Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

Overall, RAG and RALMs overcome the limits of language models’ memory by grounding responses in external information.

Vector databases can be used to store and serve machine learning models and their corresponding embeddings. The primary application is similarity search (also semantic search),

Fine-tuning involves modifying a pre-trained language model by training it on a specific task using supervised learning.

By calculating distances between embeddings, we can perform tasks like search and similarity scoring, or classify objects, for example by topic or category.

Connecting language models with online shopping tools allows them to perform actions like searching for items, loading detailed information about products, selecting item features, going through shopping pages, and making purchase decisions based on specific user instructions.

Embeddings can be created using different methods. For texts, one simple method is the bag-of-words approach, where each word is represented by a count of how many times it appears in a text.

Once we start making a lot of calls, especially in the map step, if we use a cloud provider, we’ll see tokens and, therefore, costs increase.

LangChain simplifies the development of sophisticated LLM applications by providing reusable components and pre-assembled chains.

Retrievers play a crucial role in answering questions over documents, as they are responsible for retrieving relevant information based on the given query.