ArangoDB
ArangoDB 是一个可扩展的图形数据库系统，可以更快地从连接数据中获取价值。原生图形、集成搜索引擎和 JSON 支持，通过单一查询语言。
ArangoDB可以在本地或云端运行。
本笔记本展示了如何使用 LLM 提供一个自然语言接口来访问 ArangoDB 数据库。
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 Python Driver 将我们的 GameOfThrones 数据导入到数据库中。
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，会生成初始的 ArangoDB Schema。以下是模式的获取和设置方法，如果您有兴趣查看或修改模式：
# 此时模式应该是空的，
# 因为 `graph` 在 ArangoDB 数据摄取之前已初始化（见上文）。
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
我们现在可以使用 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("Is Ned Stark alive?")
[1m> 进入新的 ArangoGraphQAChain 链...[0m
AQL 查询 (1):[32;1m[1;3m
WITH Characters
FOR character IN Characters
FILTER character.name == "Ned" AND character.surname == "Stark"
RETURN character.alive
[0m
AQL 结果:
[32;1m[1;3m[True][0m
[1m> 完成链。[0m
'是的，奈德·史塔克还活着。'
chain.run("How old is Arya Stark?")
[1m> 进入新的 ArangoGraphQAChain 链...[0m
AQL 查询 (1):[32;1m[1;3m
WITH Characters
FOR character IN Characters
FILTER character.name == "Arya" && character.surname == "Stark"
RETURN character.age
[0m
AQL 结果:
[32;1m[1;3m[11][0m
[1m> 完成链。[0m
'艾莉亚·史塔克11岁。'
chain.run("Are Arya Stark and Ned Stark related?")
[1m> 进入新的 ArangoGraphQAChain 链...[0m
AQL 查询 (1):[32;1m[1;3m
WITH Characters, ChildOf
FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf
FILTER p.vertices[-1]._key == 'NedStark'
RETURN p
[0m
AQL 结果:
[32;1m[1;3m[{'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]}][0m
[1m> 完成链。[0m
'是的，艾莉亚·史塔克和奈德·史塔克有亲属关系。根据从数据库检索到的信息，他们之间存在关系。艾莉亚·史塔克是奈德·史塔克的孩子。'
chain.run("Does Arya Stark have a dead parent?")
[1m> 进入新的 ArangoGraphQAChain 链...[0m
AQL 查询 (1):[32;1m[1;3m
WITH Characters, ChildOf
FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf
FILTER v.alive == false
RETURN e
[0m
AQL 结果:
[32;1m[1;3m[{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}][0m
[1m> 完成链。[0m
'是的，艾莉亚·史塔克有一个已故的父母。这个父母是凯特琳·史塔克。'
链修饰符
您可以更改以下 ArangoDBGraphQAChain 类变量的值，以修改链结果的行为
# Specify the maximum number of AQL Query Results to return
chain.top_k = 10
# Specify whether or not to return the AQL Query in the output dictionary
chain.return_aql_query = True
# Specify whether or not to return the AQL JSON Result in the output dictionary
chain.return_aql_result = True
# Specify the maximum amount of AQL Generation attempts that should be made
chain.max_aql_generation_attempts = 5
# Specify a set of AQL Query Examples, which are passed to
# the AQL Generation Prompt Template to promote few-shot-learning.
# Defaults to an empty string.
chain.aql_examples = """
# Is Ned Stark alive?
RETURN DOCUMENT('Characters/NedStark').alive
# Is Arya Stark the child of Ned Stark?
FOR e IN ChildOf
FILTER e._from == "Characters/AryaStark" AND e._to == "Characters/NedStark"
RETURN e
"""
chain.run("Is Ned Stark alive?")
# chain("Is Ned Stark alive?") # Returns a dictionary with the AQL Query & AQL Result
[1m> Entering new ArangoGraphQAChain chain...[0m
AQL Query (1):[32;1m[1;3m
RETURN DOCUMENT('Characters/NedStark').alive
[0m
AQL Result:
[32;1m[1;3m[True][0m
[1m> Finished chain.[0m
'是的，根据数据库中的信息，奈德·史塔克是活着的。'
chain.run("Is Bran Stark the child of Ned Stark?")
[1m> Entering new ArangoGraphQAChain chain...[0m
AQL Query (1):[32;1m[1;3m
FOR e IN ChildOf
FILTER e._from == "Characters/BranStark" AND e._to == "Characters/NedStark"
RETURN e
[0m
AQL Result:
[32;1m[1;3m[{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}][0m
[1m> Finished chain.[0m
'是的，根据 ArangoDB 数据库中的信息，布兰·史塔克确实是奈德·史塔克的孩子。'