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Mason Daugherty 2025-08-04 14:46:10 -04:00
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5
libs/partners/ollama/langchain_ollama/_utils.py
View File

@ -29,7 +29,10 @@ def validate_model(client: Client, model_name: str) -> None:
)
raise ValueError(msg)
except ConnectError as e:
msg = "Failed to connect to Ollama. Please check that Ollama is downloaded, running and accessible. https://ollama.com/download" # noqa: E501
msg = (
"Failed to connect to Ollama. Please check that Ollama is downloaded, "
"running and accessible. https://ollama.com/download"
)
raise ValueError(msg) from e
except ResponseError as e:
msg = (

2
libs/partners/ollama/langchain_ollama/chat_models.py
View File

@ -482,7 +482,7 @@ class ChatOllama(BaseChatModel):
repeat_last_n: Optional[int] = None
"""Sets how far back for the model to look back to prevent
repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = num_ctx)"""
repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = ``num_ctx``)"""
repeat_penalty: Optional[float] = None
"""Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``)

75
libs/partners/ollama/langchain_ollama/embeddings.py
View File

@ -21,12 +21,12 @@ class OllamaEmbeddings(BaseModel, Embeddings):
"""Ollama embedding model integration.
Set up a local Ollama instance:
Install the Ollama package and set up a local Ollama instance
using the instructions here: https://github.com/ollama/ollama .
`Install the Ollama package <https://github.com/ollama/ollama>`__ and set up a
local Ollama instance.
You will need to choose a model to serve.
You can view a list of available models via the model library (https://ollama.com/library).
You can view a list of available models via `the model library <https://ollama.com/library>`__.
To fetch a model from the Ollama model library use ``ollama pull <name-of-model>``.
@ -39,8 +39,8 @@ class OllamaEmbeddings(BaseModel, Embeddings):
This will download the default tagged version of the model.
Typically, the default points to the latest, smallest sized-parameter model.
* On Mac, the models will be downloaded to ~/.ollama/models
* On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models
* On Mac, the models will be downloaded to ``~/.ollama/models``
* On Linux (or WSL), the models will be stored at ``/usr/share/ollama/.ollama/models``
You can specify the exact version of the model of interest
as such ``ollama pull vicuna:13b-v1.5-16k-q4_0``.
@ -132,6 +132,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
"""Whether to validate the model exists in ollama locally on initialization.
.. versionadded:: 0.3.4
"""
base_url: Optional[str] = None
@ -139,60 +140,62 @@ class OllamaEmbeddings(BaseModel, Embeddings):
client_kwargs: Optional[dict] = {}
"""Additional kwargs to pass to the httpx clients.
These arguments are passed to both synchronous and async clients.
Use sync_client_kwargs and async_client_kwargs to pass different arguments
Use ``sync_client_kwargs`` and ``async_client_kwargs`` to pass different arguments
to synchronous and asynchronous clients.
"""
async_client_kwargs: Optional[dict] = {}
"""Additional kwargs to merge with client_kwargs before passing to the httpx
"""Additional kwargs to merge with ``client_kwargs`` before passing to the httpx
AsyncClient.
For a full list of the params, see the `HTTPX documentation <https://www.python-httpx.org/api/#asyncclient>`__.
"""
sync_client_kwargs: Optional[dict] = {}
"""Additional kwargs to merge with client_kwargs before passing to the HTTPX Client.
"""Additional kwargs to merge with ``client_kwargs`` before
passing to the HTTPX Client.
For a full list of the params, see the `HTTPX documentation <https://www.python-httpx.org/api/#client>`__.
"""
_client: Optional[Client] = PrivateAttr(default=None)
"""
The client to use for making requests.
"""
"""The client to use for making requests."""
