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AnythingLLM, LocalGPT and PrivateGPT are all related toLanguage ModelThere are two related projects, AnythingLLM, that allow users to interact with documents in a local environment, but they have some differences in implementation and features. AnythingLLM uses Pinecone and ChromaDB to handle vector embeddings, and uses the OpenAI API to implement its LLM and session functions.

AnythingLLM

An open source tool developed by Mintplex Labs Inc., designed to create private localknowledge base. It realizes the retrieval and generation of knowledge base by combining large models with RAG (Retrieval-Augmented Generation) retrieval enhancement. AnythingLLM supports multi-user use and can set permission management. It supports multiple document types such as PDF, TXT, DOCX, etc. and provides a simple document management interface. In addition, it also supports multiple LLMs, embedded models and vector databases, allowing users to answer questions and generate summaries through dialogue or search.

Document address:https://docs.useanything.com/

LocalGPT

A project that allows users to chat with documents using a GPT model on their local device. It is a project inspired by the original privateGPT, using the Vicuna-7B model and InstructorEmbeddings instead of LlamaEmbeddings. LocalGPT can be run on GPUs, but also supports CPUs, although it may be slower to run on CPUs. LocalGPT utilizes the LangChain tool to parse documents and create embeddings, which are then stored in a local vector database, using Chroma vector storage. It uses a local LLM to understand the question and create the answer, extracting the context of the answer from the document.

Document address:https://github.com/PromtEngineer/localGPT

PrivateGPT

PrivateGPT is an advanced language model platform that combines efficient language processing with strong privacy protection.OpenAIThe GPT architecture provides an API that supports normal responses and streaming responses.
Document address:https://docs.privategpt.dev/overview/welcome/introduction

Key features of PrivateGPT include:

• Privacy protection: PrivateGPT removes more than 50 types of human identifiable information (PII) in user input prompts and then re-populates this information into the generated answers to ensure a seamless and secure user experience.

• Localized operation: PrivateGPT can be run in a local environment without uploading data to the Internet or sharing it with others, thus protecting data privacy.

• Multiple application scenarios: PrivateGPT can be applied to a variety of scenarios, including online chatbots, automatic email replies, article generation, code generation, etc. In addition, it can also be used for a variety of natural language processing tasks such as text generation, question-answering systems, automatic summarization, sentiment analysis, etc.

• Intelligent Writing: PrivateGPT can help creators quickly draft article frameworks and generate creative content.

• Open source project: PrivateGPT is an open source project that allows users to build their own personalized GPT-4 model through a Python development environment and can be used without any coding or technical knowledge.

• Data control capabilities: PrivateGPT has complete data control capabilities, enabling users to interact with powerful language models in a local environment, ensuring the privacy and security of data.

PrivateGPT not only provides an efficient language model platform, but also meets the strict requirements of modern enterprises in terms of data privacy and security through features such as privacy protection and localized operation.

In general, AnythingLLM, LocalGPT, and PrivateGPT all provide a way to enable users to interact with documents in a local environment, protect data privacy, and leverage the power of large language models. The differences lie in the specific technology stacks they use, the hardware they support, and aspects such as user interface and permission management.

Both LocalGPT and PrivateGPT require LLM to be run locally and have certain requirements for the local machine. AnythingLLM is slightly lighter and the local computer does not need to run LLM to enjoy the benefits of LLM.