{"data":{"slug":"supervc-stack-vectorchord","name":"VectorChord","tagline":"Scalable, fast, and disk-friendly vector search in Postgres","github_url":"https://github.com/supervc-stack/VectorChord","owner":"supervc-stack","repo":"VectorChord","owner_avatar_url":"https://avatars.githubusercontent.com/u/284745899?v=4","primary_language":"Rust","stars":1795,"forks":72,"topics":["artificial-intelligence","llmops","postgresql","vector-database","vector-search"],"archived":false,"github_pushed_at":"2026-08-06T06:34:15+00:00","maintenance_label":"Steady","stars_delta_30d":37,"url":"https://www.graphcanon.com/tools/supervc-stack-vectorchord","markdown_url":"https://www.graphcanon.com/tools/supervc-stack-vectorchord.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/supervc-stack-vectorchord","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=supervc-stack-vectorchord","description":"Scalable, fast, and disk-friendly vector search in Postgres, the successor of pgvecto.rs.","homepage_url":"https://docs.vectorchord.ai/vectorchord/getting-started/overview.html","license":"Other","open_issues":19,"watchers":16,"ai_summary":"VectorChord is a vector database extension for PostgreSQL, offering scalable and efficient vector search capabilities. It supports vector indexing and retrieval, making it suitable for applications requiring fast similarity searches.","readme_excerpt":"## Quick Start\n\nFor new users, we recommend using the Docker image to get started quickly. If you do not prefer Docker, please read [installation guide](https://docs.vectorchord.ai/vectorchord/getting-started/installation.html) for other installation methods.\n\n```bash\ndocker run \\\n  --name vectorchord-demo \\\n  -e POSTGRES_PASSWORD=mysecretpassword \\\n  -p 5432:5432 \\\n  -d ghcr.io/tensorchord/vchord-postgres:pg18-v1.1.1\n```\n\n> [!NOTE]\n> In addition to the base image with the VectorChord extension, we provide an all-in-one image, `tensorchord/vchord-suite:pg17-latest`. This comprehensive image includes all official TensorChord extensions, including `VectorChord`, `VectorChord-bm25` and `pg_tokenizer.rs` . Developers should select an image tag that is compatible with their extension's version, as indicated in [the support matrix](https://github.com/tensorchord/VectorChord-images?tab=readme-ov-file#support-matrix).\n\nThen you can connect to the database using the `psql` command line tool. The default username is `postgres`, and the default password is `mysecretpassword`.\n\n```bash\npsql -h localhost -p 5432 -U postgres\n```\n\nNow you can play with VectorChord!\n\nVectorChord depends on pgvector, including the vector representation. Since you can use them directly, your application can be easily migrated without pain!\n\n```sql\nCREATE EXTENSION IF NOT EXISTS vchord CASCADE;\n```\n\nSimilar to pgvector, you can create a table with vector column and insert some rows to it.\n\n```sql\nCREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));\nINSERT INTO items (embedding) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);\n```\n\nWith VectorChord, you can create `vchordrq` indexes.\n\n```SQL\nCREATE INDEX ON items USING vchordrq (embedding vector_l2_ops);\n```\n\nAnd then perform a vector search using `SELECT ... ORDER BY ... LIMIT ...`.\n\n```SQL\nSELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;\n```\n\nFor more usage, please read:\n\n- [Indexing](https://docs.vectorchord.ai/vectorchord/usage/indexing.html)\n- [Multi-Vector Retrieval](https://docs.vectorchord.ai/vectorchord/usage/indexing-with-maxsim-operators.html)\n- [Quantization Types](https://docs.vectorchord.ai/vectorchord/usage/quantization-types.html)\n- [Graph Index](https://docs.vectorchord.ai/vectorchord/usage/graph-index.html)\n- [Quantization Types](https://docs.vectorchord.ai/vectorchord/usage/quantization-types.html)\n- [Similarity Filter](https://docs.vectorchord.ai/vectorchord/usage/range-query.html)\n- [PostgreSQL Tuning](https://docs.vectorchord.ai/vectorchord/usage/performance-tuning.html)\n- [Monitoring](https://docs.vectorchord.ai/vectorchord/usage/monitoring.html)\n- [Fallback