What is Memory?
Memory is a collection of your files that can be attached to a Pipe to build a Retrive Augmented Generation (RAG) system.
Memory is a collection of files that enhance AI systems by providing information for better responses. In a RAG system, Memory is used with Pipe to retrieve relevant data for queries.
The process involves creating query embeddings, retrieving matching information from Memory, augmenting the query with this data, and using it to generate accurate, context-aware responses. This integration ensures precise answers and enables use cases like documents summarization, question-answering, and more.
Retrieval Augmented Generation (RAG)
In a RAG system, when an LLM is queried, it is provided with additional information relevant to the query from the memory. This additional information helps the LLM to provide more accurate and relevant responses.
Below is the list of steps performed in a RAG system:
- Query: User queries the LLM through Pipe. Embeddings are generated for the query.
- Retrieval: Pipe retrieves query-relevant information from the memory through similarity search.
- Augmentation: Retrieved information is augmented with the query.
- Generation: The augmented information is fed to the LLM to generate a response.
