ArangoDB
ArangoDB 是一个可扩展的图形数据库系统，可更快地从连接的数据中获取价值。通过单一的查询语言，它支持原生图形、集成搜索引擎和 JSON。ArangoDB 可在本地或云端运行。
这个笔记本展示了如何使用LLMs为ArangoDB数据库提供自然语言接口。
设置
您可以通过ArangoDB Docker镜像来运行本地ArangoDB实例：
docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb
另一种选择是使用ArangoDB Cloud Connector包来运行临时云实例：
%%capture
%pip install --upgrade --quiet python-arango # The ArangoDB Python Driver
%pip install --upgrade --quiet adb-cloud-connector # The ArangoDB Cloud Instance provisioner
%pip install --upgrade --quiet langchain-openai
%pip install --upgrade --quiet langchain
# 实例化ArangoDB数据库
import json
from adb_cloud_connector import get_temp_credentials
from arango import ArangoClient
con = get_temp_credentials()
db = ArangoClient(hosts=con["url"]).db(
con["dbName"], con["username"], con["password"], verify=True
)
print(json.dumps(con, indent=2))
Log: requesting new credentials...
Succcess: new credentials acquired
{
"dbName": "TUT3sp29s3pjf1io0h4cfdsq",
"username": "TUTo6nkwgzkizej3kysgdyeo8",
"password": "TUT9vx0qjqt42i9bq8uik4v9",
"hostname": "tutorials.arangodb.cloud",
"port": 8529,
"url": "https://tutorials.arangodb.cloud:8529"
}
# 实例化ArangoDB-LangChain图
from langchain_community.graphs import ArangoGraph
graph = ArangoGraph(db)
填充数据库
我们将依赖于Python Driver将我们的GameOfThrones数据导入到我们的数据库中。
if db.has_graph("GameOfThrones"):
db.delete_graph("GameOfThrones", drop_collections=True)
db.create_graph(
"GameOfThrones",
edge_definitions=[
{
"edge_collection": "ChildOf",
"from_vertex_collections": ["Characters"],
"to_vertex_collections": ["Characters"],
},
],
)
documents = [
{
"_key": "NedStark",
"name": "Ned",
"surname": "Stark",
"alive": True,
"age": 41,
"gender": "male",
},
{
"_key": "CatelynStark",
"name": "Catelyn",
"surname": "Stark",
"alive": False,
"age": 40,
"gender": "female",
},
{
"_key": "AryaStark",
"name": "Arya",
"surname": "Stark",
"alive": True,
"age": 11,
"gender": "female",
},
{
"_key": "BranStark",
"name": "Bran",
"surname": "Stark",
"alive": True,
"age": 10,
"gender": "male",
},
]
edges = [
{"_to": "Characters/NedStark", "_from": "Characters/AryaStark"},
{"_to": "Characters/NedStark", "_from": "Characters/BranStark"},
{"_to": "Characters/CatelynStark", "_from": "Characters/AryaStark"},
{"_to": "Characters/CatelynStark", "_from": "Characters/BranStark"},
]
db.collection("Characters").import_bulk(documents)
db.collection("ChildOf").import_bulk(edges)
{'error': False,
'created': 4,
'errors': 0,
'empty': 0,
'updated': 0,
'ignored': 0,
'details': []}
获取和设置ArangoDB模式
在实例化ArangoDBGraph对象时生成了初始的ArangoDB Schema。以下是模式的获取器和设置器方法，如果您有兴趣查看或修改模式：
# 这里应该是空的模式，因为在ArangoDB数据摄入之前已经初始化了`graph`。
import json
print(json.dumps(graph.schema, indent=4))
{
"Graph Schema": [],
"Collection Schema": []
}
graph.set_schema()
# 现在我们可以查看生成的模式
import json
print(json.dumps(graph.schema, indent=4))
{
"Graph Schema": [
{
"graph_name": "GameOfThrones",
"edge_definitions": [
{
"edge_collection": "ChildOf",
"from_vertex_collections": [
"Characters"
],
"to_vertex_collections": [
"Characters"
]
}
]
}
],
"Collection Schema": [
{
"collection_name": "ChildOf",
"collection_type": "edge",
"edge_properties": [
{
"name": "_key",
"type": "str"
},
{
"name": "_id",
"type": "str"
},
{
"name": "_from",
"type": "str"
},
{
"name": "_to",
"type": "str"
},
{
"name": "_rev",
"type": "str"
}
],
"example_edge": {
"_key": "266218884025",
"_id": "ChildOf/266218884025",
"_from": "Characters/AryaStark",
"_to": "Characters/NedStark",
"_rev": "_gVPKGSq---"
}
},
{
"collection_name": "Characters",
"collection_type": "document",
"document_properties": [
{
"name": "_key",
"type": "str"
},
{
"name": "_id",
"type": "str"
},
{
"name": "_rev",
"type": "str"
},
{
"name": "name",
"type": "str"
},
{
"name": "surname",
"type": "str"
},
{
"name": "alive",
"type": "bool"
},
{
"name": "age",
"type": "int"
},
{
"name": "gender",
"type": "str"
}
],
"example_document": {
"_key": "NedStark",
"_id": "Characters/NedStark",
"_rev": "_gVPKGPi---",
"name": "Ned",
"surname": "Stark",
"alive": true,
"age": 41,
"gender": "male"
}
}
]
}
查询 ArangoDB 数据库
现在我们可以使用 ArangoDB Graph QA Chain 来查询我们的数据。
import os
os.environ["OPENAI_API_KEY"] = "your-key-here"
from langchain.chains import ArangoGraphQAChain
from langchain_openai import ChatOpenAI
chain = ArangoGraphQAChain.from_llm(
ChatOpenAI(temperature=0), graph=graph, verbose=True
)
chain.run("Ned Stark 是否还活着？")
> 进入新的 ArangoGraphQAChain 链...
