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langchain/libs/partners/ollama/langchain_ollama/llms.py
Mason Daugherty d40e340479 chore: attribute package change versions (#33854) 
Needed to disambiguate for within inherited docs
2025-11-06 16:57:30 -05:00

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"""Ollama large language models."""
from __future__ import annotations
from collections.abc import AsyncIterator, Iterator, Mapping
from typing import Any, Literal
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import BaseLLM, LangSmithParams
from langchain_core.outputs import GenerationChunk, LLMResult
from ollama import AsyncClient, Client, Options
from pydantic import PrivateAttr, model_validator
from typing_extensions import Self
from ._utils import merge_auth_headers, parse_url_with_auth, validate_model
class OllamaLLM(BaseLLM):
"""Ollama large language models.
Setup:
Install `langchain-ollama` and install/run the Ollama server locally:
```bash
pip install -U langchain-ollama
# Visit https://ollama.com/download to download and install Ollama
# (Linux users): start the server with `ollama serve`
```
Download a model to use:
```bash
ollama pull llama3.1
```
Key init args — generation params:
model: str
Name of the Ollama model to use (e.g. `'llama4'`).
temperature: float | None
Sampling temperature. Higher values make output more creative.
num_predict: int | None
Maximum number of tokens to predict.
top_k: int | None
Limits the next token selection to the K most probable tokens.
top_p: float | None
Nucleus sampling parameter. Higher values lead to more diverse text.
mirostat: int | None
Enable Mirostat sampling for controlling perplexity.
seed: int | None
Random number seed for generation reproducibility.
Key init args — client params:
base_url:
Base URL where Ollama server is hosted.
keep_alive:
How long the model stays loaded into memory.
format:
Specify the format of the output.
See full list of supported init args and their descriptions in the params section.
Instantiate:
```python
from langchain_ollama import OllamaLLM
model = OllamaLLM(
model="llama3.1",
temperature=0.7,
num_predict=256,
# base_url="http://localhost:11434",
# other params...
)
```
Invoke:
```python
input_text = "The meaning of life is "
response = model.invoke(input_text)
print(response)
```
```txt
"a philosophical question that has been contemplated by humans for
centuries..."
```
Stream:
```python
for chunk in model.stream(input_text):
print(chunk, end="")
```
```txt
a philosophical question that has been contemplated by humans for
centuries...
```
Async:
```python
response = await model.ainvoke(input_text)
# stream:
# async for chunk in model.astream(input_text):
# print(chunk, end="")
```
"""
model: str
"""Model name to use."""
reasoning: bool | None = None
"""Controls the reasoning/thinking mode for
[supported models](https://ollama.com/search?c=thinking).
- `True`: Enables reasoning mode. The model's reasoning process will be
captured and returned separately in the `additional_kwargs` of the
response message, under `reasoning_content`. The main response
content will not include the reasoning tags.
- `False`: Disables reasoning mode. The model will not perform any reasoning,
and the response will not include any reasoning content.
- `None` (Default): The model will use its default reasoning behavior. If
the model performs reasoning, the `<think>` and `</think>` tags will
be present directly within the main response content."""
validate_model_on_init: bool = False
"""Whether to validate the model exists in ollama locally on initialization.
!!! version-added "Added in `langchain-ollama` 0.3.4"
"""
mirostat: int | None = None
"""Enable Mirostat sampling for controlling perplexity.
(default: `0`, `0` = disabled, `1` = Mirostat, `2` = Mirostat 2.0)"""
mirostat_eta: float | None = 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`)"""
mirostat_tau: float | None = 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`)"""
num_ctx: int | None = None
"""Sets the size of the context window used to generate the
next token. (Default: `2048`)"""
num_gpu: int | None = None
"""The number of GPUs to use. On macOS it defaults to `1` to
enable metal support, `0` to disable."""
num_thread: int | None = None
"""Sets the number of threads to use during computation.
By default, Ollama will detect this for optimal performance.
It is recommended to set this value to the number of physical
CPU cores your system has (as opposed to the logical number of cores)."""
num_predict: int | None = None
"""Maximum number of tokens to predict when generating text.
