# `Vllm.BeamSearch`
[🔗](https://github.com/nshkrdotcom/vllm/blob/v0.3.0/lib/snakebridge_generated/vllm/beam_search/__init__.ex#L6)

Submodule bindings for `vllm.beam_search`.

## Version

- Requested: 0.14.0
- Observed at generation: 0.14.0

## Runtime Options

All functions accept a `__runtime__` option for controlling execution behavior:

    Vllm.BeamSearch.some_function(args, __runtime__: [timeout: 120_000])

### Supported runtime options

- `:timeout` - Call timeout in milliseconds (default: 120,000ms / 2 minutes)
- `:timeout_profile` - Use a named profile (`:default`, `:ml_inference`, `:batch_job`, `:streaming`)
- `:stream_timeout` - Timeout for streaming operations (default: 1,800,000ms / 30 minutes)
- `:session_id` - Override the session ID for this call
- `:pool_name` - Target a specific Snakepit pool (multi-pool setups)
- `:affinity` - Override session affinity (`:hint`, `:strict_queue`, `:strict_fail_fast`)

### Timeout Profiles

- `:default` - 2 minute timeout for regular calls
- `:ml_inference` - 10 minute timeout for ML/LLM workloads
- `:batch_job` - Unlimited timeout for long-running jobs
- `:streaming` - 2 minute timeout, 30 minute stream_timeout

### Example with timeout override

    # For a long-running ML inference call
    Vllm.BeamSearch.predict(data, __runtime__: [timeout_profile: :ml_inference])

    # Or explicit timeout
    Vllm.BeamSearch.predict(data, __runtime__: [timeout: 600_000])

    # Route to a pool and enforce strict affinity
    Vllm.BeamSearch.predict(data, __runtime__: [pool_name: :strict_pool, affinity: :strict_queue])

See `SnakeBridge.Defaults` for global timeout configuration.

# `create_sort_beams_key_function`

```elixir
@spec create_sort_beams_key_function(integer(), float(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

Python binding for `vllm.beam_search.create_sort_beams_key_function`.

## Parameters

- `eos_token_id` (integer())
- `length_penalty` (float())

## Returns

- `term()`

# `get_beam_search_score`

```elixir
@spec get_beam_search_score([integer()], float(), integer()) ::
  {:ok, float()} | {:error, Snakepit.Error.t()}
```

Calculate the beam search score with length penalty.

Adapted from

https://github.com/huggingface/transformers/blob/ccb92be23def445f2afdea94c31286f84b89eb5b/src/transformers/generation/beam_search.py#L938

## Parameters

- `tokens` (list(integer()))
- `cumulative_logprob` (float())
- `eos_token_id` (integer())
- `length_penalty` (float() default: 1.0)

## Returns

- `float()`

# `get_beam_search_score`

```elixir
@spec get_beam_search_score([integer()], float(), integer(), keyword()) ::
  {:ok, float()} | {:error, Snakepit.Error.t()}
@spec get_beam_search_score([integer()], float(), integer(), float()) ::
  {:ok, float()} | {:error, Snakepit.Error.t()}
```

# `get_beam_search_score`

```elixir
@spec get_beam_search_score([integer()], float(), integer(), float(), keyword()) ::
  {:ok, float()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict() :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

Python binding for `vllm.beam_search.MultiModalDataDict`.

## Returns

- `term()`

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(term()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(
  term(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(term(), term()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(term(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(term(), term(), term()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(term(), term(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(term(), term(), term(), term()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(term(), term(), term(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(term(), term(), term(), term(), term()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(term(), term(), term(), term(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(term(), term(), term(), term(), term(), term()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(term(), term(), term(), term(), term(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(term(), term(), term(), term(), term(), term(), term()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(
  term(),
  term(),
  term(),
  term(),
  term(),
  term(),
  term(),
  keyword()
) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
@spec multi_modal_data_dict(
  term(),
  term(),
  term(),
  term(),
  term(),
  term(),
  term(),
  term()
) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(
  term(),
  term(),
  term(),
  term(),
  term(),
  term(),
  term(),
  term(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

---

*Consult [api-reference.md](api-reference.md) for complete listing*