vault-ai VS marqo

There's this GitHub repo for Pinecone Vector with custom knowledge base: VaultAI. But I'm sure the costs would be exorbitant at scale. Basically trains it on specific files, but the API is expensive as expected. Edit: I didn't read and thought you were talking about training your own, sorry. But I'll leave the second paragraph up anyways lol. Someone mentioned LLaMA and another Falcon, the latter of which I hadn't heard of but which looks good too.

Check out the instructions readme here! You may need a little bit of command line know-how but chatgpt can help guide you if you provide it the contents of the readme

Yes, this use-case is a perfect fit actually – This deals very well with any type of manual with lots of human readable text (as opposed to charts or code). It is also better at answering more specific questions, so the example you gave regarding diagnosing engine issues is a really good match for what this is capable of. If you want to try it out you can check out the deployed version of the code here: https://vault.pash.city

Have you tried Vault AI? Saw it pop up on a couple of other Reddits!

OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (https://github.com/pashpashpash/vault-ai)

In cases where a company possesses a strong technological foundation and faces a substantial workload demanding advanced vector search capabilities, its ideal solution lies in adopting a specialized vector database. Prominent options in this domain include Chroma (having raised $20 million), Zilliz (having raised $113 million), Pinecone (having raised $138 million), Qdrant (having raised $9.8 million), Weaviate (having raised $67.7 million), LanceDB (YC W22), Vespa, Marqo, and others. Many of these players have secured significant funding in recent years and are well-positioned to capture notable market share. These vector databases offer efficient storage, indexing, and similarity search functionalities for vectors. They often incorporate specific optimizations tailored for vector data, such as similarity search based on inverted indexes and efficient vector computations. As a result, they cater to the requirements of companies operating in areas like recommendation systems, image search, and natural language processing.

I have a hosted code-first agent builder platform in production, so I respond these question a lot from our customers.

1. Probably the best is fly.io IMHO. It has a nice balance between running ephemeral containers that can support long running tasks, and quickly booting up to respond to a tool call. [1]

2. If your task is truly long running, (I'm thinking several minutes), probably wise to put trigger [2] or temporal [3] under it.

3. A mix of prompt caching, context shedding, progressive context enrichment [4].

4. I'm building a platform that can be self-hosted to do a few of the above, so I can't speak to this. But most of my customers do not.

5. To start with, a simple postgres table and pgvector is all you need. But I've recently been delighted with the DX of Upstash vector [5]. They handle the embeddings for you and give you a text-in, text-out experience. If you want more control, and savings on a higher scale, have heard good things about marqo.ai [6].

Happy to talk more about this at length. (E-mail in the profile)

[1] https://fly.io/docs/reference/architecture/

[2] trigger.dev

[3] temporal.io

[4] https://www.inferable.ai/blog/posts/llm-progressive-context-...

[5] https://upstash.com/docs/vector/overall/getstarted

[6] https://www.marqo.ai/

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.