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"2.0" python-versions = "^3.8.1" -content-hash = "ff3141131bbe26cdd25356cdddaf29dd8d4d41d4551b60169b04915b5b046671" +content-hash = "0f032d183073bab44107943f0dbeff75905c9237eb5dfead382b8f2718db6cfe" diff --git a/pyproject.toml b/pyproject.toml index ee35a5979..caa3936a9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,6 +40,7 @@ pre-commit = { version = "^3.6.2", python = "^3.9" } coverage = "^7.4.3" ruff = "^0.3.0" langchain-openai = "^0.1.1" +weaviate-client = "^4.6.1" [build-system] requires = ["poetry-core"] diff --git a/src/neo4j_genai/neo4j_queries.py b/src/neo4j_genai/neo4j_queries.py index e974a0476..d4b31d36c 100644 --- a/src/neo4j_genai/neo4j_queries.py +++ b/src/neo4j_genai/neo4j_queries.py @@ -40,6 +40,13 @@ "YIELD node, score" ) +MATCH_QUERY = ( + "UNWIND $match_params AS match_param " + "WITH match_param[0] AS match_id_value, match_param[1] AS score " + "MATCH (node) " + "WHERE {match_query}" +) + def _get_hybrid_query() -> str: return ( @@ -117,6 +124,7 @@ def get_search_query( embedding_node_property: Optional[str] = None, embedding_dimension: Optional[int] = None, filters: Optional[dict[str, Any]] = None, + match_query: Optional[str] = None, ) -> tuple[str, dict[str, Any]]: """Build the search query, including pre-filtering if needed, and return clause. @@ -130,6 +138,7 @@ def get_search_query( embedding_node_property (str): the name of the property holding the embeddings embedding_dimension (int): the dimension of the embeddings filters (dict[str, Any]): filters used to pre-filter the nodes before vector search + match_query: Optional[str]: the query to use to match the search results Returns: tuple[str, dict[str, Any]]: query and parameters @@ -144,6 +153,11 @@ def get_search_query( query, params = _get_vector_query( filters, node_label, embedding_node_property, embedding_dimension ) + elif search_type == SearchType.MATCH: + if not match_query: + raise ValueError("Match query is required for MATCH search type") + query = MATCH_QUERY.format(match_query=match_query) + params = {} else: raise ValueError(f"Search type is not supported: {search_type}") query_tail = _get_query_tail( diff --git a/src/neo4j_genai/retrievers/base.py b/src/neo4j_genai/retrievers/base.py index cbf8abb36..5f2844072 100644 --- a/src/neo4j_genai/retrievers/base.py +++ b/src/neo4j_genai/retrievers/base.py @@ -13,8 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod -from typing import Any - +from typing import Optional, Any import neo4j @@ -75,3 +74,37 @@ def _fetch_index_infos(self): self._node_label = result["labels"][0] self._embedding_node_property = result["properties"][0] self._embedding_dimension = result["dimensions"] + + +class ExternalRetriever(ABC): + """ + Abstract class for External Vector Stores + """ + + def __init__(self): + pass + + @property + @abstractmethod + def id_property_external(self): + pass + + @property + @abstractmethod + def id_property_neo4j(self): + pass + + @abstractmethod + def search( + self, + query_vector: Optional[list[float]] = None, + query_text: Optional[str] = None, + top_k: int = 5, + ) -> list[neo4j.Record]: + """ + + Returns: + list[neo4j.Record]: List of Neo4j Records + + """ + pass diff --git a/src/neo4j_genai/retrievers/external/weaviate/__init__.py b/src/neo4j_genai/retrievers/external/weaviate/__init__.py new file mode 100644 index 000000000..ae3da220a --- /dev/null +++ b/src/neo4j_genai/retrievers/external/weaviate/__init__.py @@ -0,0 +1,18 @@ +# 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 .weaviate import WeaviateNeo4jRetriever + +__all__ = ["WeaviateNeo4jRetriever"] diff --git a/src/neo4j_genai/retrievers/external/weaviate/examples/README.md b/src/neo4j_genai/retrievers/external/weaviate/examples/README.md new file mode 100644 index 