Using the Burr UI to monitor is not the only way. You can integrate your own by leveraging lifecycle hooks, enabling you to log data in a custom format to, say, datadog, langsmith, or langfuse. - Source: dev.to / about 2 months ago

Langfuse (YC W23) | https://langfuse.com | Full-Time | Berlin, Germany | on-site | LLM Observability and Analytics Langfuse is open source [1] observability and analytics tool for LLM applications — think Amplitude and Datadog for LLM apps. Our users use Langfuse to understand what happens in production and use our insights to improve their applications. We have built a number of... - Source: Hacker News / 8 months ago

Langfuse makes tracing and analyzing LLM applications accessible. It is an open-source project under MIT license. - Source: dev.to / 9 months ago

Nice to see yet another open source approach to LLM/RAG. For those who do not want to meddle with the complexity of do-it-youself, Vectara (https://vectara.com) provides a RAG-as-a-service approach - pretty helpful if you want to stay away from having to worry about all the details, scalability, security, etc - and just focus on building your RAG application. - Source: Hacker News / 4 months ago

You should also check us out (https://vectara.com) - we provide RAG as a service so you don't have to do all the heavy lifting and putting together the pieces yourself. Source: 6 months ago

Hi HN! I lead product for Vectara (https://vectara.com) and we recently worked with OpenSource connections to both evaluate our new home-grown embedding model (Boomerang) as well as to help users start more quantitatively evaluating these systems on their own data/with their own queries. OSC maintains a fantastic open source tool, Quepid, and we worked with them to integrate Vectara (and to use it to... - Source: Hacker News / 8 months ago

RAG is a very useful flow but I agree the complexity is often overwhelming, esp as you move from a toy example to a real production deployment. It's not just choosing a vector DB (last time I checked there were about 50), managing it, deciding on how to chunk data, etc. You also need to ensure your retrieval pipeline is accurate and fast, ensuring data is secure and private, and manage the whole thing as it... - Source: Hacker News / 9 months ago

I agree. My experience is that hybrid search does provide better results in many cases, and is honestly not as easy to implement as may seem at first. In general, getting search right can be complicated today and the common thinking of "hey I'm going to put up a vector DB and use that" is simplistic. Disclaimer: I'm with Vectara (https://vectara.com), we provide an end-to-end platform for building GenAI products. - Source: Hacker News / 10 months ago