11–20 of 32 posts
According to the article, it is either costly (if using OpenAI), or slow using open source AI models. In both cases, predicting the quality of generated KG using LLMs is hard.
This is a post that summarizes some reading that I had done in the space of LLMs + Knowledge Graphs with the goal of identifying technically deep and interesting directions. The post cover retrieval augmented generation (RAG) systems that use unstructured data (RAG-U) and the role folks envision knowledge graphs to play in it. Briefly the design spectrum of RAG-U systems have two dimensions: 1) What additional data t…
Knowledge graphs improve vector search by providing a "back of the book" index for the content. This can be done using knowledge extraction from an LLM during indexing, such as pulling out keyterms of a given chunk before embedding, or asking a question of the content and then answering it using the keyterms in addition to the embeddings. One challenge I found with this is determining keyterms to use with prompts tha…
This is a post that summarizes some reading that I had done in the space of LLMs + Knowledge Graphs with the goal of identifying technically deep and interesting directions. The post cover retrieval augmented generation (RAG) systems that use unstructured data (RAG-U) and the role folks envision knowledge graphs to play in it. Briefly the design spectrum of RAG-U systems have two dimensions: 1) What additional data t…
Earlier quoted context omitted.
Knowledge graphs improve vector search by providing a "back of the book" index for the content. This can be done using knowledge extraction from an LLM during indexing, such as pulling out keyterms of a given chunk before embedding, or asking a question of the content and then answering it using the keyterms in addition to the embeddings. One challenge I found with this is determining keyterms to use with prompts tha…
What open source model is good at pulling keyterms?
Earlier quoted context omitted.
Knowledge graphs improve vector search by providing a "back of the book" index for the content. This can be done using knowledge extraction from an LLM during indexing, such as pulling out keyterms of a given chunk before embedding, or asking a question of the content and then answering it using the keyterms in addition to the embeddings. One challenge I found with this is determining keyterms to use with prompts tha…
What open source model is good at pulling keyterms?
As some others here have pointed out, information extraction and searching with relevant context are the hardest parts of any search system, and it's clear that simply chunking vectors up and throwing them into a vector DB has limitations, no matter what the vector DB vendors tell you. Just like this article says, I hope that 2024 is the year where we actually get some papers that perform more rigorous evaluations of systems that use vector DBs, graph DBs, or a combination of them for building RAGs.
It’s interesting to see more developed KG + LLM use cases that aren’t just NL to Graph DB Query Lang.
If you're interested in graphs + RAG and want an alternate approach, txtai has a semantic graph component. https://neuml.hashnode.dev/introducing-the-semantic-graph https://github.com/neuml/txtai Disclaimer: I'm the primary author of txtai
Most RAG tools seem to start with the LLM and add Vector building and retrieval around it, while this tool seems like it started with Vector / Graph building and retrieval, then added LLM support later.
When I started working in search 10+ years ago, people would build a beautiful UI, and then, only on shipping, realize the search results were trash + irrelevant. They imagined a search system like Elasticsearch was basically Google. When in reality, Elasticsearch is just a bit of infrastructure. A framework, not a solution. There's a similar thing happening on RAG. Where people think building the chat interaction is…
When I started working in search 10+ years ago, people would build a beautiful UI, and then, only on shipping, realize the search results were trash + irrelevant. They imagined a search system like Elasticsearch was basically Google. When in reality, Elasticsearch is just a bit of infrastructure. A framework, not a solution. There's a similar thing happening on RAG. Where people think building the chat interaction is…