> ## Documentation Index
> Fetch the complete documentation index at: https://docs.uptrain.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Query Resolution

> Evaluates the ability of the LLM to resolve the user's query.

A good AI assistant should be able to effectively address the user's query. Query resolution evaluates the ability of the AI assistant to resolve the user's query effectively.

### How to use it?

```python theme={null}
from uptrain import EvalLLM, QueryResolution

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    'conversation' : [
        {"role": "patient", "content": "Help"}, 
        {"role": "nurse", "content": "what do you need"}, 
        {"role": "patient", "content": "Having chest pain"}, 
        {"role": "nurse", "content": "Sorry, I am not sure what that means"},
        {"role": "patient", "content": "You don't understand. Do something! I am having severe pain in my chest"}
    ]  
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data=data,
    checks=[QueryResolution(user_persona="patient", llm_persona="nurse")],
)
```

<Info>By default, we are using GPT 3.5 Turbo. If you want to use a different model, check out this [tutorial](https://github.com/uptrain-ai/uptrain/blob/main/examples/open_source_evaluator_tutorial.ipynb).</Info>

Sample Response:

```json theme={null}
{
    "score_query_resolution": 0.0,
    "explanation_query_resolution": "The AI assistant failed to effectively address the patient's urgent concern of chest pain, which could indicate a potential medical emergency. The responses provided by the AI assistant did not offer appropriate assistance or guidance in such a critical situation."
}
```

<Note>A higher query resolution score reflects that the LLM effectively addresses the user's query.</Note>

The nurse in the conversation was not able to address the patient's query, which was about chest pain, indicating a potential medical emergency.

Resulting in a low query resolution score.

### How it works?

We evaluate query resolution by determining which of the following cases apply for the given task data:

* The given responses effectively resolve the user's query.
* The given responses partially resolve the user's query.
* The given responses do not resolve the user's query.

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