huggingface / huggingface/cookbook
RAG: Question Answering using Gemma, Elasticsearch & langchain
- Dominant language
- Jupyter Notebook
- Stars
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- Forks
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- Avg merge
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- Merged PRs (30d)
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Description
This is Ashish from [Elastic](https://www.elastic.co/). I'm interested in sharing a notebook that showcases RAG implementation using Elasticsearch. The notebook will illustrate how we generate vectors using Elastic's ELSER model and retrieve semantic results. We'll be utilizing Gemma for contextual question answering.
Contributor guide
No contributing guide indexed for this repository
Research direction
Review the cookbook's existing notebook structure and identify where a RAG example using Elasticsearch, ELSER, and Gemma would fit. Confirm the expected setup and presentation conventions before adding the notebook. Done means a self-contained notebook demonstrates vector generation, semantic retrieval, and contextual question answering.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- elasticsearch, jupyter-notebook
- Domain
- machine-learning, search
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100