GenAI
LangGraphVectifyAI/PageIndex: Document Index System for Reasoning-Based RAG
Document Index System for Reasoning-Based RAG
RAG
How to Build a Knowledge Graph in 7 Steps
Discover how to build a knowledge graph in 7 simple steps, from defining your use case to creating a model to ingesting your data.
GraphRAG
Building Your Own RAG System: Enhancing Claude with Your Documentation
Connecting Claude Desktop to Your Documentation Through MCP and Qdrant
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning
We introduce THINK-AND-EXECUTE, a framework that performs reasoning with a pseudocode that contains the common logical structure of a given task.
Deep Dive
AgentsDevelopers guide how to build knowledge graph
How to Build a Knowledge Graph in 7 Steps
Discover how to build a knowledge graph in 7 simple steps, from defining your use case to creating a model to ingesting your data.
Design and Develop a RAG Solution - Azure Architecture Center
How to plan a RAG project
Introduction to LlamaIndex - Hugging Face Agents Course
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Tutorial
A Visual Guide to LLM Agents
Explore the main components of what makes LLM Agents special.
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Agents interact with their environment and typically consist of several important components
chain-of-thought
This is where planning comes in. Planning in LLM Agents involves breaking a given task up into actionable steps.
reasoning” and “thinking” a bit loosely as we can argue whether this is human-like thinking or merely breaking the answer down to structured steps.
without any examples (zero-shot prompting)
Providing examples (also called few-shot prompting7)
ReAct
Reflecting
These Multi-Agent systems usually consist of specialized Agents, each equipped with their own toolset and overseen by a supervisor.
three LLM roles
SELF-REFINE
To enable planning in LLM Agents, let’s first look at the foundation of this technique, namely reasoning.
Evaluating Chunking Strategies for Retrieval | Chroma Research
Transformation Agent | Weaviate
This Weaviate Agent is in technical preview.
Document ParsersDo you know the answer to these three questions? You should... 1. What are vector embeddings and embedding models? 2. What’s the benefit of having a vector database for vector search? 3. What’s on the next horizon for AI applications? I just finished a 3-part webinar series… pic.twitter.com/nFOAPHQw2q— Victoria Slocum (@victorialslocum) March 5, 2025
Vector Store
Multi-vector embeddings (ColBERT, ColPali, etc.) | Weaviate
Learn how to use multi-vector embeddings in Weaviate.
How to Hack AI Agents and Applications
Learn how to hack AI agents and applications with this expert guide. Find vulnerabilities, prompt injection risks, and testing strategies for AI security.
Security
SolutionsAIEBootcamp/09_Finetuning_Embeddings/Fine_tuning_Embedding_Models_for_RAG_using_RAGAS.ipynb at main · apatti/AIEBootcamp
AI Engineering bootcamp. Contribute to apatti/AIEBootcamp development by creating an account on GitHub.
Fine-Tuning
15 Best Graph Visualization Tools for Your Neo4j Graph Database
Discover the best graph visualization tools for visualizing your Neo4j graph database, including development, exploration, dashboarding, and embedded tools.
Neo4jAI-Tools
Many students and researchers are already using them - tools with integrated artificial intelligence (AI). What can AI-supported tools achieve, what opportunities do they offer and what are their limitations? The following list is an introductory selection which is not based on any value judgement.
ToolsAWS Flash - AWS Partner: Generative AI on AWS for Financial Services Industries (Technical) - AWS Skill Builder
Your learning center to build in-demand cloud skills.
Jérémy Ravenel on LinkedIn: What are the key ontology standards you should have in mind? Ontology… | 100 comments
What are the key ontology standards you should have in mind?
Ontology standards are crucial for knowledge representation and reasoning in AI and data… | 100 comments on LinkedIn
From PDFs to Insights: Structured Outputs from PDFs with Gemini 2.0
Learn how to extract structured data from PDFs with Gemini 2.0 and Pydantic.
GitHub - langchain-ai/langgraph-supervisor
Contribute to langchain-ai/langgraph-supervisor development by creating an account on GitHub.
recipes/weaviate-features/generative-search/generative_search_anthropic/rag_with_anthropic_citations.ipynb at main · weaviate/recipes
This repository shares end-to-end notebooks on how to use various Weaviate features and integrations! - weaviate/recipes
"regular people don't fine-tune VLMs"
but wtf not?
- skill gap
- high fine-tuning costs
- lack of standards and unified approaches
over the past few weeks I've been working on maestro - streamlined tool for VLM fine-tuning
link:
— SkalskiP (@skalskip92)
VLMOpen source: it works!
Two months ago user durable-racoon posted about DocumentContextExtractor, their iteration on a technique for improving the accuracy of RAG that both and had made demo implementations of.
Contextual Retrieval improves the…
— LlamaIndex 🦙 (@llama_index)