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2026-05-12 19:40:31 +09:00
parent 0f34a451fc
commit 2e9204243d
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30
참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/pinecone/README.md vendored Normal file
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### Usage Instructions
You will need both a Pinecone vector database and a Neo4j database to use this retriever.
### Writing Test Data
Update `NEO4J_AUTH`, `NEO4J_URL`, and `PC_API_KEY` variables in the `tests/e2e/pinecone_e2e/populate_dbs.py` script then run this from the project root to write test data to both dbs.
```
uv run python -m tests/e2e/pinecone_e2e/populate_dbs.py
```
### Install Pinecone client
You need to install the `pinecone-client` package to use this retriever.
```bash
pip install pinecone-client
```
### Search
Update the `NEO4J_AUTH`, `NEO4J_URL`, and `PC_API_KEY` variables in each file then run one of the following from the project root to test the retriever.
```
# Search by vector
uv run python -m examples.customize.retrievers.external.pinecone.vector_search
# Search by text, with embeddings generated locally
uv run python -m examples.customize.retrievers.external.pinecone.text_search
```

40
참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/pinecone/pinecone_text_search.py vendored Normal file
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"""This example demonstrates how to use PineconeNeo4jRetriever, ie vectors are
stored in the Pinecone database.
See the [README](./README.md) for more
information about how spin up a Pinecone and Neo4j databases if needed.
In this example, search is performed from a text. Embeddings are computed
using OpenAI models. See [../../embeddings/](../../embeddings/) for examples
using other supported embedders.
"""
from neo4j import GraphDatabase
from neo4j_graphrag.embeddings import OpenAIEmbeddings
from neo4j_graphrag.retrievers import PineconeNeo4jRetriever
from pinecone import Pinecone
NEO4J_AUTH = ("neo4j", "password")
NEO4J_URL = "neo4j://localhost:7687"
PC_API_KEY = "API_KEY"
def main() -> None:
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
pc_client = Pinecone(PC_API_KEY)
embedder = OpenAIEmbeddings()
retriever = PineconeNeo4jRetriever(
driver=neo4j_driver,
client=pc_client,
index_name="jeopardy",
id_property_neo4j="id",
embedder=embedder,
)
res = retriever.search(query_text="biology", top_k=2)
print(res)
if __name__ == "__main__":
main()

39
참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/pinecone/pinecone_vector_search.py vendored Normal file
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"""This example demonstrates how to use PineconeNeo4jRetriever, ie vectors are
stored in the Pinecone database.
See the [README](./README.md) for more
information about how spin up a Pinecone and Neo4j databases if needed.
In this example, search is performed from an already computed vector.
"""
from neo4j import GraphDatabase
from neo4j_graphrag.embeddings.sentence_transformers import (
SentenceTransformerEmbeddings,
)
from neo4j_graphrag.retrievers import PineconeNeo4jRetriever
from pinecone import Pinecone
NEO4J_AUTH = ("neo4j", "password")
NEO4J_URL = "neo4j://localhost:7687"
PC_API_KEY = "API_KEY"
def main() -> None:
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
pc_client = Pinecone(PC_API_KEY)
embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
retriever = PineconeNeo4jRetriever(
driver=neo4j_driver,
client=pc_client,
index_name="jeopardy",
id_property_neo4j="id",
embedder=embedder,
)
res = retriever.search(query_text="biology", top_k=2)
print(res)
if __name__ == "__main__":
main()

31
참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/qdrant/README.md vendored Normal file
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### Start services locally
Run the following command to spin up Neo4j and Qdrant containers.
```bash
docker compose -f tests/e2e/docker-compose.yml up
```
### Write data (once)
Run this from the project root to write data to both Neo4J and Qdrant.
```bash
uv run python -m examples.customize.retrievers.external.qdrant.populate_dbs
```
### Install Qdrant client
```bash
pip install qdrant-client
```
### Search
```bash
# search by vector
uv run python -m examples.customize.retrievers.external.qdrant.vector_search
# search by text, with embeddings generated locally
uv run python -m examples.customize.retrievers.external.qdrant.text_search
```

16
참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/qdrant/docker-compose.yml vendored Normal file
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version: "3.9"
services:
neo4j:
image: neo4j:5.24-enterprise
ports:
- 7687:7687
- 7474:7474
environment:
NEO4J_AUTH: neo4j/password
NEO4J_ACCEPT_LICENSE_AGREEMENT: "eval"
NEO4J_PLUGINS: "[\"apoc\"]"
qdrant:
image: qdrant/qdrant
ports:
- 6333:6333

