A Native Vector Index Design for Graph DBMSs With Robust and Fast Predicate-Agnostic Search Performance.
- A novel Prefiltering-based predicate agnostic filtered vector search algorithm (adaptive-local) that is fast and robust acorss various selectivities and correlation scenarios. This algorithm works directly on top of HNSW thus making it easily integrable into most existing systems.
- Disk-Based HNSW Index backed by Buffer Manager.
- Zero Copy Fast Distance Computations through the buffer manager.
- Easy to use as implemented in an embedded database.
Our Paper for more info and benchmarks against SOTA baselines: https://cs.uwaterloo.ca/~ssalihog/papers/navix-tr.pdf
We have also implemented Navix's adaptive-local algorithm in Faiss HNSW implementation. https://github.com/gaurav8297/faiss-navix
There is an increasing demand for extending existing DBMSs with vector indices to become unified systems that can support modern predictive applications, which require joint querying of vector embeddings and structured properties and connections of objects. We present NaviX, a Native vector indeX for graph DBMSs (GDBMSs) that has two main design goals.
First, we aim to implement a disk-based vector index that leverages the core storage and query processing capabilities of the underlying GDBMS. To this end, NaviX is a hiearchical navigable small world (HNSW) index, which is itself a graph-based structure.
Second, we aim to evaluate predicate-agnostic vector search queries, where the k nearest neighbors (kNNs) of a query vector 𝑣𝑄 is searched across an arbitrary subset 𝑆 of vectors that is specified by an ad-hoc selection sub-query 𝑄𝑆 . We adopt a prefiltering-based approach that evaluates 𝑄𝑆 first and passes the full information about 𝑆 to the kNN search operator. We study how to design a pre-filtering-based search algorithm that is robust under different selectivities as well as correlations of 𝑆 with 𝑣𝑄 . We propose an adaptive algorithm that utilizes local selectivity of each vector in the HNSW graph to pick a suitable heuristic at each iteration of the kNN search algorithm. We demonstrate NaviX’s robustness and efficiency through extensive experiments against both existing prefiltering- and postfiltering-based baselines that include specialized vector databases (Weaviate and Milvus) as well as DBMSs (PGVectorScale and VBase).
Example Predicate Agnostic Search Query:
-- Simple Filtering Query
MATCH (p:PersonChunk)
p.birth_date < date('1975-01-01')
CALL ANN_SEARCH(e.embedding, [0.1, ...], <K>, <efS>, <bool_use_inmem_quantized_vectors>, <bool_enable_brute_force_knn>, "navix")
RETURN c.name;
-- Join Query for use case such as graph rag (https://blog.langchain.dev/enhancing-rag-based-applications-accuracy-by-constructing-and-leveraging-knowledge-graphs/)
MATCH (p:Person)-[e:PersonChunk]->(c:Chunk)
WHERE p.birth_date >= date('1971-01-01') AND p.birth_date < date('1975-01-01')
CALL ANN_SEARCH(c.embedding, [0.1, ...], <K>, <efS>, <bool_use_inmem_quantized_vectors>, <bool_enable_brute_force_knn>, "navix")
RETURN c.id;https://huggingface.co/datasets/gaurav8297/navix/
$ git clone https://github.com/gaurav8297/kuzu.git
$ cd kuzu
$ make release NUM_THREADS=32Build on MacOS: Use this gcc https://formulae.brew.sh/formula/gcc
$ export CXX=/opt/homebrew/bin/g++-14
$ export CC=/opt/homebrew/bin/gcc-14
$ make release NUM_THREADS=32Kuzu support multiple ways to ingest data into the database. Take a look at the Import Data for more information.
For this example, we will import the SIFT1M using the parquet file format.
