pydantic_ai.direct
Methods for making imperative requests to language models with minimal abstraction.
These methods allow you to make requests to LLMs where the only abstraction is input and output schema translation so you can use all models with the same API.
These methods are thin wrappers around Model
implementations.
model_request
async
model_request(
model: Model | KnownModelName | str,
messages: list[ModelMessage],
*,
model_settings: ModelSettings | None = None,
model_request_parameters: (
ModelRequestParameters | None
) = None,
instrument: InstrumentationSettings | bool | None = None
) -> ModelResponse
Make a non-streamed request to a model.
from pydantic_ai.direct import model_request
from pydantic_ai.messages import ModelRequest
async def main():
model_response = await model_request(
'anthropic:claude-3-5-haiku-latest',
[ModelRequest.user_text_prompt('What is the capital of France?')] # (1)!
)
print(model_response)
'''
ModelResponse(
parts=[TextPart(content='Paris')],
usage=Usage(requests=1, request_tokens=56, response_tokens=1, total_tokens=57),
model_name='claude-3-5-haiku-latest',
timestamp=datetime.datetime(...),
)
'''
- See
ModelRequest.user_text_prompt
for details.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model
|
Model | KnownModelName | str
|
The model to make a request to. We allow |
required |
messages
|
list[ModelMessage]
|
Messages to send to the model |
required |
model_settings
|
ModelSettings | None
|
optional model settings |
None
|
model_request_parameters
|
ModelRequestParameters | None
|
optional model request parameters |
None
|
instrument
|
InstrumentationSettings | bool | None
|
Whether to instrument the request with OpenTelemetry/Logfire, if |
None
|
Returns:
Type | Description |
---|---|
ModelResponse
|
The model response and token usage associated with the request. |
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|
model_request_sync
model_request_sync(
model: Model | KnownModelName | str,
messages: list[ModelMessage],
*,
model_settings: ModelSettings | None = None,
model_request_parameters: (
ModelRequestParameters | None
) = None,
instrument: InstrumentationSettings | bool | None = None
) -> ModelResponse
Make a Synchronous, non-streamed request to a model.
This is a convenience method that wraps model_request
with
loop.run_until_complete(...)
. You therefore can't use this method inside async code or if there's an active event loop.
from pydantic_ai.direct import model_request_sync
from pydantic_ai.messages import ModelRequest
model_response = model_request_sync(
'anthropic:claude-3-5-haiku-latest',
[ModelRequest.user_text_prompt('What is the capital of France?')] # (1)!
)
print(model_response)
'''
ModelResponse(
parts=[TextPart(content='Paris')],
usage=Usage(requests=1, request_tokens=56, response_tokens=1, total_tokens=57),
model_name='claude-3-5-haiku-latest',
timestamp=datetime.datetime(...),
)
'''
- See
ModelRequest.user_text_prompt
for details.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model
|
Model | KnownModelName | str
|
The model to make a request to. We allow |
required |
messages
|
list[ModelMessage]
|
Messages to send to the model |
required |
model_settings
|
ModelSettings | None
|
optional model settings |
None
|
model_request_parameters
|
ModelRequestParameters | None
|
optional model request parameters |
None
|
instrument
|
InstrumentationSettings | bool | None
|
Whether to instrument the request with OpenTelemetry/Logfire, if |
None
|
Returns:
Type | Description |
---|---|
ModelResponse
|
The model response and token usage associated with the request. |
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|
model_request_stream
model_request_stream(
model: Model | KnownModelName | str,
messages: list[ModelMessage],
*,
model_settings: ModelSettings | None = None,
model_request_parameters: (
ModelRequestParameters | None
) = None,
instrument: InstrumentationSettings | bool | None = None
) -> AbstractAsyncContextManager[StreamedResponse]
Make a streamed async request to a model.
from pydantic_ai.direct import model_request_stream
from pydantic_ai.messages import ModelRequest
async def main():
messages = [ModelRequest.user_text_prompt('Who was Albert Einstein?')] # (1)!
async with model_request_stream('openai:gpt-4.1-mini', messages) as stream:
chunks = []
async for chunk in stream:
chunks.append(chunk)
print(chunks)
'''
[
PartStartEvent(index=0, part=TextPart(content='Albert Einstein was ')),
PartDeltaEvent(
index=0, delta=TextPartDelta(content_delta='a German-born theoretical ')
),
PartDeltaEvent(index=0, delta=TextPartDelta(content_delta='physicist.')),
]
'''
- See
ModelRequest.user_text_prompt
for details.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model
|
Model | KnownModelName | str
|
The model to make a request to. We allow |
required |
messages
|
list[ModelMessage]
|
Messages to send to the model |
required |
model_settings
|
ModelSettings | None
|
optional model settings |
None
|
model_request_parameters
|
ModelRequestParameters | None
|
optional model request parameters |
None
|
instrument
|
InstrumentationSettings | bool | None
|
Whether to instrument the request with OpenTelemetry/Logfire, if |
None
|
Returns:
Type | Description |
---|---|
AbstractAsyncContextManager[StreamedResponse]
|
A stream response async context manager. |
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|
model_request_stream_sync
model_request_stream_sync(
model: Model | KnownModelName | str,
messages: list[ModelMessage],
*,
model_settings: ModelSettings | None = None,
model_request_parameters: (
ModelRequestParameters | None
) = None,
instrument: InstrumentationSettings | bool | None = None
) -> StreamedResponseSync
Make a streamed synchronous request to a model.
This is the synchronous version of model_request_stream
.
It uses threading to run the asynchronous stream in the background while providing a synchronous iterator interface.
from pydantic_ai.direct import model_request_stream_sync
from pydantic_ai.messages import ModelRequest
messages = [ModelRequest.user_text_prompt('Who was Albert Einstein?')]
with model_request_stream_sync('openai:gpt-4.1-mini', messages) as stream:
chunks = []
for chunk in stream:
chunks.append(chunk)
print(chunks)
'''
[
PartStartEvent(index=0, part=TextPart(content='Albert Einstein was ')),
PartDeltaEvent(
index=0, delta=TextPartDelta(content_delta='a German-born theoretical ')
),
PartDeltaEvent(index=0, delta=TextPartDelta(content_delta='physicist.')),
]
'''
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model
|
Model | KnownModelName | str
|
The model to make a request to. We allow |
required |
messages
|
list[ModelMessage]
|
Messages to send to the model |
required |
model_settings
|
ModelSettings | None
|
optional model settings |
None
|
model_request_parameters
|
ModelRequestParameters | None
|
optional model request parameters |
None
|
instrument
|
InstrumentationSettings | bool | None
|
Whether to instrument the request with OpenTelemetry/Logfire, if |
None
|
Returns:
Type | Description |
---|---|
StreamedResponseSync
|
A sync stream response context manager. |
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|
StreamedResponseSync
dataclass
Synchronous wrapper to async streaming responses by running the async producer in a background thread and providing a synchronous iterator.
This class must be used as a context manager with the with
statement.
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|
__iter__
__iter__() -> Iterator[ModelResponseStreamEvent]
Stream the response as an iterable of ModelResponseStreamEvent
s.
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|
get
get() -> ModelResponse
Build a ModelResponse from the data received from the stream so far.
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|
usage
usage() -> Usage
Get the usage of the response so far.
Source code in pydantic_ai_slim/pydantic_ai/direct.py
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|