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community[minor]: add graph store implementation for apache age (#20582)

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**Description:** implemented GraphStore class for Apache Age graph db

**Dependencies:** depends on psycopg2

Unit and integration tests included. Formatting and linting have been
run.

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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docs/docs/integrations/graphs/apache_age.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "c94240f5",
"metadata": {},
"source": [
"# Apache AGE\n",
"\n",
">[Apache AGE](https://age.apache.org/) is a PostgreSQL extension that provides graph database functionality. AGE is an acronym for A Graph Extension, and is inspired by Bitnine’s fork of PostgreSQL 10, AgensGraph, which is a multi-model database. The goal of the project is to create single storage that can handle both relational and graph model data so that users can use standard ANSI SQL along with openCypher, the Graph query language. The data elements `Apache AGE` stores are nodes, edges connecting them, and attributes of nodes and edges.\n",
"\n",
">This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the `Cypher` query language.\n",
"\n",
">[Cypher](https://en.wikipedia.org/wiki/Cypher_(query_language)) is a declarative graph query language that allows for expressive and efficient data querying in a property graph.\n"
]
},
{
"cell_type": "markdown",
"id": "dbc0ee68",
"metadata": {},
"source": [
"## Settin up\n",
"\n",
"You will need to have a running `Postgre` instance with the AGE extension installed. One option for testing is to run a docker container using the official AGE docker image.\n",
"You can run a local docker container by running the executing the following script:\n",
"\n",
"```\n",
"docker run \\\n",
" --name age \\\n",
" -p 5432:5432 \\\n",
" -e POSTGRES_USER=postgresUser \\\n",
" -e POSTGRES_PASSWORD=postgresPW \\\n",
" -e POSTGRES_DB=postgresDB \\\n",
" -d \\\n",
" apache/age\n",
"```\n",
"\n",
"Additional instructions on running in docker can be found [here](https://hub.docker.com/r/apache/age)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "62812aad",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import GraphCypherQAChain\n",
"from langchain_community.graphs.age_graph import AGEGraph\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0928915d",
"metadata": {},
"outputs": [],
"source": [
"conf = {\n",
" \"database\": \"postgresDB\",\n",
" \"user\": \"postgresUser\",\n",
" \"password\": \"postgresPW\",\n",
" \"host\": \"localhost\",\n",
" \"port\": 5432,\n",
"}\n",
"\n",
"graph = AGEGraph(graph_name=\"age_test\", conf=conf)"
]
},
{
"cell_type": "markdown",
"id": "995ea9b9",
"metadata": {},
"source": [
"## Seeding the database\n",
"\n",
"Assuming your database is empty, you can populate it using Cypher query language. The following Cypher statement is idempotent, which means the database information will be the same if you run it one or multiple times."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fedd26b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph.query(\n",
" \"\"\"\n",
"MERGE (m:Movie {name:\"Top Gun\"})\n",
"WITH m\n",
"UNWIND [\"Tom Cruise\", \"Val Kilmer\", \"Anthony Edwards\", \"Meg Ryan\"] AS actor\n",
"MERGE (a:Actor {name:actor})\n",
"MERGE (a)-[:ACTED_IN]->(m)\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "58c1a8ea",
"metadata": {},
"source": [
"## Refresh graph schema information\n",
"If the schema of database changes, you can refresh the schema information needed to generate Cypher statements."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4e3de44f",
"metadata": {},
"outputs": [],
"source": [
"graph.refresh_schema()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1fe76ccd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Node properties are the following:\n",
" [{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}, {'properties': [{'property': 'property_a', 'type': 'STRING'}], 'labels': 'LabelA'}, {'properties': [], 'labels': 'LabelB'}, {'properties': [], 'labels': 'LabelC'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}]\n",
" Relationship properties are the following:\n",
" [{'properties': [], 'type': 'ACTED_IN'}, {'properties': [{'property': 'rel_prop', 'type': 'STRING'}], 'type': 'REL_TYPE'}]\n",
" The relationships are the following:\n",
" ['(:`Actor`)-[:`ACTED_IN`]->(:`Movie`)', '(:`LabelA`)-[:`REL_TYPE`]->(:`LabelB`)', '(:`LabelA`)-[:`REL_TYPE`]->(:`LabelC`)']\n",
" \n"
]
}
],
"source": [
"print(graph.schema)"
]
},
{
"cell_type": "markdown",
"id": "68a3c677",
"metadata": {},
"source": [
"## Querying the graph\n",
"\n",
"We can now use the graph cypher QA chain to ask question of the graph"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7476ce98",
"metadata": {},
"outputs": [],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ef8ee27b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
"WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Who played in Top Gun?',\n",
" 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Who played in Top Gun?\")"
]
},
{
"cell_type": "markdown",
"id": "2d28c4df",
"metadata": {},
"source": [
"## Limit the number of results\n",
"You can limit the number of results from the Cypher QA Chain using the `top_k` parameter.\n",
"The default is 10."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "df230946",
"metadata": {},
"outputs": [],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3f1600ee",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
"RETURN a.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Who played in Top Gun?',\n",
" 'result': 'Tom Cruise, Val Kilmer played in Top Gun.'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Who played in Top Gun?\")"
]
},
{
"cell_type": "markdown",
"id": "88c16206",
"metadata": {},
"source": [
"## Return intermediate results\n",
"You can return intermediate steps from the Cypher QA Chain using the `return_intermediate_steps` parameter"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "e412f36b",
"metadata": {},
"outputs": [],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "4f4699dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
"WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"Intermediate steps: [{'query': \"MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\\nWHERE m.name = 'Top Gun'\\nRETURN a.name\"}, {'context': [{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]}]\n",
"Final answer: Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.\n"
]
}
],
