What is large language model?

Lessons that use this term

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96 lessons use this term. The 40 with the highest term density are listed here.

RAG PipelineIntegrating Neo4j with Generative AIof a RAG (Retrieval-Augmented Generation) pipeline to provide context to a LLM17 mentions
ConsiderationsGenerative AIWhile GenAI and LLMs provide a lot of potential, you should also be14 mentions
What is Generative AIGenerative AIwill focus on text-generating models, specifically Large Language Models (LLMs11 mentions
GenAI FrameworksIntegrating Neo4j with Generative AIto help you integrate Neo4j with generative AI and large language models (LLMs6 mentions
ContextGenerative AIYou can improve the accuracy of responses from LLMs by providing context in your5 mentions
Creating Knowledge GraphsKnowledge Graphsyou can use the text analysis capabilities of Large Language Models (LLMs) to help automate knowledge graph6 mentions
GraphRAGRetrieval Augmented Generation (RAG)the strengths of graph databases to provide relevant and useful context to LLMs7 mentions

LLM configurationCustomizationcan modify which large language model (LLM) you use to suit your own10 mentions
Constructing knowledge graphsIntroductionthe process of constructing knowledge graphs from unstructured text using an LLM12 mentions
Extracting a schema from textKnowledge Graph Pipelinea knowledge graph and retrievers to extract information from the graph using LLMs5 mentions
Text to Cypher retrieverRetrievalresponse includes the Cypher statement that the LLM generated and the results from executing the5 mentions
Vector + Cypher retrieverRetrievalthe context to an LLM to generate a response to the original7 mentions

What is Generative AIGenerative AIwill focus on text-generating models, specifically Large Language Models (LLMs23 mentions
What is an Agent?AgentsAn AI agent is a system that combines a Large Language Model (LLM) with the ability to take actions in the real7 mentions
Constructing knowledge graphsKnowledge Graph Constructionthe process of constructing knowledge graphs from unstructured text using an LLM12 mentions
Extracting a schema from textKnowledge Graph Constructiona knowledge graph and retrievers to extract information from the graph using LLMs5 mentions
Text to Cypher retrieverRetrievalresponse includes the Cypher statement that the LLM generated and the results from executing the5 mentions
Vector + Cypher retrieverRetrievalthe context to an LLM to generate a response to the original7 mentions

Neo4j integrationNeo4j and LangChainpopular framework for building applications powered by large language models (LLMs6 mentions
SchemaText to CypherThe LLM generates Cypher queries based on the schema of the5 mentions
Cypher QA ChainText to Cypherrequires the graph connection and an LLM model to generate the Cypher query and7 mentions
Cypher GenerationText to Cypherspecific data or business rules, you can provide specific instructions to the LLM when generating the6 mentions
Simple LangChain AgentNeo4j and LangChainCreate an LLM and5 mentions

Neo4j LLM Graph BuilderLLM Graph Builderthis lesson, you will use the LLM Graph Builder to construct a simple knowledge18 mentions
How to Construct a Knowledge Graph with an LLMLLM Graph Builderlearn the process of constructing a knowledge graph using a Language Model (LLM13 mentions
Explore the Knowledge GraphExploring your knowledge graphthis lesson, you will explore the knowledge graph created by the LLM Graph6 mentions
Create your Knowledge GraphExploring your knowledge graphOpen the [Neo4j LLM Graph1 mention
Complete the GraphLLM Graph Builderthe genai-fundamentals_.pdf files to the LLM Graph2 mentions
Querying using CypherExploring your knowledge graphgraph generated by an LLM may not conform to a strict schema (depending on how you configured the14 mentions

Turning data into knowledgeImporting unstructured datacan use a knowledge graph to give context and ground an LLM, giving it access to structured data beyond its initial training3 mentions

Advanced MCP FeaturesIntegration and Advanced Featuresallows your tools to call the LLM during4 mentions
Building PromptsBuilding Database-Connected Featuresbuilt tools that LLMs can call and resources that clients can3 mentions
Using ResourcesBuilding Database-Connected Featuresyou have provided access to your Neo4j database through tools, which allow the LLM to query the database dynamically based on a fixed set of6 mentions
Building MCP serversGetting Started with MCPare functions that LLMs can call to perform actions or retrieve8 mentions

Creating Cypher Template toolsBuilding an Agentdefine the query once and the LLM extracts parameter values from the user's question and runs6 mentions

Advanced MCP FeaturesIntegration and Advanced Featuresallows your tools to call the LLM during4 mentions
Building MCP serversGetting Started with MCPdata at runtime, and they generate the JSON Schema descriptions that tell the LLM what each parameter11 mentions
Using ResourcesBuilding Database-Connected Featuresyou have provided access to your Neo4j database through tools, which allow the LLM to query the database dynamically based on a fixed set of6 mentions

Introducing the GraphRAG hackathonIntroduction to GraphRAGa GraphRAG application — an AI system that uses a knowledge graph to give LLMs accurate, connected, contextual3 mentions

AgentsAccess your dataAI agent is a system that combines a Large Language Model (LLM) with the ability to take actions in the real7 mentions