Embeddings are not for that. Embeddings take text and encode it into a high dimensional vector space. Similar texts will be closer together in the vector space.
The idea I was proposing was to use embeddings as a way to store and retrieve relevant "memories" so the AI could maintain coherence across time. I.e. whenever the user sends a message, we pull up the N most relevant memories (where relevance == closeness in the vector space) and include those in the prompt, so GPT3 can use the information when it forms its response.
The idea I was proposing was to use embeddings as a way to store and retrieve relevant "memories" so the AI could maintain coherence across time. I.e. whenever the user sends a message, we pull up the N most relevant memories (where relevance == closeness in the vector space) and include those in the prompt, so GPT3 can use the information when it forms its response.