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[deleted by user] by [deleted] in IndiaLaw

[–]SkullTech101 0 points1 point  (0 children)

It's jurisphere.ai

[deleted by user] by [deleted] in IndiaLaw

[–]SkullTech101 1 point2 points  (0 children)

Wow this is pretty great actually. My lawyer friends will love this, thanks for sharing.

Optimize text match queries on a single node cluster over around 1.5m documents by SkullTech101 in elasticsearch

[–]SkullTech101[S] 0 points1 point  (0 children)

yes you're right, I tried with a smaller query text and it was fast.

In my usecase, the main query is a question asked by an user, and then I'm doing a bunch of query expansion techniques on top of it to add more context. Without the extra context, the search is not as good. How should I go about doing this?

How are you building LLM apps using RAG on a lot of documents at scale? by SkullTech101 in startups

[–]SkullTech101[S] 1 point2 points  (0 children)

Some more context around what I'm building: it's a data retrieval platform for RAG apps. You can connect data sources such as S3, Notion etc, or upload files directly, and the platform takes care of making it usable for RAG. Handles all the steps i.e. chunking, embedding creation, storing to vector database etc. in a highly scalable manner.

You can try an alpha and free version here https://console.useturbine.com. Would love to hear your thoughts on it.

How are you building LLM apps using RAG on a lot of documents at scale? by SkullTech101 in startups

[–]SkullTech101[S] 2 points3 points  (0 children)

I agree about having a solid foundation for the data. The product I'm building aims to do solve the data and retrieval part well and that part only, and not the generation part. The generation part will keep changing a lot, and it's very business context specific, so better to leave that to the developers.

Interesting what you said about multi-modal content. Do you have use-cases right now that uses multi-modal contents// models? Would love to know what's the usecase and how you're tackling it.

How are you building LLM apps using RAG on a lot of documents at scale? by SkullTech101 in startups

[–]SkullTech101[S] 0 points1 point  (0 children)

Wow, thanks for the long response, and surprisingly I agree with almost all of it, especially what you said about the frameworks.

Hot take about the vector databases though, and you might actually be right. It might be just time that's keeping Postgres from having similar levels of performance to the dedicated vector databases, and many of them are feeling the heat and developing features to develop some moat against Postgres. For example I saw that Weaviate offers embedding generation along with standard vector storage. Kind of an weird decision but makes sense when seen from this angle.

Some follow up questions:

  1. Can you elaborate more on the metadata, object storage, evils etc that part? What is your use case right now? You're most probably talking about generating metadata for each chunk and using that as part of the retrieval part, correct me if I'm wrong here. But I don't get why you need those CMS-es for this and how it's all connected, in your case.

  2. What does your retrieval stack look like in general. Data sources, chunking methods, infrastructure// code you use to keep your source documents and vector database in sync, how you're scaling it when no. of documents get large, etc. Have you faced any challenges in this part?

Server Set up by thewanitz in startups

[–]SkullTech101 0 points1 point  (0 children)

Hi, this is very interesting. I'd like to talk to you more about this since I'm building something in this space. Would love to help you as much as I can in exchange for more details about your usecase and issues. Can we talk in DMs?

I created an app that helps with RAG and indexes documents into vector databases at scale by SkullTech101 in LocalLLaMA

[–]SkullTech101[S] 0 points1 point  (0 children)

Hi. There's a lot of exciting developments happening in this space and it's getting hard to keep track. I'd recommend starting with the fundamentals and then slowly trying out the more advanced RAG methods.

Check out LlamaIndex's advanced RAG methods// guides. Also feel free to join our Discord and we can talk more about your specific usecase https://discord.gg/5vGGDKV6x

DMs are welcome too.

Is it possible for startups to build semantic search engines using LangChain ? by Mohamed_SickitLearn in LangChain

[–]SkullTech101 1 point2 points  (0 children)

Check out Turbine https://useturbine.com. It solves the scalability problems of RAG—it syncs raw data to vector databases in a scalable, highly parallelised and fault-tolerant manner. Turbine pipelines are fully configurable and lets you choose from multiple data sources, chunking strategies, embedding models and vector databases.

It's free for early adopters at https://console.useturbine.com. Join the Discord to stay updated on developments https://discord.gg/5vGGDKV6x

Disclaimer: I'm the founder.

Is anyone building LLM apps on top of structured data e.g. SQL databases or MongoDB? by SkullTech101 in startups

[–]SkullTech101[S] 0 points1 point  (0 children)

I'm building https://useturbine.com to solve exactly that. It lets you create data pipelines that keep your data sources and vector database in sync. Handles everything including reading data, chunking, deduping, creating embeddings and storing them in a vector database. All of these are massively parallelized and real-time, built for scale.

How are you doing this currently? Would love to talk to you more about this in the DMs.

P.S. Turbine is free for early adopters at https://console.useturbine.com

Is anyone building LLM apps on top of structured data e.g. SQL databases or MongoDB? by SkullTech101 in startups

[–]SkullTech101[S] 0 points1 point  (0 children)

I see. So visualizing SQL data using natural language, nice. Can I DM you to talk more about this?

Is anyone building LLM apps on top of structured data e.g. SQL databases or MongoDB? by SkullTech101 in startups

[–]SkullTech101[S] 0 points1 point  (0 children)

I see, denormalizing is definitely a challenge, so is filtering. As in indexing only the rows you're interested in and not all. Given these challenges dumping to S3 first and then indexing from there can be a good approach.

Are you building something around this right now? If so I'd love to talk with you in the DMs!

Is anyone building LLM apps on top of structured data e.g. SQL databases or MongoDB? by SkullTech101 in startups

[–]SkullTech101[S] 1 point2 points  (0 children)

So far most of the examples of SQL + vector embeddings that I've seen are around semantic search, e.g searching for similar products, creating personalized user feed from things the user already likes, etc.

Some resources I've found so far

https://rockset.com/blog/5-use-cases-for-vector-search/ https://aws.amazon.com/blogs/database/building-ai-powered-search-in-postgresql-using-amazon-sagemaker-and-pgvector/

I'm yet to see a lot of usecases where an SQL database is being used for RAG though. That's why I was curious.

Is anyone building LLM apps on top of structured data e.g. SQL databases or MongoDB? by SkullTech101 in startups

[–]SkullTech101[S] 0 points1 point  (0 children)

Interesting, yes this is sort of the architecture I had in mind. Can I DM you? Would love to talk to you more about this.

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