pathwaycom/llm-app
Ready-to-run cloud templates for building real-time RAG, AI pipelines, and enterprise search applications that synchronize with various live data sources.
Awesome Infra for AI › Vector Databases & Retrieval Infrastructure
run-llama/llama_index
LlamaIndex is a data framework designed to facilitate the development of Large Language Model (LLM) applications through the integration of custom data sources. Its core functionality revolves around data ingestion, indexing, and enabling Retrieval Augmented Generation (RAG) workflows. The project provides tools to connect various data formats—such as PDFs, APIs, and databases—to LLMs, making it easier for models to access and reason over private or domain-specific information. Key components include data connectors to ingest data from diverse sources, data indexes to structure and store this data (often using vector databases), and query engines to retrieve relevant information and synthesize responses with LLMs. LlamaIndex offers modularity, allowing users to select and integrate their preferred LLMs, embedding models, and vector stores through a rich ecosystem of integrations. It supports advanced RAG techniques, agents for complex interactions, and tools for document parsing and structured data extraction. The framework is central to building intelligent applications that require LLMs to interact with and understand proprietary knowledge bases, moving beyond foundational model capabilities to create more informed and context-aware AI systems.
https://github.com/run-llama/llama_index
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