Trulens Running Evaluations

TruLens Running Evaluations

Execute your configured evaluations and analyze results.

Prerequisites

Before running evaluations, ensure you have:

1. Instrumented your app (see instrumentation skill)
2. Configured your feedback functions (see evaluation-setup skill)

Instructions

Step 1: Wrap Your App with Feedbacks

Pass your configured feedbacks to the appropriate wrapper:

from trulens.core import TruSession

session = TruSession()

# Use the wrapper that matches your framework
tru_app = YourWrapper(
    your_app,
    app_name="MyApp",
    app_version="v1",
    feedbacks=your_feedbacks,  # From evaluation-setup
)

Step 2: Run Your App with Recording

Use the context manager to record traces and run evaluations:

# Single query
with tru_app as recording:
    result = your_app.query("What is TruLens?")

# Multiple queries
test_queries = [
    "What is machine learning?",
    "How does RAG work?",
    "Explain transformers.",
]

with tru_app as recording:
    for query in test_queries:
        your_app.query(query)

Step 3: Wait for and View Results

Evaluations run asynchronously. Use retrieve_feedback_results() to wait for them to complete:

# Wait for evaluations to complete and get results as a DataFrame
# The timeout parameter controls how long to wait (default: 180 seconds)
feedback_results = recording.retrieve_feedback_results(timeout=300)
print(feedback_results)

# For a single record:
single_record_results = recording[0].retrieve_feedback_results(timeout=300)

# View leaderboard summary across all records
print(session.get_leaderboard())

# Launch interactive dashboard
from trulens.dashboard import run_dashboard

run_dashboard(session)

Important: Do NOT use time.sleep() to wait for evaluations. The retrieve_feedback_results() method properly waits for:

1. Records to be written to the database
2. Feedback evaluations to complete
3. Results to be available

Common Patterns

Comparing App Versions

# Version A
tru_v1 = TruLlama(
    query_engine_v1, app_name="MyRAG", app_version="v1", feedbacks=feedbacks
)
with tru_v1 as recording:
    for q in test_queries:
        query_engine_v1.query(q)

# Version B
tru_v2 = TruLlama(
    query_engine_v2, app_name="MyRAG", app_version="v2", feedbacks=feedbacks
)
with tru_v2 as recording:
    for q in test_queries:
        query_engine_v2.query(q)

# Compare on leaderboard (same app_name, different app_version)
print(session.get_leaderboard())

Batch Evaluation with Test Dataset

import pandas as pd

# Load test dataset
test_df = pd.read_csv("test_queries.csv")

with tru_app as recording:
    for _, row in test_df.iterrows():
        result = your_app.query(row["query"])
        # Optionally store results
        # results.append({"query": row["query"], "response": result})

Evaluating with Ground Truth

from trulens.feedback import GroundTruthAgreement

# Load ground truth dataset (see dataset-curation skill)
ground_truth_df = session.get_ground_truth("my_dataset")

# Add ground truth feedback
ground_truth = GroundTruthAgreement(ground_truth_df, provider=provider)
f_agreement = Metric(
    implementation=ground_truth.agreement_measure,
    name="Ground Truth Agreement",
    selectors={
        "prompt": Selector.select_record_input(),
        "response": Selector.select_record_output(),
    },
)

# Include with other feedbacks
all_feedbacks = your_feedbacks + [f_agreement]

Troubleshooting

Deep Agents / LangGraph Specific Issues

If evaluating a Deep Agent or LangGraph app:

1. Use TruGraph instead of TruApp + manual instrumentation:

   from trulens.apps.langgraph import TruGraph
   
   tru_agent = TruGraph(agent, app_name="DeepAgent", feedbacks=[...])
   

2. Why? TruGraph automatically:

   • Creates RECORD_ROOT spans (required for .on_input()/.on_output())
   • Captures all graph nodes and transitions
   • Handles LangGraph-specific data structures

3. Common mistake: Using @instrument(span_type=SpanType.AGENT) instead of RECORD_ROOT will cause feedback selector shortcuts to fail silently