WhyHow.AI

🔍 Want to master Rule-based Retrieval? Explore how to use WhyHow.AI and #Milvus Lite to build advanced RAG applications! The Rule-based Retrieval package open-source by WhyHow can be used with the Milvus metadata filtering feature to put guardrails on the retrieval result. 🛤️ ✨ Link to our detailed guide: https://lnkd.in/gFP5Gyvh #AI #Milvus #RAG #Tech

We are excited to be able to accept your existing triples constructed yourself or through a range of packages like LangChain or LlamaIndex. With WhyHow.AI's KG Studio, you unlock the ability for modular, multi-agent graph infrastructure, flexible querying and granular human in the loop entity resolution and triple creation.

🚀⚡ GraphRAG at the helm: Catalyzing unprecedented advancements in AI renaissance 🧠 🚢 Relying solely on traditional LLMs can lead to hallucinations and struggles with proprietary data. RAG or fine-tuning can help, but they often miss real-world context and fail to explain answers. Here enters GraphRAG..... GraphRAG enhances traditional RAG by incorporating knowledge graphs alongside vector searches. Supported by Microsoft, Lyft, and LinkedIn, it consistently delivers more accurate and holistic answers. Vectors vs. Graphs Vector Representations: Vectors are great for context-rich, scalable data retrieval but struggle to capture relationships within data. Graph Representations: Knowledge graphs map out how entities are connected, making it easier to maintain and update applications. Why GraphRAG stands out??? Community summaries: Detects "communities" of data for comprehensive answers. Map-Reduce approach: Synthesizes information across entire datasets, unlike traditional RAG. Applications 1) Query Augmentation: Uses entities from knowledge graphs to enhance queries. Example: LinkedIn cut support ticket resolution time from 40 hrs to 15 hrs. 2) Answer Augmentation: Uses knowledge graphs for detailed, accurate responses. Example: Microsoft added semantic clusters to news data queries. 3) Answer Control: Verifies accuracy using external sources. Example: Checks historical facts against Wikipedia, the Free Encyclopedia. Top GraphRAG Providers: Graphlit, by Unstruk Data: Focuses on entity-to-content relationships. FalkorDB: High-performance, scalable graph database. Cognee: Offers customizable GraphRAG pipelines. Neo4j: Open-source graph database with vector search. WhyHow.AI: Manages specialized Knowledge graphs. Why Adopt GraphRAG? Cost-Efficiency: Microsoft found up to 97% fewer tokens needed. Faster Development: Neo4j users find it easier to build and debug. Explainability: Clarifies reasoning logic and inputs. Hidden Connections: Reveals new relationships in large datasets. #AI #DataScience #KnowledgeGraphs #GraphRAG #LLMs #MachineLearning #AIInnovation #DataDiscovery #TechInnovation #ArtificialIntelligence #BigData #VectorSearch #EnterpriseAI #TechCommunity #Microsoft #LinkedIn #Neo4j #DataAnalytics #SmartData #AIExplainability #CostEfficiency

Understanding the 3-step process for accurate knowledge graphs, and the features to streamline the process for: - Schema construction - Graph Information Verification - Question Interpretation

At WhyHow.AI, we are diving into the 3 simple steps it takes to build RAG-Native Knowledge Graphs. We down the framework for building accurate knowledge graphs by focusing on three critical aspects: schema correctness, graph information completeness, and question understanding. Explore how these aspects interrelate, and discover tools to optimize and build and debug your graphs. Whether you're new to knowledge graphs or already incorporating them in RAG for accuracy and determinism, this read is for you. https://lnkd.in/ejH-gtXs Chris Rec Thomas Smoker

💼 GraphRAG in Finance: Nvidia & BlackRock's system combines the strengths of vector and graph-based retrieval to analyze earnings call transcripts. Outperforming traditional methods, it offers improved faithfulness, relevance, and precision 📊 GraphRAG has implications beyond finance, potentially revolutionizing how professionals in many fields including law, medicine, and scientific research extract insights from complex, unstructured data [ cc Neo4j WhyHow.AI Diffbot Kùzu Inc. ] ⇶ https://lnkd.in/gjgmCFa9

I'm watching the interesting work being done by Chia Jeng Yang and his colleagues at WhyHow.AI. They advocate an approach that emphasizes using small (knowledge) graph systems aimed to enhance control and determinism in RAG (Retrieval Augmented Generation) processes. That matters in domain-specific applications.

I’m looking for folks who might be interested to intern with me at WhyHow.AI. The vast majority of this work is technical, non-engineering work, and will cover the gamut of BD, Product, Research. You’ll leave this experience knowing way more than you need to about knowledge graphs, semantics, and knowledge representation. As semantics is at the heart of what we do, I’m looking for a clear communicator above all. If you’re interested, hit me up at team[@]whyhow.ai with 200 words on something about KGs that you think I might not know. Bonus points if you've built something on top of our SDK. Chris Rec Thomas Smoker

Excited to announce our SDK is out to build programmatic flows on top of our modular KG Studio!

🔍 Want to master Rule-based Retrieval? Explore how to use WhyHow.AI and #Milvus Lite to build advanced RAG applications! The Rule-based Retrieval package open-source by WhyHow can be used with the Milvus metadata filtering feature to put guardrails on the retrieval result. 🛤️ ✨ Link to our detailed guide: https://lnkd.in/gFP5Gyvh #AI #Milvus #RAG #Tech