RAG Pipeline Telemetry

RAG Observability & Pipeline Debugging

Diagnose retrieval failures, inspect retrieved context chunks with custom spans, isolate latency bottlenecks across vector stores, and verify factual grounding with async evaluations.

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Root Cause Isolation

Where Did the RAG Response Fail?

When a retrieval-augmented model produces an incorrect answer, traditional logs only show the final output. Observyze enables you to inspect each layer of the pipeline:

Query & Embedding Layer

Automatic or Custom Span

Did the query translation lose semantic meaning or fail embedding generation?

Trace embedding generation latency and input query transformations.

Vector Store Retrieval

Custom Span

Did similarity search return irrelevant chunks due to bad indexing or low top-k?

Track query latency, similarity score distribution, and chunk IDs.

Context Assembly & Prompting

Automatic via SDK / Proxy

Were retrieved chunks truncated or diluted by conflicting documents?

Inspect the complete assembled prompt payload sent to the model.

Model Generation

Automatic via SDK / Proxy

Did the LLM ignore the provided context or hallucinate external facts?

Track generation latency, token spend, and completion outputs.

Asynchronous Factual Evaluation

Observyze Eval Engine

Was the final answer factually grounded in the retrieved sources?

Score traces asynchronously with factual grounding evaluation metrics.

Instrumentation Model

Instrumenting Vector Lookups & Model Generation

Use custom spans to demarcate vector retrieval queries while letting the Observyze SDK automatically record model token counts and completions.

Automatic prompt and completion payload capture

Custom spans for Pinecone, Qdrant, Chroma, or pgvector

Zero latency impact on user-facing streaming requests

rag-trace-example.ts

TypeScript

import { ObservyzeClient } from "@observyze/sdk";
import OpenAI from "openai";

const observyze = new ObservyzeClient({
  apiKey: process.env.OBSERVYZE_API_KEY!,
  projectId: process.env.OBSERVYZE_PROJECT_ID!,
});

const openai = observyze.wrapOpenAI(new OpenAI());

// Trace the complete RAG execution
const trace = observyze.startTrace("rag.qa_pipeline");

// 1. Trace vector retrieval via custom span
const retrievalSpan = trace.startSpan("vector_retrieval");
const chunks = await vectorDb.query({ vector, topK: 3 });
retrievalSpan.setMetadata({ chunkCount: chunks.length });
retrievalSpan.end();

// 2. Model call is automatically traced
const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: buildRagPrompt(userQuery, chunks),
});

await trace.end();

Developer FAQ

Frequently Asked Questions

Technical details, integration patterns, and operational controls.

RAG (Retrieval-Augmented Generation) observability is the practice of tracing and evaluating each phase of a knowledge-augmented AI application—from initial query embedding and vector retrieval to context injection, model generation, and factual grounding verification.

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