我工作的很大一部分是改善用户使用 Neo4j 的体验。通常，将数据导入 Neo4j 并对其进行有效建模是用户面临的一个关键挑战，尤其是在早期。尽管初始数据模型很重要且需要深思熟虑，但随着数据大小或用户数量的增长，可以轻松重构以提高性能。
因此，作为对自己的挑战，我想看看 LLM 是否可以帮助建立初始数据模型。即使没有其他事情，它也可以展示事物之间的联系，并为用户提供一些可以向其他人展示的快速结果。
直观地讲，我知道数据建模是一个迭代过程，某些 LLM 很容易被大量数据分散注意力，因此这为使用 LangGraph 循环处理数据提供了一个很好的机会。
让我们深入了解实现这一目标的提示。
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1、图建模基础
GraphAcademy 上的图数据建模基础课程将指导你完成图形数据建模的基础知识，但作为第一步，我使用以下经验法则：
- 名词变成标签------它们描述节点所代表的事物。
- 动词变成关系类型------它们描述事物的连接方式。
- 其他一切都变成属性（特别是副词）------你有一个名字，可能开着一辆灰色汽车。
动词也可以是节点；你可能很高兴知道某人订购了产品，但该基本模型不允许你知道产品的订购地点和时间。在这种情况下，订单成为模型中的新节点。
我相信这可以提炼成一个提示，以创建图形数据建模的零样本方法。
2、迭代方法
几个月前我尝试过这种方法，发现我使用的模型在处理较大的模式时很容易分心，提示很快就达到了 LLM 的令牌限制。
我想这次尝试一种迭代方法，一次只考虑一个键。这应该有助于避免分心，因为 LLM 只需要一次考虑一个项目。
最后的方法使用了以下步骤：
- 将 CSV 文件加载到 Pandas 数据框中。
- 分析 CSV 中的每一列并将其附加到基于 JSON Schema 松散的数据模型中。
- 识别并添加每个实体的缺失唯一 ID。
- 检查数据模型的准确性。
- 生成 Cypher 语句以导入节点和关系。
- 生成支持导入语句的唯一约束。
- 创建约束并运行导入。
3、数据
我在 Kaggle 上快速浏览了一个有趣的数据集。脱颖而出的数据集是 Spotify 播放次数最多的歌曲：
import pandas as pd
csv_file = '/Users/adam/projects/datamodeller/data/spotify/spotify-most-streamed-songs.csv'
df = pd.read_csv(csv_file)
df.head()
track_name artist(s)_name artist_count released_year released_month released_day in_spotify_playlists in_spotify_charts streams in_apple_playlists ... key mode danceability_% valence_% energy_% acousticness_% instrumentalness_% liveness_% speechiness_% cover_url
0 Seven (feat. Latto) (Explicit Ver.) Latto, Jung Kook 2 2023 7 14 553 147 141381703 43 ... B Major 80 89 83 31 0 8 4 Not Found
1 LALA Myke Towers 1 2023 3 23 1474 48 133716286 48 ... C# Major 71 61 74 7 0 10 4 https://i.scdn.co/image/ab67616d0000b2730656d5...
2 vampire Olivia Rodrigo 1 2023 6 30 1397 113 140003974 94 ... F Major 51 32 53 17 0 31 6 https://i.scdn.co/image/ab67616d0000b273e85259...
3 Cruel Summer Taylor Swift 1 2019 8 23 7858 100 800840817 116 ... A Major 55 58 72 11 0 11 15 https://i.scdn.co/image/ab67616d0000b273e787cf...
4 WHERE SHE GOES Bad Bunny 1 2023 5 18 3133 50 303236322 84 ... A Minor 65 23 80 14 63 11 6 https://i.scdn.co/image/ab67616d0000b273ab5c9c...
5 行 × 25 列
这相对简单，但我可以立即看出曲目和艺术家之间应该存在关系。
还需要克服数据清洁度挑战，即艺术家姓名列中的列名和艺术家是逗号分隔的值。
4、选择LLM
我真的很想为此使用本地LLM，但我很早就发现 Llama 3 不行。如果有疑问，请依靠 OpenAI：
from langchain_core.prompts import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from typing import List
from langchain_core.output_parsers import JsonOutputParser
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o")
5、创建数据模型
我使用了一组简化的建模说明来创建数据建模提示。我不得不多次设计提示以获得一致的输出。
零样本示例运行得相对较好，但我发现输出不一致。定义结构化输出来保存 JSON 输出确实很有帮助：
class JSONSchemaSpecification(BaseModel):
notes: str = Field(description="Any notes or comments about the schema")
jsonschema: str = Field(description="A JSON array of JSON schema specifications that describe the entities in the data model")
6、少量样本示例输出
JSON 本身也不一致，因此我最终根据电影推荐数据集定义了一个架构。
示例输出：
example_output = [
dict(
title="Person",
type="object",
description="Node",
properties=[
dict(name="name", column_name="person_name", type="string", description="The name of the person", examples=["Tom Hanks"]),
dict(name="date_of_birth", column_name="person_dob", type="date", description="The date of birth for the person", examples=["1987-06-05"]),
dict(name="id", column_name="person_name, date_of_birth", type="string", description="The ID is a combination of name and date of birth to ensure uniqueness", examples=["tom-hanks-1987-06-05"]),
],
),
dict(
title="Director",
type="object",
description="Node",
properties=[
dict(name="name", column_name="director_names", type="string", description="The name of the directors. Split values in column by a comma", examples=["Francis Ford Coppola"]),
],
),
dict(
title="Movie",
type="object",
description="Node",
properties=[
dict(name="title", column_name="title", type="string", description="The title of the movie", examples=["Toy Story"]),
dict(name="released", column_name="released", type="integer", description="The year the movie was released", examples=["1990"]),
],
),
dict(
title="ACTED_IN",
type="object",
description="Relationship",
properties=[
dict(name="_from", column_name="od", type="string", description="Person found by the `id`. The ID is a combination of name and date of birth to ensure uniqueness", examples=["Person"]),
dict(name="_to", column_name="title", type="string", description="The movie title", examples=["Movie"]),
dict(name="roles", type="string", column_name="person_roles", description="The roles the person played in the movie", examples=["Woody"]),
],
),
dict(
title="DIRECTED",
type="object",
description="Relationship",
properties=[
dict(name="_from", type="string", column_name="director_names", description="Director names are comma separated", examples=["Director"]),
dict(name="_to", type="string", column_name="title", description="The label of the node this relationship ends at", examples=["Movie"]),
],
),
]
我不得不偏离严格的 JSON 模式，将 column_name 字段添加到输出中，以帮助 LLM 生成导入脚本。提供描述示例也有助于这一点，否则 MATCH 子句中使用的属性不一致。
7、链
这是最后的提示：
model_prompt = PromptTemplate.from_template("""
You are an expert Graph Database administrator.
