Open-source alternatives to Agriculture KnowledgeGraph

KnowledgeGraphData is a collection of structured datasets and corpora designed to provide a foundational layer for cognitive intelligence and artificial intelligence systems. It primarily consists of large-scale Chinese knowledge graph datasets, including entity-relation data and NLP training sets used to drive semantic understanding and automated question answering. The project focuses on the construction and export of massive entity-attribute-value graphs, organizing knowledge into portable formats. It provides specialized domain partitioning to tailor information retrieval for professional

Hyper-Extract is a framework designed for automated knowledge extraction, graph construction, and retrieval-augmented generation. It functions as a command-line tool that transforms unstructured text into structured knowledge graphs and hypergraphs, enabling users to build interconnected, searchable, and machine-readable data repositories from their documents. The system distinguishes itself through its focus on personal knowledge management and incremental processing. It allows users to update existing knowledge bases by processing only new document deltas, avoiding redundant computation. Th

Knwl.js is a JavaScript named entity recognition library and rule-based text parser. It serves as an extensible information extraction tool designed to identify and pull structured entities, such as dates, times, and locations, from unstructured text strings. The library allows for the definition of specialized rules and custom plugins to identify and extract unique pieces of information. This extensibility enables the automation of information retrieval by converting human-readable text into structured formats for applications and databases. The system utilizes regular expression matching a

Duckling is a deterministic named entity recognizer and natural language entity extractor. It transforms unstructured text into machine-readable data by mapping language-specific input strings to universal structured formats. The system utilizes a rule-based engine and composable language rules to resolve entities without relying on probabilistic models. It supports multilingual text parsing across various regional locales, employing a logic-driven approach to normalize diverse natural language expressions into standardized numeric values. The project covers the extraction and normalization

Named entity recognition is a natural language processing library that implements statistical and neural sequence labeling models to extract entities from text. The toolkit provides implementations for hidden Markov models, conditional random fields, and bidirectional recurrent neural networks combined with conditional random field layers. The library supports training machine learning models on annotated training corpora using maximum likelihood estimation for parameter and transition structure estimation. It includes ensemble majority voting consensus strategies to combine independent outpu

Cocoindex is an incremental data processing engine that builds and maintains live indexes for AI agents, with a core focus on codebase indexing and knowledge graph extraction. The engine uses a function-graph execution model where user-defined Python functions are composed into a directed acyclic graph, and it processes data incrementally so only changed source records or code paths are re-computed, avoiding full recomputation at any scale. It supports automatic schema inference from transformation pipeline type annotations and provides full data lineage tracing, tagging every output record wi

JioNLP is a Chinese natural language processing toolkit designed for cleaning, normalizing, and extracting structured information from unstructured text. It functions as a linguistic analyzer for Chinese characters and a rule-based named entity extractor, providing a specialized system for sentiment scoring and synthetic data generation for machine learning workflows. The project features a lexicon-based sentiment analysis engine that computes numerical emotional tone scores and a data augmentation library that uses back-translation and synonym replacement to expand training datasets. It incl

KnowledgeGraphCourse is a structured collection of graduate-level academic materials, lecture notes, and a comprehensive curriculum focused on the theory and application of knowledge graphs. It serves as a markdown-based educational resource that provides navigable course modules and study guides. The material covers specialized research on integrating knowledge graphs with large language models to reduce hallucinations. It includes detailed guides on using the SPARQL language for storing large-scale graph datasets and executing optimized queries. The curriculum spans a broad range of capabi

This project is a tool for transforming unstructured text into semantic knowledge graphs. It uses local language models to extract entities and their relationships, converting text corpora into a structured network of linked concepts. The system provides a web interface for interactive network visualization, allowing users to navigate the resulting nodes and edges. It includes a topology analysis tool that calculates node degrees and identifies community clusters to determine the visual size and color of graph elements. Beyond visualization, the project enables graph-based information retrie

DeepPavlov is a conversational AI framework and deep learning NLP library designed for building end-to-end dialogue systems and chatbots. It functions as an NLP pipeline orchestrator that allows users to compose pre-trained models and text processing components into sequential data flows for complex linguistic tasks. The system is distinguished by its ability to act as a chatbot deployment server, exposing trained conversational models as web services via REST and Socket APIs. It utilizes JSON-based pipeline configurations and dynamic variable interpolation to decouple model logic from infras

SimpleTransformers is a high-level framework for training and fine-tuning transformer models for diverse natural language processing tasks. It functions as a toolkit for developing text classification, named entity recognition, and question answering models, while also serving as a sequence-to-sequence tool and a text embedding generator. The library distinguishes itself by providing a multi-modal model trainer capable of processing and classifying data that combines both text and image inputs. It further supports specialized workflows for conversational AI training, language model generation

Spark NLP is a toolkit for scalable text analysis and machine learning built on the Apache Spark distributed computing framework. It provides a multimodal machine learning framework and a distributed pipeline system for sequencing annotators to process large-scale linguistic data. The library includes a transformer text processor for generating contextual vector embeddings and a dedicated inference engine for managing large language models. The project distinguishes itself through its ability to process heterogeneous data types, including text, audio, and images, within a unified vision-langu

