exa instrumentation

examples/exa_instrumentation_example.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""
+Example showing how to use Laminar's Exa search API instrumentation.
+
+This example demonstrates how to automatically instrument Exa API calls
+with comprehensive input/output tracing using Laminar.
+
+The instrumentation captures:
+- All input parameters via lmnr.span.input
+- Complete response data via lmnr.span.output
+- Service-specific metadata as JSON (search params, results, etc.)
+- Cost tracking with actual costs from Exa API responses
+- Performance metrics (response size, status, duration)
+- Streaming response chunks with aggregated final content
+
+Uses unified service instrumentation specification:
+- service.name: "exa"
+- service.operation: "search" | "answer" | "research"
+- service.cost.*: Cost tracking attributes
+- service.metadata: Exa-specific parameters as JSON string
+"""
+
+import os
+from lmnr import Laminar
+
+# Initialize Laminar with Exa instrumentation
+# This will automatically instrument all Exa API calls with comprehensive tracing
+Laminar.initialize(
+    project_api_key="your-laminar-api-key",  # Replace with your actual API key
+    # EXA instrumentation is included by default, but you can explicitly specify it:
+    # instruments={"EXA"}
+)
+
+# Content tracing is always enabled - all inputs and outputs are captured automatically
+
+# Now you can use Exa normally, and all calls will be automatically traced
+try:
+    from exa_py import Exa
+
+    # Initialize Exa client
+    exa = Exa(os.getenv('EXA_API_KEY'))  # Set your EXA_API_KEY environment variable
+
+    # All of these calls will be automatically instrumented:
+
+    # Basic search
+    print("Performing basic search...")
+    results = exa.search("hottest AI startups", num_results=2)
+    print(f"Found {len(results.results)} results")
+
+    # Search with content
+    print("\nPerforming search with content...")
+    results_with_content = exa.search_and_contents(
+        "AI in healthcare", 
+        text=True, 
+        num_results=2
+    )
+    print(f"Found {len(results_with_content.results)} results with content")
+
+    # Find similar content
+    print("\nFinding similar content...")
+    similar_results = exa.find_similar(
+        "https://www.adept.ai/", 
+        num_results=2
+    )
+    print(f"Found {len(similar_results.results)} similar results")
+
+    # Generate answer
+    print("\nGenerating answer...")
+    answer = exa.answer("What is the capital of France?")
+    print(f"Answer: {answer}")
+
+    # Research task (if available)
+    print("\nCreating research task...")
+    try:
+        task = exa.research.create_task(
+            instructions="What are the main benefits of meditation?",
+            infer_schema=True
+        )
+        print(f"Created research task with ID: {task.id}")
+
+        # Poll for completion
+        result = exa.research.poll_task(task.id)
+        print(f"Research completed with status: {result.status}")
+
+    except AttributeError:
+        print("Research methods not available in this version of exa_py")
+
+    print("\n✓ All Exa API calls have been automatically instrumented!")
+    print("Check your Laminar dashboard to see:")
+    print("  - lmnr.span.input: Complete input parameters")
+    print("  - lmnr.span.output: Full response data (no truncation)")
+    print("  - service.name: 'exa'")
+    print("  - service.cost.amount: Actual costs from Exa API (fallback to estimates)")
+    print("  - service.metadata: Exa-specific parameters as JSON")
+    print("  - service.response.status: 'success' or 'error'")
+    print("  - Regular CLIENT spans following unified service spec")
+
+except ImportError:
+    print("exa_py not installed. Install it with: pip install exa_py")
+    print("The instrumentation is still ready and will work once exa_py is installed.")
+
+except Exception as e:
+    print(f"Error: {e}")
+    print("Make sure to set your EXA_API_KEY environment variable.")
+    print("The instrumentation is working - this is just an API key issue.")

pyproject.toml

@@ -136,6 +136,7 @@ dev = [
   "groq>=0.30.0",
   "anthropic[bedrock]>=0.60.0",
   "langchain-openai>=0.3.32",
+  "exa-py>=1.15.6",
 ]
 
 [build-system]
@@ -146,4 +147,4 @@ build-backend = "uv_build"
 members = ["examples/fastapi-app"]
 
 [tool.ruff]
-target-version = "py310"
+target-version = "py310"