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英文字典中文字典相关资料:


  • Highly Performant, Modular and Memory Safe - GitHub
    Whether you're working with text, images, audio, PDFs, websites, or other media, EmbedAnything streamlines the process of generating embeddings from various sources and seamlessly streaming (memory-efficient-indexing) them to a vector database
  • Using Documents in AnythingLLM ~ AnythingLLM
    AnythingLLM supports both attaching documents and embedding documents (RAG Reranking) for your convenience and flexibility Uploaded documents in the chat are workspace and thread scoped This means that documents uploaded in one thread will not be available in another chat
  • GitHub Pages - Starlight Search
    Whether you're working with text, images, audio, PDFs, websites, or other media, EmbedAnything streamlines the process of generating embeddings from various sources and seamlessly streaming (memory-efficient-indexing) them to a vector database
  • embed-anything · PyPI
    Whether you're working with text, images, audio, PDFs, websites, or other media, EmbedAnything streamlines the process of generating embeddings from various sources and seamlessly streaming (memory-efficient-indexing) them to a vector database
  • AnythingLLM Default Embedder ~ AnythingLLM
    AnythingLLM ships with a built-in embedder model that runs on CPU The model is the popular all-MiniLM-L6-v2 model, which is primarily trained on English documents All-in-one AI application that can do RAG, AI Agents, and much more with no code or infrastructure headaches
  • RAG + Embedding with AnythingLLM and Ollama - My Playground
    AnythingLLM - is an all-in-one AI application that simplifies the interaction with Large Language Models (LLMs) for business intelligence purposes It allows users to chat with any document, such as PDFs or Word files, using various LLMs, including enterprise models like GPT-4 or open-source models like Llama and Mistral
  • embed_anything - Rust - Docs. rs
    Whether you’re working with text, images, audio, PDFs, websites, or other media, embed_anything streamlines the process of generating embeddings from various sources and seamlessly streaming (memory-efficient-indexing) them to a vector database
  • AnythingLLM: Complete Guide to Setup, RAG, and Use Cases
    Embedding (the standard RAG approach) breaks the document into chunks, converts them to vectors, and stores them in the workspace Once embedded, documents work across all your chats in that workspace
  • Embedding Models - AnythingLLM
    Embedding models are specific types of models that turn text into vectors, which can be stored and searched in a vector database - which is the foundation of RAG
  • EmbedAnything README. md at main - GitHub
    Whether you're working with text, images, audio, PDFs, websites, or other media, EmbedAnything streamlines the process of generating embeddings from various sources and seamlessly streaming (memory-efficient-indexing) them to a vector database





中文字典-英文字典  2005-2009