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Hey @ss2342, MultiHopEmbeddingRetriever was an external contribution by @deutschmn: maybe he can help here? In case this doesn't work and you can still consider using a generative model instead, PromptNode support a variety of open-source models as well, so you're not limited to OpenAI for this. Have a look: https://docs.haystack.deepset.ai/docs/agent |
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I have been using Haystack to build out an Extractive QA Engine for my project. However, I wanted to go a step further and try to see if I can extend my capabilities to Multi-Hop questions. I came across the MultiHopEmbeddingRetriever and have been trying to use that on the Hotpot QA dataset to see how it would work.
Setup
I first loaded in the dev set from Hotpot QA website. I took the contexts which are in lists and joined them into strings, which I then chunked up into 400-word chunks using Haystack's PreProcessor. After that, I loaded the data into the FAISSDocumentStore. I then initialized the MultiHopEmbeddingRetriever and FARMReader to set up my ExtractiveQAPipeline
Methodology
Before running my pipeline against all of the questions from the HotpotQA dataset, I tried running against just a few questions to see what the output looked like. However, I was unable to get the right answer against any of the questions I tried. I tried with various combinations of num_iterations, pooling_strategy, top_k_retriever, top_k_reader but none of them seemed to every yield the correct answer.
Example
This is one of the examples from the hotpot qa dataset that I tried.
And these are what the results look like from the ExtractiveQAPipeline (num_iterations=20, pooling_strategy="reduce_mean", top_k_retriever=10, top_k_reader=5)
I was curious if I am doing something incorrectly from my end. I would like to be able to use the MultiHopEmbeddingRetriever for my application as these kinds of multi-hop questions are frequently asked.
Limitations
I am limited to use open-source models so I cannot try to use openai agents.
Additional Info
After writing the chunked up docs into my FAISS Document Store, I have 66k documents in the document store, all coming from Hotpot QA Dataset.
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