Your task is to design a data model based on the information provided from an existing data source.
You must decide where the following column fits in with the existing data model. Consider:
* Does the column represent an entity, for example a Person, Place, or Movie? If so, this should be a node in its own right.
* Does the column represent a relationship between two entities? If so, this should be a relationship between two nodes.
* Does the column represent an attribute of an entity or relationship? If so, this should be a property of a node or relationship.
* Does the column represent a shared attribute that could be interesting to query through to find similar nodes, for example a Genre? If so, this should be a node in its own right.
## Instructions for Nodes
* Node labels are generally nouns, for example Person, Place, or Movie
* Node titles should be in UpperCamelCase
## Instructions for Relationships
* Relationshops are generally verbs, for example ACTED_IN, DIRECTED, or PURCHASED
* Examples of good relationships are (:Person)-[:ACTED_IN]->(:Movie) or (:Person)-[:PURCHASED]->(:Product)
* Relationships should be in UPPER_SNAKE_CASE
* Provide any specific instructions for the field in the description. For example, does the field contain a list of comma separated values or a single value?
## Instructions for Properties
* Relationships should be in lowerPascalCase
* Prefer the shorter name where possible, for example "person_id" and "personId" should simply be "id"
* If you are changing the property name from the original field name, mention the column name in the description
* Do not include examples for integer or date fields
* Always include instructions on data preparation for the field. Does it need to be cast as a string or split into multiple fields on a delimiting value?
* Property keys should be letters only, no numbers or special characters.
## Important!
Consider the examples provided. Does any data preparation need to be done to ensure the data is in the correct format?
You must include any information about data preparation in the description.
## Example Output
Here is an example of a good output:
```
{example_output}
```
## New Data:
Key: {key}
Data Type: {type}
Example Values: {examples}
## Existing Data Model
Here is the existing data model:
```
{existing_model}
```
## Keep Existing Data Model
Apply your changes to the existing data model but never remove any existing definitions.
""", partial_variables=dict(example_output=dumps(example_output)))
model_chain = model_prompt | llm.with_structured_output(JSONSchemaSpecification)
8、执行链
为了迭代更新模型，我迭代了数据框中的键，并将每个键、其数据类型和前五个唯一值传递给提示：
from json_repair import dumps, loads
existing_model = {}
for i, key in enumerate(df):
print("\n", i, key)
print("----------------")
try:
res = try_chain(model_chain, dict(
existing_model=dumps(existing_model),
key=key,
type=df[key].dtype,
examples=dumps(df[key].unique()[:5].tolist())
))
print(res.notes)
existing_model = loads(res.jsonschema)
print([n['title'] for n in existing_model])
except Exception as e:
print(e)
pass
existing_model
控制台输出：
0 track_name
----------------
Adding 'track_name' to an existing data model. This represents a music track entity.
['Track']
1 artist(s)_name
----------------
Adding a new column 'artist(s)_name' to the existing data model. This column represents multiple artists associated with tracks and should be modeled as a new node 'Artist' and a relationship 'PERFORMED_BY' from 'Track' to 'Artist'.
['Track', 'Artist', 'PERFORMED_BY']
2 artist_count
----------------
Added artist_count as a property of Track node. This property indicates the number of artists performing in the track.
['Track', 'Artist', 'PERFORMED_BY']
3 released_year
----------------
Add the released_year column to the existing data model as a property of the Track node.
['Track', 'Artist', 'PERFORMED_BY']
4 released_month
----------------
Adding the 'released_month' column to the existing data model, considering it as an attribute of the Track node.
['Track', 'Artist', 'PERFORMED_BY']
5 released_day
----------------
Added a new property 'released_day' to the 'Track' node to capture the day of the month a track was released.
['Track', 'Artist', 'PERFORMED_BY']
6 in_spotify_playlists
----------------
Adding the new column 'in_spotify_playlists' to the existing data model as a property of the 'Track' node.
['Track', 'Artist', 'PERFORMED_BY']
7 in_spotify_charts
----------------
Adding the 'in_spotify_charts' column to the existing data model as a property of the Track node.
['Track', 'Artist', 'PERFORMED_BY']
8 streams
----------------
Adding a new column 'streams' to the existing data model, representing the number of streams for a track.
['Track', 'Artist', 'PERFORMED_BY']
9 in_apple_playlists
----------------
Adding new column 'in_apple_playlists' to the existing data model
['Track', 'Artist', 'PERFORMED_BY']
10 in_apple_charts
----------------
Adding 'in_apple_charts' as a property to the 'Track' node, representing the number of times the track appeared in the Apple charts.
