redis.json

{
  "name": "Redis Search",
  "links": {
    "docs": "https://redis.io/docs/get-started/vector-database/",
    "github": "https://github.com/redis/redis",
    "website": "https://redis.io/solutions/vector-database/",
    "vendor_discussion": "https://github.com/superlinked/VectorHub/discussions/81",
    "poc_github": "https://github.com/abid-redis",
    "slug": "redis"
  },
  "oss": {
    "support": "full",
    "source_url": "https://github.com/redis/redis",
    "comment": "Starting with Redis 8.0, both Redis and its vector search functionality are fully open source, with vector search integrated natively into Redis instead of provided as a separate RediSearch library."
  },
  "license": {
    "value": "(i) Redis Source Available License 2.0 (RSALv2) or (ii) the Server Side Public License v1 (SSPLv1) or (iii) the GNU Affero General Public License v3 (AGPLv3)",
    "source_url": "https://redis.io/legal/licenses/",
    "comment": "Starting with Redis 8, Redis Open Source is moving to a tri-licensing model with all new Redis code contributions governed by the updated Redis Software Grant and Contributor License Agreement. After this release, contributions are subject to your choice of: (a) the Redis Source Available License v2 (RSALv2);or (b) the Server Side Public License v1 (SSPLv1); or (c) the GNU Affero General Public License v3 (AGPLv3)."
  },
  "dev_languages": {
    "value": [
      "c"
    ],
    "source_url": "",
    "comment": ""
  },
  "vector_launch_year": 2021,
  "metadata_filter": {
    "support": "full",
    "source_url": "",
    "comment": ""
  },
  "hybrid_search": {
    "support": "full",
    "source_url": "https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb",
    "comment": "Redis provides a unified hybrid search interface via the FT.HYBRID command (introduced in Redis Open Source 8.4.0); previously, hybrid search was only available through the aggregations API."
  },
  "facets": {
    "support": "full",
    "source_url": "",
    "comment": ""
  },
  "geo_search": {
    "support": "full",
    "source_url": "",
    "comment": ""
  },
  "multi_vec": {
    "support": "full",
    "source_url": "https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/",
    "comment": "https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/#storing-vectors-in-json"
  },
  "sparse_vectors": {
    "support": "none",
    "source_url": "",
    "comment": ""
  },
  "bm25": {
    "support": "full",
    "source_url": "https://redis.io/docs/interact/search-and-query/advanced-concepts/scoring/",
    "comment": "https://redis.io/docs/interact/search-and-query/advanced-concepts/scoring/"
  },
  "full_text": {
    "support": "full",
    "source_url": "https://redis.io/docs/interact/search-and-query/query/full-text/",
    "comment": ""
  },
  "embeddings_text": {
    "support": "none",
    "source_url": "https://github.com/RedisVentures/redisvl",
    "comment": "Embedding generation support available via client like https://github.com/RedisVentures/redisvl"
  },
  "embeddings_image": {
    "support": "",
    "source_url": "",
    "comment": ""
  },
  "embeddings_structured": {
    "support": "",
    "source_url": "",
    "comment": ""
  },
  "rag": {
    "support": "",
    "source_url": "",
    "comment": ""
  },
  "recsys": {
    "support": "",
    "source_url": "",
    "comment": ""
  },
  "langchain": {
    "support": "full",
    "source_url": "",
    "comment": ""
  },
  "llamaindex": {
    "support": "full",
    "source_url": "",
    "comment": ""
  },
  "managed_cloud": {
    "support": "full",
    "source_url": "",
    "comment": ""
  },
  "pricing": {
    "value": "",
    "source_url": "",
    "comment": ""
  },
  "in_process": {
    "support": "none",
    "source_url": "",
    "comment": ""
  },
  "multi_tenancy": {
    "support": "full",
    "source_url": "https://redis.com/blog/multi-tenancy-redis-enterprise/",
    "comment": "https://redis.com/blog/multi-tenancy-redis-enterprise/"
  },
  "disk_index": {
    "support": "none",
    "source_url": "",
    "comment": ""
  },
  "ephemeral": {
    "support": "full",
    "source_url": "https://redis.io/blog/the-case-for-ephemeral-search/",
    "comment": ""
  },
  "sharding": {
    "support": "full",
    "source_url": "",
    "comment": ""
  },
  "doc_size": {
    "bytes": 0,
    "unlimited": false,
    "source_url": "",
    "comment": ""
  },
  "vector_dims": {
    "value": 0,
    "unlimited": true,
    "source_url": "",
    "comment": ""
  },
  "index_types": {
    "value": [
      "FLAT",
      "HNSW",
      "SVS-VAMANA"
    ],
    "source_url": "https://redis.io/docs/latest/develop/ai/search-and-query/vectors/#svs-vamana-index",
    "comment": "Redis supports the following index types: FLAT, HNSW, and SVS-VAMANA. SVS-VAMANA is a new index type introduced in Redis 8.2."
  },
  "github_stars": {
    "value": 72680,
    "source_url": "https://github.com/redis/redis",
    "comment": "Starting with Redis 8.0, both Redis and its vector search functionality are fully open source, with vector search integrated natively into Redis instead of provided as a separate RediSearch library.",
    "value_90_days": 0
  },
  "docker_pulls": {
    "value": 10279832320,
    "source_url": "https://hub.docker.com/_/redis",
    "comment": "Starting with Redis 8.0, both Redis and its vector search functionality are fully open source, with vector search integrated natively into Redis instead of provided as a separate RediSearch library.",
    "value_90_days": 0
  },
  "pypi_downloads": {
    "value": 1392107,
    "source_url": "https://pypi.org/project/redisearch/",
    "comment": "",
    "value_90_days": 87977
  },
  "npm_downloads": {
    "value": 246155752,
    "source_url": "https://www.npmjs.com/package/@redis/search",
    "comment": "",
    "value_90_days": 58871818
  },
  "crates_io_downloads": {
    "value": 0,
    "source_url": "",
    "comment": "",
    "value_90_days": 0
  }
}