613
참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/qdrant/populate_dbs.py vendored Normal file
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# Copyright (c) "Neo4j"
# Neo4j Sweden AB [https://neo4j.com]
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# https://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import hashlib
import json
from typing import Any, Literal
import neo4j
from neo4j import GraphDatabase
from neo4j_graphrag.indexes import create_vector_index, drop_index_if_exists
try:
from qdrant_client import QdrantClient, models
except ImportError as e:
missing_module = str(e).split("'")[1]
if missing_module == "qdrant_client":
raise ImportError(
"The 'qdrant-client' package is missing. Please install it by running "
"`uv sync --extra qdrant` or `pip install qdrant-client`, or follow the instructions "
"in the Qdrant examples section of the README at https://github.com/neo4j/neo4j-graphrag-python/"
) from e
else:
raise
# biology
EMBEDDING_BIOLOGY = [
-0.04312172904610634,
0.027684900909662247,
-0.03451697900891304,
0.033050164580345154,
-0.02548302337527275,
-0.02422613650560379,
0.06078026071190834,
0.05728934705257416,
0.026537826284766197,
0.06596288084983826,
-0.011962010525166988,
-0.03995208814740181,
-0.08753148466348648,
0.048919592052698135,
-0.12121172994375229,
0.0046921405009925365,
-0.11684603989124298,
0.011236454360187054,
-0.08468407392501831,
0.00615630391985178,
-0.01894194260239601,
0.07591243833303452,
0.010966621339321136,
-0.0035629894118756056,
-0.07222436368465424,
-0.0038335833232849836,
-0.013435023836791515,
-0.01072753593325615,
-0.019481584429740906,
-0.08576139807701111,
-0.005984509363770485,
0.06511891633272171,
0.047047052532434464,
-0.008832206949591637,
-0.02516607940196991,
0.030757833272218704,
0.01017201878130436,
-0.035205237567424774,
0.0754496157169342,
0.049298327416181564,
-0.008700424805283546,
-0.042502958327531815,
-0.007827227003872395,
0.022037239745259285,
0.01526452787220478,
0.057564627379179,
0.01117363478988409,
-0.022880272939801216,
0.002632720861583948,
-0.06985171139240265,
-0.04440313205122948,
-0.02955677919089794,
-0.12585243582725525,
0.02834390103816986,
0.047075871378183365,
0.039754725992679596,
-0.062267716974020004,
-0.02022026665508747,
-0.02561432123184204,
-0.06196143478155136,
0.06283645331859589,
-0.010700947605073452,
-0.009793519042432308,
0.0676884651184082,
0.07930963486433029,
-0.05287032946944237,
0.005661565810441971,
0.04567752033472061,
0.01226893998682499,
0.0024454575031995773,
0.03628166392445564,
-0.01688201352953911,
-0.0009241311927326024,
0.10099577158689499,
0.0851878672838211,
-0.0056961155496537685,
-0.012224642559885979,
0.03202442824840546,
0.07562054693698883,
-0.030286865308880806,
0.031771108508110046,
-0.07060682028532028,
-0.01805405505001545,
0.03888492286205292,
0.013361049816012383,
-0.025815952569246292,
0.06777660548686981,
0.05500587448477745,
-0.09657344222068787,
0.06346127390861511,
-0.03499232605099678,
-0.06173454597592354,
0.03703204542398453,
-0.008920160122215748,
-0.1170671284198761,
0.05101491138339043,
0.02324659377336502,
-0.11062400788068771,
0.05613391101360321,
0.1953216791152954,
-0.046223606914281845,
0.0207725428044796,
-0.037569694221019745,
0.09747911989688873,
0.03182050585746765,
-0.09000813961029053,
-0.03741106390953064,
-0.001107109128497541,
0.05828772485256195,
0.03561848774552345,
-0.012867141515016556,
0.04318442568182945,
-0.007168568670749664,
0.10120371729135513,
0.015526260249316692,
0.046518243849277496,
0.09026871621608734,
-0.025434499606490135,
0.07185306400060654,
0.006204536650329828,
-0.025380434468388557,
-0.03805668652057648,
-0.046537771821022034,
-0.028066400438547134,
-0.04620446264743805,
-0.04175203666090965,
-0.05594577267765999,
-7.522815605061209e-33,
0.0032660849392414093,
-0.0987364873290062,
0.048021912574768066,
0.026744935661554337,
-0.04386494308710098,
0.033061958849430084,
-0.0033691301941871643,
-0.09897749125957489,
-0.0019302349537611008,
0.011837359517812729,
-0.05590499937534332,
-0.010936262086033821,
0.0011399721261113882,
0.043522097170352936,
0.024741550907492638,
0.030598435550928116,
-0.10856965184211731,
0.09524074196815491,
-0.011383255943655968,
0.0199135635048151,
-0.055631425231695175,
0.02365952730178833,
-0.02242668904364109,
-0.046286772936582565,
0.0020421564113348722,
-0.029465770348906517,
-0.0544230192899704,
-0.08019289374351501,
0.055107232183218,
-0.010339600965380669,
0.016182616353034973,
-0.019025344401597977,
-0.06670568138360977,
0.0005314883892424405,
0.01192407961934805,
-0.07481196522712708,
0.04253043606877327,
-0.05555248260498047,
-0.007614267058670521,
-0.012775871902704239,
-0.0022697255481034517,
-0.03448570892214775,
0.036854878067970276,
-0.04938317835330963,
0.08296490460634232,
0.03675423562526703,
-0.003109127515926957,
0.00267212837934494,
-0.06396905332803726,
0.019167274236679077,
