[
  {
    "id": "weaviate-docs-1",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/concepts/search/hybrid-search",
    "excerpt": "Hybrid search combines vector search and keyword search (BM25) to leverage the strengths of both approaches.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-2",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/concepts/search/hybrid-search",
    "excerpt": "Weaviate supports two strategies (`relativeScoreFusion` and `rankedFusion`) for combining vector and keyword search scores",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-3",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io",
    "excerpt": "Weaviate can serve as a robust backend for RAG workflows, where vector search is used to retrieve context that enhances the output of generative models",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-4",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io",
    "excerpt": "These agents can leverage semantic insights to make decisions or trigger actions based on the data stored in Weaviate.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-5",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/quickstart",
    "excerpt": "Set up a collection - Create a collection and import data into it.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-6",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/quickstart",
    "excerpt": "Get answers from your data by using a natural language prompt/question.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-7",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy",
    "excerpt": "Multi-tenancy provides data isolation. Each tenant is stored on a separate shard. Data stored in one tenant is not visible to another tenant.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-8",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy",
    "excerpt": "To change this behavior so Weaviate creates a new tenant, set `autoTenantCreation` to `true` in the collection definition.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-9",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/concepts/replication-architecture",
    "excerpt": "Weaviate allows data replication across a multi-node cluster by setting a replication factor > 1. This enables a variety of benefits such as high availability",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-10",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/configuration/backups",
    "excerpt": "Seamless integration with widely-used cloud blob storage, such as AWS S3, GCS, or Azure Storage",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-11",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/configuration/backups",
    "excerpt": "Backup and Restore between different storage providers",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-12",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/model-providers",
    "excerpt": "Import objects directly into Weaviate without having to manually specify embeddings",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-gh-1",
    "tier": "github",
    "url": "https://github.com/weaviate/weaviate",
    "excerpt": "Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models ... or direct import of pre-computed vector embeddings",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-gh-2",
    "tier": "github",
    "url": "https://github.com/weaviate/weaviate",
    "excerpt": "You can easily start Weaviate and a local vector embedding model with Docker.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-13",
    "tier": "claimed-docs",
    "url": "https://weaviate.io/pricing",
    "excerpt": "Always free — 1 cluster per user, upgrade to paid anytime.",
    "fetchedAt": "2026-09-04T22:08:12.781Z"
  },
  {
    "id": "weaviate-docs-14",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/concepts/search/hybrid-search",
    "excerpt": "A hybrid search runs both search types in parallel and combines their scores to produce a final ranking of results.",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-15",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/quickstart",
    "excerpt": "Import objects and vectorize them with the Weaviate Embeddings service.\n\n](/weaviate/quickstart?import=vectorization#create-a-collection)[\n\nImport vectors\n\nImport pre-computed vector embeddings along with your data.",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-16",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy",
    "excerpt": "By default, Weaviate returns an error if you try to insert an object into a non-existent tenant. To change this behavior so Weaviate creates a new tenant, set `autoTenantCreation` to `true`",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-17",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/concepts/replication-architecture",
    "excerpt": "Weaviate allows data replication across a multi-node cluster by setting a replication factor > 1.",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-18",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/configuration/backups",
    "excerpt": "Incremental backups that only store changed data, reducing backup and speeding up backup times",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-19",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/configuration/backups",
    "excerpt": "Choice of backing up an entire instance, or selected collections only",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-20",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/model-providers",
