VectorHub/docs/tools/vdb_table/data/apachedoris.json at 7f76faed165d34e6ff4396b9eb434d5ccb046463 · superlinked/VectorHub · GitHub

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{
  "name": "Apache Doris",
  "links": {
    "docs": "https://doris.apache.org/docs/dev/ai/vector-search/",
    "github": "https://github.com/apache/doris",
    "website": "https://doris.apache.org/",
    "vendor_discussion": "https://github.com/superlinked/VectorHub/discussions/575",
    "poc_github": "https://github.com/zhiqiang-hhhh",
    "slug": "Fastest Search & Analytics in One Unified System"
  },
  "oss": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/dev/compute-storage-decoupled/overview/",
    "comment": ""
  },
  "license": {
    "value": "Apache 2.0",
    "source_url": "",
    "comment": ""
  },
  "dev_languages": {
    "value": [
      "java",
      "c++"
    ],
    "source_url": "https://github.com/apache/doris",
    "comment": ""
  },
  "vector_launch_year": 2025,
  "metadata_filter": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/dev/ai/text-search/overview/",
    "comment": ""
  },
  "hybrid_search": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/dev/ai/vector-search#ann-search-with-additional-filters",
    "comment": ""
  },
  "facets": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/dev/ai/vector-search#approximate-range-search",
    "comment": "Doris has full support for SQL. You can use standard SQL syntax to perform faceted search on vector data."
  },
  "geo_search": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/dev/sql-manual/basic-element/sql-data-types/semi-structured/GEO",
    "comment": ""
  },
  "multi_vec": {
    "support": "full",
    "source_url": "",
    "comment": "You can create a teble with multiple vector columns to store different types of vectors."
  },
  "sparse_vectors": {
    "support": "none",
    "source_url": "",
    "comment": ""
  },
  "bm25": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/dev/ai/text-search/scoring",
    "comment": ""
  },
  "full_text": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/dev/ai/text-search/search-operators",
    "comment": ""
  },
  "embeddings_text": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/4.x/sql-manual/sql-functions/ai-functions/distance-functions/embed/",
    "comment": "You can convert text to embeddings using built-in AI functions."
  },
  "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://doris.apache.org/blog/multi-tenant-workload-isolation-in-apache-doris/",
    "comment": ""
  },
  "disk_index": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/4.x/ai/vector-search#approximate-nearest-neighbor-search",
    "comment": "Via Faiss"
  },
  "ephemeral": {
    "support": "none",
    "source_url": "",
    "comment": ""
  },
  "sharding": {
    "support": "full",
    "source_url": "",
    "comment": "Support partitioning and sharding for tables with vector columns"
  },
  "doc_size": {
    "bytes": 0,
    "unlimited": true,
    "source_url": "",
    "comment": ""
  },
  "vector_dims": {
    "value": 0,
    "unlimited": true,
    "source_url": "",
    "comment": ""
  },
  "int8_quantization": {
    "support": "full",
    "source_url": "https://doris.apache.org/docs/4.x/ai/vector-search#vector-quantization",
    "comment": ""
  },
  "index_types": {
    "value": [
      "HNSW",
      "IVF",
      "FLAT"
    ],
    "source_url": "https://doris.apache.org/docs/4.x/ai/vector-search#approximate-nearest-neighbor-search",
    "comment": "via Faiss"
  },
  "github_stars": {
    "value": 14600,
    "source_url": "https://github.com/ClickHouse/ClickHouse",
    "comment": "",
    "value_90_days": 0
  },
  "docker_pulls": {
    "value": 0,
    "source_url": "",
    "comment": "",
    "value_90_days": 0
  },
  "pypi_downloads": {
    "value": 0,
    "source_url": "",
    "comment": "",
    "value_90_days": 0
  },
  "npm_downloads": {
    "value": 0,
    "source_url": "",
    "comment": "",
    "value_90_days": 0
  },
  "crates_io_downloads": {
    "value": 0,
    "source_url": "",
    "comment": "",
    "value_90_days": 0
  }
}