Adaptive RAG with Mistral and Pathway notebook #31
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
Hi @avriiil thanks for the PR! Could you move the notebook to the third-party folder and could you make sure the notebook runs on colab? Thanks! |
…okbook into pathway-adapative-rag
| "outputs": [], | ||
| "source": [ | ||
| "# # Uncomment and run if you need to install Pathway and Mistral packages\n", | ||
| "# !pip install -U --prefer-binary pathway~= 0.9.0 \n", |
There was a problem hiding this comment.
I believe "pathway~=0.9.0" has to be in between ", also run_local needs to be declared even if It's False.
|
@sophiamyang -- changes have been made! I will update the Colab link when the PR is merged so it will be generated from the code on |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR adds a notebook showcasing Adaptive RAG with Mistral and Pathway. It shows how you can dynamically adapt the number of documents in a RAG prompt using feedback from the LLM. This can give you significant cost reduction of RAG LLM question answering pipelines while maintaining good accuracy. Mistral makes it easy to run Adaptive RAG locally with
ollamaor remotely using the Mistral API.Thoughts and feedback welcome :)