"source": [
"result = chain(\"Who played in Top Gun?\")\n",
"print(f\"Intermediate steps: {result['intermediate_steps']}\")\n",
"print(f\"Final answer: {result['result']}\")"
]
},
{
"cell_type": "markdown",
"id": "d6e1b054",
"metadata": {},
"source": [
"## Return direct results\n",
"You can return direct results from the Cypher QA Chain using the `return_direct` parameter"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2d3acf10",
"metadata": {},
"outputs": [],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b0a9d143",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
"RETURN a.name\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Who played in Top Gun?',\n",
" 'result': [{'name': 'Tom Cruise'},\n",
" {'name': 'Val Kilmer'},\n",
" {'name': 'Anthony Edwards'},\n",
" {'name': 'Meg Ryan'}]}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Who played in Top Gun?\")"
]
},
{
"cell_type": "markdown",
"id": "f01dfb72-24ec-4ae7-883a-ee6646889b59",
"metadata": {},
"source": [
"## Add examples in the Cypher generation prompt\n",
"You can define the Cypher statement you want the LLM to generate for particular questions"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "59baeb88-adfa-4c26-8334-fcbff3a98efb",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts.prompt import PromptTemplate\n",
"\n",
"CYPHER_GENERATION_TEMPLATE = \"\"\"Task:Generate Cypher statement to query a graph database.\n",
"Instructions:\n",
"Use only the provided relationship types and properties in the schema.\n",
"Do not use any other relationship types or properties that are not provided.\n",
"Schema:\n",
"{schema}\n",
"Note: Do not include any explanations or apologies in your responses.\n",
"Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.\n",
"Do not include any text except the generated Cypher statement.\n",
"Examples: Here are a few examples of generated Cypher statements for particular questions:\n",
"# How many people played in Top Gun?\n",
"MATCH (m:Movie {{title:\"Top Gun\"}})<-[:ACTED_IN]-()\n",
"RETURN count(*) AS numberOfActors\n",
"\n",
"The question is:\n",
"{question}\"\"\"\n",
"\n",
"CYPHER_GENERATION_PROMPT = PromptTemplate(\n",
" input_variables=[\"schema\", \"question\"], template=CYPHER_GENERATION_TEMPLATE\n",
")\n",
"\n",
"chain = GraphCypherQAChain.from_llm(\n",
" ChatOpenAI(temperature=0),\n",
" graph=graph,\n",
" verbose=True,\n",
" cypher_prompt=CYPHER_GENERATION_PROMPT,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "47c64027-cf42-493a-9c76-2d10ba753728",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (:Movie {name:\"Top Gun\"})<-[:ACTED_IN]-(:Actor)\n",
"RETURN count(*) AS numberOfActors\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'numberofactors': 4}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'How many people played in Top Gun?',\n",
" 'result': \"I don't know the answer.\"}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"How many people played in Top Gun?\")"
]
},
{
"cell_type": "markdown",
"id": "3e721cad-aa87-4526-9231-2dfc0e365939",
"metadata": {},
"source": [
"## Use separate LLMs for Cypher and answer generation\n",
"You can use the `cypher_llm` and `qa_llm` parameters to define different llms"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6f9becc2-f579-45bf-9b50-2ce02bde92da",
"metadata": {},
"outputs": [],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" graph=graph,\n",
" cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
" qa_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
" verbose=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ff18e3e3-3402-4683-aec4-a19898f23ca1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
"WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Who played in Top Gun?',\n",
" 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Who played in Top Gun?\")"
]
},
{
"cell_type": "markdown",
"id": "eefea16b-508f-4552-8942-9d5063ed7d37",
"metadata": {},
"source": [
"## Ignore specified node and relationship types\n",
"\n",
"You can use `include_types` or `exclude_types` to ignore parts of the graph schema when generating Cypher statements."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a20fa21e-fb85-41c4-aac0-53fb25e34604",
"metadata": {},
"outputs": [],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" graph=graph,\n",
" cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
" qa_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
" verbose=True,\n",
" exclude_types=[\"Movie\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3ad7f6b8-543e-46e4-a3b2-40fa3e66e895",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Node properties are the following:\n",
"Actor {name: STRING},LabelA {property_a: STRING},LabelB {},LabelC {}\n",
"Relationship properties are the following:\n",
"ACTED_IN {},REL_TYPE {rel_prop: STRING}\n",
"The relationships are the following:\n",
"(:LabelA)-[:REL_TYPE]->(:LabelB),(:LabelA)-[:REL_TYPE]->(:LabelC)\n"
]
}
],
"source": [
"# Inspect graph schema\n",
"print(chain.graph_schema)"
]
},
{
"cell_type": "markdown",
"id": "f0202e88-d700-40ed-aef9-0c969c7bf951",
"metadata": {},
"source": [
"## Validate generated Cypher statements\n",
"You can use the `validate_cypher` parameter to validate and correct relationship directions in generated Cypher statements"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "53665d03-7afd-433c-bdd5-750127bfb152",
"metadata": {},
"outputs": [],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
" graph=graph,\n",
" verbose=True,\n",
" validate_cypher=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "19e1a591-9c10-4d7b-aa36-a5e1b778a97b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
"WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'name': 'Tom Cruise'}, {'name': 'Val Kilmer'}, {'name': 'Anthony Edwards'}, {'name': 'Meg Ryan'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Who played in Top Gun?',\n",
" 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, Meg Ryan played in Top Gun.'}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Who played in Top Gun?\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

749
libs/community/langchain_community/graphs/age_graph.py Normal file
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from __future__ import annotations
import json
import re
from hashlib import md5
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Tuple, Union
from langchain_community.graphs.graph_document import GraphDocument
from langchain_community.graphs.graph_store import GraphStore
if TYPE_CHECKING:
import psycopg2.extras
class AGEQueryException(Exception):
"""Exception for the AGE queries."""
