Fusionpact Vectordb

const { create } = require('fusionpact');
const fp = create({ embedder: 'ollama' }); // or 'mock' for zero-config
// Ingest a document — auto-chunks, embeds, indexesawait fp.rag.ingest('Your document text here...', { source: 'doc.pdf' });
// Hybrid search — vector + reasoning + keyword, fused automaticallyconst results = await fp.retriever.retrieve('What safety protocols exist?', {  collection: 'default',  strategy: 'hybrid'});
// Or build LLM-ready context directlyconst context = await fp.rag.buildContext('What safety protocols exist?');console.log(context.prompt); // Ready to paste into any LLM

const { create } = require('fusionpact');
const fp = create({  embedder: 'ollama',        // Local, free, private  llmProvider: 'ollama',     // For tree reasoning  enableHybrid: true});
// Index a structured document with tree structureawait fp.treeIndex.indexDocument('annual-report', reportText, {  format: 'markdown'});
// Hybrid retrieval — automatically uses the best strategyconst results = await fp.retriever.retrieve(  'What were the total deferred tax assets in Q3?',  {    collection: 'documents',       // Vector search here    docId: 'annual-report',        // Tree reasoning here    topK: 5,    strategy: 'hybrid'            // Fuse all strategies  });
// Each result includes:// - score: Fused relevance score// - content: Retrieved text// - sources: Which strategies contributed { vector: 0.8, tree: 0.9, keyword: 0.3 }// - citation: "Section 3 > Financial Data > Table 3.2.1"// - reasoning: Full tree traversal reasoning trace

const retriever = new HybridRetriever({  engine, treeIndex, embedder,  weights: {    vector: 0.4,   // 40% weight to vector similarity    tree: 0.4,     // 40% weight to reasoning-based retrieval    keyword: 0.2   // 20% weight to keyword matching  }});

// Record feedback on result qualityretriever.recordFeedback('financial query', 'tree', 0.95);retriever.recordFeedback('general search', 'vector', 0.85);
// Get recommended weights for a new queryconst weights = retriever.getAdaptiveWeights('new financial query');// → { vector: 0.25, tree: 0.6, keyword: 0.15 }

const { TreeIndex, LLMProvider } = require('fusionpact');
const llm = new LLMProvider({ provider: 'ollama' }); // Free, localconst tree = new TreeIndex({ llmProvider: llm });
// Index a documentawait tree.indexDocument('sec-filing', filingText, {  format: 'markdown',  metadata: { source: '10-K', year: 2024 }});
// Reasoning-based searchconst results = await tree.search('sec-filing', 'Total deferred tax assets', {  maxResults: 3,  includeReasoning: true});
// results[0]:// {//   content: "Table 5.2: Deferred Tax Assets...",//   relevanceScore: 0.95,//   citation: "Financial Statements > Note 5 > Tax Assets > Table 5.2",//   reasoningPath: [//     { title: "Financial Statements", reasoning: "Deferred tax assets are in financial notes", action: "explore" },//     { title: "Note 5: Income Taxes", reasoning: "This note covers tax-related assets", action: "explore" },//     { title: "Table 5.2", reasoning: "Contains the deferred tax asset breakdown", action: "retrieve" }//   ]// }

const tree = new TreeIndex(); // No LLM — keyword fallbackawait tree.indexDocument('doc', text, { format: 'markdown' });const results = await tree.search('doc', 'safety protocols');

const { create } = require('fusionpact');const fp = create({ embedder: 'ollama', enableMemory: true });
// Episodic — remember what happenedawait fp.memory.remember('agent-1', {  content: 'User prefers dark mode and concise answers',  role: 'system',  importance: 0.8});
// Semantic — learn knowledgeawait fp.memory.learn('agent-1',  'OSHA 29 CFR 1910 covers general industry safety standards.',  { source: 'regulations', category: 'compliance' });
// Procedural — register toolsawait fp.memory.registerTool('agent-1', {  name: 'search_incidents',  description: 'Search EHS incident reports by category and severity',  schema: { type: 'object', properties: { severity: { type: 'string' } } }});
// Recall — cross-memory searchconst memories = await fp.memory.recall('agent-1', 'safety compliance');// → { episodic: [...], semantic: [...], procedural: [...], shared: [...] }
// Conversation memoryfp.memory.addMessage('agent-1', 'thread-001', { role: 'user', content: 'What are the PPE requirements?' });fp.memory.addMessage('agent-1', 'thread-001', { role: 'assistant', content: 'PPE requirements include...' });const history = fp.memory.getConversation('agent-1', 'thread-001');
// GDPR-friendly forgetfp.memory.forget('agent-1', { type: 'all' });

const { create, AgentOrchestrator } = require('fusionpact');
const fp = create({ embedder: 'ollama', enableMemory: true });const orchestrator = new AgentOrchestrator({  engine: fp.engine,  memory: fp.memory,  retriever: fp.retriever});
// Register agentsorchestrator.registerAgent({  agentId: 'researcher',  name: 'Research Agent',  role: 'Find and analyze information',  capabilities: ['search', 'analysis', 'summarization']});
orchestrator.registerAgent({  agentId: 'writer',  name: 'Writing Agent',  role: 'Generate reports and documentation',  capabilities: ['writing', 'formatting', 'editing']});
// Agent-to-agent communicationawait orchestrator.send({  from: 'researcher',  to: 'writer',  type: 'result',  payload: { findings: 'Safety incidents decreased 12% YoY...' }});
// Capability-based task delegationawait orchestrator.delegate('coordinator', 'Write a safety summary report', {  requiredCapabilities: ['writing', 'formatting']});// → Automatically routes to 'writer' agent
// Collaborative retrieval across all agentsconst results = await orchestrator.collaborativeRecall('safety compliance');// → Returns memories from all agents, plus shared knowledge
// Message handlingorchestrator.onMessage('writer', async (msg) => {  console.log(`Writer received: ${msg.type} from ${msg.from}`);  // Process task...});

const fp = require('fusionpact').create({ embedder: 'ollama' });
// Ingest — auto-chunks, embeds, indexesawait fp.rag.ingest(documentText, {  source: 'safety-manual.pdf',  title: 'Safety Manual 2024'});
// Build context for any LLMconst ctx = await fp.rag.buildContext('What PPE is required?', {  topK: 5,  maxTokens: 4000,  strategy: 'hybrid'  // Uses HybridRetriever if available});
// ctx.prompt → Ready for any LLM// ctx.sources → Source citations// ctx.chunks → Number of chunks used

const rag = new RAGPipeline(engine, {  chunkStrategy: 'recursive',  // 'recursive' | 'sentence' | 'paragraph'  chunkSize: 512,  chunkOverlap: 50});

const tenantA = engine.tenant('shared-collection', 'acme_corp');const tenantB = engine.tenant('shared-collection', 'globex_inc');
tenantA.insert([{ id: 'doc-1', vector: [...], metadata: { doc: 'Acme Plan' } }]);
// Tenant A queries — only sees Acme data. Always.const results = tenantA.search(queryVec, { topK: 10 });

// Ollama (local, free, private)const fp = create({ embedder: 'ollama' });
// OpenAIconst fp = create({ embedder: 'openai', openaiConfig: { apiKey: 'sk-...' } });
// Mock (for demos/testing — no dependencies)const fp = create({ embedder: 'mock' });