{
  "uid": "cogitave.learn.query-the-estate-with-cogitave-query.introduction",
  "kind": "moduleUnit",
  "href": "/modules/query-the-estate-with-cogitave-query/introduction/",
  "title": "Introduction",
  "summary": "",
  "type": null,
  "products": [],
  "roles": [],
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  "headings": [
    "Why fuse three signals instead of picking the best one",
    "Read this as a Day 0 spec",
    "What you will get from this module"
  ],
  "source": "Core holds one canonical model: a single typed property graph shared by every product, standard, and agent in the estate (@cogitave.learn.core-model-fundamentals covers that graph directly). Holding the graph is only half the story - you also need one way to *ask* it something. That layer is **Cogitave Query**.\n\n> One query layer over the [canonical graph](../../../../core/docs/architecture.md). Same model, same retrieval for humans (API/UI) and agents (MCP). Retrieval is **hybrid**: lexical recall, semantic recall, and graph structure are three signals fused into one ranking - none alone is enough.\n\nThat single sentence from [Cogitave Query](../../../../core/docs/query.md) is this module's whole subject. There is no separate \"agent view\" of the estate and a different \"human view\" - `docs_search` over MCP and the human-facing search box call the same pipeline and get the same ranking. This module teaches you to reason about what that pipeline actually does to a query, not to reimplement it.\n\n## Why fuse three signals instead of picking the best one\n\nLexical retrieval (BM25) and dense-vector retrieval each fail on inputs the other handles well: BM25 misses paraphrase and synonymy, dense retrieval misses the rare exact tokens - identifiers, error codes, API symbols, version strings - that dominate technical and agent queries. Cogitave adds a third signal neither text index can see at all: the **graph** itself, with its prerequisite edges, cross-references, and authority structure.\n\n## Read this as a Day 0 spec\n\nCogitave Core is [a Day 0 design](../../../../core/README.md) - the canonical model, its query layer, and its native MCP surface are specified and built against, not a black box you take on faith. This module follows that same posture: every mechanism it names - BM25 segments, HNSW vectors, Reciprocal Rank Fusion, the bounded `query_graph` profile - is stated exactly as [Cogitave Query](../../../../core/docs/query.md) and [ADR-0002](../../../../core/docs/decisions/0002-hybrid-retrieval.md) specify it, with a link back to the source.\n\n## What you will get from this module\n\nBy the end, you will be able to trace a query through the pipeline end to end, explain why rank-based fusion was chosen over a weighted score blend, and read the bounded graph-query contract that lets an agent call the graph directly without an unbounded write path opening underneath it.\n",
  "partOf": "cogitave.learn.query-the-estate-with-cogitave-query",
  "durationInMinutes": 4,
  "quiz": null
}