, and meaningful retrieval in our paid plans.

Why Graphs Belong in Retrieval

Conventional embeddings capture meaning by mapping text to vectors, enabling “near-neighbour” lookups (e.g., a query for “machine learning” finding “neural networks” or “AI”).

But real knowledge isn't just a pile of similar ideas—it's a web of relationships, contexts, and timelines. Graphs model these relationships explicitly, allowing systems to factor in context such as part–whole structure, causal links, or temporal progression.

Typically, AI memory stacks keep semantic vectors and graphs in separate silos. Our platform bridges that gap: as an open-source AI memory engine, it converts documents and data into dynamic knowledge graphs, layers in embeddings for semantic depth, and unifies both for advanced retrieval and reasoning.

cognee's Open-Source Roots: Graphs Meet Embeddings

In our open-source core, cognee pulls in data from all sorts of sources—structured or unstructured—and turns it into a graph of nodes, entities, and relationships. For semantic indexing, we use publicly available embeddings like those from sentence-transformers or OpenAI's text models.

This combination keeps cognee flexible and domain-agnostic while remaining extensible and adaptable for bespoke workloads.

Why Pure Embeddings Aren't Enough—and How Graphs Step In

But here's the gist: pure embeddings look at text in a vacuum, ignoring the broader structure and context that real memory thrives on. This creates some predictable blind spots:

• Hierarchy awareness: Think of a doc on "machine learning" with subsections on "supervised" and "unsupervised learning." Embeddings might jumble the main topic with its sub-branches, losing the tree-like organization.
• Temporal progression: A 2020 paper on "GPT-3" and a 2024 one on "GPT-4" are clearly linked evolutionarily, but embeddings alone won't highlight that progression.
• Contextual variation: Polysemous terms (e.g., “model”) vary by field; vectors alone may mis-rank without structural cues.

We’ve been envisioning embeddings that are inherently structure-aware, context-sensitive, and tuned for top-tier retrieval. It sounds ambitious, but by weaving in graph signals, we can approximate these qualities without endless tweaks.

This naturally sparks the idea of evolving embeddings themselves to be graph-aware, pulling in that structural wisdom right from the start.

BtS: cognee’s Graph-Aware Embeddings

Our approach starts with the basics: we compute regular embeddings as usual, providing the semantic foundation. Then, we layer on our proprietary graph-aware embeddings, computed from those originals and enriched with inputs like graph topology, temporal hints, and type constraints. These enriched vectors are stored alongside the originals for efficient use at query time.

At query time, questions get embedded the old-school way—no changes there. But under the hood, when we swap in these graph-aware embeddings for ranking, the magic happens: different, semantically and structurally more refined results surface. It's essentially reranking powered by smarter vectors, leading to better outcomes without overcomplicating the query path.

We skip graph neural networks (for now) and instead use a mix of deterministic and stochastic methods to modify and enrich the base embeddings. Our retrievers blend these with graph signals to rank results and return the most relevant subgraphs, striking a perfect balance between semantic match and structural relevance.

Plus, cognee supports custom, tunable graph embeddings tailored to your domain's unique data structure—ideal for everything from scientific research to enterprise knowledge bases. While we’re still keeping a lid on metrics, our internal testing has shown these enriched embeddings, whether solo or paired with regulars, significantly boost retrieval quality. We're seeing:

• Faster retrieval: Simpler retrievers operating over graph-aware embeddings achieve stronger rankings with less computation, improving latency and throughput at production scale.
• Higher precision: Results reflect both content and its position in a hierarchy or workflow.
• Improved context fidelity: Graph-based signals inferred from temporal and type nodes guide retrieval toward contextually precise content.
• Lean reasoning scaffolding: With structural cues embedded into the representation, fewer and shallower graph-reasoning passes are required after retrieval.

The core idea here isn't just that graphs and embeddings play nice together—that's old news. It's about transforming embeddings with graph context to synergize meaning and structure. Some of the teams we’ve let in to trial this capability are also seeing more consistently accurate retrievals across the board, and we're all thrilled with the potential.

Baking Smarter Intelligence into Your AI Memory

With graph-aware embeddings, the benefits stack up in real-world ways. Retrieval speeds up because ranking draws on both semantics and structure. Results hit the mark more precisely since context is embedded directly in the vectors. And overall, your memory system gets more expressive, pulling up info in ways that mimic how the human mind connects dots.

It's like offloading a bit of reasoning to the embeddings themselves. Still, though, we have to be real: embeddings aren't full-blown reasoning machines. But making them graph-aware does lighten the load on the representation layer, opening up innovative and practical pathways for retrieval and memory.

At cognee, we don't chase massive indexes; we craft intelligent ones. By enriching embeddings with graph contexts and structures, we're paving the way for leaner, more intuitive memory systems that deliver real value.