000000000..b6890b340 --- /dev/null +++ b/src/neo4j_genai/retrievers/external/weaviate/examples/README.md @@ -0,0 +1,54 @@ +### Start services locally + +This is a manual task you need to do in the terminal. + +```bash +docker run \ + --name testweaviate \ + --rm \ + -p8080:8080 -p 50051:50051 \ + cr.weaviate.io/semitechnologies/weaviate:1.24.11 + +docker run \ + --name testneo4j \ + --rm \ + -p7474:7474 -p7687:7687 \ + --env NEO4J_ACCEPT_LICENSE_AGREEMENT=eval \ + --env NEO4J_AUTH=neo4j/password \ + neo4j:enterprise +``` + +To run Weaviate with OpenAI Vectorizer enabled + +```bash +docker run \ + --name testweaviate \ + --rm \ + -p8080:8080 -p 50051:50051 \ + --env ENABLE_MODULES=text2vec-openai \ + --env DEFAULT_VECTORIZER_MODULE=text2vec-openai \ + cr.weaviate.io/semitechnologies/weaviate:1.24.11 +``` + +### Write data (once) + +Run this from the project root to write data to both dbs + +``` +poetry run python src/neo4j_genai/retrievers/external/weaviate/examples/populate_dbs.py +``` + +### Search + +To run the text search examples you'd need to create a `.env` file and add a variable named `OPENAI_API_KEY=` inside. + +``` +# search by vector +poetry run python src/neo4j_genai/retrievers/external/weaviate/examples/vector_search.py + +# search by text, with embeddings generated locally (via embedder argument) +poetry run python src/neo4j_genai/retrievers/external/weaviate/examples/text_search_local_embedder.py + +# search by text, with embeddings generated on the Weaviate side, via configured vectorizer +poetry run python src/neo4j_genai/retrievers/external/weaviate/examples/text_search_remote_embedder.py +``` diff --git a/src/neo4j_genai/retrievers/external/weaviate/examples/populate_dbs.py b/src/neo4j_genai/retrievers/external/weaviate/examples/populate_dbs.py new file mode 100644 index 000000000..144271d76 --- /dev/null +++ b/src/neo4j_genai/retrievers/external/weaviate/examples/populate_dbs.py @@ -0,0 +1,161 @@ +# 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. + +import weaviate.classes as wvc +import weaviate +from neo4j import GraphDatabase +import os.path +import json +import hashlib + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +NEO4J_URL = "neo4j://localhost:7687" +NEO4J_AUTH = ("neo4j", "password") + + +def main(): + neo4j_objects, w_question_objs = build_data_objects() + with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver: + with weaviate.connect_to_local() as w_client: + w_client.collections.create( + "Question", + vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_openai( + model="ada", model_version="002" + ), + vector_index_config=wvc.config.Configure.VectorIndex.hnsw( + distance_metric=wvc.config.VectorDistances.COSINE # select prefered distance metric + ), + properties=[ + wvc.config.Property( + name="neo4j_id", data_type=wvc.config.DataType.TEXT + ), + ], + ) + + # Populate Weaviate + populate_weaviate(w_client, w_question_objs) + + # Populate Neo4j + populate_neo4j(neo4j_driver, neo4j_objects) + + +def populate_neo4j(neo4j_driver, neo4j_objs): + 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} + ) + neo4j_driver.execute_query( + has_answer_relationships_cypher, {"relationships": has_answer_relationships} + ) + + +def populate_weaviate(w_client, w_question_objs): + questions = w_client.collections.get("Question") + questions.data.insert_many(w_question_objs) + + +def build_data_objects(): + # read file from disk + # this file is from https://github.com/weaviate-tutorials/quickstart/tree/main/data + # MIT License + file_name = os.path.join( + BASE_DIR, + "../../../../../tests/e2e/data/jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json", + ) + with open(file_name, "r") as f: + data = json.load(f) + + w_question_objs = list() + neo4j_objs = {"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() + neo4j_objs["nodes"].append( + { + "label": "Question", + "properties": { + "id": f"question_{id}", + "question": d["Question"], + }, + } + ) + neo4j_objs["nodes"].append( + { + "label": "Answer", + "properties": { + "id": f"answer_{id}", + "answer": d["Answer"], + }, + } + ) + 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": {}, + } + ) + w_question_objs.append( + wvc.data.DataObject( + properties={ + "neo4j_id": f"question_{id}", + }, + vector=d["vector"], + ) + ) + + return neo4j_objs, w_question_objs + + +if __name__ == "__main__": + main() diff --git a/src/neo4j_genai/retrievers/external/weaviate/examples/text_search_local_embedder.py b/src/neo4j_genai/retrievers/external/weaviate/examples/text_search_local_embedder.py new file mode 100644 index 000000000..b735f0e80 --- /dev/null +++ b/src/neo4j_genai/retrievers/external/weaviate/examples/text_search_local_embedder.py @@ -0,0 +1,48 @@ +# 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. + +import os +from neo4j_genai.retrievers.external.weaviate import WeaviateNeo4jRetriever +from neo4j import GraphDatabase +import weaviate +from langchain_openai import OpenAIEmbeddings + +NEO4J_URL = "neo4j://localhost:7687" +NEO4J_AUTH = ("neo4j", "password") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") + + +def main(): + neo4j_driver = GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) + with weaviate.connect_to_local() as w_client: + embedder = OpenAIEmbeddings( + api_key=OPENAI_API_KEY, model="text-embedding-ada-002" + ) + retriever = WeaviateNeo4jRetriever( + driver=neo4j_driver, + client=w_client, + collection="Question", + id_property_external="neo4j_id", + id_property_neo4j="id", + embedder=embedder, + ) + + res = retriever.search(query_text="biology", top_k=2) + print(res) + neo4j_driver.close() + + +if __name__ == "__main__": + main() diff --git a/src/neo4j_genai/retrievers/external/weaviate/examples/text_search_remote_embedder.py b/src/neo4j_genai/retrievers/external/weaviate/examples/text_search_remote_embedder.py new file mode 100644 index 000000000..e36f186f0 --- /dev/null +++ b/src/neo4j_genai/retrievers/external/weaviate/examples/text_search_remote_embedder.py @@ -0,0 +1,47 @@ +# 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. + +import os +from neo4j_genai.retrievers.external.weaviate import WeaviateNeo4jRetriever +from neo4j import GraphDatabase +import weaviate + +NEO4J_URL = "neo4j://localhost:7687" +NEO4J_AUTH = ("neo4j", "password") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") + + +def main(): + neo4j_driver = GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) + with weaviate.connect_to_local( + headers={ + "X-OpenAI-Api-Key": OPENAI_API_KEY, + } + ) as w_client: + retriever = WeaviateNeo4jRetriever( + driver=neo4j_driver, + client=w_client, + collection="Question", + id_property_external="neo4j_id", + id_property_neo4j="id", + ) + + res = retriever.search(query_text="biology", top_k=2) + print(res) + neo4j_driver.close() + + +if __name__ == "__main__": + main() diff --git a/src/neo4j_genai/retrievers/external/weaviate/examples/vector_search.py b/src/neo4j_genai/retrievers/external/weaviate/examples/vector_search.py new file mode 100644 index 000000000..f368ca6f3 --- /dev/null +++ b/src/neo4j_genai/retrievers/external/weaviate/examples/vector_search.py @@ -0,0 +1,1583 @@ +# 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 neo4j_genai.retrievers.external.weaviate import WeaviateNeo4jRetriever +from neo4j import GraphDatabase +import weaviate + +NEO4J_URL = "neo4j://localhost:7687" +NEO4J_AUTH = ("neo4j", "password") + + +def main(): + neo4j_driver = GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) + with weaviate.connect_to_local() as w_client: + retriever = WeaviateNeo4jRetriever( + driver=neo4j_driver, + client=w_client, + collection="Question", + id_property_external="neo4j_id", + id_property_neo4j="id", + ) + + res = retriever.search(query_vector=get_query_vector(), top_k=2) + print(res) + neo4j_driver.close() + + +def get_query_vector(): + # biology + return [ + 0.0042927247, + -0.007413445, + 0.00034457954, + -0.01897398, + -0.00251218, + 0.020693844, + -0.027351381, + -0.008647864, + -0.000042449385, + -0.012337249, + -0.006678342, + 0.0072608762, + 0.0051838635, + -0.020555146, + 0.00017055604, + 0.028821588, + 0.047351733, + -0.0045319796, + 0.008959935, + -0.012323379, + -0.008585449, + 0.022483058, + 0.0013869869, + -0.01801696, + -0.0014806085, + 0.01269093, + 0.021539906, + -0.03592296, + -0.0024116235, + -0.012087591, + 0.014369184, + -0.0019261781, + -0.03506303, + -0.028932547, + -0.008162417, + -0.026949156, + -0.011782453, + 0.00049628125, + 0.018044699, + 0.011213789, + 0.014452403, + 0.028072614, + -0.010957196, + -0.0006033393, + -0.008876716, + -0.008079199, + -0.011789389, + -0.007517469, + -0.028960286, + -0.0059328363, + 0.006758094, + 0.021997612, + -0.004521577, + -0.015506513, + -0.010208224, + -0.014577232, + -0.004726158, + 0.025714736, + -0.0025104464, + -0.016421925, + -0.025922785, + -0.00038467214, + -0.021054462, + 0.019611996, + -0.013315074, + 0.0014346646, + -0.0017857456, + 0.01572843, + -0.01003485, + -0.008675603, + 0.02323203, + 0.0033079637, + 0.0056970487, + -0.0129405875, + 0.035534605, + -0.010769953, + -0.023010112, + -0.001464138, + -0.008932196, + 0.015700692, + 0.010166614, + -0.022483058, + 0.00788502, + 0.0029698857, + 0.018710453, + 0.0029768206, + -0.014341445, + 0.008897521, + -0.013141701, + -0.037115768, + 0.0148546295, + 0.004067339, + -0.0060680676, + 0.02529864, + -0.027601039, + 0.021345729, + -0.010721409, + 0.033676043, + -0.0191959, + -0.06807332, + -0.009812932, + 0.015312335, + -0.010825433, + -0.018613365, + -0.02110994, + 0.006248376, + 0.008550774, + 0.0044556954, + 0.023134941, + -0.026990766, + -0.010097264, + 0.021623125, + -0.0070979055, + -0.04798975, + 0.0006449489, + -0.0011338618, + 0.0033894493, + -0.011172179, + 0.0053641717, + -0.010430141, + 0.021484427, + -0.012885109, + 0.010298378, + -0.013003002, + 0.019917132, + 0.010832367, + -0.04235858, + -0.010222093, + -0.0017658077, + 0.009466185, + 0.023564907, + 0.01998648, + 0.0036928526, + 0.01162295, + -0.022413708, + 0.016421925, + -0.008842042, + 0.0043065944, + -0.017503774, + -0.031262685, + 0.007739387, + 0.033787, + -0.040583238, + -0.00029863563, + 0.003266354, + 0.025423469, + 0.012579971, + 0.023759086, + 0.0168935, + 0.0042996593, + 0.015437164, + -0.0035125443, + 0.029043505, + 0.0031709988, + -0.0038350187, + -0.0030479038, + -0.009889216, + 0.008696408, + 0.0022053092, + 0.003027099, + -0.009507795, + 0.0028918677, + 0.012406598, + -0.022344358, + 0.009695038, + 0.017462164, + 0.007059763, + 0.012316444, + -0.0020596755, + -0.019487167, + -0.0132734645, + 0.038197618, + -0.022385968, + 0.030153096, + 0.00017857457, + 0.019736823, + 0.010624319, + 0.0026023341, + -0.014715931, + 0.0079405, + -0.024965765, + 0.026200183, + 0.018627234, + 0.0108670425, + -0.0010913853, + 0.009181853, + 0.01484076, + -0.0072192666, + 0.031207206, + -0.020638365, + -0.004466098, + 0.016061308, + 0.0054855333, + 0.004379411, + -0.6910524, + -0.032178096, + 0.009660364, + -0.005471663, + 0.012371923, + 0.0264221, + 0.007586818, + -0.0030929807, + -0.012434337, + 0.011678429, + 0.019320728, + -0.0058392147, + -0.009098634, + -0.015132027, + -0.013571667, + -0.030014396, + 0.008328856, + -0.03281611, + 0.008481425, + 0.002597133, + -0.024008743, + -0.0026543462, + -0.009015415, + -0.011983567, + 0.009154114, + 0.0044799675, + 0.018155659, + -0.018821413, + -0.008890586, + -0.007989044, + -0.0075729485, + 0.0119211525, + -0.000356499, + -0.0021550308, + 0.019487167, + 