Introducing New Embedding Models
Explore the latest new embedding models; leverage improved accuracy and reduced costs for your machine learning applications.
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Embedding
Chat with Documents Locally for Privacy
Chat with your documents securely using LocalGPT, an open-source project supporting various models, embeddings, and formats. Requires Python 3.10+, C++ compiler, and optional CUDA or Docker for GPU inference.
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PrivateGPT: A Private and Offline LLM Solution
PrivateGPT: A private and offline project using Large Language Models for document-based queries, featuring a FastAPI-based API with high-level and low-level functionalities for document ingestion, chat, completions, and contextual chunks retrieval. Includes RAG pipeline and extensive documentation for installation, configuration, usage, and deployment, with a GitHub repository for contributions and citation information.
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All-in-One Embeddings Database – TxtAI
Learn how to utilize TxtAI for semantic search, language model orchestration, and workflows, including creating, indexing, saving, querying embeddings, and performing various search types like hybrid, graph, and rag-based searches. Master language model pipelines and workflows with TxtAI through YAML files.
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