def __init__(self, exception: Union[str, Dict]) -> None:
if isinstance(exception, dict):
self.message = exception["message"] if "message" in exception else "unknown"
self.details = exception["details"] if "details" in exception else "unknown"
else:
self.message = exception
self.details = "unknown"
def get_message(self) -> str:
return self.message
def get_details(self) -> Any:
return self.details
class AGEGraph(GraphStore):
"""
Apache AGE wrapper for graph operations.
Args:
graph_name (str): the name of the graph to connect to or create
conf (Dict[str, Any]): the pgsql connection config passed directly
to psycopg2.connect
create (bool): if True and graph doesn't exist, attempt to create it
*Security note*: Make sure that the database connection uses credentials
that are narrowly-scoped to only include necessary permissions.
Failure to do so may result in data corruption or loss, since the calling
code may attempt commands that would result in deletion, mutation
of data if appropriately prompted or reading sensitive data if such
data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
"""
# python type mapping for providing readable types to LLM
types = {
"str": "STRING",
"float": "DOUBLE",
"int": "INTEGER",
"list": "LIST",
"dict": "MAP",
"bool": "BOOLEAN",
}
# precompiled regex for checking chars in graph labels
label_regex = re.compile("[^0-9a-zA-Z]+")
def __init__(
self, graph_name: str, conf: Dict[str, Any], create: bool = True
) -> None:
"""Create a new AGEGraph instance."""
self.graph_name = graph_name
# check that psycopg2 is installed
try:
import psycopg2
except ImportError:
raise ValueError(
"Could not import psycopg2 python package. "
"Please install it with `pip install psycopg2`."
)
self.connection = psycopg2.connect(**conf)
with self._get_cursor() as curs:
# check if graph with name graph_name exists
graph_id_query = (
"""SELECT graphid FROM ag_catalog.ag_graph WHERE name = '{}'""".format(
graph_name
)
)
curs.execute(graph_id_query)
data = curs.fetchone()
# if graph doesn't exist and create is True, create it
if data is None:
if create:
create_statement = """
SELECT ag_catalog.create_graph('{}');
""".format(graph_name)
try:
curs.execute(create_statement)
self.connection.commit()
except psycopg2.Error as e:
raise AGEQueryException(
{
"message": "Could not create the graph",
"detail": str(e),
}
)
else:
raise Exception(
(
'Graph "{}" does not exist in the database '
+ 'and "create" is set to False'
).format(graph_name)
)
curs.execute(graph_id_query)
data = curs.fetchone()
# store graph id and refresh the schema
self.graphid = data.graphid
self.refresh_schema()
def _get_cursor(self) -> psycopg2.extras.NamedTupleCursor:
"""
get cursor, load age extension and set search path
"""
try:
import psycopg2.extras
except ImportError as e:
raise ImportError(
"Unable to import psycopg2, please install with "
"`pip install -U psycopg2`."
) from e
cursor = self.connection.cursor(cursor_factory=psycopg2.extras.NamedTupleCursor)
cursor.execute("""LOAD 'age';""")
cursor.execute("""SET search_path = ag_catalog, "$user", public;""")
return cursor
def _get_labels(self) -> Tuple[List[str], List[str]]:
"""
Get all labels of a graph (for both edges and vertices)
by querying the graph metadata table directly
Returns
Tuple[List[str]]: 2 lists, the first containing vertex
labels and the second containing edge labels
"""
e_labels_records = self.query(
"""MATCH ()-[e]-() RETURN collect(distinct label(e)) as labels"""
)
e_labels = e_labels_records[0]["labels"] if e_labels_records else []
n_labels_records = self.query(
"""MATCH (n) RETURN collect(distinct label(n)) as labels"""
)
n_labels = n_labels_records[0]["labels"] if n_labels_records else []
return n_labels, e_labels
def _get_triples(self, e_labels: List[str]) -> List[Dict[str, str]]:
"""
Get a set of distinct relationship types (as a list of dicts) in the graph
to be used as context by an llm.
Args:
e_labels (List[str]): a list of edge labels to filter for
Returns:
List[Dict[str, str]]: relationships as a list of dicts in the format
"{'start':<from_label>, 'type':<edge_label>, 'end':<from_label>}"
"""
# age query to get distinct relationship types
try:
import psycopg2
except ImportError as e:
raise ImportError(
"Unable to import psycopg2, please install with "
"`pip install -U psycopg2`."
) from e
triple_query = """
SELECT * FROM ag_catalog.cypher('{graph_name}', $$
MATCH (a)-[e:`{e_label}`]->(b)
WITH a,e,b LIMIT 3000
RETURN DISTINCT labels(a) AS from, type(e) AS edge, labels(b) AS to
LIMIT 10
$$) AS (f agtype, edge agtype, t agtype);
"""
triple_schema = []
# iterate desired edge types and add distinct relationship types to result
with self._get_cursor() as curs:
for label in e_labels:
q = triple_query.format(graph_name=self.graph_name, e_label=label)
try:
curs.execute(q)
data = curs.fetchall()
for d in data:
# use json.loads to convert returned
# strings to python primitives
triple_schema.append(
{
"start": json.loads(d.f)[0],
"type": json.loads(d.edge),
"end": json.loads(d.t)[0],
}
)
except psycopg2.Error as e:
raise AGEQueryException(
{
"message": "Error fetching triples",
"detail": str(e),
}
)
return triple_schema
def _get_triples_str(self, e_labels: List[str]) -> List[str]:
"""
Get a set of distinct relationship types (as a list of strings) in the graph
to be used as context by an llm.