-0.020589588209986687,
-0.017604926601052284,
0.10695572197437286,
-0.09441392868757248,
0.021013183519244194,
0.04480829834938049,
-0.027837105095386505,
-0.0011123925214633346,
-0.08835164457559586,
0.017125461250543594,
0.06728456914424896,
0.12567774951457977,
-0.06033247709274292,
0.03521595522761345,
0.07578440755605698,
-0.03819991648197174,
-0.09275069832801819,
-0.09814322739839554,
0.021795116364955902,
0.025850528851151466,
-0.0532839410007,
-0.054003648459911346,
-0.026674047112464905,
-0.10661919414997101,
0.004801975563168526,
0.016014324501156807,
0.0004556076892185956,
0.01613032817840576,
-0.09400207549333572,
0.012587584555149078,
0.01425306685268879,
-0.03173312172293663,
-0.04417256638407707,
-0.034732427448034286,
-0.03740209341049194,
0.07315755635499954,
0.022384824231266975,
-0.0658881664276123,
5.2405772294150665e-05,
0.019976867362856865,
-0.054833319038152695,
-0.04896378144621849,
-0.013568416237831116,
-0.011525219306349754,
0.000531611789483577,
3.594654745316847e-33,
0.006438549142330885,
-0.07441601902246475,
-0.04541584476828575,
0.04658213630318642,
0.05004657804965973,
0.06633730232715607,
-0.009470561519265175,
-0.020702792331576347,
0.003479442559182644,
0.017732439562678337,
0.0058183688670396805,
0.0337534099817276,
0.06103092059493065,
-0.010244826786220074,
-0.049173060804605484,
-0.039285846054553986,
-0.019474590197205544,
-0.005957481451332569,
-0.007637818343937397,
-0.001945863594301045,
-0.10105801373720169,
0.07999070733785629,
-0.002908281981945038,
-0.055638041347265244,
-0.013490298762917519,
0.07804659008979797,
-0.017962178215384483,
0.05879015102982521,
-0.02929818443953991,
0.010783063247799873,
-0.017044004052877426,
0.03597024083137512,
-0.010958723723888397,
-0.020666809752583504,
0.030345136299729347,
0.08541561663150787,
0.06714903563261032,
-0.004256050102412701,
0.05138356238603592,
-0.061394669115543365,
0.06958435475826263,
0.029510458931326866,
0.006212680600583553,
0.05935342609882355,
0.06217789649963379,
0.03869514539837837,
0.016871990635991096,
0.09106622636318207,
0.01593017764389515,
0.03309919685125351,
3.424335227464326e-05,
0.025418603792786598,
-0.0019248499302193522,
-0.04490964487195015,
0.05094093829393387,
-0.007762531749904156,
0.0022712810896337032,
-0.048982247710227966,
0.04316263645887375,
0.03385719656944275,
-0.02204400859773159,
-0.008660282008349895,
-0.0358533076941967,
0.11571227014064789,
-0.11273631453514099,
0.06849292665719986,
-0.03595784679055214,
0.04161537066102028,
-0.013967745006084442,
-0.004196549765765667,
0.04396875202655792,
0.07947150617837906,
-0.038232240825891495,
-0.004747415892779827,
-0.012230468913912773,
0.039619915187358856,
-0.11451079696416855,
0.0012758959783241153,
-0.007560120429843664,
0.015182306058704853,
-0.039508406072854996,
-0.06489536166191101,
-0.003909661900252104,
0.0037100922781974077,
0.010909723117947578,
-0.07211877405643463,
0.024324269965291023,
0.04425453022122383,
0.02042572572827339,
-0.07636857777833939,
-0.03965488821268082,
-0.04338192194700241,
-0.10347922146320343,
-0.04393737390637398,
-0.01368421409279108,
-1.568378671379378e-08,
0.10027588903903961,
-0.043567851185798645,
0.050843071192502975,
-0.08256983011960983,
0.02588729001581669,
0.056297384202480316,
-0.010702059604227543,
0.08518678694963455,
0.02700129710137844,
0.07154033333063126,
-0.05953506380319595,
0.0778793916106224,
0.036633629351854324,
0.08145243674516678,
0.07252412289381027,
-0.002973137190565467,
-0.013765356503427029,
-0.046995650976896286,
-0.0247399490326643,
-0.027804242447018623,
-0.025088943541049957,
-0.01294120866805315,
-0.03852837160229683,
0.11392410844564438,
0.030737342312932014,
-0.065057672560215,
0.06248151883482933,
-0.01869017630815506,
0.02368609979748726,
-0.0654701292514801,
0.058893367648124695,
0.06793640553951263,
0.008447104133665562,
0.015255128964781761,
-0.04546090587973595,
-0.04216332733631134,
0.02343529835343361,
-0.07064592838287354,
0.01851677894592285,
-0.006000120658427477,
-0.025140153244137764,
0.003474486293271184,
-0.01600506715476513,
0.0020763161592185497,
0.010104725137352943,
0.0021877381950616837,
0.07691455632448196,
0.008766746148467064,
-0.012105572037398815,
-0.047985345125198364,
-0.012077542021870613,
-0.02539162151515484,
0.01326005533337593,
-0.05451769009232521,
-0.08339792490005493,
0.052714310586452484,
-0.011956708505749702,
-0.01412263885140419,
-0.05299930274486542,
0.0125426622107625,
0.12013623863458633,
0.09160523116588593,
0.12442110478878021,
-0.03318800404667854,
]
def populate_neo4j(
neo4j_driver: neo4j.Driver,
neo4j_objs: dict[str, Any],
should_create_vector_index: bool = False,
) -> neo4j.EagerResult:
question_nodes = list(
filter(lambda x: x["label"] == "Question", neo4j_objs["nodes"])
)
answer_nodes = list(filter(lambda x: x["label"] == "Answer", neo4j_objs["nodes"]))
category_nodes = list(
