📄️ Long-term memory for the LLM
The LLM app requires both long-term and short-term memories. Long-term memory includes factual knowledge, historical facts, background stories etc. They are best added to the context as complete chapters instead of small chunks of text to maintain the internal consistency of the knowledge.
📄️ Knowledge base from a plain text file
In this section, we will discuss how to create a vector collection snapshot from a plain text file. The
📄️ Knowledge base from a markdown file
In this section, we will discuss how to create a vector collection snapshot from a markdown file. The
📄️ Use the API server
The LlamaEdge RAG API server provides an API endpoint /create/rag that takes a text file, segments it into small chunks, turns the chunks into embeddings (i.e., vectors), and then stores the embeddings into the Qdrant database.