# Get Context Length

API to get the maximum context length of a model.

LLMs and embedding models, due to their fundamental architecture, have a property called `context length`, and more specifically a **maximum** context length. Loosely speaking, this is how many tokens the models can "keep in memory" when generating text or embeddings. Exceeding this limit will result in the model behaving erratically.

## Use the `get_context_length()` function on the model object

It's useful to be able to check the context length of a model, especially as an extra check before providing potentially long input to the model.

```python
context_length = model.get_context_length()
```

The `model` in the above code snippet is an instance of a loaded model you get from the `llm.model` method.

### Example: Check if the input will fit in the model's context window

You can determine if a given conversation fits into a model's context by doing the following:

1. Convert the conversation to a string using the prompt template.
2. Count the number of tokens in the string.
3. Compare the token count to the model's context length.

```python
import lmstudio as lms

def does_chat_fit_in_context(model: lms.LLM, chat: lms.Chat) -> bool:
    # Convert the conversation to a string using the prompt template.
    formatted = model.apply_prompt_template(chat)
    # Count the number of tokens in the string.
    token_count = len(model.tokenize(formatted))
    # Get the current loaded context length of the model
    context_length = model.get_context_length()
    return token_count < context_length

model = lms.llm()

chat = lms.Chat.from_history({
    "messages": [
        { "role": "user", "content": "What is the meaning of life." },
        { "role": "assistant", "content": "The meaning of life is..." },
        # ... More messages
    ]
})

print("Fits in context:", does_chat_fit_in_context(model, chat))
```

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