filter(lambda x: x["label"] == "Category", neo4j_objs["nodes"])
)
belongs_to_relationships = list(
filter(lambda x: x["type"] == "BELONGS_TO", neo4j_objs["relationships"])
)
has_answer_relationships = list(
filter(lambda x: x["type"] == "HAS_ANSWER", neo4j_objs["relationships"])
)
question_nodes_cypher = "UNWIND $nodes as node MERGE (n:Question {id: node.properties.id}) ON CREATE SET n = node.properties"
answer_nodes_cypher = "UNWIND $nodes as node MERGE (n:Answer {id: node.properties.id}) ON CREATE SET n = node.properties"
category_nodes_cypher = (
"UNWIND $nodes as node MERGE (n:Category {id: node.id}) ON CREATE SET n = node"
)
belongs_to_relationships_cypher = "UNWIND $relationships as rel MATCH (q:Question {id: rel.start_node_id}), (c:Category {id: rel.end_node_id}) MERGE (q)-[r:BELONGS_TO]->(c)"
has_answer_relationships_cypher = "UNWIND $relationships as rel MATCH (q:Question {id: rel.start_node_id}), (a:Answer {id: rel.end_node_id}) MERGE (q)-[r:HAS_ANSWER]->(a)"
neo4j_driver.execute_query(question_nodes_cypher, {"nodes": question_nodes})
neo4j_driver.execute_query(answer_nodes_cypher, {"nodes": answer_nodes})
neo4j_driver.execute_query(category_nodes_cypher, {"nodes": category_nodes})
neo4j_driver.execute_query(
belongs_to_relationships_cypher, {"relationships": belongs_to_relationships}
)
res = neo4j_driver.execute_query(
has_answer_relationships_cypher, {"relationships": has_answer_relationships}
)
if should_create_vector_index:
vector_index_name = "vector-index-name"
drop_index_if_exists(neo4j_driver, vector_index_name)
# Create a vector index
create_vector_index(
neo4j_driver,
vector_index_name,
label="Question",
embedding_property="vector",
dimensions=384,
similarity_fn="cosine",
)
return res
def build_data_objects(
q_vector_fmt: Literal["neo4j", "qdrant"],
) -> tuple[dict[str, Any], list[Any]]:
# read file from disk
# this file is from https://github.com/weaviate-tutorials/quickstart/tree/main/data
# MIT License
file_name = "tests/e2e/data/jeopardy_tiny_with_vectors_all-MiniLM-L6-v2.json"
with open(file_name, "r") as f:
data = json.load(f)
question_objs: list[Any] = []
neo4j_objs: dict[str, list[Any]] = {"nodes": [], "relationships": []}
# only unique categories and IDs for them
unique_categories_list = list(set([c["Category"] for c in data]))
unique_categories = [
{"label": "Category", "name": c, "id": c} for c in unique_categories_list
]
neo4j_objs["nodes"] += unique_categories
for i, d in enumerate(data):
id_ = hashlib.md5(d["Question"].encode()).hexdigest()
question_properties = {
"id": f"question_{id_}",
"question": d["Question"],
}
if q_vector_fmt == "neo4j":
# Store the vector directly on the question node for Neo4j
question_properties["vector"] = d["vector"]
# Add the question node
neo4j_objs["nodes"].append(
{
"label": "Question",
"properties": question_properties,
}
)
# Add the answer node
neo4j_objs["nodes"].append(
{
"label": "Answer",
"properties": {
"id": f"answer_{id_}",
"answer": d["Answer"],
},
}
)
# Add relationships
neo4j_objs["relationships"].append(
{
"start_node_id": f"question_{id_}",
"end_node_id": f"answer_{id_}",
"type": "HAS_ANSWER",
"properties": {},
}
)
neo4j_objs["relationships"].append(
{
"start_node_id": f"question_{id_}",
"end_node_id": d["Category"],
"type": "BELONGS_TO",
"properties": {},
}
)
# If Qdrant, we build PointStruct objects
if q_vector_fmt == "qdrant":
question_objs.append(
models.PointStruct(
id=i,
payload={"neo4j_id": f"question_{id_}"},
vector=d["vector"],
)
)
elif q_vector_fmt == "neo4j":
# For Neo4j, the vector is already added to question_properties
pass
else:
raise ValueError("q_vector_fmt must be either 'neo4j' or 'qdrant'")
return neo4j_objs, question_objs
def populate_dbs(
neo4j_driver: neo4j.Driver,
qdrant_client: QdrantClient,
collection_name: str = "Jeopardy",
) -> None:
"""
Populates both Neo4j and Qdrant with the Jeopardy data set.
"""
neo4j_objects, question_objs = build_data_objects("qdrant")
# Recreate the Qdrant collection
if qdrant_client.collection_exists(collection_name):
qdrant_client.delete_collection(collection_name)
qdrant_client.create_collection(
collection_name=collection_name,
vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE),
)
_populate_qdrant(qdrant_client, question_objs, collection_name)
populate_neo4j(neo4j_driver, neo4j_objects)
def _populate_qdrant(
client: QdrantClient, question_objs: list[Any], collection_name: str
) -> None:
"""
Inserts (upserts) question objects into the specified Qdrant collection.
"""
client.upsert(
collection_name=collection_name,
points=question_objs,
)
def main() -> None:
"""
Entry point for running the database population script.
"""
NEO4J_URL = "neo4j://localhost:7687"
NEO4J_AUTH = ("neo4j", "password")
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
qdrant_client = QdrantClient(url="http://localhost:6333")
populate_dbs(neo4j_driver, qdrant_client, collection_name="Jeopardy")
if __name__ == "__main__":
main()

