> ## Documentation Index > Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # Mistral AI > Use Mistral AI as the judge LLM for evaluations with the arize-phoenix-evals library. The [`arize-phoenix-evals`](https://pypi.org/project/arize-phoenix-evals/) library uses an LLM-as-judge to grade model output — hallucinations, factuality, helpfulness, toxicity, custom rubrics. Phoenix Evals does not ship a native Mistral adapter, so plug Mistral in via the LiteLLM proxy by passing `provider="litellm"` and `model="mistral/"` to the `LLM(...)` wrapper, then build a `create_classifier(...)` evaluator and run it over a DataFrame with `evaluate_dataframe(...)`. ## Prerequisites * Python 3.11+ * A `MISTRAL_API_KEY` from the [Mistral AI Console](https://console.mistral.ai/) ## Install ```bash theme={null} pip install arize-phoenix-evals litellm pandas ``` `litellm` is the proxy that routes the eval calls to Mistral. You don't need the `mistralai` SDK installed separately. ## Configure credentials ```bash theme={null} export MISTRAL_API_KEY="" ``` LiteLLM reads `MISTRAL_API_KEY` from the environment when it sees a `mistral/...` model id. ## Setup the eval LLM ```python theme={null} # eval_setup.py from phoenix.evals import LLM # `provider="litellm"` routes through the LiteLLM proxy. # The `mistral/` prefix on the model id tells LiteLLM which provider # to dispatch to and which env var (MISTRAL_API_KEY) to read. llm = LLM(provider="litellm", model="mistral/mistral-large-latest") ``` `mistral-large-latest` is a strong default judge; for cheaper batch evals swap in `mistral/mistral-small-latest`. The judge's job is classification, not generation, so a smaller model is often sufficient. ## 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. ```python theme={null} # example.py import pandas as pd from phoenix.evals import LLM, create_classifier, evaluate_dataframe llm = LLM(provider="litellm", model="mistral/mistral-large-latest") 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()) ``` ### Expected output ```text wrap theme={null} 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. ## Troubleshooting * **`401` / `403` from Mistral.** Verify `MISTRAL_API_KEY` is set and has access to the model. Generate a new key at [console.mistral.ai](https://console.mistral.ai/). * **`model_not_found` or `404`.** Confirm the model id is correct — LiteLLM expects `mistral/` (e.g. `mistral/mistral-large-latest`, `mistral/mistral-small-latest`). See the [LiteLLM Mistral provider docs](https://docs.litellm.ai/docs/providers/mistral) for the current list. * **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`](/ax/cookbooks/evaluation/evaluations-quickstart#log-evaluations-back-to-arize) on `arize.Client`. * **Using a different LiteLLM-supported provider.** Swap the `mistral/...` prefix for any [LiteLLM-supported model](https://docs.litellm.ai/docs/providers) — `provider="litellm"` is the generic escape hatch when no native Phoenix adapter exists. ## Resources