Build LLM applications with Retrieval-Augmented Generation over ingested documents.
npx skills add https://github.com/choice5346/BiSHE --skill llamaindex-choice5346
Please help me install this Agent Skill. Skill: llamaindex Source: https://github.com/choice5346/BiSHE/tree/main/.github/skills/llamaindex Command: npx skills add https://github.com/choice5346/BiSHE --skill llamaindex-choice5346
This Skill simplifies the process of building applications powered by Large Language Models (LLMs) by providing a robust framework for connecting LLMs to your data, enabling advanced retrieval and generation capabilities.
Use the llamaindex skill to load documents from the 'data' directory and query them about what the author did growing up.
High-intent search queries and answers about installing and using this skill.
To build a RAG pipeline, you ingest custom documents, index them for efficient retrieval, and route queries to an LLM. This framework handles the entire workflow, connecting your data to language models for accurate document-based Q&A.
Indexing internal documents for knowledge retrieval is best handled by creating vector indices from ingested files. This approach enables efficient querying across over 300 data sources, allowing chatbots to answer employee questions directly from your knowledge base.
Yes, the data framework supports multi-modal data ingestion and querying. It integrates with OpenAI and Anthropic models, allowing you to build applications that process text, images, and other formats using your preferred LLM provider.
Creating query engines for document Q&A involves loading documents, building a vector index, and configuring the engine with custom prompts. The framework offers flexible query engines that support streaming responses for real-time interactions.
Yes, you can build intelligent agents that interact with your data. The framework provides components to create agents capable of using tools, enabling them to autonomously navigate and query indices to fulfill complex user requests.
Vector indices for document search can struggle with exact keyword matching and require sufficient context window size. However, the framework mitigates this by offering various index types like list and tree indices to optimize retrieval for different data structures.
myaiartist360-makerSummarize web content, local files, and video transcripts via LLM extraction.
ldmrepoBuild real-time streaming AI agent applications using the AG-UI protocol.
peterlianpiCreate document embeddings and index them in a vector store for retrieval-augmented generation.
Harmeet10000Assemble end-to-end RAG pipelines with LangChain loaders, splitters, embeddings, and vector stores.
