import { RpcClient } from '@hivehub/vectorizer-sdk';

const client = await RpcClient.connectUrl('vectorizer://127.0.0.1:15503');
await client.hello({ clientName: 'my-app' });

console.log(await client.listCollections());
const hits = await client.searchBasic('docs', 'vector database', 5);
for (const hit of hits) console.log(hit.id, hit.score);

client.close();

import { VectorizerClient } from "@hivehub/vectorizer-sdk";

// Create client
const client = new VectorizerClient({
  baseURL: "http://localhost:15002",
  apiKey: "your-api-key-here",
});

import { SearchClient } from "@hivehub/vectorizer-sdk";

const search = new SearchClient({ baseURL: "http://localhost:15002" });
const results = await search.searchVectors("documents", { query_vector: [...], limit: 5 });

// Test / RPC-readiness regression guard:
class MockTransport implements Transport { /* ... */ }
const client = new VectorizerClient({ transport: new MockTransport() });

// Health check
const health = await client.healthCheck();
console.log("Server status:", health.status);

// Create collection
const collection = await client.createCollection({
  name: "documents",
  dimension: 768,
  similarity_metric: "cosine",
});

// Insert vectors
const vectors = [
  {
    data: [0.1, 0.2, 0.3 /* ... 768 dimensions */],
    metadata: { source: "document1.pdf" },
  },
];

await client.insertVectors("documents", vectors);

// Search vectors
const results = await client.searchVectors("documents", {
  query_vector: [0.1, 0.2, 0.3 /* ... 768 dimensions */],
  limit: 5,
});

// Text search
const textResults = await client.searchText("documents", {
  query: "machine learning algorithms",
  limit: 5,
});

// Generate embeddings
const embedding = await client.embedText({
  text: "machine learning algorithms",
});

// Hybrid search (dense + sparse vectors)
const hybridResults = await client.hybridSearch({
  collection: "documents",
  query: "machine learning",
  query_sparse: {
    indices: [0, 5, 10, 15],
    values: [0.8, 0.6, 0.9, 0.7],
  },
  alpha: 0.7,
  algorithm: "rrf",
  dense_k: 20,
  sparse_k: 20,
  final_k: 10,
});

// Graph Operations (requires graph enabled in collection config)
// List all graph nodes
const nodes = await client.listGraphNodes("documents");
console.log(`Graph has ${nodes.count} nodes`);

// Get neighbors of a node
const neighbors = await client.getGraphNeighbors("documents", "document1");
console.log(`Node has ${neighbors.neighbors.length} neighbors`);

// Find related nodes within 2 hops
const related = await client.findRelatedNodes("documents", "document1", {
  max_hops: 2,
  relationship_type: "SIMILAR_TO",
});
console.log(`Found ${related.related.length} related nodes`);

// Find shortest path between two nodes
const path = await client.findGraphPath({
  collection: "documents",
  source: "document1",
  target: "document2",
});
if (path.found) {
  console.log(`Path found: ${path.path.map(n => n.id).join(" -> ")}`);
}

// Create explicit relationship
const edge = await client.createGraphEdge({
  collection: "documents",
  source: "document1",
  target: "document2",
  relationship_type: "REFERENCES",
  weight: 0.9,
});
console.log(`Created edge: ${edge.edge_id}`);

// Discover SIMILAR_TO edges for entire collection
const discoveryResult = await client.discoverGraphEdges("documents", {
  similarity_threshold: 0.7,
  max_per_node: 10,
});
console.log(`Discovered ${discoveryResult.edges_created} edges`);

// Discover edges for a specific node
const nodeDiscovery = await client.discoverGraphEdgesForNode(
  "documents",
  "document1",
  {
    similarity_threshold: 0.7,
    max_per_node: 10,
  }
);
console.log(`Discovered ${nodeDiscovery.edges_created} edges for node`);

// Get discovery status
const status = await client.getGraphDiscoveryStatus("documents");
console.log(
  `Discovery status: ${status.total_nodes} nodes, ` +
    `${status.total_edges} edges, ` +
    `${status.progress_percentage.toFixed(1)}% complete`
);