(Default: `128`, `-1` = infinite generation, `-2` = fill context)"""
repeat_last_n: int | None = None
"""Sets how far back for the model to look back to prevent
repetition. (Default: `64`, `0` = disabled, `-1` = `num_ctx`)"""
repeat_penalty: float | None = 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`)"""
temperature: float | None = None
"""The temperature of the model. Increasing the temperature will
make the model answer more creatively. (Default: `0.8`)"""
seed: int | None = None
"""Sets the random number seed to use for generation. Setting this
to a specific number will make the model generate the same text for
the same prompt."""
stop: list[str] | None = None
"""Sets the stop tokens to use."""
tfs_z: float | None = 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`)"""
top_k: int | None = 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`)"""
top_p: float | None = 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`)"""
format: Literal["", "json"] = ""
"""Specify the format of the output (options: `'json'`)"""
keep_alive: int | str | None = None
"""How long the model will stay loaded into memory."""
base_url: str | None = None
"""Base url the model is hosted under.
If none, defaults to the Ollama client default.
Supports `userinfo` auth in the format `http://username:password@localhost:11434`.
Useful if your Ollama server is behind a proxy.
!!! warning
`userinfo` is not secure and should only be used for local testing or
in secure environments. Avoid using it in production or over unsecured
networks.
!!! note
If using `userinfo`, ensure that the Ollama server is configured to
accept and validate these credentials.
!!! note
`userinfo` headers are passed to both sync and async clients.
"""
client_kwargs: dict | None = {}
"""Additional kwargs to pass to the httpx clients. Pass headers in here.
These arguments are passed to both synchronous and async clients.
Use `sync_client_kwargs` and `async_client_kwargs` to pass different arguments
to synchronous and asynchronous clients.
"""
async_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the async client; for shared args use `client_kwargs`.
For a full list of the params, see the [httpx documentation](https://www.python-httpx.org/api/#asyncclient).
"""
sync_client_kwargs: dict | None = {}
"""Additional kwargs to merge with `client_kwargs` before passing to httpx client.
These are clients unique to the sync client; for shared args use `client_kwargs`.
For a full list of the params, see the [httpx documentation](https://www.python-httpx.org/api/#client).
"""
_client: Client | None = PrivateAttr(default=None)
"""The client to use for making requests."""
_async_client: AsyncClient | None = PrivateAttr(default=None)
"""The async client to use for making requests."""
def _generate_params(
self,
prompt: str,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict[str, Any]:
if self.stop is not None and stop is not None:
msg = "`stop` found in both the input and default params."
raise ValueError(msg)
if self.stop is not None:
stop = self.stop
options_dict = kwargs.pop(
"options",
{
"mirostat": self.mirostat,
"mirostat_eta": self.mirostat_eta,
"mirostat_tau": self.mirostat_tau,
"num_ctx": self.num_ctx,
"num_gpu": self.num_gpu,
"num_thread": self.num_thread,
"num_predict": self.num_predict,
"repeat_last_n": self.repeat_last_n,
"repeat_penalty": self.repeat_penalty,
"temperature": self.temperature,
"seed": self.seed,
"stop": self.stop if stop is None else stop,
"tfs_z": self.tfs_z,
"top_k": self.top_k,
"top_p": self.top_p,
},
)
return {
"prompt": prompt,
"stream": kwargs.pop("stream", True),
"model": kwargs.pop("model", self.model),
"think": kwargs.pop("reasoning", self.reasoning),
"format": kwargs.pop("format", self.format),
"options": Options(**options_dict),
"keep_alive": kwargs.pop("keep_alive", self.keep_alive),
**kwargs,
}
@property
def _llm_type(self) -> str:
"""Return type of LLM."""
return "ollama-llm"
def _get_ls_params(
self, stop: list[str] | None = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = super()._get_ls_params(stop=stop, **kwargs)
if max_tokens := kwargs.get("num_predict", self.num_predict):
params["ls_max_tokens"] = max_tokens
return params
@model_validator(mode="after")
def _set_clients(self) -> Self:
"""Set clients to use for ollama."""