Args:
e_labels (List[str]): a list of edge labels to filter for
Returns:
List[str]: relationships as a list of strings in the format
"(:`<from_label>`)-[:`<edge_label>`]->(:`<to_label>`)"
"""
triples = self._get_triples(e_labels)
return self._format_triples(triples)
@staticmethod
def _format_triples(triples: List[Dict[str, str]]) -> List[str]:
"""
Convert a list of relationships from dictionaries to formatted strings
to be better readable by an llm
Args:
triples (List[Dict[str,str]]): a list relationships in the form
{'start':<from_label>, 'type':<edge_label>, 'end':<from_label>}
Returns:
List[str]: a list of relationships in the form
"(:`<from_label>`)-[:`<edge_label>`]->(:`<to_label>`)"
"""
triple_template = "(:`{start}`)-[:`{type}`]->(:`{end}`)"
triple_schema = [triple_template.format(**triple) for triple in triples]
return triple_schema
def _get_node_properties(self, n_labels: List[str]) -> List[Dict[str, Any]]:
"""
Fetch a list of available node properties by node label to be used
as context for an llm
Args:
n_labels (List[str]): a list of node labels to filter for
Returns:
List[Dict[str, Any]]: a list of node labels and
their corresponding properties in the form
"{
'labels': <node_label>,
'properties': [
{
'property': <property_name>,
'type': <property_type>
},...
]
}"
"""
try:
import psycopg2
except ImportError as e:
raise ImportError(
"Unable to import psycopg2, please install with "
"`pip install -U psycopg2`."
) from e
# cypher query to fetch properties of a given label
node_properties_query = """
SELECT * FROM ag_catalog.cypher('{graph_name}', $$
MATCH (a:`{n_label}`)
RETURN properties(a) AS props
LIMIT 100
$$) AS (props agtype);
"""
node_properties = []
with self._get_cursor() as curs:
for label in n_labels:
q = node_properties_query.format(
graph_name=self.graph_name, n_label=label
)
try:
curs.execute(q)
except psycopg2.Error as e:
raise AGEQueryException(
{
"message": "Error fetching node properties",
"detail": str(e),
}
)
data = curs.fetchall()
# build a set of distinct properties
s = set({})
for d in data:
# use json.loads to convert to python
# primitive and get readable type
for k, v in json.loads(d.props).items():
s.add((k, self.types[type(v).__name__]))
np = {
"properties": [{"property": k, "type": v} for k, v in s],
"labels": label,
}
node_properties.append(np)
return node_properties
def _get_edge_properties(self, e_labels: List[str]) -> List[Dict[str, Any]]:
"""
Fetch a list of available edge properties by edge label to be used
as context for an llm
Args:
e_labels (List[str]): a list of edge labels to filter for
Returns:
List[Dict[str, Any]]: a list of edge labels
and their corresponding properties in the form
"{
'labels': <edge_label>,
'properties': [
{
'property': <property_name>,
'type': <property_type>
},...
]
}"
"""
try:
import psycopg2
except ImportError as e:
raise ImportError(
"Unable to import psycopg2, please install with "
"`pip install -U psycopg2`."
) from e
# cypher query to fetch properties of a given label
edge_properties_query = """
SELECT * FROM ag_catalog.cypher('{graph_name}', $$
MATCH ()-[e:`{e_label}`]->()
RETURN properties(e) AS props
LIMIT 100
$$) AS (props agtype);
"""
edge_properties = []
with self._get_cursor() as curs:
for label in e_labels:
q = edge_properties_query.format(
graph_name=self.graph_name, e_label=label
)
try:
curs.execute(q)
except psycopg2.Error as e:
raise AGEQueryException(
{
"message": "Error fetching edge properties",
"detail": str(e),
}
)
data = curs.fetchall()
# build a set of distinct properties
s = set({})
for d in data:
# use json.loads to convert to python
# primitive and get readable type
for k, v in json.loads(d.props).items():
s.add((k, self.types[type(v).__name__]))
np = {
"properties": [{"property": k, "type": v} for k, v in s],
"type": label,
}
edge_properties.append(np)
return edge_properties
def refresh_schema(self) -> None:
"""
Refresh the graph schema information by updating the available
labels, relationships, and properties
"""
# fetch graph schema information
n_labels, e_labels = self._get_labels()
triple_schema = self._get_triples(e_labels)
node_properties = self._get_node_properties(n_labels)
edge_properties = self._get_edge_properties(e_labels)
# update the formatted string representation
self.schema = f"""
Node properties are the following:
{node_properties}
Relationship properties are the following:
{edge_properties}
The relationships are the following:
{self._format_triples(triple_schema)}
"""
# update the dictionary representation
self.structured_schema = {
"node_props": {el["labels"]: el["properties"] for el in node_properties},
"rel_props": {el["type"]: el["properties"] for el in edge_properties},
"relationships": triple_schema,
"metadata": {},
}
@property
def get_schema(self) -> str:
"""Returns the schema of the Graph"""
return self.schema
@property
def get_structured_schema(self) -> Dict[str, Any]:
"""Returns the structured schema of the Graph"""
return self.structured_schema
@staticmethod
def _get_col_name(field: str, idx: int) -> str:
