Best AI tools for< Persist Chat Data >

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The ai-chat-local repository is a project that implements a local model customization using ollama, allowing data privacy and bypassing detection. It integrates with the esp32 development board to create a simple chat assistant. The project includes features like database integration for chat persistence, user recognition, and AI-based document processing. It also offers a graphical configuration tool for Dify.AI implementation.

Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asyncronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands. Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core memory features, but is not designed to be a general purpose vector database. Zep allows you to be more intentional about constructing your prompt: 1. automatically adding a few recent messages, with the number customized for your app; 2. a summary of recent conversations prior to the messages above; 3. and/or contextually relevant summaries or messages surfaced from the entire chat session. 4. and/or relevant Business data from Zep Document Collections.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

Smart Connections is an AI-powered plugin for Obsidian that helps you discover hidden connections and insights in your notes. With features like Smart View for real-time relevant note suggestions and Smart Chat for chatting with your notes, Smart Connections makes it easier than ever to stay organized and uncover hidden connections between your notes. Its intuitive interface and customizable settings ensure a seamless experience, tailored to your unique needs and preferences.

Cortex is a tool that simplifies and accelerates the process of creating applications utilizing modern AI models like chatGPT and GPT-4. It provides a structured interface (GraphQL or REST) to a prompt execution environment, enabling complex augmented prompting and abstracting away model connection complexities like input chunking, rate limiting, output formatting, caching, and error handling. Cortex offers a solution to challenges faced when using AI models, providing a simple package for interacting with NL AI models.

AWS AI Stack is a full-stack boilerplate project designed for building serverless AI applications on AWS. It provides a trusted AWS foundation for AI apps with access to powerful LLM models via Bedrock. The architecture is serverless, ensuring cost-efficiency by only paying for usage. The project includes features like AI Chat & Streaming Responses, Multiple AI Models & Data Privacy, Custom Domain Names, API & Event-Driven architecture, Built-In Authentication, Multi-Environment support, and CI/CD with Github Actions. Users can easily create AI Chat bots, authentication services, business logic, and async workers using AWS Lambda, API Gateway, DynamoDB, and EventBridge.

ellmer is a tool that facilitates the use of large language models (LLM) from R. It supports various LLM providers and offers features such as streaming outputs, tool/function calling, and structured data extraction. Users can interact with ellmer in different ways, including interactive chat console, interactive method call, and programmatic chat. The tool provides support for multiple model providers and offers recommendations for different use cases, such as exploration or organizational use.

Elmer is a user-friendly wrapper over common APIs for calling llm’s, with support for streaming and easy registration and calling of R functions. Users can interact with Elmer in various ways, such as interactive chat console, interactive method call, programmatic chat, and streaming results. Elmer also supports async usage for running multiple chat sessions concurrently, useful for Shiny applications. The tool calling feature allows users to define external tools that Elmer can request to execute, enhancing the capabilities of the chat model.

RubyLLM is a delightful Ruby tool for working with AI, providing a beautiful API for various AI providers like OpenAI, Anthropic, Gemini, and DeepSeek. It simplifies AI usage by offering a consistent format, minimal dependencies, and a joyful coding experience. Users can chat, analyze images, audio, and documents, generate images, create vector embeddings, and integrate AI with Ruby code effortlessly. The tool also supports Rails integration, streaming responses, and tool creation, making AI tasks seamless and enjoyable.

