ArangoDB
Open In Colab
ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language.
ArangoDBruns on-prem or in the cloud.
This notebook shows how to use LLMs to provide a natural language interface to an ArangoDB database.
Setting up
You can get a local ArangoDB instance running via the ArangoDB Docker
image:
docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb
An alternative is to use the ArangoDB Cloud Connector package to get a temporary cloud instance running:
%%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"
}
# Instantiate the ArangoDB-LangChain Graph
from langchain_community.graphs import ArangoGraph
graph = ArangoGraph(db)
API Reference:
Populating database
We will rely on the Python Driver to import our
GameOfThrones
data into our database.
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': []}
Getting and setting the ArangoDB schema
An initial ArangoDB Schema is generated upon instantiating the
ArangoDBGraph object. Below are the schema’s getter & setter methods
should you be interested in viewing or modifying the schema:
# 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"
}
}
]
}
Querying the ArangoDB database
We can now use the ArangoDB Graph QA Chain to inquire about our data
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
)
API Reference:
chain.run("Is Ned Stark alive?")
> Entering new ArangoGraphQAChain chain...
AQL Query (1):
WITH Characters
FOR character IN Characters
FILTER character.name == "Ned" AND character.surname == "Stark"
RETURN character.alive
AQL Result:
[True]
> Finished chain.
'Yes, Ned Stark is alive.'
chain.run("How old is Arya Stark?")
> Entering new ArangoGraphQAChain chain...
AQL Query (1):
WITH Characters
FOR character IN Characters
FILTER character.name == "Arya" && character.surname == "Stark"
RETURN character.age
AQL Result:
[11]
> Finished chain.
'Arya Stark is 11 years old.'
chain.run("Are Arya Stark and Ned Stark related?")
> Entering new ArangoGraphQAChain chain...
AQL Query (1):
WITH Characters, ChildOf
FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf
FILTER p.vertices[-1]._key == 'NedStark'
RETURN p
AQL Result:
[{'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]}]
> Finished chain.
'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?")
> Entering new ArangoGraphQAChain chain...
AQL Query (1):
WITH Characters, ChildOf
FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf
FILTER v.alive == false
RETURN e
AQL Result:
[{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}]
> Finished chain.
'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'
Chain modifiers
You can alter the values of the following ArangoDBGraphQAChain class
variables to modify the behaviour of your chain results
# 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
> Entering new ArangoGraphQAChain chain...
AQL Query (1):
RETURN DOCUMENT('Characters/NedStark').alive
AQL Result:
[True]
> Finished chain.
'Yes, according to the information in the database, Ned Stark is alive.'
chain.run("Is Bran Stark the child of Ned Stark?")
> Entering new ArangoGraphQAChain chain...
AQL Query (1):
FOR e IN ChildOf
FILTER e._from == "Characters/BranStark" AND e._to == "Characters/NedStark"
RETURN e
AQL Result:
[{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}]
> Finished chain.
'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'