llm-lab-org/Multimodal-RAG-Survey

• hightopics#1

  Add 'survey' and 'literature-review' to repository topics.

  原因：

  当前

  multimodal-learning, rag, retrieval-augmented-generation

  复制粘贴的修复

  复制

  multimodal-learning, rag, retrieval-augmented-generation, survey, literature-review

  待办完成忽略
• highlicense#2

  Add a LICENSE file and reference it in the README.

  原因：

  复制粘贴的修复

  复制

  Create a `LICENSE` file in the repository root with the chosen license (e.g., MIT, Apache-2.0, or a custom license). Then, add a line to the README, for example: "This project is released under the [Your Chosen License Name] license. See the [LICENSE file](LICENSE) for details."

  待办完成忽略
• mediumreadme#3

  Add a clear disclaimer in the README that this is a survey, not an implementation.

  原因：

  当前

  This repository is designed to collect and categorize papers related to Multimodal Retrieval-Augmented Generation (RAG) according to our survey paper: Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation.

  复制粘贴的修复

  复制

  This repository is designed to collect and categorize papers related to Multimodal Retrieval-Augmented Generation (RAG) according to our survey paper: Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation. **Please note: This repository is a comprehensive literature survey and resource collection, not an implementation or a deployable system.** Given the rapid growth in this field, we will continuously update both the paper and this repository to serve as a resource for researchers working on future projects.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find a comprehensive survey on techniques for multimodal retrieval-augmented generation?

  你：未被推荐
  AI 推荐顺序：

  1. Retrieval-Augmented Generation for Large Language Models: A Survey
  2. A Survey of Retrieval-Augmented Generation for LLMs
  3. A Survey on Multimodal Large Language Models
  4. Multimodal Foundation Models: A Survey
  5. Deep Cross-Modal Hashing: A Survey
  6. A Survey on Cross-Modal Retrieval

  AI 推荐了 6 个替代方案，却始终没点名 llm-lab-org/Multimodal-RAG-Survey。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are effective strategies for integrating diverse data modalities into RAG systems?

  你：未被推荐
  AI 推荐顺序：

  1. Pinecone
  2. Weaviate (weaviate/weaviate)
  3. Qdrant (qdrant/qdrant)
  4. Chroma (chroma-core/chroma)
  5. OpenAI CLIP
  6. Google LaMDA/PaLM 2/Gemini
  7. Hugging Face Transformers (huggingface/transformers)
  8. Neo4j (neo4j/neo4j)
  9. Amazon Neptune
  10. Grakn (vaticle/typedb)
  11. LangChain (langchain-ai/langchain)
  12. LlamaIndex (run-llama/llama_index)
  13. Haystack (deepset-ai/haystack)

  AI 推荐了 13 个替代方案，却始终没点名 llm-lab-org/Multimodal-RAG-Survey。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of llm-lab-org/Multimodal-RAG-Survey?

  pass
  ⚠AI 未点名 llm-lab-org/Multimodal-RAG-Survey —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts llm-lab-org/Multimodal-RAG-Survey in production, what risks or prerequisites should they evaluate first?

  pass
  ⚠AI 未点名 llm-lab-org/Multimodal-RAG-Survey —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo llm-lab-org/Multimodal-RAG-Survey solve, and who is the primary audience?

  pass
  ⚠AI 未点名 llm-lab-org/Multimodal-RAG-Survey —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？