Trace Azure AI Search
Vector Databases
Auto-instrument Azure AI Search with traceAI in under 3 minutes. Every LLM call, tool use, retrieval, and chain step becomes an OpenTelemetry span you can search, replay, and debug.
Recipes for Azure AI Search
Prerequisites
Before you start
- · A working Azure AI Search app — local or already in production.
- · A free Future AGI account with
FI_API_KEYandFI_SECRET_KEY. - · Python 3.9+ / Node 18+ / Java 17+ depending on which SDK you're installing.
Install
<dependency>
<groupId>ai.futureagi</groupId>
<artifactId>traceai-java-azure-search</artifactId>
<version>LATEST</version>
</dependency>Trace recipe
import ai.futureagi.fi.instrumentation.TraceProvider;
import ai.futureagi.traceai.azure_search.AzureSearchInstrumentor;
TraceProvider provider = TraceProvider.builder()
.projectName("azure_search_app")
.projectType("observe")
.build();
new AzureSearchInstrumentor().instrument(provider);
// Your existing Azure AI Search code runs unchanged.
// Every call is now an OpenTelemetry span in Future AGI.What Future AGI captures
Trace fields you'll see in the dashboard
-
Spans for every Azure AI Search call: input, output, latency, tokens, cost, model name, errors
-
Trace tree across LLM, tool, retrieval, embedding, and chain spans
-
Custom attributes via `using_attributes` (session_id, user_id, prompt_template, tags, custom dicts)
-
Streaming-safe — partial chunks aggregated into a single span
Common gotchas
Read these before you ship
- 01
Set `FI_API_KEY` and `FI_SECRET_KEY` in env before calling `register()` — silent fallback otherwise.
- 02
Async frameworks: instantiate the instrumentor *before* you create the client, not after.
- 03
Streaming responses are aggregated into a single span only when you use the official SDK iterator.
Next: chain it with the other recipes
Trace is the first step. Most teams add an evaluator the same week, and start optimising or simulating once they have a baseline. Each recipe takes minutes to wire up.
Adjacent integrations
More integrations like Azure AI Search
Pinecone
Managed vector database with hybrid search and metadata filtering.
Weaviate
Open-source vector database with built-in vectorizers and modules.
Qdrant
Vector search engine with payload filtering and quantisation.
Chroma
Embeddings database for AI applications with first-class collections.