I am building a graph based semantic search engine. We can use low cost LLMs, like Haiku, or local models to extract semantics (named entity recognition). Then the nodes in the graph maintain types (things like people, date, currency) as extracted and allow queries. https://github.com/pixlie/PixlieAI Currently building a demo where we crawl startup investment data to build a knowledge graph that can be filtered for p…
Very interesting, I've been thinking about this kind of approach but haven't had the time to really work on it. So what kind of business model do you have? Is it a kind of drop-in replacement for vector dbs? Out of curiosity, if it's not a trade secret, how do you plan to handle conflicting data (two sources saying different things on the same topic/data)?
At this moment, we have not entered the territory of conflict resolution but I know what you mean. Interestingly I just came across this: https://arxiv.org/pdf/2410.18415 (released on Oct 24, 2024).