ArangoDB

ArangoDB 是一个可扩展的图数据库系统，用于从连接数据中提取价值，更快。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大型语言模型（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包来获取一个临时的云实例运行：

%%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

# Instantiate ArangoDB Database
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"
}

<!--IMPORTS:[{"imported": "ArangoGraph", "source": "langchain_community.graphs", "docs": "https://python.langchain.com/api_reference/community/graphs/langchain_community.graphs.arangodb_graph.ArangoGraph.html", "title": "ArangoDB"}]-->
# Instantiate the ArangoDB-LangChain Graph
from langchain_community.graphs import ArangoGraph

graph = ArangoGraph(db)

填充数据库

我们将依赖Python Driver将我们的权力的游戏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：

# The schema should be empty here,
# since `graph` was initialized prior to ArangoDB Data ingestion (see above).

import json

print(json.dumps(graph.schema, indent=4))

{
    "Graph Schema": [],
    "Collection Schema": []
}

graph.set_schema()

# We can now view the generated schema

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链来查询我们的数据

import os

os.environ["OPENAI_API_KEY"] = "your-key-here"

<!--IMPORTS:[{"imported": "ArangoGraphQAChain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/community/chains/langchain_community.chains.graph_qa.arangodb.ArangoGraphQAChain.html", "title": "ArangoDB"}, {"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "ArangoDB"}]-->
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> Entering new ArangoGraphQAChain chain...[0m
AQL Query (1):[32;1m[1;3m
WITH Characters
FOR character IN Characters
FILTER character.name == "Ned" AND character.surname == "Stark"
RETURN character.alive
[0m
AQL Result:
[32;1m[1;3m[True][0m

[1m> Finished chain.[0m

'Yes, Ned Stark is alive.'

chain.run("How old is Arya Stark?")

[1m> Entering new ArangoGraphQAChain chain...[0m
AQL Query (1):[32;1m[1;3m
WITH Characters
FOR character IN Characters
FILTER character.name == "Arya" && character.surname == "Stark"
RETURN character.age
[0m
AQL Result:
[32;1m[1;3m[11][0m

[1m> Finished chain.[0m

'Arya Stark is 11 years old.'

chain.run("Are Arya Stark and Ned Stark related?")

[1m> Entering new ArangoGraphQAChain chain...[0m
AQL Query (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 Result:
[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> Finished chain.[0m

'Yes, Arya Stark and Ned Stark are related. According to the information retrieved from the database, there is a relationship between them. Arya Stark is the child of Ned Stark.'

chain.run("Does Arya Stark have a dead parent?")

[1m> Entering new ArangoGraphQAChain chain...[0m
AQL Query (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 Result:
[32;1m[1;3m[{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}][0m

[1m> Finished chain.[0m

'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'

链修改器

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

'Yes, according to the information in the database, Ned Stark is alive.'

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

'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'

ArangoDB

设置

填充数据库

获取和设置ArangoDB模式

查询ArangoDB数据库

链修改器

Was this page helpful?

You can also leave detailed feedback on GitHub.

设置​

填充数据库​

获取和设置ArangoDB模式​

查询ArangoDB数据库​

链修改器​

Was this page helpful?

You can also leave detailed feedback on GitHub.

设置

填充数据库

获取和设置ArangoDB模式

查询ArangoDB数据库

链修改器