Neo4j Live: Mastering Retrieval Queries with Vector + Graph
Ойын-сауық
Curious about how Retrieval Augmented Generation (RAG) enhances LLMs and cuts down on hallucinations? Join our livestream to demystify RAG and see it in action!
We'll kick off with a brief overview of RAG, followed by a live demo using the LangChain framework and Neo4j database. Then, dive deep with us as we build a retrieval query from scratch, uncovering the presenter’s techniques and testing methods.
Don't miss out on this chance to elevate your understanding of RAG and become proficient in building powerful retrieval queries!
Guest: Jennifer Reif, Neo4j
Blog: jmhreif.com/blog/rag-demo-ret...
GraphRAG Demo Repository: github.com/neo4j-examples/rag...
Jennifers Podcast: jmhreif.podbean.com/
Get started with GraphRAG: neo4j.com/developer-blog/grap...
0:00 - Introduction
3:23 - Introduction of Jennifer Reif
5:00 - Retrieval Queries and Vector Search
8:00 - Technical Demonstration
25:04 - Enhancing the Query
36:00 - Application Testing and Debugging
40:00 - Q&A and Discussion
57:57 - WrapUp
#neo4j #graphdatabase #rag #graphrag #vector #database #query
Пікірлер: 2
The "Why knowledge graph?" question i feel was not answered adequately. The question is implicitly comparing KG with NOSQL/SQL so only a thoughtful and specific comparison between the two is appropriate; I'll give it a shot: The answer is "the KG is able to express ambiguity in it's syntax that isn't possible in NOSQL/SQL, or is prohibitively expensive compared to KG's which is very cheap. In cypher, we easily express Patterns, that personify ambiguity/non-completeness. This ability to speak to your Database in patterns, allows us to instruct the KG to 'Give me whatever Entity type exists at this location; node or relationship, I'm not sure what it is, but i know it matches this Pattern: ' Another mental model: 'Ask a partial question to your KG, and ask it to give you a complete answer, and the complete question it answers, as an output.'. Ironically that's a very similar processes for which we use LLM's. Ambiguity in, Clarity out. KG + LLM are perfectly complementary tools. The best SQL can do as a comparison is the LIKE operator which is prohibitively expensive on a large corpus."
@neo4j
6 күн бұрын
cool! thank you very much!