interface VectorStore {
  initialize(): Promise<void>;
  upsert(collection: string, doc: VectorDocument): Promise<void>;
  upsertBatch(collection: string, docs: VectorDocument[]): Promise<void>;
  search(
    collection: string,
    query: number[] | string,
    options?: VectorSearchOptions
  ): Promise<VectorSearchResult[]>;
  get(collection: string, id: string): Promise<VectorDocument | null>;
  delete(collection: string, id: string): Promise<void>;
  dropCollection(collection: string): Promise<void>;
  close(): Promise<void>;
}

import { InMemoryVectorStore, OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding({ apiKey: process.env.OPENAI_API_KEY });
const store = new InMemoryVectorStore(embedder);

await store.initialize(); // no-op, but call for consistency
await store.upsert("docs", {
  id: "1",
  content: "Agentium is a TypeScript agent framework.",
});
const results = await store.search("docs", "What is Agentium?");

import { PgVectorStore, OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding();
const store = new PgVectorStore(
  {
    connectionString: process.env.DATABASE_URL!,
    dimensions: 1536, // optional, from embedder if omitted
  },
  embedder
);

await store.initialize(); // Creates vector extension + tables
await store.upsert("docs", { id: "1", content: "..." });
const results = await store.search("docs", "query", { topK: 5 });

import { QdrantVectorStore, OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding();
const store = new QdrantVectorStore(
  {
    url: "http://localhost:6333",
    apiKey: process.env.QDRANT_API_KEY,
    dimensions: 1536,
  },
  embedder
);

await store.initialize();
await store.upsert("docs", { id: "1", content: "..." });
const results = await store.search("docs", "query", { topK: 5, minScore: 0.7 });

import { MongoDBVectorStore, OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding();
const store = new MongoDBVectorStore(
  {
    uri: process.env.MONGO_URI!,
    dbName: "agentium_vectors",
    indexName: "vector_index", // Atlas Search index name
    dimensions: 1536,
  },
  embedder
);

await store.initialize();
await store.upsert("docs", { id: "1", content: "..." });
const results = await store.search("docs", "query", { topK: 5 });

// With embedder—documents without embedding get one automatically
const store = new InMemoryVectorStore(embedder);
await store.upsert("docs", { id: "1", content: "Hello" }); // embedding computed

// Without embedder—documents must include embedding
await store.upsert("docs", {
  id: "1",
  content: "Hello",
  embedding: [0.1, -0.2, ...],
});