0.0044314233, + -0.0039737173, + 0.028280662, + 0.021317989, + 0.043828785, + -0.01319718, + -0.030097615, + 0.0011035214, + 0.012815759, + -0.022691106, + 0.028211314, + 0.016602233, + -0.0020648767, + 0.023981003, + 0.015756171, + 0.012337249, + 0.0019556514, + 0.01583939, + 0.018710453, + 0.02334299, + -0.0115536, + 0.017531514, + -0.008946066, + -0.0024619016, + -0.0015447567, + -0.010069525, + 0.0008317587, + -0.014743671, + -0.027295902, + -0.02328751, + 0.027878437, + -0.032483235, + 0.009743583, + 0.019917132, + -0.010215159, + 0.022330489, + 0.022815935, + -0.014604972, + -0.0008096536, + -0.0001595035, + 0.016075179, + 0.022372099, + -0.0052601476, + -0.015132027, + 0.0032004723, + 0.0068309107, + -0.010083395, + -0.02106833, + -0.0066228625, + 0.028904807, + -0.020915762, + -0.007628428, + 0.010478686, + 0.0037240598, + 0.0037136574, + 0.014299835, + 0.02868289, + 0.0009223463, + -0.0052428106, + -0.014702061, + 0.009701974, + -0.011810194, + -0.0084952945, + 0.00659859, + -0.013044612, + -0.005218538, + 0.000095517884, + 0.021248639, + 0.010346922, + 0.011352488, + 0.0045909267, + -0.004670678, + 0.020763194, + 0.044022962, + -0.021539906, + 0.0077809966, + -0.00095181976, + -0.0062934533, + 0.010714474, + -0.00017369844, + -0.033592824, + 0.0066089923, + -0.0025104464, + 0.021997612, + -0.01805857, + -0.0023474754, + 0.0060819373, + 0.006276116, + -0.011962762, + 0.0061720917, + 0.01590874, + -0.018946242, + -0.020888023, + -0.017670212, + 0.0003892232, + 0.017795041, + 0.013377489, + 0.011269269, + -0.015464904, + 0.028252924, + 0.009681169, + 0.015534253, + -0.020541277, + 0.010679799, + -0.007836476, + -0.027073985, + -0.015561993, + 0.005603427, + -0.0010081661, + -0.013342814, + -0.006154754, + -0.022427578, + -0.0022053092, + -0.019279119, + 0.012933653, + 0.017448295, + 0.012815759, + 0.0019105745, + 0.028849328, + 0.034674674, + -0.023689736, + -0.0062449086, + -0.0008872382, + -0.0068690525, + 0.013516188, + 0.010561905, + 0.01814179, + -0.018391447, + 0.003644308, + -0.0026716834, + -0.017032199, + -0.0070181536, + 0.015173636, + -0.0076006884, + -0.04776783, + 0.01110283, + -0.03919625, + -0.015451034, + 0.010242898, + 0.011123635, + 0.0003929074, + -0.017808912, + 0.01603357, + 0.021387339, + -0.0055479477, + -0.016297096, + -0.0038107466, + -0.020333229, + -0.0032958277, + 0.018405316, + 0.0031796675, + 0.032316796, + 0.020555146, + -0.004365541, + 0.012933653, + 0.009951631, + 0.02227501, + 0.0012266166, + 0.0085299695, + 0.019182028, + 0.009819867, + 0.0014468007, + 0.010020981, + -0.0030704422, + 0.02314881, + 0.04449454, + -0.00095702097, + 0.01794761, + -0.011095895, + 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__name__ == "__main__": + main() diff --git a/src/neo4j_genai/retrievers/external/weaviate/weaviate.py b/src/neo4j_genai/retrievers/external/weaviate/weaviate.py new file mode 100644 index 000000000..dbf69d694 --- /dev/null +++ b/src/neo4j_genai/retrievers/external/weaviate/weaviate.py @@ -0,0 +1,138 @@ +# 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 typing import Optional +from neo4j_genai.retrievers.base import ExternalRetriever +from neo4j_genai.embedder import Embedder +from neo4j_genai.types import SearchType +from neo4j_genai.retrievers.utils import validate_search_query_input +import weaviate.classes as wvc +import neo4j +import logging +from neo4j_genai.neo4j_queries import get_search_query + +logger = logging.getLogger(__name__) + + +class WeaviateNeo4jRetriever(ExternalRetriever): + def __init__( + self, + driver: neo4j.Driver, + client: any, + collection: str, + id_property_external: str, + id_property_neo4j: str = None, + embedder: Optional[Embedder] = None, + return_properties: Optional[list[str]] = None, + retrieval_query: Optional[str] = None, + ): + self.driver = driver + self.client = client + self.search_collection = client.collections.get(collection) + self._id_property_external = id_property_external + self._id_property_neo4j = id_property_neo4j + self.embedder = embedder + self.return_properties = return_properties + self.retrieval_query = retrieval_query + + @property + def id_property_external(self): + return self._id_property_external + + @property + def id_property_neo4j(self): + return self._id_property_neo4j + + def search( + self, + query_vector: Optional[list[float]] = None, + query_text: Optional[str] = None, + top_k: int = 5, + ) -> list[neo4j.Record]: + """Get the top_k nearest neighbor embeddings using Weaviate for either provided query_vector or query_text. + Both query_vector and query_text can be provided. + If query_vector is provided, then it will be preferred over the embedded query_text + for the vector search. + If query_text is provided, then it will check if an embedder is provided and use it to generate the query_vector. + If no embedder is provided, then it will assume that the vectorizer is used in Weaviate. + + See the following documentation for more details: + - [Query a vector index](https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/#indexes-vector-query) + - [db.index.vector.queryNodes()](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_vector_queryNodes) + - [db.index.fulltext.queryNodes()](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_fulltext_querynodes) + Args: + query_text (str): The text to get the closest neighbors of. + query_vector (Optional[list[float]], optional): The vector embeddings to get the closest neighbors of. Defaults to None. + top_k (int, optional): The number of neighbors to return. Defaults to 5. + Raises: + ValueError: If validation of the input arguments fail. + Returns: + list[neo4j.Record]: The results of the search query + """ + + validate_search_query_input(query_text=query_text, query_vector=query_vector) + + # If we want to use a local embedder, we still want to call the near_vector method + # so we want to create the vector as early as possible here + if query_text: + if self.embedder: + query_vector = self.embedder.embed_query(query_text) + logger.debug("Locally generated query vector: %s", query_vector) + else: + logger.debug( + "No embedder provided, assuming vectorizer is used in Weaviate." + ) + + if query_vector: + response = self.search_collection.query.near_vector( + near_vector=query_vector, + limit=top_k, + return_metadata=wvc.query.MetadataQuery(certainty=True), + ) + logger.debug("Weaviate query vector: %s", query_vector) + logger.debug("Response: %s", response) + else: + response = self.search_collection.query.near_text( + query=query_text, + limit=top_k, + return_metadata=wvc.query.MetadataQuery(certainty=True), + ) + logger.debug("Query text: %s", query_text) + logger.debug("Response: %s", response) + + result_tuples = [ + [f"{o.properties[self._id_property_external]}", o.metadata.certainty or 0.0] + for o in response.objects + ] + + match_query = "node[$id_property] = match_id_value" + search_query, _ = get_search_query( + SearchType.MATCH, + match_query=match_query, + return_properties=self.return_properties, + retrieval_query=self.retrieval_query, + ) + + parameters = { + "match_params": result_tuples, + "id_property": self.id_property_neo4j, + } + + logger.debug("Weaviate Store Cypher parameters: %s", parameters) + logger.debug("Weaviate Store Cypher query: %s", search_query) + + records, _, _ = self.driver.execute_query(search_query, parameters) + + return records diff --git a/src/neo4j_genai/retrievers/utils.py b/src/neo4j_genai/retrievers/utils.py new file mode 100644 index 000000000..ce9ec37ad --- /dev/null +++ b/src/neo4j_genai/retrievers/utils.py @@ -0,0 +1,23 @@ +# 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 