# Download Sift 1M Data
$ wget https://huggingface.co/datasets/gaurav8297/navix/resolve/main/sift_1M/sift_base.parquet
# Start Kùzu Shell and Build Index
$ ./build/release/tools/shell/kuzu /path/to/kuzu/data
> CREATE NODE TABLE sift (id INT64, embedding FLOAT[128], PRIMARY KEY (id));
> COPY sift FROM (LOAD FROM "/path/to/sift_base.parquet" RETURN cast(id as INT64) as id, cast(embedding as FLOAT[128]) as embedding);
> CREATE VECTOR INDEX ON sift.embedding (efConstruction=200, maxNbrsAtUpperLevel=32, maxNbrsAtLowerLevel=64, distanceFunc='L2');
> call threads=32; # Set the number of threads for index building (Optional)
> UPDATE VECTOR INDEX ON sift.embedding;Once the index is built, you can query the data using the following query template:
MATCH (s:dataset)
WHERE <filtering_condition>
CALL ANN_SEARCH(s.<embedding_property>, [0.1, ...], <K>, <efS>, <bool_use_inmem_quantized_vectors>, <bool_enable_brute_force_knn>, "navix")
RETURN s.<property>;Another example query with one hop graph traversal:
MATCH (p:Person)-[e:PersonChunk]->(c:Chunk)
WHERE <filtering_condition>
CALL ANN_SEARCH(c.<embedding_property>, [0.1, ...], <K>, <efS>, <bool_use_inmem_quantized_vectors>, <bool_enable_brute_force_knn>, "navix")
RETURN c.<embedding_property>;Now coming back to our sift dataset, let's query the data:
# Simple vector search query
> MATCH (s:sift)
> CALL ANN_SEARCH(s.embedding, [23.0, 0.0, 0.0, 0.0, 1.0, 5.0, 43.0, 114.0, 3.0, 0.0, 0.0, 0.0, 14.0, 121.0, 120.0, 78.0, 81.0, 4.0, 0.0, 0.0, 31.0, 126.0, 23.0, 18.0, 126.0, 12.0, 0.0, 0.0, 0.0, 1.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 8.0, 29.0, 96.0, 43.0, 0.0, 0.0, 0.0, 0.0, 1.0, 81.0, 126.0, 44.0, 126.0, 1.0, 0.0, 0.0, 1.0, 45.0, 66.0, 96.0, 126.0, 0.0, 0.0, 0.0, 1.0, 16.0, 12.0, 63.0, 1.0, 2.0, 0.0, 0.0, 11.0, 40.0, 26.0, 0.0, 5.0, 20.0, 28.0, 1.0, 0.0, 17.0, 36.0, 5.0, 126.0, 45.0, 10.0, 1.0, 0.0, 2.0, 12.0, 29.0, 126.0, 6.0, 0.0, 0.0, 2.0, 110.0, 96.0, 46.0, 18.0, 13.0, 0.0, 0.0, 3.0, 5.0, 1.0, 2.0, 29.0, 50.0, 30.0, 7.0, 8.0, 3.0, 0.0, 1.0, 55.0, 24.0, 14.0, 5.0, 9.0, 15.0, 8.0, 10.0, 10.0, 1.0, 0.0, 0.0, 19.0, 79.0, 16.0, 4.0], 10, 64, false, false, 'navix')
> RETURN s.id;
# Vector search query with filtering
> MATCH (s:sift)
> WHERE s.id < 300000
> CALL ANN_SEARCH(s.embedding, [23.0, 0.0, 0.0, 0.0, 1.0, 5.0, 43.0, 114.0, 3.0, 0.0, 0.0, 0.0, 14.0, 121.0, 120.0, 78.0, 81.0, 4.0, 0.0, 0.0, 31.0, 126.0, 23.0, 18.0, 126.0, 12.0, 0.0, 0.0, 0.0, 1.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 8.0, 29.0, 96.0, 43.0, 0.0, 0.0, 0.0, 0.0, 1.0, 81.0, 126.0, 44.0, 126.0, 1.0, 0.0, 0.0, 1.0, 45.0, 66.0, 96.0, 126.0, 0.0, 0.0, 0.0, 1.0, 16.0, 12.0, 63.0, 1.0, 2.0, 0.0, 0.0, 11.0, 40.0, 26.0, 0.0, 5.0, 20.0, 28.0, 1.0, 0.0, 17.0, 36.0, 5.0, 126.0, 45.0, 10.0, 1.0, 0.0, 2.0, 12.0, 29.0, 126.0, 6.0, 0.0, 0.0, 2.0, 110.0, 96.0, 46.0, 18.0, 13.0, 0.0, 0.0, 3.0, 5.0, 1.0, 2.0, 29.0, 50.0, 30.0, 7.0, 8.0, 3.0, 0.0, 1.0, 55.0, 24.0, 14.0, 5.0, 9.0, 15.0, 8.0, 10.0, 10.0, 1.0, 0.0, 0.0, 19.0, 79.0, 16.0, 4.0], 10, 64, false, false, 'navix')