client_kwargs = self.client_kwargs or {}
cleaned_url, auth_headers = parse_url_with_auth(self.base_url)
merge_auth_headers(client_kwargs, auth_headers)
sync_client_kwargs = client_kwargs
if self.sync_client_kwargs:
sync_client_kwargs = {**sync_client_kwargs, **self.sync_client_kwargs}
async_client_kwargs = client_kwargs
if self.async_client_kwargs:
async_client_kwargs = {**async_client_kwargs, **self.async_client_kwargs}
self._client = Client(host=cleaned_url, **sync_client_kwargs)
self._async_client = AsyncClient(host=cleaned_url, **async_client_kwargs)
if self.validate_model_on_init:
validate_model(self._client, self.model)
return self
async def _acreate_generate_stream(
self,
prompt: str,
stop: list[str] | None = None,
**kwargs: Any,
) -> AsyncIterator[Mapping[str, Any] | str]:
if self._async_client:
async for part in await self._async_client.generate(
**self._generate_params(prompt, stop=stop, **kwargs)
):
yield part
def _create_generate_stream(
self,
prompt: str,
stop: list[str] | None = None,
**kwargs: Any,
) -> Iterator[Mapping[str, Any] | str]:
if self._client:
yield from self._client.generate(
**self._generate_params(prompt, stop=stop, **kwargs)
)
async def _astream_with_aggregation(
self,
prompt: str,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
verbose: bool = False, # noqa: FBT002
**kwargs: Any,
) -> GenerationChunk:
final_chunk = None
thinking_content = ""
async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
if not isinstance(stream_resp, str):
if stream_resp.get("thinking"):
thinking_content += stream_resp["thinking"]
chunk = GenerationChunk(
text=stream_resp.get("response", ""),
generation_info=(
dict(stream_resp) if stream_resp.get("done") is True else None
),
)
if final_chunk is None:
final_chunk = chunk
else:
final_chunk += chunk
if run_manager:
await run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=verbose,
)
if final_chunk is None:
msg = "No data received from Ollama stream."
raise ValueError(msg)
if thinking_content:
if final_chunk.generation_info:
final_chunk.generation_info["thinking"] = thinking_content
else:
final_chunk.generation_info = {"thinking": thinking_content}
return final_chunk
def _stream_with_aggregation(
self,
prompt: str,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
verbose: bool = False, # noqa: FBT002
**kwargs: Any,
) -> GenerationChunk:
final_chunk = None
thinking_content = ""
for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
if not isinstance(stream_resp, str):
if stream_resp.get("thinking"):
thinking_content += stream_resp["thinking"]
chunk = GenerationChunk(
text=stream_resp.get("response", ""),
generation_info=(
dict(stream_resp) if stream_resp.get("done") is True else None
),
)
if final_chunk is None:
final_chunk = chunk
else:
final_chunk += chunk
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=verbose,
)
if final_chunk is None:
msg = "No data received from Ollama stream."
raise ValueError(msg)
if thinking_content:
if final_chunk.generation_info:
final_chunk.generation_info["thinking"] = thinking_content
else:
final_chunk.generation_info = {"thinking": thinking_content}
return final_chunk
def _generate(
self,
prompts: list[str],
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
generations = []
for prompt in prompts:
final_chunk = self._stream_with_aggregation(
prompt,
stop=stop,
run_manager=run_manager,
verbose=self.verbose,
**kwargs,
)
generations.append([final_chunk])
return LLMResult(generations=generations) # type: ignore[arg-type]
async def _agenerate(
self,
prompts: list[str],
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
generations = []
for prompt in prompts:
final_chunk = await self._astream_with_aggregation(
prompt,
stop=stop,
run_manager=run_manager,
verbose=self.verbose,
**kwargs,
)
generations.append([final_chunk])
return LLMResult(generations=generations) # type: ignore[arg-type]
def _stream(
self,
prompt: str,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
reasoning = kwargs.get("reasoning", self.reasoning)
for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
if not isinstance(stream_resp, str):
additional_kwargs = {}
if reasoning and (thinking_content := stream_resp.get("thinking")):
additional_kwargs["reasoning_content"] = thinking_content
chunk = GenerationChunk(
text=(stream_resp.get("response", "")),
generation_info={
"finish_reason": self.stop,
**additional_kwargs,
**(
dict(stream_resp) if stream_resp.get("done") is True else {}
),
},
)
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
verbose=self.verbose,
)
yield chunk
async def _astream(
self,
prompt: str,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
reasoning = kwargs.get("reasoning", self.reasoning)
async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
if not isinstance(stream_resp, str):
additional_kwargs = {}
if reasoning and (thinking_content := stream_resp.get("thinking")):
additional_kwargs["reasoning_content"] = thinking_content
chunk = GenerationChunk(
text=(stream_resp.get("response", "")),
generation_info={
"finish_reason": self.stop,
**additional_kwargs,
**(
dict(stream_resp) if stream_resp.get("done") is True else {}
),
},
)
if run_manager:
await run_manager.on_llm_new_token(
chunk.text,
verbose=self.verbose,
)
yield chunk
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