"""
Convert a cypher return field to a pgsql select field
If possible keep the cypher column name, but create a generic name if necessary
Args:
field (str): a return field from a cypher query to be formatted for pgsql
idx (int): the position of the field in the return statement
Returns:
str: the field to be used in the pgsql select statement
"""
# remove white space
field = field.strip()
# if an alias is provided for the field, use it
if " as " in field:
return field.split(" as ")[-1].strip()
# if the return value is an unnamed primitive, give it a generic name
elif field.isnumeric() or field in ("true", "false", "null"):
return f"column_{idx}"
# otherwise return the value stripping out some common special chars
else:
return field.replace("(", "_").replace(")", "")
@staticmethod
def _wrap_query(query: str, graph_name: str) -> str:
"""
Convert a cypher query to an Apache Age compatible
sql query by wrapping the cypher query in ag_catalog.cypher,
casting results to agtype and building a select statement
Args:
query (str): a valid cypher query
graph_name (str): the name of the graph to query
Returns:
str: an equivalent pgsql query
"""
# pgsql template
template = """SELECT {projection} FROM ag_catalog.cypher('{graph_name}', $$
{query}
$$) AS ({fields});"""
# if there are any returned fields they must be added to the pgsql query
if "return" in query.lower():
# parse return statement to identify returned fields
fields = (
query.lower()
.split("return")[-1]
.split("distinct")[-1]
.split("order by")[0]
.split("skip")[0]
.split("limit")[0]
.split(",")
)
# raise exception if RETURN * is found as we can't resolve the fields
if "*" in [x.strip() for x in fields]:
raise ValueError(
"AGE graph does not support 'RETURN *'"
+ " statements in Cypher queries"
)
# get pgsql formatted field names
fields = [
AGEGraph._get_col_name(field, idx) for idx, field in enumerate(fields)
]
# build resulting pgsql relation
fields_str = ", ".join(
[field.split(".")[-1] + " agtype" for field in fields]
)
# if no return statement we still need to return a single field of type agtype
else:
fields_str = "a agtype"
select_str = "*"
return template.format(
graph_name=graph_name,
query=query,
fields=fields_str,
projection=select_str,
)
@staticmethod
def _record_to_dict(record: NamedTuple) -> Dict[str, Any]:
"""
Convert a record returned from an age query to a dictionary
Args:
record (): a record from an age query result
Returns:
Dict[str, Any]: a dictionary representation of the record where
the dictionary key is the field name and the value is the
value converted to a python type
"""
# result holder
d = {}
# prebuild a mapping of vertex_id to vertex mappings to be used
# later to build edges
vertices = {}
for k in record._fields:
v = getattr(record, k)
# agtype comes back '{key: value}::type' which must be parsed
if isinstance(v, str) and "::" in v:
dtype = v.split("::")[-1]
v = v.split("::")[0]
if dtype == "vertex":
vertex = json.loads(v)
vertices[vertex["id"]] = vertex.get("properties")
# iterate returned fields and parse appropriately
for k in record._fields:
v = getattr(record, k)
if isinstance(v, str) and "::" in v:
dtype = v.split("::")[-1]
v = v.split("::")[0]
else:
dtype = ""
if dtype == "vertex":
d[k] = json.loads(v).get("properties")
# convert edge from id-label->id by replacing id with node information
# we only do this if the vertex was also returned in the query
# this is an attempt to be consistent with neo4j implementation
elif dtype == "edge":
edge = json.loads(v)
d[k] = (
vertices.get(edge["start_id"], {}),
edge["label"],
vertices.get(edge["end_id"], {}),
)
else:
d[k] = json.loads(v) if isinstance(v, str) else v
return d
def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]:
"""
Query the graph by taking a cypher query, converting it to an
age compatible query, executing it and converting the result
Args:
query (str): a cypher query to be executed
params (dict): parameters for the query (not used in this implementation)
Returns:
List[Dict[str, Any]]: a list of dictionaries containing the result set
"""
try:
import psycopg2
except ImportError as e:
raise ImportError(
"Unable to import psycopg2, please install with "
"`pip install -U psycopg2`."
) from e
# convert cypher query to pgsql/age query
wrapped_query = self._wrap_query(query, self.graph_name)
# execute the query, rolling back on an error
with self._get_cursor() as curs:
try:
curs.execute(wrapped_query)
self.connection.commit()
except psycopg2.Error as e:
self.connection.rollback()
raise AGEQueryException(
{
"message": "Error executing graph query: {}".format(query),
"detail": str(e),
}
)
data = curs.fetchall()
if data is None:
result = []
# convert to dictionaries
else:
result = [self._record_to_dict(d) for d in data]
return result
@staticmethod
def _format_properties(
properties: Dict[str, Any], id: Union[str, None] = None
) -> str:
"""
Convert a dictionary of properties to a string representation that
can be used in a cypher query insert/merge statement.