['Track', 'Artist', 'PERFORMED_BY']
11 in_deezer_playlists
----------------
Add 'in_deezer_playlists' to the existing data model for a music track database.
['Track', 'Artist', 'PERFORMED_BY']
12 in_deezer_charts
----------------
Adding a new property 'inDeezerCharts' to the existing 'Track' node to represent the number of times the track appeared in Deezer charts.
['Track', 'Artist', 'PERFORMED_BY']
13 in_shazam_charts
----------------
Adding new data 'in_shazam_charts' to the existing data model. This appears to be an attribute of the 'Track' node, indicating the number of times a track appeared in the Shazam charts.
['Track', 'Artist', 'PERFORMED_BY']
14 bpm
----------------
Added bpm column as a property to the Track node as it represents a characteristic of the track.
['Track', 'Artist', 'PERFORMED_BY']
15 key
----------------
Adding the 'key' column to the existing data model. The 'key' represents the musical key of a track, which is a shared attribute that can be interesting to query through to find similar tracks.
['Track', 'Artist', 'PERFORMED_BY']
16 mode
----------------
Adding 'mode' to the existing data model. It represents a musical characteristic of a track, which is best captured as an attribute of the Track node.
['Track', 'Artist', 'PERFORMED_BY']
17 danceability_%
----------------
Added 'danceability_%' to the existing data model as a property of the Track node. The field represents the danceability percentage of the track.
['Track', 'Artist', 'PERFORMED_BY']
18 valence_%
----------------
Adding the valence percentage column to the existing data model as a property of the Track node.
['Track', 'Artist', 'PERFORMED_BY']
19 energy_%
----------------
Integration of the new column 'energy_%' into the existing data model. This column represents an attribute of the Track entity and should be added as a property of the Track node.
['Track', 'Artist', 'PERFORMED_BY']
20 acousticness_%
----------------
Adding acousticness_% to the existing data model as a property of the Track node.
['Track', 'Artist', 'PERFORMED_BY']
21 instrumentalness_%
----------------
Adding the new column 'instrumentalness_%' to the existing Track node as it represents an attribute of the Track entity.
['Track', 'Artist', 'PERFORMED_BY']
22 liveness_%
----------------
Adding the new column 'liveness_%' to the existing data model as an attribute of the Track node
['Track', 'Artist', 'PERFORMED_BY']
23 speechiness_%
----------------
Adding the new column 'speechiness_%' to the existing data model as a property of the 'Track' node.
['Track', 'Artist', 'PERFORMED_BY']
24 cover_url
----------------
Adding a new property 'cover_url' to the existing 'Track' node. This property represents the URL of the track's cover image.
['Track', 'Artist', 'PERFORMED_BY']
在对提示进行一些处理用例的调整后，我最终得到了一个非常满意的模型。LLM设法确定数据集由曲目、艺术家和连接两者的 PERFORMED_BY 关系组成：
[
{
"title": "Track",
"type": "object",
"description": "Node",
"properties": [
{
"name": "name",
"column_name": "track_name",
"type": "string",
"description": "The name of the track",
"examples": [
"Seven (feat. Latto) (Explicit Ver.)",
"LALA",
"vampire",
"Cruel Summer",
"WHERE SHE GOES",
],
},
{
"name": "artist_count",
"column_name": "artist_count",
"type": "integer",
"description": "The number of artists performing in the track",
"examples": [2, 1, 3, 8, 4],
},
{
"name": "released_year",
"column_name": "released_year",
"type": "integer",
"description": "The year the track was released",
"examples": [2023, 2019, 2022, 2013, 2014],
},
{
"name": "released_month",
"column_name": "released_month",
"type": "integer",
"description": "The month the track was released",
"examples": [7, 3, 6, 8, 5],
},
{
"name": "released_day",
"column_name": "released_day",
"type": "integer",
"description": "The day of the month the track was released",
"examples": [14, 23, 30, 18, 1],
},
{
"name": "inSpotifyPlaylists",
"column_name": "in_spotify_playlists",
"type": "integer",
"description": "The number of Spotify playlists the track is in. Cast the value as an integer.",
"examples": [553, 1474, 1397, 7858, 3133],
},
{
"name": "inSpotifyCharts",
"column_name": "in_spotify_charts",
"type": "integer",
"description": "The number of times the track appeared in the Spotify charts. Cast the value as an integer.",
"examples": [147, 48, 113, 100, 50],
},
{
"name": "streams",
"column_name": "streams",
"type": "array",
"description": "The list of stream IDs for the track. Maintain the array format.",
"examples": [
"141381703",
"133716286",
"140003974",
"800840817",
"303236322",
],
},
{
"name": "inApplePlaylists",
"column_name": "in_apple_playlists",
"type": "integer",
"description": "The number of Apple playlists the track is in. Cast the value as an integer.",
"examples": [43, 48, 94, 116, 84],
},
{
"name": "inAppleCharts",
"column_name": "in_apple_charts",
"type": "integer",
"description": "The number of times the track appeared in the Apple charts. Cast the value as an integer.",
"examples": [263, 126, 207, 133, 213],
},
{
"name": "inDeezerPlaylists",
"column_name": "in_deezer_playlists",
"type": "array",
"description": "The list of Deezer playlist IDs the track is in. Maintain the array format.",
"examples": ["45", "58", "91", "125", "87"],
},
{
"name": "inDeezerCharts",
"column_name": "in_deezer_charts",
"type": "integer",
"description": "The number of times the track appeared in the Deezer charts. Cast the value as an integer.",
"examples": [10, 14, 12, 15, 17],
},
{
"name": "inShazamCharts",
