all AI news
Graph-based metadata filtering for improving vector search in RAG applications
April 25, 2024, 3:30 p.m. | LangChain
LangChain blog.langchain.dev
Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j
Editor's Note: the following is a guest blog post from Tomaz Bratanic, who focuses on Graph ML and GenAI research at Neo4j. Neo4j is a graph database and analytics company which helps organizations find
advanced analytics applications blog database filtering genai graph graph-based graph database improving langchain metadata neo4j organizations rag research retrieval search vector vector search
More from blog.langchain.dev / LangChain
LangChain v0.2: A Leap Towards Stability
1 day, 18 hours ago |
blog.langchain.dev
Access Control Updates for LangSmith
3 days, 19 hours ago |
blog.langchain.dev
[Week of 4/29] LangChain Release Notes
1 week, 1 day ago |
blog.langchain.dev
Regression Testing with LangSmith
1 week, 3 days ago |
blog.langchain.dev
Jobs in AI, ML, Big Data
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York