_async_client: Optional[AsyncClient] = PrivateAttr(default=None)
"""
The async client to use for making requests.
"""
"""The async client to use for making requests."""
mirostat: Optional[int] = None
"""Enable Mirostat sampling for controlling perplexity.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
(default: ``0``, ``0`` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)"""
mirostat_eta: Optional[float] = None
"""Influences how quickly the algorithm responds to feedback
from the generated text. A lower learning rate will result in
slower adjustments, while a higher learning rate will make
the algorithm more responsive. (Default: 0.1)"""
the algorithm more responsive. (Default: ``0.1``)"""
mirostat_tau: Optional[float] = None
"""Controls the balance between coherence and diversity
of the output. A lower value will result in more focused and
coherent text. (Default: 5.0)"""
coherent text. (Default: ``5.0``)"""
num_ctx: Optional[int] = None
"""Sets the size of the context window used to generate the
next token. (Default: 2048) """
next token. (Default: ``2048``) """
num_gpu: Optional[int] = None
"""The number of GPUs to use. On macOS it defaults to 1 to
enable metal support, 0 to disable."""
"""The number of GPUs to use. On macOS it defaults to ``1`` to
enable metal support, ``0`` to disable."""
keep_alive: Optional[int] = None
"""controls how long the model will stay loaded into memory
following the request (default: 5m)
"""Controls how long the model will stay loaded into memory
following the request (default: ``5m``)
"""
num_thread: Optional[int] = None
@ -203,34 +206,34 @@ class OllamaEmbeddings(BaseModel, Embeddings):
repeat_last_n: Optional[int] = None
"""Sets how far back for the model to look back to prevent
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = ``num_ctx``)"""
repeat_penalty: Optional[float] = None
"""Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
will be more lenient. (Default: 1.1)"""
"""Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``)
will penalize repetitions more strongly, while a lower value (e.g., ``0.9``)
will be more lenient. (Default: ``1.1``)"""
temperature: Optional[float] = None
"""The temperature of the model. Increasing the temperature will
make the model answer more creatively. (Default: 0.8)"""
make the model answer more creatively. (Default: ``0.8``)"""
stop: Optional[list[str]] = None
"""Sets the stop tokens to use."""
tfs_z: Optional[float] = None
"""Tail free sampling is used to reduce the impact of less probable
tokens from the output. A higher value (e.g., 2.0) will reduce the
impact more, while a value of 1.0 disables this setting. (default: 1)"""
tokens from the output. A higher value (e.g., ``2.0``) will reduce the
impact more, while a value of ``1.0`` disables this setting. (default: ``1``)"""
top_k: Optional[int] = None
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
will give more diverse answers, while a lower value (e.g. 10)
will be more conservative. (Default: 40)"""
"""Reduces the probability of generating nonsense. A higher value (e.g. ``100``)
will give more diverse answers, while a lower value (e.g. ``10``)
will be more conservative. (Default: ``40``)"""
top_p: Optional[float] = None
"""Works together with top-k. A higher value (e.g., 0.95) will lead
to more diverse text, while a lower value (e.g., 0.5) will
generate more focused and conservative text. (Default: 0.9)"""
"""Works together with top-k. A higher value (e.g., ``0.95``) will lead
to more diverse text, while a lower value (e.g., ``0.5``) will
generate more focused and conservative text. (Default: ``0.9``)"""
model_config = ConfigDict(
extra="forbid",
@ -257,7 +260,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
@model_validator(mode="after")
def _set_clients(self) -> Self:
"""Set clients to use for ollama."""
"""Set clients to use for Ollama."""
client_kwargs = self.client_kwargs or {}
sync_client_kwargs = client_kwargs