This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals

Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin

Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall and structured knowledge. It functions as a framework for building persistent memory systems that connect large language models to graph-based knowledge and vector storage, enabling agents to maintain context across complex tasks and multiple sessions. The platform distinguishes itself through a hybrid approach that combines semantic similarity search with structural graph traversal, allowing for context-aware information retrieval. It features a modular architecture that orche

This project is a comprehensive framework for developing, orchestrating, and deploying autonomous agents. It provides a structured environment for building agents that utilize reasoning loops to perform multi-step tasks, manage state through graph-based workflows, and interact with external tools. By mapping unstructured model outputs into typed schemas, the framework ensures reliable integration with downstream application logic. The platform distinguishes itself through a focus on production-grade reliability and security. It incorporates hybrid memory systems that combine vector embeddings

This repository serves as a comprehensive library of architectural blueprints and code examples for integrating large language models into software applications. It functions as a developer learning resource, providing structured tutorials and implementation patterns that demonstrate how to build intelligent features using advanced prompting and data processing techniques. The collection distinguishes itself by focusing on complex reasoning and data-grounding workflows. It provides practical guidance on implementing retrieval-augmented generation pipelines, which connect language models to pr

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

This project is a high-performance semantic graph database engine designed for storing and querying massive RDF datasets. It functions as a specialized platform for managing linked data and complex relationship models, utilizing standard semantic web protocols to integrate and analyze distributed information sources. The system distinguishes itself through its use of B-Tree indexing to enable rapid traversal of relationships within large-scale datasets and its support for the Triple Pattern Fragments protocol to facilitate scalable web-based access. It provides automated tools for transformin

Datascript is an immutable, in-memory state store and schema-based triple store. It manages application state as a versioned database, storing data as immutable facts consisting of an entity, attribute, value, and transaction. The project provides a logic engine for executing Datalog queries with support for implicit joins, recursive rules, and negation. It also features a declarative pull API for retrieving deeply nested entity graphs and related data structures. The database enforces data integrity through schema-driven constraints and attribute types. It supports atomic transactions, plug

This project is a natural language processing system designed for named entity recognition and text classification. It uses a machine learning approach to identify specific names and key information from raw text to organize unstructured content into a structured format. The system implements a multi-layer architecture that combines a pre-trained transformer for embeddings, bidirectional long short-term memory for sequence modeling, and a conditional random field for label transitions. It supports transfer learning through the fine-tuning of these models on task-specific datasets. The projec

Entity-Relation-Extraction is a machine learning framework designed to identify entities and their logical connections within unstructured text. It functions as a pipeline that transforms raw documents into structured knowledge graphs by utilizing deep learning models and transformer architectures. The project distinguishes itself through a schema-driven approach, which maps extracted information to predefined relational templates to ensure output consistency. It employs a multi-stage process that combines sequence-labeling token classification with contextual encoding to delineate entity bou

This project is a named entity recognition framework and TensorFlow-based natural language processing model. It provides a pipeline for adapting pre-trained language models to specific entity recognition and text classification tasks. The system implements a sequence labeling architecture that combines transformer-based embeddings with bidirectional sequence modeling and conditional random field decoding. It includes tools for fine-tuning model weights and training the network to identify and categorize entities within unstructured text. The framework also includes a client-server architectu

llm-graph-builder is a tool for transforming unstructured data into structured Neo4j graph databases using large language models. It functions as a graph orchestrator that automates the construction of nodes and relationships from raw text based on custom schemas. The project provides a visualizer for analyzing relational data as interactive networks and a token monitor to track daily and monthly API consumption per user. It also includes a vector embedding generator that utilizes configurable model providers to enable semantic search and retrieval augmented generation. The system covers cap

GraphGPT is an LLM knowledge graph generator that extracts entities and relationships from unstructured text to create visual knowledge graphs. It functions as a natural language graph interface and an unstructured data extraction pipeline, transforming raw text into structured triples for mapping complex information networks. The system enables dynamic knowledge mapping by allowing users to build and update network visualizations through conversational queries and text-based instructions. This allows for the conversion of unstructured data into visual graphs to identify patterns and connecti

Knwl.js is a JavaScript named entity recognition library and text entity extractor. It functions as an extensible text parsing engine designed to scan unstructured strings for specific data patterns and convert them into structured information. The engine utilizes a modular framework that allows for the recognition of custom data types. This is achieved through a plugin system for language pattern matching, enabling the integration of custom logic to identify unique data types within text. The library identifies and isolates entities such as dates, times, phone numbers, emails, and locations

LAC is a Chinese lexical analysis engine and toolkit designed for joint word segmentation, part-of-speech tagging, and named entity recognition. It functions as a high-performance system that identifies word boundaries and grammatical categories using trained machine learning models. The project features a lightweight, compiled native runtime that enables on-device natural language processing and embedding into mobile applications. It includes model compression and conversion to optimize for resource-constrained environments and supports multi-threaded parallel execution to increase throughpu