Search Results for "squid-graph"
Sort By:
Showing 16 open source projects for "squid-graph"
View related business solutions-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
1
nano-graphrag
A simple, easy-to-hack GraphRAG implementation
...The system extracts entities and relationships from documents using language models and organizes them into graph structures that can be queried during generation. Developers can integrate different storage backends and embedding engines, including vector databases and graph databases such as Neo4j, allowing flexible experimentation with hybrid retrieval methods. -
2
AI Powered Knowledge Graph Generator
AI Powered Knowledge Graph Generator
AI-Powered Knowledge Graph is an open-source project focused on building knowledge graph systems that integrate artificial intelligence and machine learning to represent complex relationships between data entities. Knowledge graphs organize information as networks of nodes and relationships, allowing applications to analyze connections between concepts, datasets, or real-world entities. -
3
Youtu-GraphRAG
Vertically Unified Agents for Graph Retrieval-Augmented Reasoning
Youtu-GraphRAG is a research framework developed by Tencent for performing complex reasoning using graph-based retrieval-augmented generation. The system combines knowledge graphs, retrieval mechanisms, and agent-based reasoning into a unified architecture designed to handle knowledge-intensive tasks. Instead of relying solely on text retrieval, the framework organizes information into structured graph schemas that represent entities, relationships, and attributes. -
4
kg-gen
Knowledge Graph Generation from Any Text
...Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing higher-quality graph structures from raw text. The framework addresses common problems in automatic knowledge graph construction, particularly sparsity and duplication of entities, by applying a clustering and entity-resolution process that merges semantically similar nodes. This allows the generated graphs to be denser, more coherent, and easier to use for downstream tasks such as retrieval-augmented generation, semantic search, and reasoning systems. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
5
SAG
SQL-Driven RAG Engine
...The architecture also includes a three-stage retrieval pipeline consisting of recall, expansion, and reranking steps to improve search accuracy. The engine integrates semantic vector similarity with traditional full-text search to improve both recall and precision. Because the knowledge graph is generated dynamically, the system can adapt to new information without requiring manual graph maintenance. -
6
DriveLM
Driving with Graph Visual Question Answering
...The system includes DriveLM-Data, a dataset built on driving environments such as nuScenes and CARLA, where human-written reasoning steps connect different layers of driving tasks. This design allows models to learn relationships between objects, behaviors, and navigation decisions through graph-structured logic. -
7
Torch Pruning
DepGraph: Towards Any Structural Pruning
...The library focuses on reducing the size and computational cost of neural networks by removing redundant parameters and channels while maintaining model performance. It introduces a graph-based algorithm called DepGraph that automatically identifies dependencies between layers, allowing parameters to be pruned safely across complex architectures. This dependency analysis makes it possible to prune large networks such as transformers, convolutional networks, and diffusion models without breaking the computational graph. -
8
Nano-vLLM
A lightweight vLLM implementation built from scratch
...The project recreates the core functionality of vLLM in a simplified architecture written in approximately a thousand lines of Python, making it easier for developers and researchers to understand how modern LLM inference systems work. Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. The engine is intended primarily for educational use, experimentation, and lightweight deployments where a full production-grade inference stack may be unnecessary. Its API closely mirrors that of the original vLLM framework, allowing developers familiar with vLLM to adopt the tool with minimal changes. -
9
LLMCompiler
An LLM Compiler for Parallel Function Calling
...LLMCompiler addresses this limitation by applying principles from classical compilers to analyze a task and construct an execution plan that allows multiple functions to run in parallel whenever possible. The framework builds a dependency graph of required operations, identifying which tasks must run sequentially and which can be executed simultaneously. Its architecture includes components such as a planning module that constructs the task graph, a task dispatcher that manages dependencies, and an executor that performs parallel calls. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
10
OmAgent
Build multimodal language agents for fast prototype and production
...Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker coordination, and node optimization behind the scenes. Its architecture uses a graph-based workflow engine where tasks are represented as nodes in a directed workflow, enabling modular composition of complex reasoning pipelines. The framework also includes support for various reasoning strategies commonly used in language agents, such as chain-of-thought prompting, self-consistency reasoning, and ReAct-style decision loops. -
11
GraphRAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. -
12
Controllable-RAG-Agent
This repository provides an advanced RAG
Controllable-RAG-Agent is an advanced Retrieval-Augmented Generation (RAG) system designed specifically for complex, multi-step question answering over your own documents. Instead of relying solely on simple semantic search, it builds a deterministic control graph that acts as the “brain” of the agent, orchestrating planning, retrieval, reasoning, and verification across many steps. The pipeline ingests PDFs, splits them into chapters, cleans and preprocesses text, then constructs vector stores for fine-grained chunks, chapter summaries, and book quotes to support nuanced queries. At query time, it anonymizes entities, creates a high-level plan, de-anonymizes and expands that plan into concrete retrieval or reasoning tasks, and executes them in sequence while continuously revising the plan. ... -
13
ML Retreat
Machine Learning Journal for Intermediate to Advanced Topics
...Rather than functioning as a traditional tutorial series, the repository is organized as a learning journey that progressively explores increasingly advanced subjects. Topics include large language models, graph neural networks, mechanistic interpretability, transformer architectures, and emerging research areas such as quantum machine learning. The repository includes references to influential research papers, lectures, and educational content from well-known machine learning educators. -
14
Agents 2.0
An Open-source Framework for Data-centric Language Agents
Agents is an open-source framework designed to build and train autonomous language agents through a data-centric and learning-oriented architecture. The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During training, the system performs a forward execution where the agent completes a task and records the trajectory of prompts, outputs, and tool usage. ... -
15
Anomaly Detection Learning Resources
Anomaly detection related books, papers, videos, and toolboxes
...The repository organizes resources into structured categories such as books, tutorials, academic papers, datasets, benchmark frameworks, and open-source toolkits. It includes materials covering a wide range of anomaly detection domains, including time series data, graph data, tabular datasets, and real-time monitoring systems. By compiling resources from multiple programming ecosystems such as Python, R, and other machine learning platforms, the repository allows users to discover both research papers and practical implementations. -
16
Graph of Thoughts
Official Implementation of "Graph of Thoughts
Graph of Thoughts is an open-source framework that implements a novel reasoning paradigm for large language models by organizing reasoning steps as a structured graph instead of a simple linear chain. Traditional reasoning methods such as chain-of-thought generate sequential reasoning steps, but Graph of Thoughts introduces a more flexible structure where multiple reasoning paths can be explored and evaluated simultaneously.
- Previous
- You're on page 1
- Next