// Qdrant-compatible API usage
const qdrantCollections = await client.qdrantListCollections();
const qdrantResults = await client.qdrantSearchPoints(
  "documents",
  embedding.embedding,
  10
);

const client = new VectorizerClient({
  baseURL: "http://localhost:15002", // API base URL
  apiKey: "your-api-key", // API key for authentication
  timeout: 30000, // Request timeout in ms
  headers: {
    // Custom headers
    "User-Agent": "MyApp/1.0",
  },
  logger: {
    // Logger configuration
    level: "info", // debug, info, warn, error
    enabled: true,
  },
});

const client = new VectorizerClient({
  connectionString: "umicp://localhost:15003",
  apiKey: "your-api-key",
});

const client = new VectorizerClient({
  protocol: "umicp",
  apiKey: "your-api-key",
  umicp: {
    host: "localhost",
    port: 15003,
    compression: "gzip", // 'gzip', 'deflate', 'lz4', or 'none'
    encryption: true, // Enable encryption
    priority: "normal", // 'low', 'normal', 'high'
  },
});

import { VectorizerClient } from "@hivehub/vectorizer-sdk";

// Configure with master and replicas - SDK handles routing automatically
const client = new VectorizerClient({
  hosts: {
    master: "http://master-node:15002",
    replicas: ["http://replica1:15002", "http://replica2:15002"],
  },
  apiKey: "your-api-key",
  readPreference: "replica", // "master" | "replica" | "nearest"
});

// Writes automatically go to master
await client.createCollection({
  name: "documents",
  dimension: 768,
  similarity_metric: "cosine",
});

await client.insertTexts("documents", [
  { id: "doc1", text: "Sample document", metadata: { source: "api" } },
]);

// Reads automatically go to replicas (load balanced)
const results = await client.searchVectors("documents", {
  query: "sample",
  limit: 10,
});

const collections = await client.listCollections();

// Option 1: Override read preference for specific operation
await client.insertTexts("docs", [newDoc]);
const result = await client.getVector("docs", newDoc.id, { readPreference: "master" });

// Option 2: Use a transaction-like pattern
const result = await client.withMaster(async (masterClient) => {
  await masterClient.insertTexts("docs", [newDoc]);
  return await masterClient.getVector("docs", newDoc.id);
});

// Single node - no replication
const client = new VectorizerClient({
  baseURL: "http://localhost:15002",
  apiKey: "your-api-key",
});

// List collections
const collections = await client.listCollections();

// Get collection info
const info = await client.getCollection("documents");

// Create collection
const collection = await client.createCollection({
  name: "documents",
  dimension: 768,
  similarity_metric: "cosine",
  description: "Document embeddings",
});

// Update collection
const updated = await client.updateCollection("documents", {
  description: "Updated description",
});

// Delete collection
await client.deleteCollection("documents");

// Insert vectors
const vectors = [
  {
    data: [0.1, 0.2, 0.3],
    metadata: { source: "doc1.pdf" },
  },
];
await client.insertVectors("documents", vectors);

// Get vector
const vector = await client.getVector("documents", "vector-id");

// Update vector
const updated = await client.updateVector("documents", "vector-id", {
  metadata: { updated: true },
});

// Delete vector
await client.deleteVector("documents", "vector-id");

// Delete multiple vectors — returns DeleteReport with per-id status.
const deleteReport = await client.deleteVectors("documents", [
  "id1",
  "id2",
  "id3",
]);
console.log(`deleted=${deleteReport.deleted} failed=${deleteReport.failed}`);

// Tier demotion: move vectors between collections without re-embedding
// (issue #265). The server inserts into `dst` BEFORE deleting from
// `src`, so a mid-batch crash leaves a recoverable duplicate, never
// data loss. Per-id outcomes (`ok | missing_in_src |
// dst_insert_failed | src_delete_failed`) populate `results` without
// aborting the batch.
const moveReport = await client.moveToCollection("hot", "warm", [
  "vec-1",
  "vec-2",
]);
for (const row of moveReport.results) {
  if (row.status !== "ok") {
    console.warn("move failed", row);
  }
}

const client = new VectorizerClient({ baseURL: "http://localhost:15002" });

// Server health, uptime, collection/vector counts
const stats = await client.getStats();
console.log("Total vectors:", stats.total_vectors);

const status = await client.getStatus();
console.log(`Server v${status.version}, uptime: ${status.uptime}s`);