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from neo4j import GraphDatabase
from neo4j_graphrag.embeddings.sentence_transformers import (
SentenceTransformerEmbeddings,
)
from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
from qdrant_client import QdrantClient
NEO4J_URL = "neo4j://localhost:7687"
NEO4J_AUTH = ("neo4j", "password")
def main() -> None:
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
retriever = QdrantNeo4jRetriever(
driver=neo4j_driver,
client=QdrantClient(url="http://localhost:6333"),
collection_name="Jeopardy",
id_property_external="neo4j_id",
id_property_neo4j="id",
embedder=embedder,
)
res = retriever.search(query_text="biology", top_k=2)
print(res)
if __name__ == "__main__":
main()

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from embedding_biology import EMBEDDING_BIOLOGY
from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
from qdrant_client import QdrantClient
NEO4J_URL = "neo4j://localhost:7687"
NEO4J_AUTH = ("neo4j", "password")
def main() -> None:
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
retriever = QdrantNeo4jRetriever(
driver=neo4j_driver,
client=QdrantClient(url="http://localhost:6333"),
collection_name="Jeopardy",
id_property_external="neo4j_id",
id_property_neo4j="id",
)
res = retriever.search(query_vector=EMBEDDING_BIOLOGY, top_k=2)
print(res)
if __name__ == "__main__":
main()