AQL 查询 (1):
WITH Characters
FOR character IN Characters
FILTER character.name == "Ned" AND character.surname == "Stark"
RETURN character.alive
AQL 结果:
[True]
> 链结束。
'是的，Ned Stark 还活着。'
chain.run("Arya Stark 多大了？")
> 进入新的 ArangoGraphQAChain 链...
AQL 查询 (1):
WITH Characters
FOR character IN Characters
FILTER character.name == "Arya" && character.surname == "Stark"
RETURN character.age
AQL 结果:
[11]
> 链结束。
'Arya Stark 11 岁了。'
chain.run("Arya Stark 和 Ned Stark 有亲戚关系吗？")
> 进入新的 ArangoGraphQAChain 链...
AQL 查询 (1):
WITH Characters, ChildOf
FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf
FILTER p.vertices[-1]._key == 'NedStark'
RETURN p
AQL 结果:
[{'vertices': [{'_key': 'AryaStark', '_id': 'Characters/AryaStark', '_rev': '_gVPKGPi--B', 'name': 'Arya', 'surname': 'Stark', 'alive': True, 'age': 11, 'gender': 'female'}, {'_key': 'NedStark', '_id': 'Characters/NedStark', '_rev': '_gVPKGPi---', 'name': 'Ned', 'surname': 'Stark', 'alive': True, 'age': 41, 'gender': 'male'}], 'edges': [{'_key': '266218884025', '_id': 'ChildOf/266218884025', '_from': 'Characters/AryaStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq---'}], 'weights': [0, 1]}
> 链结束。
'是的，Arya Stark 和 Ned Stark 有亲戚关系。根据从数据库检索到的信息，他们之间存在关系。Arya Stark 是 Ned Stark 的孩子。'
chain.run("Arya Stark 的父母中有人已故吗？")
> 进入新的 ArangoGraphQAChain 链...
AQL 查询 (1):
WITH Characters, ChildOf
FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf
FILTER v.alive == false
RETURN e
AQL 结果:
[{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}
> 链结束。
'是的，Arya Stark 的一个父母已故。父母是 Catelyn Stark。'
链修改器
您可以修改以下 ArangoDBGraphQAChain 类变量的值，以修改链结果的行为。
# 指定要返回的 AQL 查询结果的最大数量
chain.top_k = 10
# 指定是否在输出字典中返回 AQL 查询
chain.return_aql_query = True
# 指定是否在输出字典中返回 AQL JSON 结果
chain.return_aql_result = True
# 指定应进行的 AQL 生成尝试的最大数量
chain.max_aql_generation_attempts = 5
# 指定一组 AQL 查询示例，这些示例传递给 AQL 生成提示模板，以促进少量学习。
# 默认为空字符串。
chain.aql_examples = """
# Ned Stark 是否还活着？
RETURN DOCUMENT('Characters/NedStark').alive
# Arya Stark 是否是 Ned Stark 的孩子？
FOR e IN ChildOf
FILTER e._from == "Characters/AryaStark" AND e._to == "Characters/NedStark"
RETURN e
"""
chain.run("Ned Stark 是否还活着？")
> 进入新的 ArangoGraphQAChain 链...
AQL 查询 (1):
RETURN DOCUMENT('Characters/NedStark').alive
AQL 结果:
[True]
> 链结束。
'是的，根据数据库中的信息，Ned Stark 还活着。'
chain.run("Bran Stark 是否是 Ned Stark 的孩子？")
> 进入新的 ArangoGraphQAChain 链...
AQL 查询 (1):
FOR e IN ChildOf
FILTER e._from == "Characters/BranStark" AND e._to == "Characters/NedStark"
RETURN e
AQL 结果:
[{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}]
> 链结束。
'是的，根据 ArangoDB 数据库中的信息，Bran Stark 确实是 Ned Stark 的孩子。'