"column_name": "in_shazam_charts",
"type": "array",
"description": "The list of Shazam chart IDs the track is in. Maintain the array format.",
"examples": ["826", "382", "949", "548", "425"],
},
{
"name": "bpm",
"column_name": "bpm",
"type": "integer",
"description": "The beats per minute of the track. Cast the value as an integer.",
"examples": [125, 92, 138, 170, 144],
},
{
"name": "key",
"column_name": "key",
"type": "string",
"description": "The musical key of the track. Cast the value as a string.",
"examples": ["B", "C#", "F", "A", "D"],
},
{
"name": "mode",
"column_name": "mode",
"type": "string",
"description": "The mode of the track (e.g., Major, Minor). Cast the value as a string.",
"examples": ["Major", "Minor"],
},
{
"name": "danceability",
"column_name": "danceability_%",
"type": "integer",
"description": "The danceability percentage of the track. Cast the value as an integer.",
"examples": [80, 71, 51, 55, 65],
},
{
"name": "valence",
"column_name": "valence_%",
"type": "integer",
"description": "The valence percentage of the track. Cast the value as an integer.",
"examples": [89, 61, 32, 58, 23],
},
{
"name": "energy",
"column_name": "energy_%",
"type": "integer",
"description": "The energy percentage of the track. Cast the value as an integer.",
"examples": [83, 74, 53, 72, 80],
},
{
"name": "acousticness",
"column_name": "acousticness_%",
"type": "integer",
"description": "The acousticness percentage of the track. Cast the value as an integer.",
"examples": [31, 7, 17, 11, 14],
},
{
"name": "instrumentalness",
"column_name": "instrumentalness_%",
"type": "integer",
"description": "The instrumentalness percentage of the track. Cast the value as an integer.",
"examples": [0, 63, 17, 2, 19],
},
{
"name": "liveness",
"column_name": "liveness_%",
"type": "integer",
"description": "The liveness percentage of the track. Cast the value as an integer.",
"examples": [8, 10, 31, 11, 28],
},
{
"name": "speechiness",
"column_name": "speechiness_%",
"type": "integer",
"description": "The speechiness percentage of the track. Cast the value as an integer.",
"examples": [4, 6, 15, 24, 3],
},
{
"name": "coverUrl",
"column_name": "cover_url",
"type": "string",
"description": "The URL of the track's cover image. If the value is 'Not Found', it should be cast as an empty string.",
"examples": [
"https://i.scdn.co/image/ab67616d0000b2730656d5ce813ca3cc4b677e05",
"https://i.scdn.co/image/ab67616d0000b273e85259a1cae29a8d91f2093d",
],
},
],
},
{
"title": "Artist",
"type": "object",
"description": "Node",
"properties": [
{
"name": "name",
"column_name": "artist(s)_name",
"type": "string",
"description": "The name of the artist. Split values in column by a comma",
"examples": [
"Latto",
"Jung Kook",
"Myke Towers",
"Olivia Rodrigo",
"Taylor Swift",
"Bad Bunny",
],
}
],
},
{
"title": "PERFORMED_BY",
"type": "object",
"description": "Relationship",
"properties": [
{
"name": "_from",
"type": "string",
"description": "The label of the node this relationship starts at",
"examples": ["Track"],
},
{
"name": "_to",
"type": "string",
"description": "The label of the node this relationship ends at",
"examples": ["Artist"],
},
],
},
]
[
{
"title": "Track",
"type": "object",
"description": "Node",
"properties": [
{
"name": "name",
"column_name": "track_name",
"type": "string",
"description": "The name of the track",
"examples": [
"Seven (feat. Latto) (Explicit Ver.)",
"LALA",
"vampire",
"Cruel Summer",
"WHERE SHE GOES",
],
},
{
"name": "artist_count",
"column_name": "artist_count",
"type": "integer",
"description": "The number of artists performing in the track",
"examples": [2, 1, 3, 8, 4],
},
{
"name": "released_year",
"column_name": "released_year",
"type": "integer",
"description": "The year the track was released",
"examples": [2023, 2019, 2022, 2013, 2014],
},
{
"name": "released_month",
"column_name": "released_month",
"type": "integer",
"description": "The month the track was released",
"examples": [7, 3, 6, 8, 5],
},
{
"name": "released_day",
"column_name": "released_day",
"type": "integer",
"description": "The day of the month the track was released",
"examples": [14, 23, 30, 18, 1],
},
{
"name": "inSpotifyPlaylists",
"column_name": "in_spotify_playlists",
"type": "integer",
"description": "The number of Spotify playlists the track is in. Cast the value as an integer.",
"examples": [553, 1474, 1397, 7858, 3133],
},
{
"name": "inSpotifyCharts",
"column_name": "in_spotify_charts",
"type": "integer",
"description": "The number of times the track appeared in the Spotify charts. Cast the value as an integer.",
"examples": [147, 48, 113, 100, 50],
},
{
"name": "streams",
"column_name": "streams",
"type": "array",
"description": "The list of stream IDs for the track. Maintain the array format.",
"examples": [
"141381703",
"133716286",
"140003974",
"800840817",
"303236322",
],
},
{
"name": "inApplePlaylists",
"column_name": "in_apple_playlists",
"type": "integer",
"description": "The number of Apple playlists the track is in. Cast the value as an integer.",
"examples": [43, 48, 94, 116, 84],
},
{
"name": "inAppleCharts",
"column_name": "in_apple_charts",
"type": "integer",
"description": "The number of times the track appeared in the Apple charts. Cast the value as an integer.",
"examples": [263, 126, 207, 133, 213],
},
{
"name": "inDeezerPlaylists",
"column_name": "in_deezer_playlists",
"type": "array",
"description": "The list of Deezer playlist IDs the track is in. Maintain the array format.",
