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OthmanAdi avatar

OthmanAdi/planning-with-files

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14,139 stars·1,302 forks·Python·mit·26 viewswww.aikux.ai↗

Planning With Files

Planning with files is an enterprise knowledge graph platform designed to transform unstructured organizational data into a searchable, interconnected network. By utilizing a graph-based retrieval-augmented generation engine, the system grounds language model outputs in verified internal data, ensuring that responses are explainable, traceable, and free from hallucinations.

The platform distinguishes itself through a focus on data sovereignty and secure, private infrastructure deployment. It enables organizations to maintain full control over sensitive information by processing data locally or within regional cloud environments, preventing the use of internal knowledge for external model training. The architecture supports granular security through attribute-based access control and allows for the isolation of knowledge into distinct, domain-specific workspaces while maintaining a unified semantic logic across the entire organization.

Beyond core retrieval, the system provides a comprehensive suite of tools for managing the data lifecycle, including automated business workflow execution and audit-ready event logging. It facilitates collective intelligence by aggregating expert experience and project documentation into a centralized repository, which can be analyzed to identify infrastructure dependencies and optimize operational efficiency.

The project is implemented in Python and is designed for deployment within customer-managed infrastructure to meet strict regulatory compliance and data governance requirements.

Features

  • Knowledge Graphs - Transforms organizational data into interconnected knowledge graphs to provide a single source of truth for retrieval.
  • Graph-Based Retrieval Augmentation - Grounds large language models in structured knowledge graphs to eliminate hallucinations and ensure explainable, traceable outputs.
  • Graph Retrieval Augmented Generation - Grounds language model outputs in specific graph paths to ensure transparency and reduce hallucinations in enterprise data retrieval.
  • Knowledge Graphs - Utilizes knowledge graphs to provide context and persistent memory for AI agents, grounding generated content in verified internal data.

