Ask HN: How do you build per-user RAG/GraphRAG

2 points by david1542 1 year ago | 4 comments

Hey all,

I’ve been working on an AI agent system over the past year that connects to internal company tools like Slack, GitHub, Notion, etc, to help investigate production incidents. The agent needs context, so we built a system that ingests this data, processes it, and builds a structured knowledge graph (kind of a mix of RAG and GraphRAG).

What we didn’t expect was just how much infra work that would require.

We ended up:

- Using LlamaIndex's OS abstractions for chunking, embedding and retrieval.

- Adopting Chroma as the vector store.

- Writing custom integrations for Slack/GitHub/Notion. We used LlamaHub here for the actual querying, although some parts were a bit unmaintained and we had to fork + fix. We could’ve used Nango or Airbyte tbh but eventually didn't do that.

- Building an auto-refresh pipeline to sync data every few hours and do diffs based on timestamps. This was pretty hard as well.

- Handling security and privacy (most customers needed to keep data in their own environments).

- Handling scale - some orgs had hundreds of thousands of documents across different tools.

It became clear we were spending a lot more time on data infrastructure than on the actual agent logic. I think it might be ok for a company that interacts with customers' data, but definitely we felt like we were dealing with a lot of non-core work.

So I’m curious: for folks building LLM apps that connect to company systems, how are you approaching this? Are you building it all from scratch too? Using open-source tools? Is there something obvious we’re missing?

Would really appreciate hearing how others are tackling this part of the stack.

PaulHoule 1 year ago |

If it's hard for you than it's hard for your customers and they have a reason to pay for your product.

After I left a job where I developed a neural search engine for patents (years before BERT) I talked with many of the vendors in the enterprise search and what I found was that few of them did systematic work to improve the relevance of their results [1] and few of them tried to sell their product based on the quality of the results.

What they all promoted was ease of integration with hundreds of data sources, security, privacy, scale, rapid sync, etc. Looking at the way these got sold, I'd say that all of that is the core work and the actual search engine is an afterthought.

[1] See https://trec.nist.gov/

david1542 1 year ago | |

Yeh I tend to agree. Real value comes from carefully curating the data and applying smart optimizations, which is something few companies focus on. But I also get the sense that a lot of energy ends up being spent elsewhere - on integration, infrastructure, lots of fragmented OS libraries, etc at the expense of iteration speed and relevance-focused experimentation.

PaulHoule 1 year ago | | |

I was frustrated with enterprise search vendors and their customers because they didn't see it my way. Here are some ways of thinking about it.

Most cynically, enterprise software is bought by different people than those who use it. The buyers have a list of items to check and the fastest way to get eliminated is to not have an integration for a data source they have so vendors will put up a comprehensive list of them on their web site. The buyers will never test the relevance of the results against their data, though the users will feel it every day, unless the search engine is so bad that they just don't use it. (Common!)

On the other hand, if the integration doesn't work, you get recall of 0% no matter how smart and well tuned your search engine is.

I think a lot of founders and data scientists believe in a variant of the Pareto principle which comes down to "I want to do the 20% of the work that gets me 80% of the way there". The trouble is that a minimum viable product has to be viable, and you have to get to 100% of that minimum or you are always going to be a bridesmaid and never a bride.

The awful truth about data science, relevance, ML and all that is that data is dirty and takes a huge amount of work to wrangle. If you want "iteration speed and relevance-focused experimentation" you have to make investments in product, people and process to run more cycles in less calendar time. Look up my profile and ping me if you want to hear war stories.

barrenko 1 year ago |

I'd say this is normal? There may be some solutions popping up, but I haven't been drinking straight from X.com AI/ML firehose lately so I don't know of one unisolution at the moment.