Args:
properties (Dict[str,str]): a dictionary containing node/edge properties
id (Union[str, None]): the id of the node or None if none exists
Returns:
str: the properties dictionary as a properly formatted string
"""
props = []
# wrap property key in backticks to escape
for k, v in properties.items():
prop = f"`{k}`: {json.dumps(v)}"
props.append(prop)
if id is not None and "id" not in properties:
props.append(
f"id: {json.dumps(id)}" if isinstance(id, str) else f"id: {id}"
)
return "{" + ", ".join(props) + "}"
@staticmethod
def clean_graph_labels(label: str) -> str:
"""
remove any disallowed characters from a label and replace with '_'
Args:
label (str): the original label
Returns:
str: the sanitized version of the label
"""
return re.sub(AGEGraph.label_regex, "_", label)
def add_graph_documents(
self, graph_documents: List[GraphDocument], include_source: bool = False
) -> None:
"""
insert a list of graph documents into the graph
Args:
graph_documents (List[GraphDocument]): the list of documents to be inserted
include_source (bool): if True add nodes for the sources
with MENTIONS edges to the entities they mention
Returns:
None
"""
# query for inserting nodes
node_insert_query = (
"""
MERGE (n:`{label}` {properties})
"""
if not include_source
else """
MERGE (n:`{label}` {properties})
MERGE (d:Document {d_properties})
MERGE (d)-[:MENTIONS]->(n)
"""
)
# query for inserting edges
edge_insert_query = """
MERGE (from:`{f_label}` {f_properties})
MERGE (to:`{t_label}` {t_properties})
MERGE (from)-[:`{r_label}` {r_properties}]->(to)
"""
# iterate docs and insert them
for doc in graph_documents:
# if we are adding sources, create an id for the source
if include_source:
if not doc.source.metadata.get("id"):
doc.source.metadata["id"] = md5(
doc.source.page_content.encode("utf-8")
).hexdigest()
# insert entity nodes
for node in doc.nodes:
node.properties["id"] = node.id
if include_source:
query = node_insert_query.format(
label=node.type,
properties=self._format_properties(node.properties),
d_properties=self._format_properties(doc.source.metadata),
)
else:
query = node_insert_query.format(
label=AGEGraph.clean_graph_labels(node.type),
properties=self._format_properties(node.properties),
)
self.query(query)
# insert relationships
for edge in doc.relationships:
edge.source.properties["id"] = edge.source.id
edge.target.properties["id"] = edge.target.id
inputs = {
"f_label": AGEGraph.clean_graph_labels(edge.source.type),
"f_properties": self._format_properties(edge.source.properties),
"t_label": AGEGraph.clean_graph_labels(edge.target.type),
"t_properties": self._format_properties(edge.target.properties),
"r_label": AGEGraph.clean_graph_labels(edge.type).upper(),
"r_properties": self._format_properties(edge.properties),
}
query = edge_insert_query.format(**inputs)
self.query(query)

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libs/community/tests/integration_tests/graphs/test_age_graph.py Normal file
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@ -0,0 +1,337 @@
import os
import re
import unittest
from typing import Any, Dict
from langchain_core.documents import Document
from langchain_community.graphs.age_graph import AGEGraph
from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
test_data = [
GraphDocument(
nodes=[Node(id="foo", type="foo"), Node(id="bar", type="bar")],
relationships=[
Relationship(
source=Node(id="foo", type="foo"),
target=Node(id="bar", type="bar"),
type="REL",
)
],
source=Document(page_content="source document"),
)
]
class TestAGEGraph(unittest.TestCase):
def test_node_properties(self) -> None:
conf = {
"database": os.getenv("AGE_PGSQL_DB"),
"user": os.getenv("AGE_PGSQL_USER"),
"password": os.getenv("AGE_PGSQL_PASSWORD"),
"host": os.getenv("AGE_PGSQL_HOST", "localhost"),
"port": int(os.getenv("AGE_PGSQL_PORT", 5432)),
}
self.assertIsNotNone(conf["database"])
self.assertIsNotNone(conf["user"])
self.assertIsNotNone(conf["password"])
graph_name = os.getenv("AGE_GRAPH_NAME", "age_test")
graph = AGEGraph(graph_name, conf)
graph.query("MATCH (n) DETACH DELETE n")
# Create two nodes and a relationship
graph.query(
"""
CREATE (la:LabelA {property_a: 'a'})
CREATE (lb:LabelB)
CREATE (lc:LabelC)
MERGE (la)-[:REL_TYPE]-> (lb)
MERGE (la)-[:REL_TYPE {rel_prop: 'abc'}]-> (lc)
"""
)
# Refresh schema information
# graph.refresh_schema()
n_labels, e_labels = graph._get_labels()
node_properties = graph._get_node_properties(n_labels)
expected_node_properties = [
{
"properties": [{"property": "property_a", "type": "STRING"}],
"labels": "LabelA",
},
{
"properties": [],
"labels": "LabelB",
},
{
"properties": [],
"labels": "LabelC",
},
]
self.assertEqual(
sorted(node_properties, key=lambda x: x["labels"]), expected_node_properties
)
def test_edge_properties(self) -> None:
conf = {
"database": os.getenv("AGE_PGSQL_DB"),
