RAG

Create a Neo4j GraphRAG workflow using LangChain and LangGraph, combining graph queries, vector search, and dynamic prompting for advanced RAG.

Explore low-hanging fruit strategies to enhance your RAG search systems and improve user experience with practical techniques.

Building an agent with LangChain's LangGraph and Graphiti

Discover a simple way to query Neo4j through your favorite HTTP client with Neo4j Query API.

Discover a systematic approach to enhance Retrieval-Augmented Generation (RAG) systems for improved performance and user satisfaction.

Hello, AI and data professionals! Today, we’re exploring hierarchical indices — a method significantly improving information retrieval in…

Will we still need to learn query languages in the future?

GraphRAG (by way of Neo4j in this case) enhances faithfulness (a RAGAS metric most similar to precision) when compared to vector-based RAG, but does not significantly lift other RAGAS metrics related to retrieval; may not offer enough ROI to justify the hype of the accuracy benefits given the performance overhead.

Explore inverted thinking to build a terrible RAG system and learn pitfalls to avoid for better recommendations.

CalPitch is our AI-powered sales outreach tool designed to transform how our business development team connects with prospects through cutting-edge technology.

I recently embarked on a data adventure with the Northwind Traders Sales Dataset that I discovered on GitHub in CSV format. My goal was to convert this dataset into a Neo4j graph database to explore the power of graph databases for data analysis. Here's a glimpse of my journey: Data Cleaning with Pandas: I used Python's pandas library to clean the data, merge tables, drop unwanted columns, and perform various data transformations. Pandas made it easy to handle and manipulate the data efficiently. Cypher Code for Data Insertion: After preparing the data, I wrote Cypher code to insert the cleaned data into a Neo4j graph database. This involved creating nodes and relationships to represent the data structure accurately. Neo4j Cloud Instance: I utilized a free Neo4j cloud instance to host my graph database. The cloud platform provided an easy-to-use interface and powerful features to manage and query my data. The combination of pandas for data preprocessing and Neo4j for graph representation has been incredibly powerful. It has opened up new possibilities for visualizing and analyzing the data relationships in ways that were not possible with traditional databases. If you're interested in data analysis, graph databases, or just love exploring new technologies, I highly recommend giving this a try. Feel free to reach out if you have any questions or want to share your own experiences! #DataScience #GraphDatabases #Neo4j #Python #Pandas #DataCleaning #Cypher #DataAnalysis #TechJourney #pandas #programming #knowledgegraph #rag #RetrievalAugementedGeneration #GraphRAG Buy me a coffee: https://www.buymeacoffee.com/princez3 Follow me on social media: Discord community server: https://discord.gg/xpyUaEppzU twitter: https://twitter.com/Prince_krampah Channel main page: https://www.youtube.com/c/CodeWithPrince Hope you enjoy today's video. Please show your love and support by just liking and subscribing to the channel so we can grow a strong and powerful community. Activate the 🔔 beside the subscribe button to get the notification!📩 If you have any questions or requests feel free to leave them in the comments below. Thank you for watching and see you in the next video!!

Building and Deploying GenAI GraphRAG Applications and AI agents using Google Cloud’s Vertex AI Reasoning Engine with LangChain and Neo4j Authors: Michael Hunger,  Head of Product Innovation, Neo4j Maruti C, Partner Engineering Lead,Google Generative AI developers not familiar with orchestration too...

Learn how to add domain specific vocabulary to your RAG with Agents using LangChain and LangGraph and make smarter and more useful RAG.

Use the GraphRAG Python package to transform unstructured data into knowledge graphs and enhance GenAI retrieval accuracy and relevance.

Practical RAG techniques for engineers: learn production-ready solutions from industry experts to optimize performance, cut costs, and enhance the accuracy and relevance of your applications.

One AI engineer's journey to get great results when building an agentic RAG system.

Notes about my hobbies and other things I find interesting.

Notes about my hobbies and other things I find interesting.

Notes about my hobbies and other things I find interesting.

Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, all with different implementation methods. To mitigate this, we propose rerankers, a Python library which provides a simple, easy-to-use interface to all commonly used re-ranking approaches.

DocETL is an open-source system for building LLM-powered data processing pipelines, offering declarative operators and powerful optimization for complex document analysis tasks

In this tutorial, we explore and evaluate different chunking strategies to choose the right one for a sample RAG application.

An overview and comparison of the various approaches, with examples from industry search systems.

Learn how to combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.

Discover why GraphRAG will subsume vector-only RAG and emerge as the default RAG architecture for most use cases.

GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more:

Machine learning, Scala, and interactive computing

Explore the new LangChain RAG Template with Redis integration. Streamline AI development with efficient, adaptive APIs.