Function startObservation

Example

import { startObservation } from '@langfuse/tracing';

// Span for general operations (default)
const span = startObservation('user-workflow', {
  input: { userId: '123', action: 'checkout' },
  metadata: { version: '2.1.0', feature: 'new-checkout' }
});
span.update({ output: { success: true, orderId: '456' } });
span.end();

// Generation for LLM interactions
const generation = startObservation('openai-gpt-4', {
  input: [{ role: 'user', content: 'Explain quantum computing' }],
  model: 'gpt-4-turbo',
  modelParameters: { temperature: 0.7, maxTokens: 500 }
}, { asType: 'generation' });

generation.update({
  output: { role: 'assistant', content: 'Quantum computing...' },
  usageDetails: { promptTokens: 12, completionTokens: 150, totalTokens: 162 },
  costDetails: { totalCost: 0.002, currency: 'USD' }
});
generation.end();

// Agent for AI workflows with tools
const agent = startObservation('research-agent', {
  input: { query: 'Latest developments in renewable energy' },
  metadata: { tools: ['web-search', 'pdf-reader'], model: 'gpt-4' }
}, { asType: 'agent' });

// Tool for individual API calls
const weatherTool = startObservation('weather-api', {
  input: { location: 'San Francisco', units: 'metric' },
  metadata: { provider: 'openweather', timeout: 5000 }
}, { asType: 'tool' });

// Chain for multi-step RAG pipeline
const ragChain = startObservation('rag-qa-pipeline', {
  input: { question: 'How does photosynthesis work?' },
  metadata: { steps: ['retrieve', 'rerank', 'generate'], vectorDb: 'pinecone' }
}, { asType: 'chain' });

// Retriever for vector search
const retriever = startObservation('vector-search', {
  input: { query: 'machine learning algorithms', topK: 5 },
  metadata: { vectorStore: 'chroma', similarity: 'cosine' }
}, { asType: 'retriever' });

// Evaluator for quality assessment
const evaluator = startObservation('quality-check', {
  input: {
    response: 'Paris is the capital of France',
    reference: 'The capital city of France is Paris'
  },
  metadata: { metric: 'semantic-similarity', threshold: 0.8 }
}, { asType: 'evaluator' });

// Guardrail for content filtering
const guardrail = startObservation('content-filter', {
  input: { text: 'User message content', policies: ['no-profanity', 'no-pii'] },
  metadata: { strictMode: true, version: 'v2' }
}, { asType: 'guardrail' });

// Embedding for text vectorization
const embedding = startObservation('text-embedder', {
  input: { texts: ['Hello world', 'Machine learning'] },
  model: 'text-embedding-ada-002',
  metadata: { dimensions: 1536 }
}, { asType: 'embedding' });

// Event for point-in-time occurrences (auto-ended)
const event = startObservation('user-login', {
  input: { userId: '123', method: 'oauth' },
  level: 'DEFAULT',
  metadata: { ip: '192.168.1.1', userAgent: 'Chrome/120.0' }
}, { asType: 'event' });

// Nested observations with parent context
const parentSpan = startObservation('ai-pipeline');
const childRetriever = startObservation('doc-search', {
  input: { query: 'AI safety' }
}, {
  asType: 'retriever',
  parentSpanContext: parentSpan.otelSpan.spanContext()
});
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