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That field was\n> renamed to a generic **`tenantId`** on the re-home — nothing here imports\n> `@agstudio/*` or knows what a guild is; it is a plain multi-tenant partition\n> key on the point payload.\n\n## What's in the box\n\n| Export | Role |\n| --- | --- |\n| `QdrantKnowledgeAdapter` | The `IKnowledgeProvider` implementation. Config-driven — consumes a plain `QdrantAdapterConfig`; knows nothing about `process.env`. |\n| `QdrantAdapterConfigSchema` / `parseQdrantAdapterConfig` | The zod config contract (endpoint, collection, embeddings, optional `tenantId`). |\n| `translateFilter` | The `CorpusFilter` → Qdrant payload-filter translation (exported for testing). |\n| `loadQdrantKnowledgeEnv` / `qdrantEnvToConfig` | The typed env module — the ONE place `QDRANT_*` / `OPENAI_*` are read. |\n| `createStandaloneQdrantAdapter()` | Wires the typed env into a ready-to-use adapter. |\n| provider-kit family (`KNOWLEDGE_QDRANT_CATALOG`, `makeKnowledgeQdrantResolver`, `resolveKnowledgeBackend`) | Registers the `qdrant` backend under the `@agentproto/adapter-knowledge-*` discovery convention. |\n\n## Environment\n\n| Var | Required | Default | Meaning |\n| --- | --- | --- | --- |\n| `QDRANT_URL` | ✅ | — | Base URL of the Qdrant instance. |\n| `QDRANT_API_KEY` | | — | Sent as the `api-key` header (unsecured local deployments need none). |\n| `QDRANT_COLLECTION` | | `knowledge` | Collection to read/write (must exist with the right vector size). |\n| `OPENAI_API_KEY` | ✅ | — | Key for the OpenAI-compatible `/embeddings` call. |\n| `OPENAI_EMBEDDING_MODEL` | | `text-embedding-3-small` | Embedding model id (1536d). |\n| `OPENAI_BASE_URL` | | `https://api.openai.com/v1` | Embeddings base URL (Azure / Ollama / vLLM). |\n| `KNOWLEDGE_QDRANT_TENANT_ID` | | — | Tenant scope. When set, ingest tags every point and query/get/delete force a `must tenantId` clause. |\n\n## Usage\n\n```ts\nimport {\n  QdrantKnowledgeAdapter,\n  createStandaloneQdrantAdapter,\n} from \"@agentproto/adapter-knowledge-qdrant\"\n\n// From the ambient QDRANT_* / OPENAI_* env:\nconst kb = createStandaloneQdrantAdapter()\n\n// Or construct directly (e.g. a host resolving a per-KB config + vault secret):\nconst direct = new QdrantKnowledgeAdapter({\n  endpoint: \"http://127.0.0.1:6333\",\n  collection: \"knowledge\",\n  embeddingApiKey: process.env.OPENAI_API_KEY!,\n  tenantId: \"tenant-42\", // optional — isolates this tenant in a shared collection\n})\n\nawait kb.ingest({ kind: \"text\", uri: \"doc://intro\", content: \"…\" })\nconst { hits } = await kb.query({ query: \"how do I ship\", topK: 5 })\n```\n\n## Scope\n\nSupported: `ingest({ kind: \"text\" | \"url\" })`, `query` (vector), `listSources`,\n`getSource`, `deleteSource`, `healthCheck`. Out of scope (throws a clean\nerror): `kind: \"file\" | \"connector\"` ingestion (needs a host-side resolver);\n`graph` / `hybrid` query modes (Qdrant is vector-only here — the adapter falls\nback to vector and stamps `modeUsed: \"vector\"`).\n\n## Testing\n\nUnit tests run against a mocked `globalThis.fetch` + a canned embeddings\nresponse — **no live Qdrant and no real embeddings API** (CI-safe). They assert\nthe request shapes (upsert / search / scroll / delete), the result mapping, and\nthe `tenantId` payload filter.\n\n```sh\npnpm --filter @agentproto/adapter-knowledge-qdrant test\n```\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}