"examples": ["45", "58", "91", "125", "87"],
},
{
"name": "inDeezerCharts",
"column_name": "in_deezer_charts",
"type": "integer",
"description": "The number of times the track appeared in the Deezer charts. Cast the value as an integer.",
"examples": [10, 14, 12, 15, 17],
},
{
"name": "inShazamCharts",
"column_name": "in_shazam_charts",
"type": "array",
"description": "The list of Shazam chart IDs the track is in. Maintain the array format.",
"examples": ["826", "382", "949", "548", "425"],
},
{
"name": "bpm",
"column_name": "bpm",
"type": "integer",
"description": "The beats per minute of the track. Cast the value as an integer.",
"examples": [125, 92, 138, 170, 144],
},
{
"name": "key",
"column_name": "key",
"type": "string",
"description": "The musical key of the track. Cast the value as a string.",
"examples": ["B", "C#", "F", "A", "D"],
},
{
"name": "mode",
"column_name": "mode",
"type": "string",
"description": "The mode of the track (e.g., Major, Minor). Cast the value as a string.",
"examples": ["Major", "Minor"],
},
{
"name": "danceability",
"column_name": "danceability_%",
"type": "integer",
"description": "The danceability percentage of the track. Cast the value as an integer.",
"examples": [80, 71, 51, 55, 65],
},
{
"name": "valence",
"column_name": "valence_%",
"type": "integer",
"description": "The valence percentage of the track. Cast the value as an integer.",
"examples": [89, 61, 32, 58, 23],
},
{
"name": "energy",
"column_name": "energy_%",
"type": "integer",
"description": "The energy percentage of the track. Cast the value as an integer.",
"examples": [83, 74, 53, 72, 80],
},
{
"name": "acousticness",
"column_name": "acousticness_%",
"type": "integer",
"description": "The acousticness percentage of the track. Cast the value as an integer.",
"examples": [31, 7, 17, 11, 14],
},
{
"name": "instrumentalness",
"column_name": "instrumentalness_%",
"type": "integer",
"description": "The instrumentalness percentage of the track. Cast the value as an integer.",
"examples": [0, 63, 17, 2, 19],
},
{
"name": "liveness",
"column_name": "liveness_%",
"type": "integer",
"description": "The liveness percentage of the track. Cast the value as an integer.",
"examples": [8, 10, 31, 11, 28],
},
{
"name": "speechiness",
"column_name": "speechiness_%",
"type": "integer",
"description": "The speechiness percentage of the track. Cast the value as an integer.",
"examples": [4, 6, 15, 24, 3],
},
{
"name": "coverUrl",
"column_name": "cover_url",
"type": "string",
"description": "The URL of the track's cover image. If the value is 'Not Found', it should be cast as an empty string.",
"examples": [
"https://i.scdn.co/image/ab67616d0000b2730656d5ce813ca3cc4b677e05",
"https://i.scdn.co/image/ab67616d0000b273e85259a1cae29a8d91f2093d",
],
},
],
},
{
"title": "Artist",
"type": "object",
"description": "Node",
"properties": [
{
"name": "name",
"column_name": "artist(s)_name",
"type": "string",
"description": "The name of the artist. Split values in column by a comma",
"examples": [
"Latto",
"Jung Kook",
"Myke Towers",
"Olivia Rodrigo",
"Taylor Swift",
"Bad Bunny",
],
}
],
},
{
"title": "PERFORMED_BY",
"type": "object",
"description": "Relationship",
"properties": [
{
"name": "_from",
"type": "string",
"description": "The label of the node this relationship starts at",
"examples": ["Track"],
},
{
"name": "_to",
"type": "string",
"description": "The label of the node this relationship ends at",
"examples": ["Artist"],
},
],
},
]
9、添加唯一标识符
我注意到该架构不包含任何唯一标识符，这在导入关系时可能会成为问题。不同的艺术家会发行同名歌曲，而两个艺术家可能同名，这是有道理的。
因此，为曲目创建标识符非常重要，这样它们就可以在更大的数据集中区分开来：
# Add primary key/unique identifiers
uid_prompt = PromptTemplate.from_template("""
You are a graph database expert reviewing a single entity from a data model generated by a colleague.
You want to ensure that all of the nodes imported into the database are unique.
## Example
A schema contains Actors with a number of properties including name, date of birth.
Two actors may have the same name then add a new compound property combining the name and date of birth.
If combining values, include the instruction to convert the value to slug case. Call the new property 'id'.
If you have identified a new property, add it to the list of properties leaving the rest intact.
Include in the description the fields that are to be concatenated.
## Example Output
Here is an example of a good output:
```
{example_output}
```
## Current Entity Schema
```
{entity}
```
""", partial_variables=dict(example_output=dumps(example_output)))
uid_chain = uid_prompt | llm.with_structured_output(JSONSchemaSpecification)
这一步实际上只需要用于节点，因此我从模式中提取了节点，为每个节点运行链，然后将关系与更新的定义相结合：
# extract nodes and relationships
nodes = [n for n in existing_model if "node" in n["description"].lower()]
rels = [n for n in existing_model if "node" not in n["description"].lower()]
# generate a unique id for nodes
with_uids = []
for entity in nodes:
res = uid_chain.invoke(dict(entity=dumps(entity)))
json = loads(res.jsonschema)
with_uids = with_uids + json if type(json) == list else with_uids + [json]
# combine nodes and relationships
with_uids = with_uids + rels
10、数据模型审查
为了保持理智，值得检查模型是否需要优化。model_prompt 在识别名词和动词方面做得很好，但模型更复杂。
一次迭代将 *_playlists 和 _charts 列视为 ID，并尝试创建 Stream 节点和 IN_PLAYLIST 关系。我认为这是因为计数超过 1,000，包括使用逗号格式化（例如 1,001）。
想法不错，但可能有点太聪明了。但这显示了让了解数据结构的人参与其中的重要性。
# Add primary key/unique identifiers
review_prompt = PromptTemplate.from_template("""
You are a graph database expert reviewing a data model generated by a colleague.
