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before_cat_recalls_declarative_memories

Intervene before the Cat searches in the documents.

Allows intercepting when the Cat queries the memories using the embedded user's input.

The hook is executed just before the Cat searches for the meaningful context in both memories and stores it in the Working Memory.

The hook returns the values for maximum number (k) of items to retrieve from memory and the score threshold applied to the query in the vector memory (items with score under the threshold are not retrieved) It also returns the embedded query (embedding) and the conditions on recall (metadata).

📄 Arguments

Name Type Description
declarative_recall_config dict Dictionary with data needed to recall declarative memories.
cat StrayCat Cheshire Cat instance, allows you to use the framework components.

The default value of declarative_recall_config is: 

{
    "embedding": recall_query_embedding,  # embedding of the recall query
    "k": 3,  # number of memories to retrieve
    "threshold": 0.7,  # similarity threshold to retrieve memories
    "metadata": None,  # dictionary of metadata to filter memories
}

↩ī¸ Return

Type: dict

Edited dictionary that will be fed as a query to the Vector db.

✍ Example

from cat.mad_hatter.decorators import hook

@hook  # default priority = 1
def before_cat_recalls_declarative_memories(declarative_recall_config, cat):
    # filter memories using custom metadata. 
    # N.B. you must add the metadata when uploading the document! 
    declarative_recall_config["metadata"] = {"topic": "cats"}

    return declarative_recall_config

Note