Skip to content

Class swarmauri_standard.llms.MistralModel.MistralModel

swarmauri_standard.llms.MistralModel.MistralModel

MistralModel(**data)

Bases: LLMBase

A model class for interfacing with the Mistral language model API.

Provides methods for synchronous, asynchronous, and streaming conversation interactions with the Mistral language model API.

ATTRIBUTE DESCRIPTION
api_key

API key for authenticating with Mistral.

TYPE: str

allowed_models

List of model names allowed for use.

TYPE: List[str]

name

Default model name.

TYPE: str

type

Type identifier for the model.

TYPE: Literal['MistralModel']

Provider resources: https://docs.mistral.ai/getting-started/models/

Initialize the GroqAIAudio class with the provided data.

PARAMETER DESCRIPTION
**data

Arbitrary keyword arguments containing initialization data.

TYPE: Dict[str, Any] DEFAULT: {}

Source code in swarmauri_standard/llms/MistralModel.py
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
def __init__(self, **data: Dict[str, Any]):
    """
    Initialize the GroqAIAudio class with the provided data.

    Args:
        **data (Dict[str, Any]): Arbitrary keyword arguments containing initialization data.
    """
    super().__init__(**data)
    self._client = httpx.Client(
        headers={"Authorization": f"Bearer {self.api_key.get_secret_value()}"},
        base_url=self._BASE_URL,
        timeout=self.timeout,
    )
    self._async_client = httpx.AsyncClient(
        headers={"Authorization": f"Bearer {self.api_key.get_secret_value()}"},
        base_url=self._BASE_URL,
        timeout=self.timeout,
    )

allowed_models class-attribute instance-attribute

allowed_models = [
    "mistral-medium-2508",
    "magistral-medium-2507",
    "codestral-2508",
    "devstral-medium-2507",
    "mistral-ocr-2505",
    "magistral-medium-2506",
    "ministral-8b-2410",
    "mistral-medium-2505",
    "codestral-2501",
    "mistral-large-2411",
    "pixtral-large-2411",
    "mistral-small-2407",
    "mistral-embed",
    "codestral-embed",
    "mistral-moderation-2411",
    "magistral-small-2507",
    "mistral-small-2506",
    "magistral-small-2506",
    "devstral-small-2507",
    "mistral-small-2501",
    "devstral-small-2505",
    "pixtral-12b-2409",
    "open-mistral-nemo",
]

name class-attribute instance-attribute

name = 'mistral-medium-2508'

type class-attribute instance-attribute

type = 'MistralModel'

timeout class-attribute instance-attribute

timeout = 600.0

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

resource class-attribute instance-attribute

resource = Field(default=LLM.value, frozen=True)

version class-attribute instance-attribute

version = '0.1.0'

api_key class-attribute instance-attribute

api_key = None

include_usage class-attribute instance-attribute

include_usage = True

BASE_URL class-attribute instance-attribute

BASE_URL = None

get_allowed_models

get_allowed_models()

Get a list of allowed models for the Mistral API.

RETURNS DESCRIPTION
List[str]

List[str]: List of allowed model names.

Source code in swarmauri_standard/llms/MistralModel.py
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
@retry_on_status_codes((429, 529), max_retries=1)
def get_allowed_models(self) -> List[str]:
    """
    Get a list of allowed models for the Mistral API.

    Returns:
        List[str]: List of allowed model names.
    """
    response = self._client.get("models")
    response.raise_for_status()
    response_data = response.json()

    chat_models = [
        model["id"]
        for model in response_data["data"]
        if model["capabilities"]["completion_chat"]
    ]

    return chat_models

predict

predict(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1,
    enable_json=False,
    safe_prompt=False,
)

Generate a synchronous response for a conversation.

PARAMETER DESCRIPTION
conversation

The conversation to respond to.

TYPE: Conversation

temperature

Sampling temperature. Defaults to 0.7.

TYPE: int DEFAULT: 0.7

max_tokens

Maximum tokens in response. Defaults to 256.

TYPE: int DEFAULT: 256

top_p

Top-p sampling parameter. Defaults to 1.

TYPE: int DEFAULT: 1

enable_json

If True, enables JSON responses. Defaults to False.

TYPE: bool DEFAULT: False

safe_prompt

Enables safe prompt mode if True. Defaults to False.

TYPE: bool DEFAULT: False

RETURNS DESCRIPTION
Conversation

Updated conversation with the model response.

TYPE: Conversation

Source code in swarmauri_standard/llms/MistralModel.py
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
@retry_on_status_codes((429, 529), max_retries=1)
def predict(
    self,
    conversation: Conversation,
    temperature: int = 0.7,
    max_tokens: int = 256,
    top_p: int = 1,
    enable_json: bool = False,
    safe_prompt: bool = False,
) -> Conversation:
    """
    Generate a synchronous response for a conversation.

