obsidian-copilot VS tonic_validate

hadn't seen your repo yet [1] - adding it to my list right now.

Your blog post is really neat on top - thanks for sharing

https://github.com/eugeneyan/obsidian-copilot

Um... can someone explain what this actually does?

In the video the user chooses the 'Copilot: Draft' action, and wow, it generates code...

...but, the 'draft' action [1] calls `/get_chunks` and then runs 'queryLLM' [2] which then just invokes 'https://api.openai.com/v1/chat/completions' directly.

So, generating text this way is 100% not interesting or relevant.

What's interesting here is how it's building the prompt to send to the openai-api.

So... can anyone shed some light on what the actual code [3] in get_chunks() does, and why you would... hm... I guess, do a lookup and pass the results to the openai api, instead of just the raw text?

The repo says: "You write a section header and the copilot retrieves relevant notes & docs to draft that section for you.", and you can see in the linked post [4], this is basically what the OP is trying to implement here; you write 'I want X', and the plugin (a bit like copilot) does a lookup of related documents, crafts a meta-prompt and passes the prompt to the openai api.

...but, it doesn't seem to do that. It seems to ignore your actual prompt, lookup related documents by embedding similarity... and then... pass those documents in as the prompt?

I'm pretty confused as to why you would want that.

It basically requires that you write your prompt separately before hand, so you can invoke it magically with a one-line prompt later. Did I misunderstand how this works?

[1] - https://github.com/eugeneyan/obsidian-copilot/blob/bdabdc422...

[2] - https://github.com/eugeneyan/obsidian-copilot/blob/bdabdc422...

[3] - https://github.com/eugeneyan/obsidian-copilot/blob/main/src/...

[4] - https://eugeneyan.com/writing/llm-experiments/#shortcomings-...

I tried out Amazon Bedrock, and used Tonic Validate to do a head to head comparison of very simple RAG system's built using embedding and text models available in Amazon Bedrock. I compared Amazon Titan's embedding and text models to Cohere's embedding and text models in RAG systems that employ Amazon Bedrock Knowledge Bases as the vector db and retrieval components of the system.

The code for the comparison is in this jupyter notebook https://github.com/TonicAI/tonic_validate/blob/main/examples...

Let me know what you think, And your experiences building RAG with Amazon Bedrock!

Tonic's RAG evaluation platform is Tonic Validate, which has open source RAG metrics https://github.com/TonicAI/tonic_validate, and a web app for tracking and monitoring RAG performance https://www.tonic.ai/validate.

Hey HN, Joe and Ethan from Tonic.ai here again. Alongside last week’s announcement of Tonic Validate Metrics (https://news.ycombinator.com/item?id=38012126), we’ve also released an open-source SDK for logging the performance of Retrieval Augmented Generation (RAG) applications during development, Tonic Validate Logging. Tonic Validate Logging is used to log your RAG responses to the Tonic Validate App. When RAG responses are logged, metrics are calculated on the responses using Tonic Validate Metrics.

We were working on a RAG-powered app to enable companies to talk to their free-text data safely when we ran into trouble tracking the performance of our models’ responses. So we built these solutions to help us out: Tonic Validate Metrics for benchmarking, and Tonic Validate Logging + the Tonic Validate UI to track performance improvements to help us choose the best system possible. Tonic Validate provides a simple and convenient UI that you can get for free at https://validate.tonic.ai/.

Two key benefits of using the Tonic Validate tools are (1) automatic logging and metrics calculation with just a few lines of code and (2) a simple, convenient UI to help visualize your experiments, iterations, and benchmarking results for your RAG applications.

Our hope is that these packages will become a useful part of the technique layer behind the growing suite of LLM-powered applications and, more importantly, that the open-source packages evolve and thrive with your contributions.

We’re excited to hear what you all think in the comments!

Read our docs here: https://docs.tonic.ai/validate/

Get the open-source Tonic Validate Metrics package at: https://github.com/TonicAI/tvalmetrics

Get the open-source Tonic Validate Logging SDK at: https://github.com/TonicAI/tvallogging

Sign up for the Tonic Validate UI here: https://validate.tonic.ai/