Reimagining Workspace Search with Cognee, Knowledge Graphs, and Multi-Hop Reasoning

Short summary

Enterprise AI search fails when answers require connecting information across multiple platforms (GitHub, Jira, Slack, Google Docs, Salesforce). Traditional vector RAG treats text chunks as isolated, missing conceptual relationships and relying purely on semantic similarity matching. The article proposes using knowledge graphs (Cognee) and multi-hop reasoning: extract entities and relationships from all enterprise sources, normalize them into a unified graph schema, and use orchestration (LangGraph) to navigate across platforms.

• •Naive vector RAG fails to connect information across enterprise platforms—missing multi-hop reasoning
• •Knowledge graphs with entity extraction and graph schema solve the 'Context Fragment Tax' problem
• •Groq + LangGraph orchestration enables cross-platform navigation and grounded generation

Generated with AI, which can make mistakes.