// Recent logs
const logs = await client.getLogs({ lines: 50, level: "INFO" });
for (const entry of logs) {
  console.log(`${entry.timestamp}: ${entry.message}`);
}

// Per-collection indexing progress
const progress = await client.getIndexingProgress();
for (const [collection, pct] of Object.entries(progress.progress)) {
  console.log(`${collection}: ${pct.toFixed(1)}% complete`);
}

// Force flush one collection
await client.forceSaveCollection("my_docs");

// List and clean empty collections
const empty = await client.listEmptyCollections();
if (empty.length > 0) {
  const report = await client.cleanupEmptyCollections();
  console.log(`Cleaned up ${report.deleted} empty collections`);
}

// List workspaces
const workspaces = await client.listWorkspaces();
console.log("Workspaces:", workspaces);

const client = new VectorizerClient({ baseURL: "http://localhost:15002" });

// Current user info
const user = await client.me();
console.log(`Logged in as: ${user.username} (roles: ${user.roles.join(", ")})`);

// Refresh token with extended TTL
const token = await client.refreshToken();
console.log(`Token refreshed, expires in: ${token.expires_in} seconds`);

// Validate password before creating account
const report = await client.validatePassword("MySecure123!");
console.log(`Valid: ${report.valid}, feedback: ${report.feedback.join(", ")}`);

// Create API key for programmatic access
const apiKey = await client.createApiKey({
  name: "integration-key",
  expires_in: 86400 * 365, // 1 year
});
console.log("API Key:", apiKey.api_key);

// List and revoke API keys
const keys = await client.listApiKeys();
for (const key of keys) {
  console.log(`Key: ${key.id} (expires: ${key.expires_at})`);
}
await client.revokeApiKey(keys[0].id);

// Change password
await client.changePassword("newPassword123!");

// Logout
await client.logout();

const client = new VectorizerClient({ baseURL: "http://localhost:15002" });

// Check replication role and status
const status = await client.getReplicationStatus();
console.log(`Role: ${status.role}, enabled: ${status.enabled}`);

// Get replication statistics (lag, bytes synced)
const stats = await client.getReplicationStats();
console.log(`Bytes synced: ${stats.bytes_synced}`);

// List all replicas connected to this master
const replicas = await client.listReplicas();
for (const replica of replicas) {
  console.log(`Replica: ${replica.address} (lag: ${replica.lag_ms}ms)`);
}

const client = new VectorizerClient({ baseURL: "http://localhost:15002" });

// Stage 1: Broad discovery — multi-query search across all collections
const broad = await client.broadDiscovery({
  query: "machine learning algorithms",
  max_results: 20,
});
console.log(`Found ${broad.results.length} broad results`);

// Stage 2: Semantic focus — narrow search to top collection
const focused = await client.semanticFocus({
  query: "neural networks",
  collection: "research",
  max_results: 10,
});
console.log(`Focused results: ${focused.results.length}`);

// Stage 3: Promote README — elevate high-quality chunks
const promoted = await client.promoteReadme({
  results: focused.results,
  readme_boost: 2.0,
});

// Stage 4: Compress evidence — distill to bullet points
const bullets = await client.compressEvidence({
  chunks: promoted.results,
  max_bullets: 15,
});
console.log("Evidence bullets:", bullets.bullets);

// Stage 5: Build answer plan — organize bullets into sections
const plan = await client.buildAnswerPlan({
  evidence: bullets.bullets,
  max_sections: 5,
});
console.log("Sections:", plan.sections);

// Stage 6: Render LLM prompt — final markdown string for LLM
const llmPrompt = await client.renderLlmPrompt({
  plan,
  style: "formal",
});
console.log("LLM prompt:\n", llmPrompt.markdown);

const client = new VectorizerClient({ baseURL: "http://localhost:15002" });

const userId = "user-123";

// List user's backups
const backups = await client.listUserBackups(userId);
for (const backup of backups) {
  console.log(`Backup: ${backup.id} (size: ${backup.size_bytes} bytes)`);
}

// Create a new backup
const newBackup = await client.createUserBackup({
  user_id: userId,
  name: "full-backup-2024-01",
  description: "January full backup",
  collections: undefined, // backup all
});
console.log("Created backup:", newBackup.id);

// Restore a backup
await client.restoreUserBackup({
  user_id: userId,
  backup_id: newBackup.id,
});
console.log("Restore started");

