[
  {
    "id": "milvus-docs-1",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/getstarted/quickstart.md",
    "excerpt": "To create a local Milvus vector database, simply instantiate a `MilvusClient` by specifying a file name to store all data",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-2",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/userGuide/search-query-get/full-text-search.md",
    "excerpt": "it simplifies vector searches by accepting raw text input, automatically converting your text data into sparse embeddings without the need to manually generate vector embeddings",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-3",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/userGuide/search-query-get/multi-vector-search.md",
    "excerpt": "Milvus supports this by allowing search on multiple vector fields, conducting several Approximate Nearest Neighbor (ANN) searches simultaneously",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-4",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/userGuide/search-query-get/filtered-search.md",
    "excerpt": "You can include filtering conditions in a search request so that Milvus conducts metadata filtering before conducting ANN searches, reducing the search scope from the whole collection to only the entities matching the specified filtering conditions",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-5",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/reference/multi_tenancy.md",
    "excerpt": "Milvus supports four multi-tenancy strategies, each offering a different trade-off between scalability, data isolation, and flexibility",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-6",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/adminGuide/rbac.md",
    "excerpt": "With RBAC, you can finely control the operations users can perform at the collection, database, and instance levels, enhancing data security",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-7",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/integrations/milvus_and_mcp.md",
    "excerpt": "allowing AI applications to perform vector searches, manage collections, and retrieve data using natural language commands—without writing custom database queries",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-8",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/getstarted/milvus_lite.md",
    "excerpt": "With the same client-side code, you can run GenAI apps with Milvus Lite on a laptop or Jupyter Notebook, or Milvus Standalone on Docker container, or Milvus Distributed on massive scale Kubernetes cluster serving billions of vectors in production",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-9",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/about/overview.md",
    "excerpt": "In 2022, Milvus supported billion-scale vectors, and in 2023, it scaled up to tens of billions with consistent stability",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-gh-1",
    "tier": "github",
    "url": "https://github.com/milvus-io/milvus",
    "excerpt": "This installs `pymilvus`, the Python SDK for Milvus. Use `MilvusClient` to create a client",
    "fetchedAt": "2026-09-04T22:09:02.024Z"
  },
  {
    "id": "milvus-docs-10",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/getstarted/quickstart.md",
    "excerpt": "Milvus is an open-source vector database that suits AI applications of every size from running a demo chatbot in Jupyter notebook to building web-scale search that serves billions of users.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-11",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/userGuide/search-query-get/full-text-search.md",
    "excerpt": "it simplifies vector searches by accepting raw text input, automatically converting your text data into sparse embeddings without the need to manually generate vector embeddings.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-12",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/userGuide/search-query-get/multi-vector-search.md",
    "excerpt": "Milvus supports this by allowing search on multiple vector fields, conducting several Approximate Nearest Neighbor (ANN) searches simultaneously.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-13",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/userGuide/search-query-get/filtered-search.md",
    "excerpt": "You can include filtering conditions in a search request so that Milvus conducts metadata filtering before conducting ANN searches, reducing the search scope from the whole collection to only the entities matching the specified filtering conditions.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-14",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/reference/multi_tenancy.md",
    "excerpt": "Milvus supports four multi-tenancy strategies, each offering a different trade-off between scalability, data isolation, and flexibility.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-15",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/adminGuide/rbac.md",
    "excerpt": "With RBAC, you can finely control the operations users can perform at the collection, database, and instance levels, enhancing data security.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-16",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/integrations/milvus_and_mcp.md",
    "excerpt": "allowing AI applications to perform vector searches, manage collections, and retrieve data using natural language commands—without writing custom database queries.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-17",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/getstarted/milvus_lite.md",
    "excerpt": "Milvus Lite shares the same API with Milvus Standalone and Distributed, and covers most of the features such as vector data persistence and management, vector CRUD operations, sparse and dense vector search, metadata filtering, multi-vector and hybrid_search.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-18",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/about/overview.md",
    "excerpt": "Compared to popular implementations like FAISS and HNSWLib, Milvus delivers 30%-70% better performance.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-gh-2",
    "tier": "github",
    "url": "https://github.com/milvus-io/milvus",
    "excerpt": "Milvus also supports Standalone mode for single machine deployment.",
    "fetchedAt": "2026-09-04T22:10:51.924Z"
  },
  {
    "id": "milvus-docs-19",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/about/overview.md",
    "excerpt": "Milvus Lite is a Python library that can be easily integrated into your applications. As a lightweight version of Milvus, it’s ideal for quick prototyping in Jupyter Notebooks or running on edge devices",
