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The arize-phoenix-evals library uses an LLM-as-judge to grade model output — hallucinations, factuality, helpfulness, toxicity, custom rubrics. Plug Bedrock-hosted models in as the judge by passing provider="bedrock" to the LLM(...) wrapper, then build a create_classifier(...) evaluator and run it over a DataFrame with evaluate_dataframe(...).

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Prerequisites

  • Python 3.11+
  • AWS credentials with bedrock:InvokeModel permission on the model you want to judge with
  • The target foundation model enabled in your AWS region’s Bedrock model access page

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Install

pip install arize-phoenix-evals litellm boto3 pandas
The bedrock provider uses the LiteLLM backend under the hood; boto3 provides the AWS SDK and SigV4 signing.

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Configure credentials

The bedrock provider picks up the standard AWS credential chain — env vars, shared credentials file (~/.aws/credentials), or an attached IAM role. Set the env vars directly if you don’t already have AWS credentials configured: 
export AWS_ACCESS_KEY_ID="<your-access-key-id>"
export AWS_SECRET_ACCESS_KEY="<your-secret-access-key>"
export AWS_REGION="us-east-1"          # region where the model is enabled
# Optional, only if you're using STS / SSO short-term credentials:
# export AWS_SESSION_TOKEN="..."

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Setup the eval LLM

# eval_setup.py
from phoenix.evals import LLM

# `provider="bedrock"` dispatches via the LiteLLM backend to the Bedrock
# Runtime API, picking up the ambient AWS credentials.
llm = LLM(provider="bedrock", model="us.anthropic.claude-sonnet-4-6")
Bedrock model ids vary by region and provider — many Anthropic models on Bedrock require the cross-region us. / eu. inference profile prefix shown above. See the Bedrock model catalog for the id to use in your region.

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Run an evaluation

This example builds a hallucination classifier and grades two sample question/answer pairs against a reference. The pattern generalizes: replace the prompt template, choices, and DataFrame columns with whatever metric you want to evaluate. 
# example.py
import pandas as pd

from phoenix.evals import LLM, create_classifier, evaluate_dataframe

llm = LLM(provider="bedrock", model="us.anthropic.claude-sonnet-4-6")

HALLUCINATION_PROMPT = """\
Determine whether the answer below is factually supported by the
reference. Reply with exactly one of: factual, hallucinated.

Question: {input}
Answer: {output}
Reference: {reference}
"""

evaluator = create_classifier(
    name="hallucination",
    prompt_template=HALLUCINATION_PROMPT,
    llm=llm,
    # `choices` maps each label the LLM may emit to a numeric score.
    # `direction="maximize"` (the default) means higher score is better.
    choices={"factual": 1.0, "hallucinated": 0.0},
)

df = pd.DataFrame([
    {
        "input":     "What is the capital of France?",
        "output":    "Paris is the capital of France.",
        "reference": "Paris is the capital and most populous city of France.",
    },
    {
        "input":     "What is the capital of France?",
        "output":    "Berlin is the capital of France.",
        "reference": "Paris is the capital and most populous city of France.",
    },
])

results = evaluate_dataframe(dataframe=df, evaluators=[evaluator])

# `hallucination_score` is a Score row (a dict-like with `score`, `label`,
# `explanation`, …) — pull the numeric out for a flat display column.
results["score"] = results["hallucination_score"].apply(lambda r: r["score"])
print(results[["input", "output", "score"]].to_string())

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Expected output

                            input                            output  score
0  What is the capital of France?   Paris is the capital of France.    1.0
1  What is the capital of France?  Berlin is the capital of France.    0.0
The full returned DataFrame also includes hallucination_execution_details (status + exceptions + timing) and the original hallucination_score column with each evaluator result’s full dict (name, score, label, explanation, metadata, kind, direction) — useful for surfacing the LLM’s reasoning, persisting eval rows back to Arize AX, or filtering retries.

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Troubleshooting

  • AccessDeniedException / UnrecognizedClientException. Your AWS credentials don’t have bedrock:InvokeModel on the target model, or the credentials aren’t being picked up. Verify with aws sts get-caller-identity and confirm the role has Bedrock permissions.
  • ValidationException: ... on-demand throughput isn't supported. The base Anthropic model id (e.g. anthropic.claude-sonnet-4-6) requires a cross-region inference profile. Switch to the regional prefix (us.anthropic.claude-sonnet-4-6 for US, eu.... for EU).
  • AccessDeniedException: You don't have access to the model. The model isn’t enabled in your region. Enable it on the Bedrock model access page.
  • All rows return the same label. Your prompt template isn’t differentiating cases. Make sure each row’s {input}/{output}/{reference} columns expose enough context for the judge to discriminate, and that choices lists every label your prompt asks the LLM to emit.
  • Some rows fail with timeout / rate-limit. Pass max_retries= to evaluate_dataframe(...) (defaults to 3). For large batches, also pass initial_per_second_request_rate=... to LLM(...) to throttle.
  • Logging results back to Arize AX. This guide stops at producing the eval DataFrame. To attach those evals to existing spans in an Arize AX project, use log_evaluations_sync on arize.Client.
  • Assuming a role from a different account. Use boto3.client("sts").assume_role(...), export the temporary credentials as env vars, then call LLM(...) — the provider will pick them up on the next request.

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Resources

Phoenix Evals Documentation

arize-phoenix-evals on PyPI

Phoenix Evals Source

Amazon Bedrock Tracing (instrument app calls)