    Args:
        conversation (Conversation): The conversation to respond to.
        temperature (int, optional): Sampling temperature. Defaults to 0.7.
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256.
        top_p (int, optional): Top-p sampling parameter. Defaults to 1.
        enable_json (bool, optional): If True, enables JSON responses. Defaults to False.
        safe_prompt (bool, optional): Enables safe prompt mode if True. Defaults to False.

    Returns:
        Conversation: Updated conversation with the model response.
    """
    formatted_messages = self._format_messages(conversation.history)

    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "safe_prompt": safe_prompt,
    }

    if enable_json:
        payload["response_format"] = {"type": "json_object"}

    with DurationManager() as prompt_timer:
        response = self._client.post("chat/completions", json=payload)
        response.raise_for_status()

    response_data = response.json()
    message_content = response_data["choices"][0]["message"]["content"]

    usage_data = response_data.get("usage", {})

    if self.include_usage:
        usage = self._prepare_usage_data(usage_data, prompt_timer.duration)

        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

    return conversation

apredict async

apredict(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1,
    enable_json=False,
    safe_prompt=False,
)

Generate an asynchronous response for a conversation.

PARAMETER DESCRIPTION
conversation

The conversation to respond to.

TYPE: Conversation

temperature

Sampling temperature. Defaults to 0.7.

TYPE: int DEFAULT: 0.7

max_tokens

Maximum tokens in response. Defaults to 256.

TYPE: int DEFAULT: 256

top_p

Top-p sampling parameter. Defaults to 1.

TYPE: int DEFAULT: 1

enable_json

Enables JSON responses. Defaults to False.

TYPE: bool DEFAULT: False

safe_prompt

Enables safe prompt mode if True. Defaults to False.

TYPE: bool DEFAULT: False

RETURNS DESCRIPTION
Conversation

Updated conversation with the model response.

TYPE: Conversation

Source code in swarmauri_standard/llms/MistralModel.py
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
@retry_on_status_codes((429, 529), max_retries=1)
async def apredict(
    self,
    conversation: Conversation,
    temperature: int = 0.7,
    max_tokens: int = 256,
    top_p: int = 1,
    enable_json: bool = False,
    safe_prompt: bool = False,
) -> Conversation:
    """
    Generate an asynchronous response for a conversation.

    Args:
        conversation (Conversation): The conversation to respond to.
        temperature (int, optional): Sampling temperature. Defaults to 0.7.
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256.
        top_p (int, optional): Top-p sampling parameter. Defaults to 1.
        enable_json (bool, optional): Enables JSON responses. Defaults to False.
        safe_prompt (bool, optional): Enables safe prompt mode if True. Defaults to False.

    Returns:
        Conversation: Updated conversation with the model response.
    """
    formatted_messages = self._format_messages(conversation.history)

    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "safe_prompt": safe_prompt,
    }

    if enable_json:
        payload["response_format"] = {"type": "json_object"}

    with DurationManager() as prompt_timer:
        response = await self._async_client.post("chat/completions", json=payload)
        response.raise_for_status()

    response_data = response.json()

    message_content = response_data["choices"][0]["message"]["content"]

    usage_data = response_data.get("usage", {})

    if self.include_usage and usage_data:
        usage = self._prepare_usage_data(usage_data, prompt_timer.duration)
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

    return conversation

stream

stream(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1,
    safe_prompt=False,
)

Stream response content iteratively.

PARAMETER DESCRIPTION
conversation

The conversation to respond to.

TYPE: Conversation

temperature

Sampling temperature. Defaults to 0.7.

TYPE: int DEFAULT: 0.7

max_tokens

Maximum tokens in response. Defaults to 256.

TYPE: int DEFAULT: 256

top_p

Top-p sampling parameter. Defaults to 1.

TYPE: int DEFAULT: 1

safe_prompt

Enables safe prompt mode if True. Defaults to False.

TYPE: bool DEFAULT: False

YIELDS DESCRIPTION
str

Chunks of response content.

TYPE:: str

Source code in swarmauri_standard/llms/MistralModel.py
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
@retry_on_status_codes((429, 529), max_retries=1)
def stream(
    self,
    conversation: Conversation,
    temperature: int = 0.7,
    max_tokens: int = 256,
    top_p: int = 1,
    safe_prompt: bool = False,
) -> Iterator[str]:
    """
    Stream response content iteratively.