"user": os.getenv("AGE_PGSQL_USER"),
"password": os.getenv("AGE_PGSQL_PASSWORD"),
"host": os.getenv("AGE_PGSQL_HOST", "localhost"),
"port": int(os.getenv("AGE_PGSQL_PORT", 5432)),
}
self.assertIsNotNone(conf["database"])
self.assertIsNotNone(conf["user"])
self.assertIsNotNone(conf["password"])
graph_name = os.getenv("AGE_GRAPH_NAME", "age_test")
graph = AGEGraph(graph_name, conf)
graph.query("MATCH (n) DETACH DELETE n")
# Create two nodes and a relationship
graph.query(
"""
CREATE (la:LabelA {property_a: 'a'})
CREATE (lb:LabelB)
CREATE (lc:LabelC)
MERGE (la)-[:REL_TYPE]-> (lb)
MERGE (la)-[:REL_TYPE {rel_prop: 'abc'}]-> (lc)
"""
)
# Refresh schema information
# graph.refresh_schema()
n_labels, e_labels = graph._get_labels()
relationships_properties = graph._get_edge_properties(e_labels)
expected_relationships_properties = [
{
"type": "REL_TYPE",
"properties": [{"property": "rel_prop", "type": "STRING"}],
}
]
self.assertEqual(relationships_properties, expected_relationships_properties)
def test_relationships(self) -> None:
conf = {
"database": os.getenv("AGE_PGSQL_DB"),
"user": os.getenv("AGE_PGSQL_USER"),
"password": os.getenv("AGE_PGSQL_PASSWORD"),
"host": os.getenv("AGE_PGSQL_HOST", "localhost"),
"port": int(os.getenv("AGE_PGSQL_PORT", 5432)),
}
self.assertIsNotNone(conf["database"])
self.assertIsNotNone(conf["user"])
self.assertIsNotNone(conf["password"])
graph_name = os.getenv("AGE_GRAPH_NAME", "age_test")
graph = AGEGraph(graph_name, conf)
graph.query("MATCH (n) DETACH DELETE n")
# Create two nodes and a relationship
graph.query(
"""
CREATE (la:LabelA {property_a: 'a'})
CREATE (lb:LabelB)
CREATE (lc:LabelC)
MERGE (la)-[:REL_TYPE]-> (lb)
MERGE (la)-[:REL_TYPE {rel_prop: 'abc'}]-> (lc)
"""
)
# Refresh schema information
# graph.refresh_schema()
n_labels, e_labels = graph._get_labels()
relationships = graph._get_triples(e_labels)
expected_relationships = [
{"start": "LabelA", "type": "REL_TYPE", "end": "LabelB"},
{"start": "LabelA", "type": "REL_TYPE", "end": "LabelC"},
]
self.assertEqual(
sorted(relationships, key=lambda x: x["end"]), expected_relationships
)
def test_add_documents(self) -> None:
conf = {
"database": os.getenv("AGE_PGSQL_DB"),
"user": os.getenv("AGE_PGSQL_USER"),
"password": os.getenv("AGE_PGSQL_PASSWORD"),
"host": os.getenv("AGE_PGSQL_HOST", "localhost"),
"port": int(os.getenv("AGE_PGSQL_PORT", 5432)),
}
self.assertIsNotNone(conf["database"])
self.assertIsNotNone(conf["user"])
self.assertIsNotNone(conf["password"])
graph_name = os.getenv("AGE_GRAPH_NAME", "age_test")
graph = AGEGraph(graph_name, conf)
# Delete all nodes in the graph
graph.query("MATCH (n) DETACH DELETE n")
# Create two nodes and a relationship
graph.add_graph_documents(test_data)
output = graph.query(
"MATCH (n) RETURN labels(n) AS label, count(*) AS count ORDER BY labels(n)"
)
self.assertEqual(
output, [{"label": ["bar"], "count": 1}, {"label": ["foo"], "count": 1}]
)
def test_add_documents_source(self) -> None:
conf = {
"database": os.getenv("AGE_PGSQL_DB"),
"user": os.getenv("AGE_PGSQL_USER"),
"password": os.getenv("AGE_PGSQL_PASSWORD"),
"host": os.getenv("AGE_PGSQL_HOST", "localhost"),
"port": int(os.getenv("AGE_PGSQL_PORT", 5432)),
}
self.assertIsNotNone(conf["database"])
self.assertIsNotNone(conf["user"])
self.assertIsNotNone(conf["password"])
graph_name = os.getenv("AGE_GRAPH_NAME", "age_test")
graph = AGEGraph(graph_name, conf)
# Delete all nodes in the graph
graph.query("MATCH (n) DETACH DELETE n")
# Create two nodes and a relationship
graph.add_graph_documents(test_data, include_source=True)
output = graph.query(
"MATCH (n) RETURN labels(n) AS label, count(*) AS count ORDER BY labels(n)"
)
expected = [
{"label": ["bar"], "count": 1},
{"label": ["Document"], "count": 1},
{"label": ["foo"], "count": 1},
]
self.assertEqual(output, expected)
def test_get_schema(self) -> None:
conf = {
"database": os.getenv("AGE_PGSQL_DB"),
"user": os.getenv("AGE_PGSQL_USER"),
"password": os.getenv("AGE_PGSQL_PASSWORD"),
"host": os.getenv("AGE_PGSQL_HOST", "localhost"),
"port": int(os.getenv("AGE_PGSQL_PORT", 5432)),
}
self.assertIsNotNone(conf["database"])
self.assertIsNotNone(conf["user"])
self.assertIsNotNone(conf["password"])
graph_name = os.getenv("AGE_GRAPH_NAME", "age_test")
graph = AGEGraph(graph_name, conf)
graph.query("MATCH (n) DETACH DELETE n")
graph.refresh_schema()
expected = """
Node properties are the following:
[]
Relationship properties are the following:
[]
The relationships are the following:
[]
"""
# check that works on empty schema
self.assertEqual(
re.sub(r"\s", "", graph.get_schema), re.sub(r"\s", "", expected)
)
expected_structured: Dict[str, Any] = {
"node_props": {},
"rel_props": {},
"relationships": [],
"metadata": {},
}
self.assertEqual(graph.get_structured_schema, expected_structured)
