RAG components summary
Last updated
Last updated
A RAG system includes the following components:
| Component | Definition | Examples |
|---|
Document store | Where the textual data is stored. | Google Docs, Notion, Word documents |
Chunker | How each document is broken into pieces (or chunks) that are then embedded. | llama hub |
Embedder | How each document chunk is transformed into a vector that stores its semantic meaning. | ada-002, sentence transformer |
Retriever | The algorithm that retrieves relevant chunks of text from the user query. Those chunks of text are used as context to answer a user query. | Take the top cosine similarity scores between the embedding of the user query and the embedded document chunks |
Prompt builder | How the user query, along with conversation history and retrieved document chunks, are put into the context window to prompt the LLM for an answer to the user query. | Here's a user query {user_query} and here's a list of context that may be helpful to answer the user's query: {context_1}, {context_2}. Answer the user's query using the given context. |
LLM | The large language model that receives the prompt from the prompt builder and returns an answer to the user's query. | gpt3.5-turbo, gpt4, llama 2, claude |