    Args:
        conversation (Conversation): The conversation to respond to.
        temperature (int, optional): Sampling temperature. Defaults to 0.7.
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256.
        top_p (int, optional): Top-p sampling parameter. Defaults to 1.
        safe_prompt (bool, optional): Enables safe prompt mode if True. Defaults to False.

    Yields:
        str: Chunks of response content.
    """
    formatted_messages = self._format_messages(conversation.history)

    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "safe_prompt": safe_prompt,
        "stream": True,
    }

    with DurationManager() as prompt_timer:
        response = self._client.post("chat/completions", json=payload)
        response.raise_for_status()

    usage_data = {}
    message_content = ""

    with DurationManager() as completion_timer:
        for line in response.iter_lines():
            json_str = line.replace("data: ", "")
            try:
                if json_str:
                    chunk = json.loads(json_str)
                    if (
                        chunk["choices"][0]["delta"]
                        and "content" in chunk["choices"][0]["delta"]
                    ):
                        delta = chunk["choices"][0]["delta"]["content"]
                        message_content += delta
                        yield delta
                    if "usage" in chunk:
                        usage_data = chunk.get("usage", {})
            except json.JSONDecodeError:
                pass

    if self.include_usage and usage_data:
        usage = self._prepare_usage_data(
            usage_data, prompt_timer.duration, completion_timer.duration
        )
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

astream async

astream(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1,
    safe_prompt=False,
)

Asynchronously stream response content.

PARAMETER DESCRIPTION
conversation

The conversation to respond to.

TYPE: Conversation

temperature

Sampling temperature. Defaults to 0.7.

TYPE: int DEFAULT: 0.7

max_tokens

Maximum tokens in response. Defaults to 256.

TYPE: int DEFAULT: 256

top_p

Top-p sampling parameter. Defaults to 1.

TYPE: int DEFAULT: 1

safe_prompt

Enables safe prompt mode if True. Defaults to False.

TYPE: bool DEFAULT: False

YIELDS DESCRIPTION
str

Chunks of response content.

TYPE:: AsyncIterator[str]

Source code in swarmauri_standard/llms/MistralModel.py
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
@retry_on_status_codes((429, 529), max_retries=1)
async def astream(
    self,
    conversation,
    temperature: int = 0.7,
    max_tokens: int = 256,
    top_p: int = 1,
    safe_prompt: bool = False,
) -> AsyncIterator[str]:
    """
    Asynchronously stream response content.

    Args:
        conversation (Conversation): The conversation to respond to.
        temperature (int, optional): Sampling temperature. Defaults to 0.7.
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256.
        top_p (int, optional): Top-p sampling parameter. Defaults to 1.
        safe_prompt (bool, optional): Enables safe prompt mode if True. Defaults to False.

    Yields:
        str: Chunks of response content.
    """
    formatted_messages = self._format_messages(conversation.history)

    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "safe_prompt": safe_prompt,
        "stream": True,
    }

    with DurationManager() as prompt_timer:
        response = await self._async_client.post("chat/completions", json=payload)
        response.raise_for_status()

    usage_data = {}
    message_content = ""

    with DurationManager() as completion_timer:
        async for line in response.aiter_lines():
            json_str = line.replace("data: ", "")
            try:
                if json_str:
                    chunk = json.loads(json_str)
                    if (
                        chunk["choices"][0]["delta"]
                        and "content" in chunk["choices"][0]["delta"]
                    ):
                        delta = chunk["choices"][0]["delta"]["content"]
                        message_content += delta
                        yield delta
                    if "usage" in chunk:
                        usage_data = chunk.get("usage", {})
            except json.JSONDecodeError:
                pass

    if self.include_usage and usage_data:
        usage = self._prepare_usage_data(
            usage_data, prompt_timer.duration, completion_timer.duration
        )
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

batch

batch(
    conversations,
    temperature=0.7,
    max_tokens=256,
    top_p=1,
    enable_json=False,
    safe_prompt=False,
)

Synchronously processes multiple conversations and generates responses for each.

PARAMETER DESCRIPTION
conversations

List of conversations to process.

TYPE: List[Conversation]

temperature

Sampling temperature for response generation.

TYPE: float DEFAULT: 0.7

max_tokens

Maximum tokens for the response.

TYPE: int DEFAULT: 256

top_p

Nucleus sampling parameter.

TYPE: int DEFAULT: 1

enable_json

If True, enables JSON output format.

TYPE: bool DEFAULT: False

safe_prompt

If True, enables safe prompting.

TYPE: bool DEFAULT: False

RETURNS DESCRIPTION
List[Conversation]

List[Conversation]: List of updated conversations with generated responses.