Parameters](https://docs.vectorchord.ai/vectorchord/usage/fallback-parameters.html)\n- [Measure Recall](https://docs.vectorchord.ai/vectorchord/usage/measure-recall.html)\n- [Prewarm](https://docs.vectorchord.ai/vectorchord/usage/prewarm.html)\n- [Prefilter](https://docs.vectorchord.ai/vectorchord/usage/prefilter.html)\n- [Prefetch](https://docs.vectorchord.ai/vectorchord/usage/prefetch.html)\n- [Rerank in Table](https://docs.vectorchord.ai/vectorchord/usage/rerank-in-table.html)\n- [Partitioning Tuning](https://docs.vectorchord.ai/vectorchord/usage/partitioning-tuning.html)\n- [External Build](https://docs.vectorchord.ai/vectorchord/usage/external-index-precomputation.html)\n\n---\n\n## License\n\nThis software is licensed under a dual license model:\n\n1. **GNU Affero General Public License v3 (AGPLv3)**: You may use, modify, and distribute this software under the terms of the AGPLv3.\n\n2. **Elastic License v2 (ELv2)**: You may also use, modify, and distribute this software under the Elastic License v2, which has specific restrictions.\n\nYou may choose either license based on your needs. We welcome any commercial collaboration or support, so please email us <vectorchord-inquiry@tensorchord.ai> with any questions or requests regarding the licenses.\n\n[cost-estimation]: https://github.com/user-","github_created_at":"2024-09-03T07:39:46+00:00","created_at":"2026-07-07T17:42:47.475314+00:00","updated_at":"2026-09-20T04:31:23.493862+00:00","categories":[{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"artificial-intelligence","name":"artificial-intelligence"},{"slug":"llmops","name":"llmops"},{"slug":"postgresql","name":"postgresql"},{"slug":"vector-database","name":"vector-database"},{"slug":"vector-search","name":"vector-search"}],"trust":{"provenance":{"is_fork":false,"github_id":851492630,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:31:13.380Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":0,"days_since_push":43,"last_release_at":"2026-02-28T04:04:07Z","stars_delta_30d":37,"open_issues_delta_30d":2},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-09-18T08:31:15.036Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:31:12.276Z"},"languages":{"value":["rust"],"source":"github.language","observed_at":"2026-09-18T08:31:12.276Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-09-18T08:31:12.276Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need a vector database extension that is compatible with PostgreSQL and requires minimal setup, as VectorChord can be easily integrated using Docker or other installation methods.","For applications that require fast similarity searches and can benefit from the disk-friendly nature of VectorChord, which optimizes storage and retrieval operations.","If your project involves multi-vector retrieval and you need advanced indexing options like `vchordrq` indexes, which are supported by VectorChord.","When you are working on projects that require quantization types and graph indexing, as VectorChord provides detailed documentation and support for these features."],"when_not_to_use":["If your application does not require PostgreSQL as the underlying database, as VectorChord is specifically designed as an extension for PostgreSQL.","When you need a standalone vector database solution that does not depend on PostgreSQL, as VectorChord is tightly integrated with PostgreSQL and may not be suitable for environments without it.","If your project does not benefit from the specific indexing and retrieval capabilities provided by VectorChord, such as `vchordrq` indexes, and you require a more generalized vector database solution.","When you are looking for a tool that supports a wider range of database systems beyond PostgreSQL, as VectorChord is limited to PostgreSQL environments."],"source":"enrich:decision_facts","observed_at":"2026-09-18T10:18:02.081Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"VectorChord is a vector database extension for PostgreSQL, offering scalable and efficient vector search capabilities. It supports vector indexing and retrieval, making it suitable for applications requiring fast and low"}]}}