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  • Private AI Infrastructure - Deploys intelligence systems within private infrastructure to ensure data remains under organizational control.
  • Knowledge Bases - Transforms unstructured documents into a dynamic, interconnected knowledge repository for artificial intelligence systems.
  • AI Grounding Services - Grounds language models in verified internal data to ensure generated responses are accurate and trustworthy.
  • Private Infrastructure Hosting - Deploys services within private networks to ensure full control over data residency and regulatory compliance.
  • Hallucination Detection - Connects large language models to structured graph databases to ground generated responses and reduce hallucinations.
  • Graph Databases - Utilizes graph databases to provide context and relationships for models, reducing hallucinations and ensuring verifiable intelligence.
  • Knowledge Graph Retrieval - Structures organizational data as interconnected nodes and edges to enable verifiable, context-aware information retrieval.
  • Data Sovereignty - Ensures sensitive information remains under organizational control by operating within private or compliant regional infrastructure.
  • Explainable AI - Implements graph-based retrieval methods to provide verifiable, trustworthy, and explainable responses from language models.
  • Grounded Answer Generation - Traces generated answers back to specific nodes and edges in the knowledge graph to ensure factual grounding.
  • Generative Answer Engines - Synthesizes grounded natural language responses directly from internal data sources to prevent hallucinations.
  • Graph Reasoning Systems - Combines language models with structured knowledge graphs to provide explainable, rule-based responses.
  • Segmented Knowledge Spaces - Organizes information into isolated, secure domains for specific teams while maintaining a unified semantic logic across the entire organization.
  • Knowledge Mapping and Graph Tools - Structures organizational information as interconnected entities and relations to create an audit-ready map of business processes.
  • Data Sovereignty Models - Provides frameworks for maintaining control over data residency and jurisdictional compliance in private environments.
  • Document and Unstructured Extraction - Extracts entities and relationships from documents, emails, and tickets to build a structured network of organizational knowledge.
  • Attribute-Based Access Control - Enforces granular security permissions at the data level using resource metadata and user attributes to ensure regulatory compliance.
  • Data Residency Controls - Restricts data processing to specific regional data centers to meet strict sovereignty and compliance requirements.
  • Enterprise Security Controls - Applies enterprise-grade security controls, including role-based access and audit logging, to ensure regulatory compliance.
  • Local-First Architectures - Deploys processing within private infrastructure to maintain full control over sensitive information and prevent external model training.
  • Business Workflow Automation - Automates recurring operational tasks and approval processes through rule-based engines with complete audit trails.
  • Knowledge Management - Operates isolated knowledge environments for different teams while maintaining a unified semantic logic across the entire organization.
  • Knowledge Management Systems - Aggregates unstructured documents into interconnected knowledge bases to facilitate collective intelligence and automated operational workflows.
  • Team Collaboration Platforms - Aggregates organizational knowledge into a centralized platform to enable collaborative information sharing and retrieval across enterprise teams.
  • Workspace Isolation - Organizes information into distinct, collaborative workspaces for specific teams or projects while maintaining unified semantic logic.
  • Knowledge Management Systems - Provides a centralized system for aggregating organizational knowledge to facilitate collaborative decision-making across enterprise teams.
  • Data Lifecycle Management - Manages the export, audit, and deletion of stored information to maintain compliance with data protection regulations.
  • Semantic Information Retrieval - Delivers precise answers by searching across integrated data sources based on meaning and context.
  • Knowledge Maps - Connects data, people, and information using graph structures to transform scattered files into a searchable network.
  • Data Privacy Tools - Enforces data privacy through local processing and secure architectural patterns that keep information under user control.
  • Audit Logs - Maintains comprehensive chronological logs of system operations and access events to facilitate compliance verification and security reviews.
  • Granular Access Controls - Manages access control at the attribute level to ensure granular security and regulatory compliance.
  • Local Data Privacy Tools - Maintains data privacy by keeping processing local and preventing the use of sensitive information for external training.
  • Data Privacy Compliance - Processes enterprise data within a framework designed to ensure regulatory compliance and traceability.
  • Equivalence Verifiers - Generates answers based on traceable paths within a knowledge graph to ensure outputs are verifiable by human users.
  • Natural Language Query Interfaces - Retrieves data from external systems by translating natural language requests into structured queries for context-aware information access.
  • Knowledge and Information Management - Captures and integrates employee contributions into a shared organizational knowledge base to maintain dynamic institutional information.
  • Data Governance - Provides frameworks for managing the security, versioning, and compliance of organizational data assets.
  • Relationship Mappings - Connects disparate information like projects, documents, and stakeholders into a knowledge graph to reveal hidden dependencies.
  • Token Optimizers - Compresses data formats to reduce token usage and lower operational costs for large-scale analysis.
  • Graph Visualizers - Visualizes connections between data points using graph-based structures to ensure transparency and traceability.
  • Professional Knowledge Curations - Filters and selects high-quality, compliant information to populate knowledge graphs for reliable artificial intelligence.
  • Data Compression - Optimizes internal data formats to reduce computational overhead and lower the cost of processing large-scale organizational knowledge.
  • Decision Support Systems - Surfaces historical project data and documented risks to inform current decision-making processes and improve organizational intelligence.
  • Response Caching - Links generated answers to specific source paths within a graph database to eliminate black-box results.
  • Process Isolation - Uses isolation techniques to ensure proprietary information remains confined to specific organizational workflows.
  • Authorization Decision Explainers - Provides transparency into technical dependencies and access rights by translating complex relationships into plain language.

    • Star history

    Star history chart for othmanadi/planning-with-filesStar history chart for othmanadi/planning-with-files

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does othmanadi/planning-with-files do?

    Planning with files is an enterprise knowledge graph platform designed to transform unstructured organizational data into a searchable, interconnected network. By utilizing a graph-based retrieval-augmented generation engine, the system grounds language model outputs in verified internal data, ensuring that responses are explainable, traceable, and free from hallucinations.

    What are the main features of othmanadi/planning-with-files?

    The main features of othmanadi/planning-with-files are: Knowledge Graphs, Graph-Based Retrieval Augmentation, Graph Retrieval Augmented Generation, Private AI Infrastructure, Knowledge Bases, AI Grounding Services, Private Infrastructure Hosting, Hallucination Detection.

    Which projects share features with othmanadi/planning-with-files?

    Projects with overlapping indexed features include: arangodb/arangodb — This project is a multi-model database system designed to store and manage information as documents, graphs, and… awesome-selfhosted/awesome-selfhosted — This project is a community-curated directory of open-source software designed for deployment in private server… camel-ai/camel — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified… voltagent/awesome-claude-code-subagents — This project provides a framework for managing multi-agent systems, designed to automate complex software development,… openspg/kag — KAG is a graph-augmented retrieval augmented generation system and knowledge graph engine. It functions as a framework… datawhalechina/all-in-rag — This project is a retrieval augmented generation framework designed to build pipelines that connect unstructured data…

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