62
libs/partners/ollama/langchain_ollama/llms.py
View File

@ -61,26 +61,26 @@ class OllamaLLM(BaseLLM):
mirostat: Optional[int] = None
"""Enable Mirostat sampling for controlling perplexity.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
(default: ``0``, ``0`` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)"""
mirostat_eta: Optional[float] = None
"""Influences how quickly the algorithm responds to feedback
from the generated text. A lower learning rate will result in
slower adjustments, while a higher learning rate will make
the algorithm more responsive. (Default: 0.1)"""
the algorithm more responsive. (Default: ``0.1``)"""
mirostat_tau: Optional[float] = None
"""Controls the balance between coherence and diversity
of the output. A lower value will result in more focused and
coherent text. (Default: 5.0)"""
coherent text. (Default: ``5.0``)"""
num_ctx: Optional[int] = None
"""Sets the size of the context window used to generate the
next token. (Default: 2048)"""
next token. (Default: ``2048``)"""
num_gpu: Optional[int] = None
"""The number of GPUs to use. On macOS it defaults to 1 to
enable metal support, 0 to disable."""
"""The number of GPUs to use. On macOS it defaults to ``1`` to
enable metal support, ``0`` to disable."""
num_thread: Optional[int] = None
"""Sets the number of threads to use during computation.
@ -90,20 +90,20 @@ class OllamaLLM(BaseLLM):
num_predict: Optional[int] = None
"""Maximum number of tokens to predict when generating text.
(Default: 128, -1 = infinite generation, -2 = fill context)"""
(Default: ``128``, ``-1`` = infinite generation, ``-2`` = fill context)"""
repeat_last_n: Optional[int] = None
"""Sets how far back for the model to look back to prevent
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = ``num_ctx``)"""
repeat_penalty: Optional[float] = None
"""Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
will be more lenient. (Default: 1.1)"""
"""Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``)
will penalize repetitions more strongly, while a lower value (e.g., ``0.9``)
will be more lenient. (Default: ``1.1``)"""
temperature: Optional[float] = None
"""The temperature of the model. Increasing the temperature will
make the model answer more creatively. (Default: 0.8)"""
make the model answer more creatively. (Default: ``0.8``)"""
seed: Optional[int] = None
"""Sets the random number seed to use for generation. Setting this
@ -115,21 +115,21 @@ class OllamaLLM(BaseLLM):
tfs_z: Optional[float] = None
"""Tail free sampling is used to reduce the impact of less probable
tokens from the output. A higher value (e.g., 2.0) will reduce the
impact more, while a value of 1.0 disables this setting. (default: 1)"""
tokens from the output. A higher value (e.g., ``2.0``) will reduce the
impact more, while a value of 1.0 disables this setting. (default: ``1``)"""
top_k: Optional[int] = None
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
will give more diverse answers, while a lower value (e.g. 10)
will be more conservative. (Default: 40)"""
"""Reduces the probability of generating nonsense. A higher value (e.g. ``100``)
will give more diverse answers, while a lower value (e.g. ``10``)
will be more conservative. (Default: ``40``)"""
top_p: Optional[float] = None
"""Works together with top-k. A higher value (e.g., 0.95) will lead
to more diverse text, while a lower value (e.g., 0.5) will
generate more focused and conservative text. (Default: 0.9)"""
"""Works together with top-k. A higher value (e.g., ``0.95``) will lead
to more diverse text, while a lower value (e.g., ``0.5``) will
generate more focused and conservative text. (Default: ``0.9``)"""
format: Literal["", "json"] = ""
"""Specify the format of the output (options: json)"""
"""Specify the format of the output (options: ``'json'``)"""
keep_alive: Optional[Union[int, str]] = None
"""How long the model will stay loaded into memory."""
@ -139,33 +139,35 @@ class OllamaLLM(BaseLLM):
client_kwargs: Optional[dict] = {}
"""Additional kwargs to pass to the httpx clients.
These arguments are passed to both synchronous and async clients.
Use sync_client_kwargs and async_client_kwargs to pass different arguments
Use ``sync_client_kwargs`` and ``async_client_kwargs`` to pass different arguments
to synchronous and asynchronous clients.
"""
async_client_kwargs: Optional[dict] = {}
"""Additional kwargs to merge with client_kwargs before passing to the HTTPX
"""Additional kwargs to merge with ``client_kwargs`` before passing to the HTTPX
AsyncClient.
For a full list of the params, see the `HTTPX documentation <https://www.python-httpx.org/api/#asyncclient>`__.
"""
sync_client_kwargs: Optional[dict] = {}
"""Additional kwargs to merge with client_kwargs before passing to the HTTPX Client.
"""Additional kwargs to merge with ``client_kwargs`` before
passing to the HTTPX Client.
For a full list of the params, see the `HTTPX documentation <https://www.python-httpx.org/api/#client>`__.
"""
_client: Optional[Client] = PrivateAttr(default=None)
"""
The client to use for making requests.
"""
"""The client to use for making requests."""
_async_client: Optional[AsyncClient] = PrivateAttr(default=None)
"""
The async client to use for making requests.
"""
"""The async client to use for making requests."""
def _generate_params(
self,