Your task is to review the data model and ensure that it is fit for purpose.
Check for:
## Check for nested objects
Remember that Neo4j cannot store arrays of objects or nested objects.
These must be converted into into separate nodes with relationships between them.
You must include the new node and a reference to the relationship to the output schema.
## Check for Entities in properties
If there is a property that represents an array of IDs, a new node should be created for that entity.
You must include the new node and a reference to the relationship to the output schema.
# Keep Instructions
Ensure that the instructions for the nodes, relationships, and properties are clear and concise.
You may improve them but the detail must not be removed in any circumstances.
## Current Entity Schema
```
{entity}
```
""")
review_chain = review_prompt | llm.with_structured_output(JSONSchemaSpecification)
review_nodes = [n for n in with_uids if "node" in n["description"].lower() ]
review_rels = [n for n in with_uids if "node" not in n["description"].lower() ]
reviewed = []
for entity in review_nodes:
res = review_chain.invoke(dict(entity=dumps(entity)))
json = loads(res.jsonschema)
reviewed = reviewed + json
# add relationships back in
reviewed = reviewed + review_rels
len(reviewed)
reviewed = with_uids
在实际场景中，我希望运行几次以迭代方式改进数据模型。我会设置一个最大限制，然后迭代到该点，否则数据模型对象不再发生变化。
11、生成导入语句
此时，架构应该足够强大，并包含尽可能多的信息，以允许 LLM 生成一组导入脚本。
根据 Neo4j 数据导入建议，应多次处理该文件，每次导入单个节点或关系，以避免急切操作和锁定。
import_prompt = PromptTemplate.from_template("""
Based on the data model, write a Cypher statement to import the following data from a CSV file into Neo4j.
Do not use LOAD CSV as this data will be imported using the Neo4j Python Driver, use UNWIND on the $rows parameter instead.
You are writing a multi-pass import process, so concentrate on the entity mentioned.
When importing data, you must use the following guidelines:
* follow the instructions in the description when identifying primary keys.
* Use the instructions in the description to determine the format of properties when a finding.
* When combining fields into an ID, use the apoc.text.slug function to convert any text to slug case and toLower to convert the string to lowercase - apoc.text.slug(toLower(row.`name`))
* If you split a property, convert it to a string and use the trim function to remove any whitespace - trim(toString(row.`name`))
* When combining properties, wrap each property in the coalesce function so the property is not null if one of the values is not set - coalesce(row.`id`, '') + '--'+ coalsece(row.`title`)
* Use the `column_name` field to map the CSV column to the property in the data model.
* Wrap all column names from the CSV in backticks - for example row.`column_name`.
* When you merge nodes, merge on the unique identifier and nothing else. All other properties should be set using `SET`.
* Do not use apoc.periodic.iterate, the files will be batched in the application.
Data Model:
```
{data_model}
```
Current Entity:
```
{entity}
```
""")
此链需要与前面步骤不同的输出对象。在这种情况下，密码成员是最重要的，但我还想包含一个 chain_of_thought 密钥来鼓励思想链：
class CypherOutputSpecification(BaseModel):
chain_of_thought: str = Field(description="Any reasoning used to write the Cypher statement")
cypher: str = Field(description="The Cypher statement to import the data")
notes: Optional[str] = Field(description="Any notes or closing remarks about the Cypher statement")
import_chain = import_prompt | llm.with_structured_output(CypherOutputSpecification)
然后，同样的过程适用于迭代每个审查过的定义并生成 Cypher：
import_cypher = []
for n in reviewed:
print('\n\n------', n['title'])
res = import_chain.invoke(dict(
data_model=dumps(reviewed),
entity=n
))
import_cypher.append((
res.cypher
))
print(res.cypher)
控制台输出：
------ Track
UNWIND $rows AS row
MERGE (t:Track {id: apoc.text.slug(toLower(coalesce(row.`track_name`, '') + '-' + coalesce(row.`released_year`, '')))})
SET t.name = trim(toString(row.`track_name`)),
t.artist_count = toInteger(row.`artist_count`),
t.released_year = toInteger(row.`released_year`),
t.released_month = toInteger(row.`released_month`),
t.released_day = toInteger(row.`released_day`),
t.inSpotifyPlaylists = toInteger(row.`in_spotify_playlists`),
t.inSpotifyCharts = toInteger(row.`in_spotify_charts`),
t.streams = row.`streams`,
t.inApplePlaylists = toInteger(row.`in_apple_playlists`),
t.inAppleCharts = toInteger(row.`in_apple_charts`),
t.inDeezerPlaylists = row.`in_deezer_playlists`,
t.inDeezerCharts = toInteger(row.`in_deezer_charts`),
t.inShazamCharts = row.`in_shazam_charts`,
t.bpm = toInteger(row.`bpm`),
