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`cat_recall_query`

Intervene before the recall query is embedded, hooking into the semantic search query.

This hook allows editing the user’s message used as a query for context retrieval from memories. As a result, the retrieved context can be conditioned editing the user’s message.

For example, this hook is a suitable to perform Hypothetical Document Embedding (HyDE). HyDE ¹ strategy exploits the user’s message to generate a hypothetical answer. This is then used to recall the relevant context from the memory. An official plugin is available to test this technique.

📄 Arguments

Name	Type	Description
`user_message`	`str`	The user’s message that will be used to query the vector memories.
`cat`	Cat	Cheshire Cat instance, allows you to use the framework components.

↩️ Return

Type: str

Edited string to be used for context retrieval in memory. The returned string is further stored in the Working Memory at cat.working_memory.recall_query.

✍ Example

from cat.mad_hatter.decorators import hook

@hook  # default priority = 1
def cat_recall_query(user_message, cat):
    # Ask the LLM to generate an answer for the question
    new_query = cat.llm(f"If the input is a question, generate a plausible answer. Input --> {user_message}")

    # Replace the original message and use the answer as a query
    return new_query

Gao, L., Ma, X., Lin, J., & Callan, J. (2022). Precise Zero-Shot Dense Retrieval without Relevance Labels. arXiv preprint arXiv:2212.10496. ↩