# Create two nodes and a relationship
graph.query(
"""
MERGE (a:a {id: 1})-[b:b {id: 2}]-> (c:c {id: 3})
"""
)
# check that schema doesn't update without refresh
self.assertEqual(
re.sub(r"\s", "", graph.get_schema), re.sub(r"\s", "", expected)
)
self.assertEqual(graph.get_structured_schema, expected_structured)
# two possible orderings of node props
expected_possibilities = [
"""
Node properties are the following:
[
{'properties': [{'property': 'id', 'type': 'INTEGER'}], 'labels': 'a'},
{'properties': [{'property': 'id', 'type': 'INTEGER'}], 'labels': 'c'}
]
Relationship properties are the following:
[
{'properties': [{'property': 'id', 'type': 'INTEGER'}], 'type': 'b'}
]
The relationships are the following:
[
'(:`a`)-[:`b`]->(:`c`)'
]
""",
"""
Node properties are the following:
[
{'properties': [{'property': 'id', 'type': 'INTEGER'}], 'labels': 'c'},
{'properties': [{'property': 'id', 'type': 'INTEGER'}], 'labels': 'a'}
]
Relationship properties are the following:
[
{'properties': [{'property': 'id', 'type': 'INTEGER'}], 'type': 'b'}
]
The relationships are the following:
[
'(:`a`)-[:`b`]->(:`c`)'
]
""",
]
expected_structured2 = {
"node_props": {
"a": [{"property": "id", "type": "INTEGER"}],
"c": [{"property": "id", "type": "INTEGER"}],
},
"rel_props": {"b": [{"property": "id", "type": "INTEGER"}]},
"relationships": [{"start": "a", "type": "b", "end": "c"}],
"metadata": {},
}
graph.refresh_schema()
# check that schema is refreshed
self.assertIn(
re.sub(r"\s", "", graph.get_schema),
[re.sub(r"\s", "", x) for x in expected_possibilities],
)
self.assertEqual(graph.get_structured_schema, expected_structured2)

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libs/community/tests/unit_tests/graphs/test_age_graph.py Normal file
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import re
import unittest
from collections import namedtuple
from typing import Any, Dict, List
from langchain_community.graphs.age_graph import AGEGraph
class TestAGEGraph(unittest.TestCase):
def test_format_triples(self) -> None:
test_input = [
{"start": "from_a", "type": "edge_a", "end": "to_a"},
{"start": "from_b", "type": "edge_b", "end": "to_b"},
]
expected = [
"(:`from_a`)-[:`edge_a`]->(:`to_a`)",
"(:`from_b`)-[:`edge_b`]->(:`to_b`)",
]
self.assertEqual(AGEGraph._format_triples(test_input), expected)
def test_get_col_name(self) -> None:
inputs = [
("a", 1),
("a as b", 1),
(" c ", 1),
(" c as d ", 1),
("sum(a)", 1),
("sum(a) as b", 1),
("count(*)", 1),
("count(*) as cnt", 1),
("true", 1),
("false", 1),
("null", 1),
]
expected = [
"a",
"b",
"c",
"d",
"sum_a",
"b",
"count_*",
"cnt",
"column_1",
"column_1",
"column_1",
]
for idx, value in enumerate(inputs):
self.assertEqual(AGEGraph._get_col_name(*value), expected[idx])
def test_wrap_query(self) -> None:
inputs = [
"""
MATCH (keanu:Person {name:'Keanu Reeves'})
RETURN keanu.name AS name, keanu.born AS born
""",
"""
MERGE (n:a {id: 1})
""",
]
expected = [
"""
SELECT * FROM ag_catalog.cypher('test', $$
MATCH (keanu:Person {name:'Keanu Reeves'})
RETURN keanu.name AS name, keanu.born AS born
$$) AS (name agtype, born agtype);
""",
"""
SELECT * FROM ag_catalog.cypher('test', $$
MERGE (n:a {id: 1})
$$) AS (a agtype);
""",
]
for idx, value in enumerate(inputs):
self.assertEqual(
re.sub(r"\s", "", AGEGraph._wrap_query(value, "test")),
re.sub(r"\s", "", expected[idx]),
)
with self.assertRaises(ValueError):
AGEGraph._wrap_query(
"""
MATCH ()
RETURN *
""",
"test",
)
def test_format_properties(self) -> None:
inputs: List[Dict[str, Any]] = [{}, {"a": "b"}, {"a": "b", "c": 1, "d": True}]
expected = ["{}", '{`a`: "b"}', '{`a`: "b", `c`: 1, `d`: true}']
for idx, value in enumerate(inputs):
self.assertEqual(AGEGraph._format_properties(value), expected[idx])
def test_clean_graph_labels(self) -> None:
inputs = ["label", "label 1", "label#$"]
expected = ["label", "label_1", "label_"]
for idx, value in enumerate(inputs):
self.assertEqual(AGEGraph.clean_graph_labels(value), expected[idx])
def test_record_to_dict(self) -> None:
Record = namedtuple("Record", ["node1", "edge", "node2"])
r = Record(
node1='{"id": 1, "label": "label1", "properties":'
+ ' {"prop": "a"}}::vertex',
edge='{"id": 3, "label": "edge", "end_id": 2, '
+ '"start_id": 1, "properties": {"test": "abc"}}::edge',
node2='{"id": 2, "label": "label1", '
+ '"properties": {"prop": "b"}}::vertex',
)
result = AGEGraph._record_to_dict(r)
expected = {
"node1": {"prop": "a"},
"edge": ({"prop": "a"}, "edge", {"prop": "b"}),
"node2": {"prop": "b"},
}
self.assertEqual(result, expected)
Record2 = namedtuple("Record2", ["string", "int", "float", "bool", "null"])
r2 = Record2('"test"', "1", "1.5", "true", None)
result = AGEGraph._record_to_dict(r2)
expected2 = {
"string": "test",
"int": 1,
"float": 1.5,
"bool": True,
"null": None,
}
self.assertEqual(result, expected2)