t.key = trim(toString(row.`key`)),
t.mode = trim(toString(row.`mode`)),
t.danceability = toInteger(row.`danceability_%`),
t.valence = toInteger(row.`valence_%`),
t.energy = toInteger(row.`energy_%`),
t.acousticness = toInteger(row.`acousticness_%`),
t.instrumentalness = toInteger(row.`instrumentalness_%`),
t.liveness = toInteger(row.`liveness_%`),
t.speechiness = toInteger(row.`speechiness_%`),
t.coverUrl = CASE row.`cover_url` WHEN 'Not Found' THEN '' ELSE trim(toString(row.`cover_url`)) END
------ Artist
UNWIND $rows AS row
WITH row, split(row.`artist(s)_name`, ',') AS artistNames
UNWIND artistNames AS artistName
MERGE (a:Artist {id: apoc.text.slug(toLower(trim(artistName)))})
SET a.name = trim(artistName)
------ PERFORMED_BY
UNWIND $rows AS row
UNWIND split(row.`artist(s)_name`, ',') AS artist_name
MERGE (t:Track {id: apoc.text.slug(toLower(row.`track_name`)) + '-' + trim(toString(row.`released_year`))})
MERGE (a:Artist {id: apoc.text.slug(toLower(trim(artist_name)))})
MERGE (t)-[:PERFORMED_BY]->(a)
这个提示需要一些工程设计才能获得一致的结果：
- 有时 Cypher 会包含定义了多个字段的 MERGE 语句，这充其量只是次优的。如果任何列为空，整个导入都会失败。
- 有时，结果会包含 apoc.period.iterate，这不再是必需的，我想要可以使用 Python 驱动程序执行的代码。
- 我不得不重申，在创建关系时应使用指定的列名。
- 在关系两端的节点上使用唯一标识符时，LLM 不会遵循说明，因此需要几次尝试才能让它遵循描述中的说明。这个提示和 model_prompt 之间有一些来回。
- 包含特殊字符（例如 energy_%）的列名需要反引号。
将其分成两个提示也会很有帮助------一个用于节点，一个用于关系。但这是另一天的任务。
12、创建唯一约束
接下来，可以使用导入脚本作为在数据库中创建唯一约束的基础：
constraint_prompt = PromptTemplate.from_template("""
You are an expert graph database administrator.
Use the following Cypher statement to write a Cypher statement to
create unique constraints on any properties used in a MERGE statement.
The correct syntax for a unique constraint is:
CREATE CONSTRAINT movie_title_id IF NOT EXISTS FOR (m:Movie) REQUIRE m.title IS UNIQUE;
Cypher:
```
{cypher}
```
""")
constraint_chain = constraint_prompt | llm.with_structured_output(CypherOutputSpecification)
constraint_queries = []
for statement in import_cypher:
res = constraint_chain.invoke(dict(cypher=statement))
statements = res.cypher.split(";")
for cypher in statements:
constraint_queries.append(cypher)
控制台输出：
CREATE CONSTRAINT track_id_unique IF NOT EXISTS FOR (t:Track) REQUIRE t.id IS UNIQUE
CREATE CONSTRAINT stream_id IF NOT EXISTS FOR (s:Stream) REQUIRE s.id IS UNIQUE
CREATE CONSTRAINT playlist_id IF NOT EXISTS FOR (p:Playlist) REQUIRE p.id IS UNIQUE
CREATE CONSTRAINT chart_id IF NOT EXISTS FOR (c:Chart) REQUIRE c.id IS UNIQUE
CREATE CONSTRAINT track_id_unique IF NOT EXISTS FOR (t:Track) REQUIRE t.id IS UNIQUE
CREATE CONSTRAINT stream_id_unique IF NOT EXISTS FOR (s:Stream) REQUIRE s.id IS UNIQUE
CREATE CONSTRAINT track_id_unique IF NOT EXISTS FOR (t:Track) REQUIRE t.id IS UNIQUE
CREATE CONSTRAINT playlist_id_unique IF NOT EXISTS FOR (p:Playlist) REQUIRE p.id IS UNIQUE
CREATE CONSTRAINT track_id_unique IF NOT EXISTS FOR (track:Track) REQUIRE track.id IS UNIQUE
CREATE CONSTRAINT chart_id_unique IF NOT EXISTS FOR (chart:Chart) REQUIRE chart.id IS UNIQUE
有时此提示会返回索引和约束的语句，因此在分号处进行拆分。
13、运行导入
一切就绪后，就可以执行 Cypher 语句了：
from os import getenv
from neo4j import GraphDatabase
driver = GraphDatabase.driver(
getenv("NEO4J_URI"),
auth=(
getenv("NEO4J_USERNAME"),
getenv("NEO4J_PASSWORD")
)
)
with driver.session() as session:
# truncate the db
session.run("MATCH (n) DETACH DELETE n")
# create constraints
for q in constraint_queries:
if q.strip() != "":
session.run(q)
# import the data
for q in import_cypher:
if q.strip() != "":
res = session.run(q, rows=rows).consume()
print(q)
print(res.counters)
14、数据集上的 QA
如果没有使用 GraphCypherQAChain 对数据集进行一些 QA，这篇文章就不完整：
from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
graph = Neo4jGraph(
url=getenv("NEO4J_URI"),
username=getenv("NEO4J_USERNAME"),
password=getenv("NEO4J_PASSWORD"),
enhanced_schema=True
)
qa = GraphCypherQAChain.from_llm(
llm,
graph=graph,
allow_dangerous_requests=True,
verbose=True
)
15、最受欢迎的艺术家
数据库中最受欢迎的艺术家是谁？
qa.invoke({"query": "Who are the most popular artists?"})
> Entering new GraphCypherQAChain chain...
Generated Cypher:
cypher
MATCH (:Track)-[:PERFORMED_BY]->(a:Artist)
RETURN a.name, COUNT(*) AS popularity
ORDER BY popularity DESC
LIMIT 10
Full Context:
[{'a.name': 'Bad Bunny', 'popularity': 40}, {'a.name': 'Taylor Swift', 'popularity': 38}, {'a.name': 'The Weeknd', 'popularity': 36}, {'a.name': 'SZA', 'popularity': 23}, {'a.name': 'Kendrick Lamar', 'popularity': 23}, {'a.name': 'Feid', 'popularity': 21}, {'a.name': 'Drake', 'popularity': 19}, {'a.name': 'Harry Styles', 'popularity': 17}, {'a.name': 'Peso Pluma', 'popularity': 16}, {'a.name': '21 Savage', 'popularity': 14}]
> Finished chain.
