What Is a Knowledge Graph?

What Is a Knowledge Graph?

A knowledge graph is a structured map of entities, facts, and relationships that a retrieval-augmented generation system can query alongside text chunks. It is a RAG reliability primitive: instead of retrieving only semantically similar passages, the pipeline can follow typed edges such as company-policy, drug-contraindication, or ticket-owner. In production traces, knowledge graphs show up as graph retrieval spans, context metadata, and grounding evidence that FutureAGI can evaluate for entity recall, multi-hop completeness, and answer support.

Why It Matters in Production LLM and Agent Systems

Flat retrieval breaks down when the answer depends on relationships. A support bot may retrieve the right cancellation policy but miss that the policy applies only to enterprise accounts in one region. A medical assistant may surface a drug note but miss a contraindication edge. A coding agent may find a function but skip the ownership or dependency link that explains why editing it breaks another service. The result is not always an obvious hallucination; it is often a plausible answer with the wrong entity, stale relationship, or missing provenance.

Developers feel the pain as brittle query rewrites and long prompts full of defensive context. SREs see p99 latency rise because the retriever sends too many chunks to compensate for poor structure. Compliance teams see audit gaps when the final answer cites a page but not the entity path that made the answer valid. End users see contradictions across turns because the system cannot preserve relationships between accounts, policies, tickets, and prior actions.

The symptoms are concrete: low entity recall, high answer variance for paraphrased questions, many retrieval spans with similar scores, and trace notes that show the correct document but not the required relationship. In 2026-era agentic pipelines, this matters more because retrieval is not a single call. Agents plan, search, call tools, update memory, and ask follow-up questions. Unlike pure vector search, a knowledge graph can encode that “EU enterprise account” and “30-day retention exception” are connected before the LLM writes the answer.

How FutureAGI Handles Knowledge Graphs

FutureAGI handles knowledge graphs through the retrieval and evaluation workflow, not as a separate graph database claim. The specific product anchor is sdk:KnowledgeBase, exposed in the SDK inventory as fi.kb.KnowledgeBase for creating, updating, deleting, and managing knowledge bases and uploaded files. In practice, an engineer loads policy documents, tables, and entity exports into a knowledge base, then attaches metadata such as entity IDs, relation types, source files, tenant IDs, and document versions.

A typical FutureAGI workflow starts with a RAG agent using LlamaIndex or LangChain. traceAI-llamaindex or traceAI-langchain records the retrieval span, the selected chunks, and relation metadata where the retriever exposes it. The engineer then runs a golden dataset of multi-hop questions: “Which EU enterprise customers have the 30-day retention exception?” or “Which integration owner must approve this API migration?” ContextEntityRecall checks whether the required entities appeared in retrieved context. Groundedness checks whether the answer stayed supported by that context. MultiHopReasoning evaluates whether the answer followed the necessary relationship chain.

FutureAGI’s approach is to evaluate the graph as retrieval evidence, not to assume that graph structure alone makes answers correct. Unlike Ragas faithfulness, which mainly scores whether an answer is supported after retrieval, this workflow also catches graph failures before generation. If entity recall drops below a release threshold, the team fixes the ontology edge, metadata filter, or query rewrite, then reruns the regression eval before shipping.

How to Measure or Detect It

Measure a knowledge graph by checking whether the right entities and edges are available before generation and used after generation:

• Entity recall: ContextEntityRecall measures whether required entities from the expected answer appear in retrieved context.
• Relationship completion: track graph paths needed per answer, then fail traces where one required edge is missing.
• Answer support: Groundedness returns whether the response is supported by the retrieved context, not only fluent.
• Multi-hop quality: MultiHopReasoning catches answers that skip an intermediate entity or relation.
• Operational signal: watch eval-fail-rate-by-cohort, retrieval p99, empty-neighborhood rate, and thumbs-down rate for entity-heavy queries.
• Trace evidence: inspect retrieval spans from traceAI-llamaindex or traceAI-langchain for entity IDs, relation labels, and source versions.

from fi.evals import ContextEntityRecall

result = ContextEntityRecall().evaluate(
    input="Which EU enterprise customers have 30-day retention?",
    context="Acme -> enterprise plan -> EU region -> 30-day retention"
)
print(result.score, result.reason)

Common Mistakes

Most failures are modeling and observability failures, not graph database failures. The pattern is a relation missing before the LLM ever generates text.

• Treating graph construction as one-time ETL. Product catalogs, ownership, permissions, and policies drift; stale edges create confident wrong answers.
• Storing every noun as an entity. Low-signal nodes bloat traversal, increase ambiguity, and reduce retrieval precision for entity-heavy questions.
• Hiding relation metadata from traces. If spans only show chunks, no one can debug the missing edge or wrong traversal.
• Evaluating only final answers. Entity recall can fail before generation while answer judges still pass on easy queries.
• Mixing tenant graphs with soft filters. Use hard namespaces or separate stores for regulated data; metadata filters alone are easy to misapply.