This text discusses the integration of LLM workflows with Knowledge Graphs using Neo4j and APOC. It highlights the availability of OpenAI and VertexAI endpoints as APOC Extended procedures. The text emphasizes the opportunities and use cases for companies to utilize LLMs in enhancing productivity, data transformation, and conversational AI systems. It explains the process of constructing text embedding representations using neighbor information and how it can improve vector similarity search applications. The text also introduces the concept of retrieval-augmented LLMs, where external information is provided at query time to generate more relevant answers. It concludes by mentioning the continuous advancements in the field and the potential for greater innovation in data handling and processing with Neo4j and APOC.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Integrating LLM workflows with Knowledge Graph | Integration of LLMs with Knowledge Graphs | More seamless integration between LLMs and Knowledge Graphs | Enhancing productivity and improving data manipulation |
| OpenAI and VertexAI endpoints as APOC Extended procedures | Availability of OpenAI and VertexAI endpoints | More available endpoints for LLM integration | Increasing accessibility and options for LLM integration |
| Using graph context to enrich text embeddings | Enriching text embeddings with graph context | Improved vector similarity search results | Enhancing the richness of embedded information |
| Retrieval-augmented LLMs | Providing LLMs with external information at query time | LLMs incorporating external context for generating answers | Improving the accuracy and relevance of LLM-generated responses |
| Integrating Large Language Models with Knowledge Graphs | Integration of LLMs with Knowledge Graphs | Advancements in LLM and Knowledge Graph integration | Enhancing data handling and processing capabilities |