Browse Tools

Pass input through unchanged for debugging and testing pipelines

Format retrieved memories into a text block suitable for LLM prompt injection. Parses JSON memory array and sorts by similarity distance.

Analyze and score the importance of a message for memory prioritization. Scores text from 0.0 (trivial) to 1.0 (critical) based on factual content, preferences, and explicit memory requests, providing reasoning for the score.

Remove old or low-importance messages from SQLite database. Deletes messages based on age, importance score, or total count criteria. Returns deleted message IDs and count.

Update last_accessed timestamp and increment access_count for message records in SQLite database

Retrieve messages from the PA2 SQLite database. Queries the messages table and returns conversation history filtered by conversation_id, role, or limit.

Save a message to the PA2 SQLite database with conversation tracking. Creates database and schema if needed, assigns unique message ID, and stores message with metadata.

Delete documents from a ChromaDB vector collection by their IDs

Add documents to a ChromaDB vector collection with embeddings. Reads JSON input with id, content, and metadata fields, uses pa-embed tool to obtain embeddings, and stores the document in the specified ChromaDB collection.

Performs semantic search against a ChromaDB vector collection. Parses query from JSON input, queries the collection using embeddings, and returns matching results with similarity distances.

Embed text into vector representations for semantic search using Ollama's embedding models. Outputs embedding vector with metadata.

bug fix (missing default for --notes argument