> RETURN s.id;Download all files in https://huggingface.co/datasets/gaurav8297/navix/tree/main/wiki_sample to some directory and run the following commands:
# Start Kùzu Shell and Build Index
$ ./build/release/tools/shell/kuzu /path/to/kuzu/data
# Create Schema
> CREATE NODE TABLE persons (resource_uri STRING, resource_name STRING, birth_date DATE, birth_place STRING, wiki_id INT64, PRIMARY KEY (wiki_id));
> CREATE NODE TABLE embeddings (wiki_id INT64, id INT64, embedding FLOAT[1024], PRIMARY KEY (id));
> CREATE REL TABLE person_embeddings (FROM persons TO embeddings);
# Load Data And Build Index
> COPY persons FROM "path/to/persons.csv" (deLim=',', header=true);
> COPY embeddings FROM "path/to/embeddings.csv" (deLim=',', header=true);
> COPY person_embeddings FROM "path/to/person_embeddings.csv" (deLim=',', header=true);
> CREATE VECTOR INDEX ON embeddings.embedding (efConstruction=128, maxNbrsAtUpperLevel=16, maxNbrsAtLowerLevel=32);
> UPDATE VECTOR INDEX ON embeddings.embedding;
# Query Data
> MATCH (p:persons)-[r:person_embeddings]->(e:embeddings) WHERE p.birth_date > DATE('1997-01-01') CALL ANN_SEARCH(e.embedding, [-0.13074035942554474, -0.11036457121372223, -1.2414557933807373, 0.6815852522850037, 0.5377219319343567, 0.353532075881958, -0.1961292326450348, -0.8198744654655457, 0.7403571009635925, -0.4667998254299164, -0.25637614727020264, 0.4690445065498352, -0.35455217957496643, -0.9163416624069214, 0.3384556770324707, 0.10572801530361176, -0.24479854106903076, -0.06863178312778473, -1.0084848403930664, -0.6793057322502136, 1.5745874643325806, -0.5046554803848267, 0.5119291543960571, -0.5269959568977356, -0.19540132582187653, 0.26367616653442383, 0.8618758320808411, -0.6465486884117126, -0.22201575338840485, 0.7980281114578247, -0.04780108109116554, 0.3250841200351715, 0.30059510469436646, -0.9767253398895264, -0.09649553149938583, 0.27558958530426025, 0.4848115146160126, 0.7396630048751831, -0.38177913427352905, -0.6647166609764099, -0.049349937587976456, 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0.29416558146476746, 0.4677976071834564, -0.45163944363594055, 0.06629878282546997, -0.21315264701843262, 1.062420129776001, -1.1194350719451904, 0.8277050256729126, 0.3637703061103821, -0.7486276030540466, -0.5353367328643799, -0.7669677138328552, 0.43453818559646606, 0.4747556149959564, 0.35538917779922485, 0.535586416721344, -0.30967506766319275, 0.6870360374450684, -1.0070325136184692, -0.13316400349140167, -0.1982230246067047, 0.6257407069206238, 0.3101324439048767, 0.42929312586784363, 0.49556252360343933, 0.5634082555770874, 0.2200520783662796, 0.67030268907547, -0.8224767446517944, 0.41406577825546265, -1.3664606809616089], 10, 64, false, false, 'navix') RETURN e.id, _distance;You can contact us at [email protected].