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Prompt

What are the major RAGs and are there any open source RAGs?

Output

Based on the search results and your question, I can provide an overview of major RAG (Retrieval-Augmented Generation) frameworks, including open-source options:

Major RAG Frameworks:

  1. Pathway: A high-throughput, low-latency framework for production AI applications with RAG capabilities.
  2. Cohere: Offers advanced RAG capabilities with a focus on enterprise-level, multilingual use cases.
  3. LlamaIndex: A SaaS-based framework enhancing RAG with efficient indexing and advanced retrieval capabilities.
  4. LangChain: An orchestrator designed to integrate LLMs with external data sources and APIs.
  5. Haystack: A flexible open-source AI framework for building end-to-end LLM-powered applications.
  6. DSPY: Not detailed in the search results, but mentioned as one of the top RAG frameworks for 2024.
  7. OpenAI API with Assistants API: Allows file uploads for RAG-like functionality.

Open-Source RAG Frameworks:

  1. Haystack: Explicitly mentioned as an open-source framework.
  2. LangChain: While it offers commercial services, it also has open-source components.
  3. LlamaIndex: Although primarily SaaS-based, it has open-source components available on GitHub.
  4. FARM: An internal framework from Deepset for building transformer-based NLP pipelines including RAG.
  5. REALM: A Google toolkit for open-domain question answering with RAG.

Other Notable Mentions:

  • Azure Machine Learning: Enables RAG through Azure Cognitive Services.
  • Hugging Face Transformer plugin: Provides a transformer to generate RAG models.
  • Meta AI: Combines retrieval and generation within a single framework.

It's worth noting that many of these frameworks, even if not fully open-source, often have open-source components or libraries that developers can use and contribute to. The field of RAG is rapidly evolving, with new tools and frameworks emerging regularly.

Extracted Prompts

Prompts/Major RAGs and open source RAGs.md#Prompt 1

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.