Source code in swarmauri_standard/llms/MistralModel.py
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
def batch(
    self,
    conversations: List[Conversation],
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: int = 1,
    enable_json: bool = False,
    safe_prompt: bool = False,
) -> List[Conversation]:
    """
    Synchronously processes multiple conversations and generates responses for each.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        temperature (float, optional): Sampling temperature for response generation.
        max_tokens (int, optional): Maximum tokens for the response.
        top_p (int, optional): Nucleus sampling parameter.
        enable_json (bool, optional): If True, enables JSON output format.
        safe_prompt (bool, optional): If True, enables safe prompting.

    Returns:
        List[Conversation]: List of updated conversations with generated responses.
    """
    return [
        self.predict(
            conv,
            temperature=temperature,
            max_tokens=max_tokens,
            top_p=top_p,
            enable_json=enable_json,
            safe_prompt=safe_prompt,
        )
        for conv in conversations
    ]

abatch async

abatch(
    conversations,
    temperature=0.7,
    max_tokens=256,
    top_p=1,
    enable_json=False,
    safe_prompt=False,
    max_concurrent=5,
)

Asynchronously processes multiple conversations with controlled concurrency.

PARAMETER DESCRIPTION
conversations

List of conversations to process.

TYPE: List[Conversation]

temperature

Sampling temperature for response generation.

TYPE: float DEFAULT: 0.7

max_tokens

Maximum tokens for the response.

TYPE: int DEFAULT: 256

top_p

Nucleus sampling parameter.

TYPE: int DEFAULT: 1

enable_json

If True, enables JSON output format.

TYPE: bool DEFAULT: False

safe_prompt

If True, enables safe prompting.

TYPE: bool DEFAULT: False

max_concurrent

Maximum number of concurrent tasks.

TYPE: int DEFAULT: 5

RETURNS DESCRIPTION
List[Conversation]

List[Conversation]: List of updated conversations with generated responses.

Source code in swarmauri_standard/llms/MistralModel.py
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
async def abatch(
    self,
    conversations: List[Conversation],
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: int = 1,
    enable_json: bool = False,
    safe_prompt: bool = False,
    max_concurrent: int = 5,
) -> List[Conversation]:
    """
    Asynchronously processes multiple conversations with controlled concurrency.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        temperature (float, optional): Sampling temperature for response generation.
        max_tokens (int, optional): Maximum tokens for the response.
        top_p (int, optional): Nucleus sampling parameter.
        enable_json (bool, optional): If True, enables JSON output format.
        safe_prompt (bool, optional): If True, enables safe prompting.
        max_concurrent (int, optional): Maximum number of concurrent tasks.

    Returns:
        List[Conversation]: List of updated conversations with generated responses.
    """
    semaphore = asyncio.Semaphore(max_concurrent)

    async def process_conversation(conv) -> Conversation:
        async with semaphore:
            return await self.apredict(
                conv,
                temperature=temperature,
                max_tokens=max_tokens,
                top_p=top_p,
                enable_json=enable_json,
                safe_prompt=safe_prompt,
            )

    tasks = [process_conversation(conv) for conv in conversations]
    return await asyncio.gather(*tasks)

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS DESCRIPTION
Callable

A decorator function that registers the model class.

TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER DESCRIPTION
resource_type

The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used.

TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None

type_name

An optional custom type name for the subtype.

TYPE: Optional[str] DEFAULT: None

RETURNS DESCRIPTION
Callable

A decorator function that registers the subtype.

TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py
12
13
14
15
16
17
18
19
20
21
22
23
24
@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py
11
12
13
14
15
16
17
18
19
20
21
22
23
@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py
23
24
25
26
27
28
def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger

add_allowed_model

add_allowed_model(model)

Add a new model to the list of allowed models.

RAISES DESCRIPTION
ValueError

If the model is already in the allowed models list.

Source code in swarmauri_base/llms/LLMBase.py
36
37
38
39
40
41
42
43
44
45
def add_allowed_model(self, model: str) -> None:
    """
    Add a new model to the list of allowed models.

    Raises:
        ValueError: If the model is already in the allowed models list.
    """
    if model in self.allowed_models:
        raise ValueError(f"Model '{model}' is already allowed.")
    self.allowed_models.append(model)

remove_allowed_model

remove_allowed_model(model)

Remove a model from the list of allowed models.

RAISES DESCRIPTION
ValueError

If the model is not in the allowed models list.

Source code in swarmauri_base/llms/LLMBase.py
47
48
49
50
51
52
53
54
55
56
def remove_allowed_model(self, model: str) -> None:
    """
    Remove a model from the list of allowed models.

    Raises:
        ValueError: If the model is not in the allowed models list.
    """
    if model not in self.allowed_models:
        raise ValueError(f"Model '{model}' is not in the allowed models list.")
    self.allowed_models.remove(model)