{
"query": "Who are the most popular artists?",
"result": "Bad Bunny, Taylor Swift, and The Weeknd are the most popular artists."
}
LLM 似乎是根据艺术家参与的曲目数量来判断其受欢迎程度，而不是根据其总的流媒体数量。
16、每分钟节拍数
哪首曲目的 BPM 最高？
qa.invoke({"query": "Which track has the highest BPM?"})
> Entering new GraphCypherQAChain chain...
Generated Cypher:
cypher
MATCH (t:Track)
RETURN t
ORDER BY t.bpm DESC
LIMIT 1
Full Context:
[{'t': {'id': 'seven-feat-latto-explicit-ver--2023'}}]
> Finished chain.
{
"query": "Which track has the highest BPM?",
"result": "I don't know the answer."
}
17、改进 Cypher 生成提示
在这种情况下，Cypher 看起来不错，提示中包含了正确的结果，但 gpt-4o 无法解释答案。传递给 GraphCypherQAChain 的 CYPHER_GENERATION_PROMPT 似乎可以使用其他指令来使列名更详细。
始终在 Cypher 语句中使用标签和属性名称来使用详细的列名。例如，使用"person_name"而不是"name"。
带有自定义提示的 GraphCypherQAChain：
YPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
Instructions:
Use only the provided relationship types and properties in the schema.
Do not use any other relationship types or properties that are not provided.
Schema:
{schema}
Note: Do not include any explanations or apologies in your responses.
Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
Do not include any text except the generated Cypher statement.
Always use verbose column names in the Cypher statement using the label and property names. For example, use 'person_name' instead of 'name'.
Include data from the immediate network around the node in the result to provide extra context. For example, include the Movie release year, a list of actors and their roles, or the director of a movie.
When ordering by a property, add an `IS NOT NULL` check to ensure that only nodes with that property are returned.
Examples: Here are a few examples of generated Cypher statements for particular questions:
# How many people acted in Top Gun?
MATCH (m:Movie {{name:"Top Gun"}})
RETURN COUNT { (m)<-[:ACTED_IN]-() } AS numberOfActors
The question is:
{question}"""
CYPHER_GENERATION_PROMPT = PromptTemplate(
input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
)
qa = GraphCypherQAChain.from_llm(
llm,
graph=graph,
allow_dangerous_requests=True,
verbose=True,
cypher_prompt=CYPHER_GENERATION_PROMPT,
)
18、最多艺术家演唱的曲目
图表擅长按类型和方向返回关系数量。
qa.invoke({"query": "Which tracks are performed by the most artists?"})
> Entering new GraphCypherQAChain chain...
Generated Cypher:
cypher
MATCH (t:Track)
WITH t, COUNT { (t)-[:PERFORMED_BY]->(:Artist) } as artist_count
WHERE artist_count IS NOT NULL
RETURN t.id AS track_id, t.name AS track_name, artist_count
ORDER BY artist_count DESC
Full Context:
[{'track_id': 'los-del-espacio-2023', 'track_name': 'Los del Espacio', 'artist_count': 8}, {'track_id': 'se-le-ve-2021', 'track_name': 'Se Le Ve', 'artist_count': 8}, {'track_id': 'we-don-t-talk-about-bruno-2021', 'track_name': "We Don't Talk About Bruno", 'artist_count': 7}, {'track_id': 'cayï-ï-la-noche-feat-cruz-cafunï-ï-abhir-hathi-bejo-el-ima--2022', 'track_name': None, 'artist_count': 6}, {'track_id': 'jhoome-jo-pathaan-2022', 'track_name': 'Jhoome Jo Pathaan', 'artist_count': 6}, {'track_id': 'besharam-rang-from-pathaan--2022', 'track_name': None, 'artist_count': 6}, {'track_id': 'nobody-like-u-from-turning-red--2022', 'track_name': None, 'artist_count': 6}, {'track_id': 'ultra-solo-remix-2022', 'track_name': 'ULTRA SOLO REMIX', 'artist_count': 5}, {'track_id': 'angel-pt-1-feat-jimin-of-bts-jvke-muni-long--2023', 'track_name': None, 'artist_count': 5}, {'track_id': 'link-up-metro-boomin-don-toliver-wizkid-feat-beam-toian-spider-verse-remix-spider-man-across-the-spider-verse--2023', 'track_name': None, 'artist_count': 5}]
> Finished chain.
{
"query": "Which tracks are performed by the most artists?",
"result": "The tracks \"Los del Espacio\" and \"Se Le Ve\" are performed by the most artists, with each track having 8 artists."
}
19、结束语
CSV 分析和建模是最耗时的部分。生成它可能需要五分钟以上的时间。
成本本身相当便宜。在八个小时的实验中，我肯定发送了数百个请求，最终只花了一美元左右。
要达到这一点，有许多挑战：
- 提示需要多次迭代才能正确。这个问题可以通过微调模型或提供少量样本来克服。
- GPT-4o 的 JSON 响应可能不一致。我被推荐使用 json-repair，这比尝试让 LLM 验证其自己的 JSON 输出要好。
我可以看到这种方法在 LangGraph 实现中效果很好，其中操作按顺序运行，使 LLM 能够构建和优化模型。随着新模型的发布，它们也可能从微调中受益。
原文链接：大模型CSV转图数据 - BimAnt