Introduction to LangChain: Retrieval Augmented Generation

Today, we dive into Retrieval Augmented Generation. This is a way to augment LLMs with additional data coming from a database. The data is first encoded into vectors, and they are stored in a vector database for fast retrieval. We are going to cover the following points:

• Indexing the data in a local index and augmenting an LLM with it

• Indexing the data in a Pinecone data and augmenting an LLM with it

• Providing the data source when answering questions with LLMs

• Indexing a website and using its data to augment an LLM

• Indexing GitHub repo to ask questions about the code base

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Below is the code used in the video!