    "excerpt": "Weaviate integrates with a variety of self-hosted and API-based models from a range of providers.",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-21",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/quickstart",
    "excerpt": "Follow the instructions below to install one of the official client libraries, available in Python, JavaScript/TypeScript, Go, and Java.",
    "fetchedAt": "2026-09-04T22:09:55.757Z"
  },
  {
    "id": "weaviate-docs-22",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io",
    "excerpt": "By indexing data with vectors, Weaviate supports searches based on both semantic similarity and keywords.",
    "fetchedAt": "2026-09-04T22:11:35.171Z"
  },
  {
    "id": "weaviate-docs-23",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/quickstart",
    "excerpt": "Get answers from your data by using a natural language prompt/question. Cloud only",
    "fetchedAt": "2026-09-04T22:11:35.171Z"
  },
  {
    "id": "weaviate-docs-24",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/quickstart",
    "excerpt": "Import objects and vectorize them with the Weaviate Embeddings service.\n\nImport pre-computed vector embeddings along with your data.",
    "fetchedAt": "2026-09-04T22:11:35.171Z"
  },
  {
    "id": "weaviate-docs-25",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io",
    "excerpt": "Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.",
    "fetchedAt": "2026-09-04T22:11:35.171Z"
  },
  {
    "id": "weaviate-docs-26",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io",
    "excerpt": "Weaviate supports searches based on both semantic similarity and keywords. This allows for more relevant results even when the query terms don’t exactly match the stored data.",
    "fetchedAt": "2026-09-04T22:13:04.622Z"
  },
  {
    "id": "weaviate-docs-27",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io",
    "excerpt": "Query Agent: Run agentic search over your Weaviate Cloud collections",
    "fetchedAt": "2026-09-04T22:13:04.622Z"
  },
  {
    "id": "weaviate-docs-28",
    "tier": "claimed-docs",
    "url": "https://docs.weaviate.io/weaviate/quickstart",
    "excerpt": "Import vectors: Import pre-computed vector embeddings along with your data.",
    "fetchedAt": "2026-09-04T22:13:04.622Z"
  },
  {
    "id": "weaviate-gh-3",
    "tier": "github",
    "url": "https://github.com/weaviate/weaviate",
    "excerpt": "Weaviate offers multiple installation and deployment options: Docker, Kubernetes, Weaviate Cloud",
    "fetchedAt": "2026-09-04T22:13:04.622Z"
  },
  {
    "id": "weaviate-gh-4",
    "tier": "github",
    "url": "https://github.com/weaviate/weaviate",
    "excerpt": "It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface.",
    "fetchedAt": "2026-09-04T22:13:04.622Z"
  },
  {
    "id": "weaviate-comm-1",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=36774093",
    "excerpt": "Weaviate is pretty cool IMO. It is open source and fairly easy to get running locally... You can even run Weaviate as an embedded python package rather than a separate process, so compare that to running an ES cluster and I think life gets a lot easier.",
    "fetchedAt": "2026-09-04T22:14:22.154Z"
  },
  {
    "id": "weaviate-comm-2",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=36774093",
    "excerpt": "The near-bot responses from people who do not clearly identify that they work at this company are very frustrating. This feels like using HN for manufactured marketing campaigns... If you work at Weaviate, I'd recommend you stop doing this stuff.",
    "fetchedAt": "2026-09-04T22:14:22.154Z"
  },
  {
    "id": "weaviate-comm-3",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=37311394",
    "excerpt": "Just migrated from Supabase + pgvector to Weaviate hoping to take advantage of langchain.retrievers.weaviate_hybrid_search.WeaviateHybridSearchRetriever. Unfortunately i can't get it work asynchronously :(",
    "fetchedAt": "2026-09-04T22:14:22.154Z"
  },
  {
    "id": "weaviate-comm-4",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=37311394",
    "excerpt": "I feel like small formulas could clarify better these 2 fusion algorithms. I have read the article twice and checked some of the references and I'm still a bit confused how each of the algorithms compute the final order",
    "fetchedAt": "2026-09-04T22:14:22.154Z"
  },
  {
    "id": "weaviate-comm-5",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=36774093",
    "excerpt": "I wonder what Weaviate users think of the GraphQL interface. When using an LLM to do this type of query generation it needs to understand the schema and be trained to generate a semantically correct request.",
    "fetchedAt": "2026-09-04T22:14:22.154Z"
  },
  {
    "id": "weaviate-probe-1",
    "tier": "probe",
    "url": "https://docs.weaviate.io/llms.txt",
    "excerpt": "PROBE llms.txt: HTTP 200 at https://docs.weaviate.io/llms.txt # Weaviate\n\n## TL;DR\n\nWeaviate is an open-source vector database (Go) that stores objects, vectors, and inverted indexes",
    "fetchedAt": "2026-09-04T22:15:09.830Z"
  },
  {
    "id": "weaviate-probe-2",
    "tier": "probe",
    "url": "https://github.com/weaviate/mcp-server-weaviate",
    "excerpt": "official MCP server documented at https://github.com/weaviate/mcp-server-weaviate",
    "fetchedAt": "2026-09-04T22:15:09.830Z"
  }
]