Requesting integration of PaperQA and Microsoft GraphRAG.
PaperQA
A simple interface to get good answers with grounded responses containing in-text citations.
State-of-the-art implementation including document metadata-awareness in embeddings and LLM-based re-ranking and contextual summarization (RCS).
Support for agentic RAG, where a language agent can iteratively refine queries and answers.
Automatic redundant fetching of paper metadata, including citation and journal quality data from multiple providers.
A usable full-text search engine for a local repository of PDF/text files.
A robust interface for customization, with default support for all LiteLLM models.
GraphRAG
GraphRAG is able to connect information across large volumes of information and use these connections to answer questions that are difficult or impossible to answer using keyword and vector-based search mechanisms. Building on the previous question, provide semi-technical, high-level information on how the system offers functionality for various uses. This lets a system using GraphRAG to answer questions where the answers span many documents as well as thematic questions such as “what are the top themes in this dataset?
Originally created by @vishaldwdi on GitHub (Sep 24, 2024).
Original GitHub issue: https://github.com/open-webui/open-webui/issues/5646
Requesting integration of PaperQA and Microsoft GraphRAG.
# PaperQA
- A simple interface to get good answers with grounded responses containing in-text citations.
- State-of-the-art implementation including document metadata-awareness in embeddings and LLM-based re-ranking and contextual summarization (RCS).
- Support for agentic RAG, where a language agent can iteratively refine queries and answers.
- Automatic redundant fetching of paper metadata, including citation and journal quality data from multiple providers.
- A usable full-text search engine for a local repository of PDF/text files.
- A robust interface for customization, with default support for all LiteLLM models.
# GraphRAG
GraphRAG is able to connect information across large volumes of information and use these connections to answer questions that are difficult or impossible to answer using keyword and vector-based search mechanisms. Building on the previous question, provide semi-technical, high-level information on how the system offers functionality for various uses. This lets a system using GraphRAG to answer questions where the answers span many documents as well as thematic questions such as “what are the top themes in this dataset?
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Originally created by @vishaldwdi on GitHub (Sep 24, 2024).
Original GitHub issue: https://github.com/open-webui/open-webui/issues/5646
Requesting integration of PaperQA and Microsoft GraphRAG.
PaperQA
GraphRAG
GraphRAG is able to connect information across large volumes of information and use these connections to answer questions that are difficult or impossible to answer using keyword and vector-based search mechanisms. Building on the previous question, provide semi-technical, high-level information on how the system offers functionality for various uses. This lets a system using GraphRAG to answer questions where the answers span many documents as well as thematic questions such as “what are the top themes in this dataset?