Part 7/12:
Encodes each message into a semantic vector.
Stores both the vector and associated metadata (timestamp, speaker, message content) in the index.
Performs similarity searches by querying with the latest message vector to retrieve the most relevant past memories for context.
This process allows Raven to recall long-term conversations and initiate responses grounded in background context, significantly surpassing simple keyword matching.
Overcoming Challenges: Response Context and Data Retrieval
One recurring challenge was ensuring the correct context for responses—particularly, avoiding the inclusion of superfluous data such as timestamps within user prompts or storing redundant metadata.
To address this, the system: