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almacenamiento de vectores

  • Framework for building retrieval-augmented AI agents using LlamaIndex for document ingestion, vector indexing, and QA.
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    What is Custom Agent with LlamaIndex?
    This project demonstrates a comprehensive framework for creating retrieval-augmented AI agents using LlamaIndex. It guides developers through the entire workflow, starting with document ingestion and vector store creation, followed by defining a custom agent loop for contextual question-answering. Leveraging LlamaIndex's powerful indexing and retrieval capabilities, users can integrate any OpenAI-compatible language model, customize prompt templates, and manage conversation flows via a CLI interface. The modular architecture supports various data connectors, plugin extensions, and dynamic response customization, enabling rapid prototyping of enterprise-grade knowledge assistants, interactive chatbots, and research tools. This solution streamlines building domain-specific AI agents in Python, ensuring scalability, flexibility, and ease of integration.
  • GenAI Processors streamlines building generative AI pipelines with customizable data loading, processing, retrieval, and LLM orchestration modules.
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    What is GenAI Processors?
    GenAI Processors provides a library of reusable, configurable processors to build end-to-end generative AI workflows. Developers can ingest documents, break them into semantic chunks, generate embeddings, store and query vectors, apply retrieval strategies, and dynamically construct prompts for large language model calls. Its plug-and-play design allows easy extension of custom processing steps, seamless integration with Google Cloud services or external vector stores, and orchestration of complex RAG pipelines for tasks such as question answering, summarization, and knowledge retrieval.
  • Transform your browsing history into a vector representation.
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    What is Max's Browser History Embedding Tool?
    This tool allows users to store a vector representation of their browsing history, utilizing OpenAI's embedding model for analysis. It is particularly useful for research purposes, assisting users in understanding patterns and trends in their web activity. By transforming traditional browsing history into a more analyzable format, users can leverage this data for various analytical tasks and gain insights into their browsing habits.
  • Build AI workflows effortlessly with Substrate.
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    What is Substrate?
    Substrate is a versatile platform designed for developing AI workflows by connecting various modular components or nodes. It offers an intuitive Software Development Kit (SDK) that encompasses essential AI functionalities, including language models, image generation, and integrated vector storage. This platform caters to diverse sectors, empowering users to construct complex AI systems with ease and efficiency. By streamlining the development process, Substrate allows individuals and organizations to focus on innovation and customization, transforming ideas into effective solutions.
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