    "fetchedAt": "2026-09-04T22:12:32.150Z"
  },
  {
    "id": "milvus-docs-20",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/getstarted/milvus_lite.md",
    "excerpt": "With the same client-side code, you can run GenAI apps with Milvus Lite on a laptop or Jupyter Notebook, or Milvus Standalone on Docker container, or Milvus Distributed on massive scale Kubernetes cluster serving billions of vectors in production.",
    "fetchedAt": "2026-09-04T22:12:32.150Z"
  },
  {
    "id": "milvus-docs-21",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/about/overview.md",
    "excerpt": "Milvus Lite is a Python library that can be easily integrated into your applications. As a lightweight version of Milvus, it's ideal for quick prototyping in Jupyter Notebooks or running on edge devices",
    "fetchedAt": "2026-09-04T22:13:46.912Z"
  },
  {
    "id": "milvus-docs-22",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/getstarted/quickstart.md",
    "excerpt": "from pymilvus import MilvusClient client = MilvusClient(\"milvus_demo.db\")",
    "fetchedAt": "2026-09-04T22:13:46.912Z"
  },
  {
    "id": "milvus-docs-23",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/integrations/milvus_and_mcp.md",
    "excerpt": "This tutorial walks you through setting up an MCP server for Milvus, allowing AI applications to perform vector searches, manage collections, and retrieve data using natural language commands—without writing custom database queries.",
    "fetchedAt": "2026-09-04T22:13:46.912Z"
  },
  {
    "id": "milvus-docs-24",
    "tier": "claimed-docs",
    "url": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v3.0.x/site/en/about/overview.md",
    "excerpt": "In 2022, Milvus supported billion-scale vectors, and in 2023, it scaled up to tens of billions with consistent stability, powering large-scale scenarios for over 300 major enterprises",
    "fetchedAt": "2026-09-04T22:13:46.912Z"
  },
  {
    "id": "milvus-gh-3",
    "tier": "github",
    "url": "https://github.com/milvus-io/milvus",
    "excerpt": "res = client.search(\n    collection_name=\"demo_collection\",  # target collection\n    data=query_vectors,  # a list of one or more query vectors, supports batch\n    limit=2,",
    "fetchedAt": "2026-09-04T22:13:46.912Z"
  },
  {
    "id": "milvus-comm-1",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/22012300",
    "excerpt": "At this moment, the IVF indices are based on FAISS, so performance is the same as Faiss. IVF_SQ8H reconstructs Faiss IVF SQ8 with much better performance but requires a GPU.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-2",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/22012300",
    "excerpt": "Milvus allows appending vectors, stored across multiple file slices; when a slice hits a threshold, Milvus builds the index for it and new data goes into a new slice. Vector deletion was not yet supported, expected by end of Q1.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-3",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/22012300",
    "excerpt": "From reading the docs, newly inserted vectors seem to be queried using brute force until indexed - an interesting design, but insertion docs still ask you to rebuild indices after inserting vectors in some cases.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-4",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/22012300",
    "excerpt": "Milvus could run on ARM CPU - ported to Nvidia Jetson NANO and Raspberry Pi 4 (4GB mem) so far, though most people found running Milvus on ARM 'looked cool' but weren't sure they needed it.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-5",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/22012300",
    "excerpt": "There's not a lot of information on the site about the architecture or storage solutions used, prompting a user to ask the authors for more info.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-6",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/40522400",
    "excerpt": "I have been playing with Milvus but as my use case evolves, I think PGVector may be a better fit. I currently store enriched data in PG and embeddings in Milvus - consolidating them into one DB makes more sense.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-7",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/40522400",
    "excerpt": "I recently used Milvus for the first time - it made sense because it was quick to implement, purpose built, and worked exactly as intended.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-8",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/46222002",
    "excerpt": "We regularly do tens of thousands of QPS on pgvector fine on massive data stores. We dropped Milvus after they started trying to force their Zilliz SaaS down our throats.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-9",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/46222002",
    "excerpt": "For people who run thousands of QPS on billions of vectors, Milvus is a solid choice... I've seen many builders migrate from pgvector to Milvus as their apps scale, but perhaps they wish they had considered scalability earlier. (from a Milvus team member, self-described as possibly biased)",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-comm-10",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/46222002",
    "excerpt": "pgVector is great and so is FAISS, but those are just a subset of what you get from Milvus - if you want hybrid search, different IVF variants, disk-based search, horizontal scaling, SIMD, or sparse vectors, Milvus is great.",
    "fetchedAt": "2026-09-04T22:15:01.063Z"
  },
  {
    "id": "milvus-probe-1",
    "tier": "probe",
    "url": "https://milvus.io/llms.txt",
    "excerpt": "PROBE llms.txt: HTTP 200 at https://milvus.io/llms.txt # Milvus\n\n> Milvus is an open-source, high-performance vector database designed for similarity search and AI application",
    "fetchedAt": "2026-09-04T22:15:13.412Z"
  },
  {
    "id": "milvus-probe-2",
    "tier": "probe",
    "url": "https://milvus.io/openapi.json",
    "excerpt": "PROBE openapi: all candidate paths 404 (https://milvus.io/openapi.json, https://milvus.io/swagger.json, https://milvus.io/api/openapi.json, https://milvus.io/.well-known/openapi.json)",
    "fetchedAt": "2026-09-04T22:15:13.412Z"
  },
  {
    "id": "milvus-probe-3",
    "tier": "probe",
    "url": "https://github.com/zilliztech/mcp-server-milvus",
    "excerpt": "official MCP server documented at https://github.com/zilliztech/mcp-server-milvus",
    "fetchedAt": "2026-09-04T22:15:13.412Z"
  }
]