Agnaistic is an AI roleplay chat tool that allows users to interact with personalized characters using their favorite AI services. It supports multiple AI services, persona schema formats, and features such as group conversations, user authentication, and memory/lore books. Agnaistic can be self-hosted or run using Docker, and it provides a range of customization options through its settings.json file. The tool is designed to be user-friendly and accessible, making it suitable for both casual users and developers.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

Cloudflare Agents is a framework for building intelligent, stateful agents that persist, think, and evolve at the edge of the network. It allows for maintaining persistent state and memory, real-time communication, processing and learning from interactions, autonomous operation at global scale, and hibernating when idle. The project is actively evolving with focus on core agent framework, WebSocket communication, HTTP endpoints, React integration, and basic AI chat capabilities. Future developments include advanced memory systems, WebRTC for audio/video, email integration, evaluation framework, enhanced observability, and self-hosting guide.

aiaio (AI-AI-O) is a lightweight, privacy-focused web UI for interacting with AI models. It supports both local and remote LLM deployments through OpenAI-compatible APIs. The tool provides features such as dark/light mode support, local SQLite database for conversation storage, file upload and processing, configurable model parameters through UI, privacy-focused design, responsive design for mobile/desktop, syntax highlighting for code blocks, real-time conversation updates, automatic conversation summarization, customizable system prompts, WebSocket support for real-time updates, Docker support for deployment, multiple API endpoint support, and multiple system prompt support. Users can configure model parameters and API settings through the UI, handle file uploads, manage conversations, and use keyboard shortcuts for efficient interaction. The tool uses SQLite for storage with tables for conversations, messages, attachments, and settings. Contributions to the project are welcome under the Apache License 2.0.

Magma is a powerful and flexible framework for building scalable and efficient machine learning pipelines. It provides a simple interface for creating complex workflows, enabling users to easily experiment with different models and data processing techniques. With Magma, users can streamline the development and deployment of machine learning projects, saving time and resources.

LlamaIndex is a data framework for building LLM applications. It provides tools for ingesting, structuring, and querying data, as well as integrating with LLMs and other tools. LlamaIndex is designed to be easy to use for both beginner and advanced users, and it provides a comprehensive set of features for building LLM applications.

This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. The testing procedure involves generating a SQL query, running both the 'gold' query and the generated query on their respective database to obtain dataframes with the results, comparing the dataframes using an 'exact' and a 'subset' match, logging these alongside other metrics of interest, and aggregating the results for reporting. The repository provides comprehensive instructions for installing dependencies, starting a Postgres instance, importing data into Postgres, importing data into Snowflake, using private data, implementing a query generator, and running the test with different runners.

Sentient is a personal, private, and interactive AI companion developed by Existence. The project aims to build a completely private AI companion that is deeply personalized and context-aware of the user. It utilizes automation and privacy to create a true companion for humans. The tool is designed to remember information about the user and use it to respond to queries and perform various actions. Sentient features a local and private environment, MBTI personality test, integrations with LinkedIn, Reddit, and more, self-managed graph memory, web search capabilities, multi-chat functionality, and auto-updates for the app. The project is built using technologies like ElectronJS, Next.js, TailwindCSS, FastAPI, Neo4j, and various APIs.

SambaNova AI Starter Kits is a collection of open-source examples and guides designed to facilitate the deployment of AI-driven use cases for developers and enterprises. The kits cover various categories such as Data Ingestion & Preparation, Model Development & Optimization, Intelligent Information Retrieval, and Advanced AI Capabilities. Users can obtain a free API key using SambaNova Cloud or deploy models using SambaStudio. Most examples are written in Python but can be applied to any programming language. The kits provide resources for tasks like text extraction, fine-tuning embeddings, prompt engineering, question-answering, image search, post-call analysis, and more.

Letta is an open source framework for building stateful LLM applications. It allows users to build stateful agents with advanced reasoning capabilities and transparent long-term memory. The framework is white box and model-agnostic, enabling users to connect to various LLM API backends. Letta provides a graphical interface, the Letta ADE, for creating, deploying, interacting, and observing with agents. Users can access Letta via REST API, Python, Typescript SDKs, and the ADE. Letta supports persistence by storing agent data in a database, with PostgreSQL recommended for data migrations. Users can install Letta using Docker or pip, with Docker defaulting to PostgreSQL and pip defaulting to SQLite. Letta also offers a CLI tool for interacting with agents. The project is open source and welcomes contributions from the community.