// Delete old backup
await client.deleteUserBackup(userId, backups[0].id);

// Vector similarity search
const results = await client.searchVectors("documents", {
  query_vector: [0.1, 0.2, 0.3],
  limit: 10,
  threshold: 0.8,
  include_metadata: true,
});

// Text semantic search
const textResults = await client.searchText("documents", {
  query: "machine learning",
  limit: 10,
  threshold: 0.8,
  include_metadata: true,
  model: "bert-base",
});

const results = await client.intelligentSearch({
  query: "machine learning algorithms",
  collections: ["documents", "research"],
  max_results: 15,
  domain_expansion: true,
  technical_focus: true,
  mmr_enabled: true,
  mmr_lambda: 0.7,
});

const results = await client.semanticSearch({
  query: "neural networks",
  collection: "documents",
  max_results: 10,
  semantic_reranking: true,
  similarity_threshold: 0.6,
});

const results = await client.contextualSearch({
  query: "API documentation",
  collection: "docs",
  context_filters: {
    category: "backend",
    language: "typescript",
  },
  max_results: 10,
});

const results = await client.multiCollectionSearch({
  query: "authentication",
  collections: ["docs", "code", "tickets"],
  max_total_results: 20,
  max_per_collection: 5,
  cross_collection_reranking: true,
});

const filtered = await client.filterCollections({
  query: "machine learning",
  min_score: 0.5,
});

const expanded = await client.expandQueries({
  query: "neural networks",
  max_expansions: 5,
});

const discovery = await client.discover({
  query: "authentication methods",
  max_results: 10,
});

const content = await client.getFileContent({
  collection: "docs",
  file_path: "src/client.ts",
});

const files = await client.listFilesInCollection({
  collection: "docs",
});

const chunks = await client.getFileChunksOrdered({
  collection: "docs",
  file_path: "README.md",
  chunk_size: 1000,
});

const outline = await client.getProjectOutline({
  collection: "codebase",
});

const related = await client.getRelatedFiles({
  collection: "codebase",
  file_path: "src/client.ts",
  max_results: 5,
});

const summary = await client.summarizeText({
  text: "Long document text...",
  method: "extractive", // 'extractive', 'abstractive', 'hybrid'
  max_length: 200,
});

const summary = await client.summarizeContext({
  context: "Document context...",
  method: "abstractive",
  focus: "key_points",
});

await client.addWorkspace({
  name: "my-project",
  path: "/path/to/project",
});

const workspaces = await client.listWorkspaces();

await client.removeWorkspace({
  name: "my-project",
});

const backup = await client.createBackup({
  name: "backup-2024-11-24",
});

const backups = await client.listBackups();

await client.restoreBackup({
  filename: "backup-2024-11-24.vecdb",
});

// Generate embeddings
const embedding = await client.embedText({
  text: "machine learning algorithms",
  model: "bert-base",
  parameters: {
    max_length: 512,
    normalize: true,
  },
});

import {
  VectorizerError,
  AuthenticationError,
  CollectionNotFoundError,
  ValidationError,
  NetworkError,
  ServerError,
} from "@hivehub/vectorizer-sdk";

try {
  await client.createCollection({
    name: "documents",
    dimension: 768,
  });
} catch (error) {
  if (error instanceof AuthenticationError) {
    console.error("Authentication failed:", error.message);
  } else if (error instanceof ValidationError) {
    console.error("Validation error:", error.message);
  } else if (error instanceof NetworkError) {
    console.error("Network error:", error.message);
  } else {
    console.error("Unknown error:", error.message);
  }
}

// Vector types
interface Vector {
  id: string;
  data: number[];
  metadata?: Record<string, unknown>;
}

// Collection types
interface Collection {
  name: string;
  dimension: number;
  similarity_metric: "cosine" | "euclidean" | "dot_product";
  description?: string;
  created_at?: Date;
  updated_at?: Date;
}

// Search result types
interface SearchResult {
  id: string;
  score: number;
  data: number[];
  metadata?: Record<string, unknown>;
}

// Client configuration
interface VectorizerClientConfig {
  baseURL?: string;
  wsURL?: string;
  apiKey?: string;
  timeout?: number;
  headers?: Record<string, string>;
  logger?: LoggerConfig;
}