typing import Optional + + +def validate_search_query_input( + query_text: Optional[str] = None, query_vector: Optional[list[float]] = None +): + if not (bool(query_vector) ^ bool(query_text)): + raise ValueError("You must provide exactly one of query_vector or query_text.") diff --git a/src/neo4j_genai/types.py b/src/neo4j_genai/types.py index f13e67483..eedf988ae 100644 --- a/src/neo4j_genai/types.py +++ b/src/neo4j_genai/types.py @@ -22,6 +22,7 @@ ConfigDict, ) import neo4j +from neo4j_genai.retrievers.utils import validate_search_query_input class VectorSearchRecord(BaseModel): @@ -71,10 +72,7 @@ def check_query(cls, values): Validates that one of either query_vector or query_text is provided exclusively. """ query_vector, query_text = values.get("query_vector"), values.get("query_text") - if not (bool(query_vector) ^ bool(query_text)): - raise ValueError( - "You must provide exactly one of query_vector or query_text." - ) + validate_search_query_input(query_text, query_vector) return values @@ -99,6 +97,7 @@ class SearchType(str, Enum): VECTOR = "vector" HYBRID = "hybrid" + MATCH = "match" class EmbedderModel(BaseModel): diff --git a/tests/e2e/data/jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json b/tests/e2e/data/jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json new file mode 100644 index 000000000..fe25084b5 --- /dev/null +++ b/tests/e2e/data/jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json @@ -0,0 +1,15432 @@ +[ + { + "Category": "SCIENCE", + "Question": "This organ removes excess glucose from the blood & stores it as glycogen", + "Answer": "Liver", + "vector": [ + -0.0065820422023534775, + -0.004173373803496361, + 0.030538693070411682, + -0.004229101352393627, + 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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 unittest.mock import MagicMock +from types import SimpleNamespace +from neo4j_genai.retrievers.external.weaviate import WeaviateNeo4jRetriever +from neo4j_genai.neo4j_queries import get_search_query +from neo4j_genai.types import SearchType + + +# Weaviate class with fake methods +class WClient: + def __init__(self, node_id_value=None, node_match_score=None): + self.collections = MagicMock() + self.collections.get = MagicMock() + query = MagicMock() + self.collections.get.return_value = SimpleNamespace(query=query) + query.near_text.return_value = SimpleNamespace( + objects=[ + SimpleNamespace( + properties={"neo4j_id": node_id_value}, + metadata=SimpleNamespace(certainty=node_match_score), + ) + ] + ) + + +def test_text_search_remote_vector_store_happy_path(driver): + query_text = "may thy knife chip and shatter" + top_k = 5 + node_id_value = "node-test-id" + node_match_score = 0.9 + + wc = WClient(node_id_value=node_id_value, node_match_score=node_match_score) + + retriever = WeaviateNeo4jRetriever( + driver=driver, + client=wc, + collection="dummy-collection", + id_property_neo4j="sync_id", + id_property_external="neo4j_id", + ) + driver.execute_query.return_value = [ + [{"node": {"sync_id": node_id_value}, "score": node_match_score}], + None, + None, + ] + search_query, _ = get_search_query( + SearchType.MATCH, match_query="node[$id_property] = match_id_value" + ) + + records = retriever.search(query_text=query_text, top_k=top_k) + + driver.execute_query.assert_called_once_with( + search_query, + { + "match_params": [ + [node_id_value, node_match_score], + ], + "id_property": "sync_id", + }, + ) + + assert records == [{"node": {"sync_id": node_id_value}, "score": node_match_score}] + + +def test_text_search_remote_vector_store_return_properties(driver): + query_text = "may thy knife chip and shatter" + top_k = 5 + node_id_value = "node-test-id" + node_match_score = 0.9 + + wc = WClient(node_id_value=node_id_value, node_match_score=node_match_score) + + retriever = WeaviateNeo4jRetriever( + driver=driver, + client=wc, + collection="dummy-collection", + id_property_neo4j="sync_id", + id_property_external="neo4j_id", + return_properties=["sync_id"], + ) + driver.execute_query.return_value = [ + [{"node": {"sync_id": node_id_value}, "score": node_match_score}], + None, + None, + ] + search_query, _ = get_search_query( + SearchType.MATCH, + match_query="node[$id_property] = match_id_value", + return_properties=["sync_id"], + ) + + records = retriever.search(query_text=query_text, top_k=top_k) + + driver.execute_query.assert_called_once_with( + search_query, + { + "match_params": [ + [node_id_value, node_match_score], + ], + "id_property": "sync_id", + }, + ) + + assert records == [{"node": {"sync_id": node_id_value}, "score": node_match_score}] + + +def test_text_search_remote_vector_store_retrieval_query(driver): + query_text = "may thy knife chip and shatter" + top_k = 5 + node_id_value = "node-test-id" + node_match_score = 0.9 + retrieval_query = "WITH node MATCH (node)--(m) RETURN n, m LIMIT 10" + + wc = WClient(node_id_value=node_id_value, node_match_score=node_match_score) + + retriever = WeaviateNeo4jRetriever( + driver=driver, + client=wc, + collection="dummy-collection", + id_property_neo4j="sync_id", + id_property_external="neo4j_id", + retrieval_query=retrieval_query, + ) + driver.execute_query.return_value = [ + [{"node": {"sync_id": node_id_value}, "score": node_match_score}], + None, + None, + ] + search_query, _ = get_search_query( + SearchType.MATCH, + match_query="node[$id_property] = match_id_value", + retrieval_query=retrieval_query, + ) + + records = retriever.search(query_text=query_text, top_k=top_k) + + driver.execute_query.assert_called_once_with( + search_query, + { + "match_params": [ + [node_id_value, node_match_score], + ], + "id_property": "sync_id", + }, + ) + + assert records == [{"node": {"sync_id": node_id_value}, "score": node_match_score}] diff --git a/tests/unit/test_neo4j_queries.py b/tests/unit/test_neo4j_queries.py index 0ef2b68e9..0f2cd9430 100644 --- a/tests/unit/test_neo4j_queries.py +++ b/tests/unit/test_neo4j_queries.py @@ -158,6 +158,50 @@ def test_hybrid_search_with_properties(): assert result.strip() == expected.strip() +def test_match_search_basic(): + match_query = "node[$id_property] = match_id_value" + expected = ( + "UNWIND $match_params AS match_param " + "WITH match_param[0] AS match_id_value, match_param[1] AS score " + "MATCH (node) " + f"WHERE {match_query} " + "RETURN node, score" + ) + result, _ = get_search_query(SearchType.MATCH, match_query=match_query) + assert result.strip() == expected.strip() + + +def test_match_search_with_properties(): + match_query = "node[$id_property] = match_id_value" + properties = ["name", "age"] + expected = ( + "UNWIND $match_params AS match_param " + "WITH match_param[0] AS match_id_value, match_param[1] AS score " + "MATCH (node) " + f"WHERE {match_query} " + "RETURN node {.name, .age} as node, score" + ) + result, _ = get_search_query( + SearchType.MATCH, match_query=match_query, return_properties=properties + ) + assert result.strip() == expected.strip() + + +def test_match_search_with_retrieval_query(): + match_query = "node[$id_property] = match_id_value" + retrieval_query = "WITH node MATCH (node)--(m) RETURN n, m LIMIT 10" + expected = ( + "UNWIND $match_params AS match_param " + "WITH match_param[0] AS match_id_value, match_param[1] AS score " + "MATCH (node) " + f"WHERE {match_query} " + retrieval_query + ) + result, _ = get_search_query( + SearchType.MATCH, match_query=match_query, retrieval_query=retrieval_query + ) + assert result.strip() == expected.strip() + + def test_get_query_tail_with_retrieval_query(): retrieval_query = "MATCH (n) RETURN n LIMIT 10" expected = retrieval_query