langchain/templates/neo4j-semantic-layer/neo4j_semantic_layer/memory_tool.py
Tomaz Bratanic 3e0cd11f51
templates: Add neo4j semantic layer template (#15652)
Co-authored-by: Tomaz Bratanic <tomazbratanic@Tomazs-MacBook-Pro.local>
Co-authored-by: Erick Friis <erick@langchain.dev>
2024-01-09 15:33:44 -08:00

73 lines
1.9 KiB
Python

from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
# Import things that are needed generically
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from neo4j_semantic_layer.utils import get_candidates, get_user_id, graph
store_rating_query = """
MERGE (u:User {userId:$user_id})
WITH u
UNWIND $candidates as row
MATCH (m:Movie {title: row.candidate})
MERGE (u)-[r:RATED]->(m)
SET r.rating = toFloat($rating)
RETURN distinct 'Noted' AS response
"""
def store_movie_rating(movie: str, rating: int):
user_id = get_user_id()
candidates = get_candidates(movie, "movie")
if not candidates:
return "This movie is not in our database"
response = graph.query(
store_rating_query,
params={"user_id": user_id, "candidates": candidates, "rating": rating},
)
try:
return response[0]["response"]
except Exception as e:
print(e)
return "Something went wrong"
class MemoryInput(BaseModel):
movie: str = Field(description="movie the user liked")
rating: int = Field(
description=(
"Rating from 1 to 5, where one represents heavy dislike "
"and 5 represent the user loved the movie"
)
)
class MemoryTool(BaseTool):
name = "Memory"
description = "useful for memorizing which movies the user liked"
args_schema: Type[BaseModel] = MemoryInput
def _run(
self,
movie: str,
rating: int,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return store_movie_rating(movie, rating)
async def _arun(
self,
movie: str,
rating: int,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
return store_movie_rating(movie, rating)