GoogleAI

In my previous Persisting LLM chat history to Firestore post, I showed how to persist chat messages in Firestore for more meaningful and context-aware conversations. Another common requirement in LLM applications is to ground responses in data for more relevant answers. For that, you need embeddings. In this post, I want to talk specifically about text embeddings and how Firestore and LangChain can help you to store text embeddings and do similarity searches against them. Read More →

Firestore has long been my go-to NoSQL backend for my serverless apps. Recently, it’s becoming my go-to backend for my LLM powered apps too. In this series of posts, I want to show you how Firestore can help for your LLM apps. In the first post of the series, I want to talk about LLM powered chat applications. I know, not all LLM apps have to be chat based apps but a lot of them are because LLMs are simply very good at chat based communication. Read More →

Introduction When you’re building a Large Language Model (LLMs) application, you typically start with the SDK of the LLM you’re trying to talk to. However, at some point, it might make sense to start using a higher level framework. This is especially true if you rely on multiple LLMs from different vendors. Instead of learning and using SDKs from multiple vendors, you can learn a higher level framework and use that to orchestrate your calls to multiple LLMs. Read More →