Enter your document types, business objects, question scenarios, or current RAG issues. Kollab helps you draft a graph schema, Neo4j modeling notes, LangChain retriever flow, evidence rules, and launch evaluation checklist.
When vector search struggles with multi-hop questions, relationships, or evidence chains, a knowledge graph can make entities and links explicit and work with retrieval-augmented generation more reliably.
Paste sample documents, database fields, business objects, or common questions. Kollab organizes the core entities, relationship types, properties, cleanup rules, and extraction prompts you need before building the graph.
Upgrade pure vector RAG into a graph-and-vector workflow: identify entities in the question, decide when to query Neo4j, draft Cypher thinking, supplement vector recall, and merge evidence into the answer.
For enterprise knowledge bases and AI Agent workflows, Kollab also outlines graph memory updates, evidence citation rules, conflict handling, and evaluation items such as accuracy, coverage, latency, and maintenance cost.
Kollab breaks “we need Graph RAG” into clear implementation steps: modeling, extraction, indexing, retrieval, answer synthesis, and evaluation.
Paste document types, business objects, sample content, frequent questions, or current RAG failure cases.
Get entities, relationships, properties, constraints, extraction prompts, and Neo4j modeling suggestions.
Plan how graph retrieval, vector search, reranking, context compression, and answer synthesis work together.
Continue into development tasks, evaluation examples, launch risks, and team responsibilities.
Use it before upgrading a knowledge base, validating a Neo4j prototype, designing a LangChain retriever, or adding long-term memory to an AI Agent.
Move beyond document chunks by modeling products, policies, customers, events, and relationships.
Prepare graph schema notes, example Cypher directions, and ingestion tasks before development starts.
Describe how entity extraction, graph lookup, vector search, reranking, and answer synthesis should cooperate.
Turn users, projects, events, decisions, and facts into graph memory that can be updated and checked.
Ordinary RAG often retrieves text chunks by similarity. Graph RAG also models entities and relationships so the system can answer multi-hop, relationship-heavy, and evidence-sensitive questions more clearly.
No. Neo4j is a common option, but you can use the plan with other graph databases or adapt the schema notes to your existing data stack.
Yes. The page is designed to produce retriever flow, prompt structure, graph lookup logic, and evaluation notes that can guide a LangChain prototype.
Yes. You can continue the result into an architecture brief, implementation checklist, task breakdown, or technical proposal inside Kollab.
Enter your knowledge base, business objects, and question goals to generate graph schema, retrieval flow, Neo4j and LangChain planning notes, and a launch evaluation checklist.