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Loyal-Elephie

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Conversational & Personal Assistants
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Basic Information

Loyal Elephie is an open source memory-enabled conversational application that combines a Next.js web user interface with a Python backend to provide a Retrieval Augmented Generation chat experience. The project is designed to let users interact with both local and OpenAI-compatible language models while selectively saving conversational moments as editable episodic memory. It integrates hybrid search using ChromaDB and BM25 to improve retrieval, supports date-aware queries, and can connect to a local embedding server. The application includes a built-in login for secure web access and optional integration with browser-based Markdown editors so the assistant can reference and update external notes in real time. The README notes the project is primarily tested on Linux and documents deployment steps for frontend and backend, model and API configuration, and language preference settings.

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App URL
https://github.com/v2rockets/Loyal-Elephie
Github Repository
https://github.com/v2rockets/Loyal-Elephie/blob/main/README.md

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App Details

Features
Controllable memory that allows users to save, edit, and manage episodic memory for the assistant. Hybrid retrieval combining ChromaDB and BM25 for more efficient and date-aware searches. Built-in login and access controls to restrict web access to authorized users. A streamlined agent format using XML-style syntax that avoids function-calling and aims to reduce token usage. Support for both open and proprietary LLMs and embeddings through OpenAI-compatible APIs and examples for local embedding deployment. Optional integration with browser Markdown editors to view and edit referenced documents during chats. An example embedding server is provided under external_example and the README lists several tested local LLMs and configuration options.
Use Cases
For end users Loyal Elephie serves as a second-brain conversational companion that can remember and recall important context across sessions, improving continuity and relevance in chats. Hybrid RAG search and local embedding options make responses more factual and date-aware while allowing deployments that reduce external API dependency. The secure login protects private conversations for web access. Editable memory and Markdown integration let users correct or enrich what the assistant knows and surface original notes during discussions. Compatibility with a range of models and an OpenAI-compatible interface offers flexibility for local or cloud deployments. The included deployment steps and example embedding server lower the barrier to run the system on a personal server.

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