Spaces:
Sleeping
Sleeping
File size: 1,693 Bytes
62516b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from __future__ import annotations
from typing import Any
from src.agent.infra.memory_store import LongTermMemoryStore
def build_internal_tools(*, memory_store: LongTermMemoryStore, user_id: str) -> list[Any]:
try:
from langchain_core.tools import tool
except ImportError as exc:
raise RuntimeError(
"langchain-core is not installed. Install dependencies before using /api/material."
) from exc
@tool
def remember_user_fact(fact: str, memory_type: str = "general") -> str:
"""Save a durable user fact into long-term memory."""
memory_id = memory_store.remember_fact(
user_id=user_id,
fact=fact,
memory_type=memory_type,
)
if not memory_id:
return "No memory saved because the fact was empty."
return f"Saved memory with id {memory_id}."
@tool
def recall_user_facts(query: str = "", limit: int = 5) -> str:
"""Recall previously saved user facts for personalization."""
docs = memory_store.recall_user_facts(
user_id=user_id,
query=query,
limit=limit,
)
if not docs:
return "No memories found for this user."
lines: list[str] = []
for idx, doc in enumerate(docs, start=1):
metadata = doc.metadata or {}
memory_type = metadata.get("memory_type", "general")
created_at = metadata.get("created_at", "unknown")
lines.append(
f"{idx}. [{memory_type}] ({created_at}) {doc.page_content}"
)
return "\n".join(lines)
return [remember_user_fact, recall_user_facts]
|