Lyzr Studio’s Knowledge Graph empowers you to transform unstructured documents into deeply connected semantic networks — enabling advanced reasoning and multi-hop question answering. This is ideal when your domain requires relationship-based understanding beyond simple retrieval.


1. Choose Knowledge Base Type

Start by selecting Knowledge Graph as the type when creating a new Knowledge Base. This will activate Lyzr’s graph-building and Neo4J integration features.

Knowledge Graphs are ideal for content where relationships between concepts, people, tools, or processes are important (e.g., legal documents, SOPs, or technical documentation).


2. Prerequisite: Set Up a Neo4J Account

Before using the Knowledge Graph feature, you must configure a Neo4J database instance. Follow these steps:

Create a Neo4J Aura Account (Cloud)

  1. Visit https://neo4j.com/cloud and sign up for a Neo4J Aura free or paid account.
  2. Create a new project and launch a free-tier database.
  3. Once created, copy the following credentials from your Neo4J dashboard:
    • URI (e.g., neo4j+s://yourdb.neo4j.io)
    • Username
    • Password
  4. You can test the connection directly inside the Neo4J Console to ensure it works.

Tip: Use the free tier during development. Upgrade only when you scale.


3. Configure Neo4J in Lyzr

Once your Neo4J instance is ready:

  1. Go to Studio > Data Connectors.
  2. Click Create New.
  3. Select Neo4J from the list of database types.
  4. Enter:
    • Name (e.g., “My Knowledge Graph DB”)
    • URI, Username, and Password from your Neo4J account.

Lyzr will now be able to construct and manage a graph-based semantic layer using this connection.


4. Upload and Parse Documents

Next, upload your documents or text files. Supported formats include:

  • PDF, DOCX, TXT, Markdown
  • Multiple files can be uploaded at once

Lyzr will automatically:

  • Parse content using intelligent NLP techniques
  • Identify entities and their types
  • Extract semantic relationships (e.g., “Person A manages Team B”, “Tool X is used for Task Y”)

5. Graph Construction and Storage

Using the parsed content and connected Neo4J backend:

  • Entities become nodes in the graph (e.g., people, tools, organizations).
  • Relationships are modeled as typed edges with directionality.
  • The resulting graph is stored in Neo4J and visualizable through native tools.

This structure enables complex reasoning and dynamic query resolution.


6. Query the Knowledge Graph

Once built, your KB can be queried via agents or directly from Studio.

  • Use natural language (e.g., “Which tools are used in the product launch process?”).
  • Lyzr translates queries into graph traversals using Cypher (Neo4J query language).
  • Results can span across multiple documents and entities.

Compared to traditional RAG, this method ensures higher accuracy and semantic depth.


Summary

FeatureDescription
Neo4J IntegrationLink to a cloud-hosted or self-managed graph database.
Relationship-Aware ParsingAutomatically detect and map entities + their relationships.
Multi-Hop Query SupportResolve complex questions across document relationships.
Visual Graph InspectionLeverage Neo4J’s visual tools for debugging and exploration.
Ideal Use CasePolicy docs, technical workflows, org charts, process documents.

The Knowledge Graph KB elevates your AI agents from basic retrieval to knowledge reasoning — making them capable of navigating context-rich, connected data spaces effortlessly.