38
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### Start services locally
This is a manual task you need to do in the terminal.
This spins up Neo4j and Weaviate containers and is configuring Weaviate to use embeddings from Hugging Face's Sentence Transformers using the "all-MiniLM-L6-v2" model, which has 384 dimensions.
```bash
docker compose -f tests/e2e/docker-compose.yml up
```
### Write data (once)
Run this from the project root to write data to both dbs.
```
uv run python -m tests/e2e/weaviate_e2e/populate_dbs.py
```
### Install Weaviate client
You need to install the `weaviate-client` package to use this retriever.
```bash
pip install weaviate-client
```
### Search
```
# search by vector
uv run python -m examples.customize.retrievers.external.weaviate.vector_search
# search by text, with embeddings generated locally (via embedder argument)
uv run python -m examples.customize.retrievers.external.weaviate.text_search_local_embedder
# search by text, with embeddings generated on the Weaviate side, via configured vectorizer
uv run python -m examples.customize.retrievers.external.weaviate.text_search_remote_embedder
```

40
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"""This example demonstrates how to use WeaviateNeo4jRetriever, ie vectors are
stored in the Weaviate database.
See the [README](./README.md) for more
information about how spin up a Weaviate and Neo4j databases if needed.
In this example, we are embeddings a text and provide a local embeder to the
WeaviateNeo4jRetriever.
"""
from neo4j import GraphDatabase
from neo4j_graphrag.embeddings.sentence_transformers import (
SentenceTransformerEmbeddings,
)
from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever
from weaviate.connect.helpers import connect_to_local
NEO4J_URL = "neo4j://localhost:7687"
NEO4J_AUTH = ("neo4j", "password")
def main() -> None:
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
with connect_to_local() as w_client:
embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
retriever = WeaviateNeo4jRetriever(
driver=neo4j_driver,
client=w_client,
collection="Jeopardy",
id_property_external="neo4j_id",
id_property_neo4j="id",
embedder=embedder,
)
res = retriever.search(query_text="biology", top_k=2)
print(res)
if __name__ == "__main__":
main()

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"""This example demonstrates how to use WeaviateNeo4jRetriever, ie vectors are
stored in the Weaviate database.
See the [README](./README.md) for more
information about how spin up a Weaviate and Neo4j databases if needed.
In this example, we are embeddings a text and provide a remote embeder to the
WeaviateNeo4jRetriever.
"""
from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever
from weaviate.connect.helpers import connect_to_local
NEO4J_URL = "neo4j://localhost:7687"
NEO4J_AUTH = ("neo4j", "password")
def main() -> None:
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
with connect_to_local() as w_client:
retriever = WeaviateNeo4jRetriever(
driver=neo4j_driver,
client=w_client,
collection="Jeopardy",
id_property_external="neo4j_id",
id_property_neo4j="id",
)
# Weaviate is configured to embed the text on the server side
# see populate_dbs.py for the configuration
res = retriever.search(query_text="biology", top_k=2)
print(res)
if __name__ == "__main__":
main()

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"""This example demonstrates how to use WeaviateNeo4jRetriever, ie vectors are
stored in the Weaviate database.
See the [README](./README.md) for more
information about how spin up a Weaviate and Neo4j databases if needed.
In this example, search is performed from an already existing vector.
"""
from embedding_biology import EMBEDDING_BIOLOGY
from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever
from weaviate.connect.helpers import connect_to_local
NEO4J_URL = "neo4j://localhost:7687"
NEO4J_AUTH = ("neo4j", "password")
def main() -> None:
with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
with connect_to_local() as w_client:
retriever = WeaviateNeo4jRetriever(
driver=neo4j_driver,
client=w_client,
collection="Jeopardy",
id_property_external="neo4j_id",
id_property_neo4j="id",
)
res = retriever.search(query_vector=EMBEDDING_BIOLOGY, top_k=2)
print(res)
if __name__ == "__main__":
main()