Comments (2)
Hi, thanks for the questions. Here we focused more on the LLM part of the REPLUG paper, where documents can be fed in parallel. In general, documents can be retrieved using any method; for the simplicity of the notebook we showed in memory DB that uses BM25 with re-ranker using off-the-shelf sentence transformer. We don't have training code for embedders here, we might add it in the future.
The PromptModel is an abstraction around LLMs where you provide a model name and it generates text based on a prompt, abstracting away whether it is a local model, the hardware specifications or perhaps even a cloud-based service. The API is part of Haystack v1, which we use for the development of our library.
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Thanks for the work, here also look forward to the implementation of the REPLUG training :)
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Related Issues (20)
- Add updates to `PLAIDDocumentStore` HOT 1
- Finetuning example HOT 5
- ImportError: cannot import name 'Seq2SeqGenerator' from 'haystack.nodes' HOT 1
- Document Lister not imported HOT 1
- Is this repo still active? HOT 5
- How to use this repo on other languages? HOT 7
- Cannot run the examples, haystack issues HOT 4
- Add Chroma HOT 1
- I need your help creating an example with fastRAG and MiniAutoGen: Lightweight and Flexible Agents for Multi-Agent Chats
- Support for Haystack v2 HOT 2
- Not working on large list of documents. HOT 2
- REPLUG compute the LM likelihood HOT 1
- Missing libs directory HOT 1
- Issues Running Different LLM Models on examples/rag_with_quantized_llm.ipynb
- Fine tuned Embedding model HOT 1
- Connection error when running fastrag.rest_api_application HOT 4
- pip install .[intel] fails HOT 1
- Question for the replug_parallel_reader HOT 1
- State of Migration to Haystack 2.0
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