marqo VS chatgpt-retrieval-plugin

We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo

Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)

Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).

That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.

As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.

Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo

Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo

Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo

Many comments are criticizing the usage of LLM for this use case but I do believe this will become more common in the future. For example, OpenAI's retrieval plugin leverages LLM to do PII detection [1] instead of using the traditional libraries [2].

[1] https://github.com/openai/chatgpt-retrieval-plugin/blob/main...

There is this

https://github.com/openai/chatgpt-retrieval-plugin

I just use SBERT which has models I can run locally

https://sbert.net/

should be yes, but even in examples from openai, they usually do splitting into chunks

For example in chatgpt-retrieval-plugin[0] repo default chunk size is just 200 tokens

[0] https://github.com/openai/chatgpt-retrieval-plugin/blob/main...

this is anyway a limitation, no doubt, but chunking is pretty often used

To begin, visit the retrieval plugin repository.

You need to run the chatgpt-retrieval-plugin. The README has a quickstart guide for doing that: https://github.com/openai/chatgpt-retrieval-plugin

If the browser plugin for ChatGPT gets better to actually exceed what a web-scraper / summarizer can do, you could use GPT for the summarization part and store the results again in some vector database after creating embeddings. You could then use ChatGPT to "talk to your data", meaning when asked for specific information, it could query the database, retrieve the relevant knowledge and provide a meaningful answer based on your question. This could be done via OpenAIs own retrieval plugin (see https://github.com/openai/chatgpt-retrieval-plugin ) or by creating your own Chat-UI that performs the semantic search, feeds the result into the GPT4 API and returns the answer back to the user. David Shapiro made a nice video with step by step instructions on how to do that: https://www.youtube.com/watch?v=2xNzB7xq8nk&pp=ygUUZ3B0NCBtZW1vcnkgcGluZWNvbmU%3D

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For things like books or papers, I guess that embedding will stay relevant for a while.