"""Ollama chat models."""
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Sequence,
Type,
Union,
cast,
)
from uuid import uuid4
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
HumanMessage,
SystemMessage,
ToolCall,
ToolMessage,
)
from langchain_core.messages.ai import UsageMetadata
from langchain_core.messages.tool import tool_call
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import convert_to_openai_tool
from ollama import AsyncClient, Client, Message, Options
from pydantic import PrivateAttr, model_validator
from typing_extensions import Self
def _get_usage_metadata_from_generation_info(
generation_info: Optional[Mapping[str, Any]],
) -> Optional[UsageMetadata]:
"""Get usage metadata from ollama generation info mapping."""
if generation_info is None:
return None
input_tokens: Optional[int] = generation_info.get("prompt_eval_count")
output_tokens: Optional[int] = generation_info.get("eval_count")
if input_tokens is not None and output_tokens is not None:
return UsageMetadata(
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=input_tokens + output_tokens,
)
return None
def _get_tool_calls_from_response(
response: Mapping[str, Any],
) -> List[ToolCall]:
"""Get tool calls from ollama response."""
tool_calls = []
if "message" in response:
if "tool_calls" in response["message"]:
for tc in response["message"]["tool_calls"]:
tool_calls.append(
tool_call(
id=str(uuid4()),
name=tc["function"]["name"],
args=tc["function"]["arguments"],
)
)
return tool_calls
def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
return {
"type": "function",
"id": tool_call["id"],
"function": {
"name": tool_call["name"],
"arguments": tool_call["args"],
},
}
[docs]
class ChatOllama(BaseChatModel):
"""Ollama chat model integration.
.. dropdown:: Setup
:open:
Install ``langchain-ollama`` and download any models you want to use from ollama.
.. code-block:: bash
ollama pull mistral:v0.3
pip install -U langchain-ollama
Key init args — completion params:
model: str
Name of Ollama model to use.
temperature: float
Sampling temperature. Ranges from 0.0 to 1.0.
num_predict: Optional[int]
Max number of tokens to generate.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_ollama import ChatOllama
llm = ChatOllama(
model = "llama3",
temperature = 0.8,
num_predict = 256,
# other params ...
)
Invoke:
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn\'t specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0')
Stream:
.. code-block:: python
messages = [
("human", "Return the words Hello World!"),
]
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890')
Async:
.. code-block:: python
messages = [
("human", "Hello how are you!"),
]
await llm.ainvoke(messages)
.. code-block:: python
AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0')
.. code-block:: python
messages = [
("human", "Say hello world!"),
]
async for chunk in llm.astream(messages):
print(chunk.content)
.. code-block:: python
HEL
LO
WORLD
!
.. code-block:: python
messages = [
("human", "Say hello world!"),
("human","Say goodbye world!")
]
await llm.abatch(messages)
.. code-block:: python
[AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'),
AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')]
JSON mode:
.. code-block:: python
json_llm = ChatOllama(format="json")
messages = [
("human", "Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only."),
]
llm.invoke(messages).content
.. code-block:: python
'{"location": "Pune, India", "time_of_day": "morning"}'
Tool Calling:
.. warning::
Ollama currently does not support streaming for tools
.. code-block:: python
from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field
class Multiply(BaseModel):
a: int = Field(..., description="First integer")
b: int = Field(..., description="Second integer")
ans = await chat.invoke("What is 45*67")
ans.tool_calls
.. code-block:: python
[{'name': 'Multiply',
'args': {'a': 45, 'b': 67},
'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
'type': 'tool_call'}]
""" # noqa: E501
model: str
"""Model name to use."""
mirostat: Optional[int] = None
"""Enable Mirostat sampling for controlling perplexity.
(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)"""
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)"""
num_ctx: Optional[int] = None
"""Sets the size of the context window used to generate the
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."""
num_thread: Optional[int] = 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: Optional[int] = None
"""Maximum number of tokens to predict when generating text.
(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)"""
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)"""
temperature: Optional[float] = None
"""The temperature of the model. Increasing the temperature will
make the model answer more creatively. (Default: 0.8)"""
seed: Optional[int] = 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: 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)"""
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)"""
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)"""
format: Literal["", "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."""
base_url: Optional[str] = None
"""Base url the model is hosted under."""
client_kwargs: Optional[dict] = {}
"""Additional kwargs to pass to the httpx Client.
For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html)
"""
_client: Client = PrivateAttr(default=None)
"""
The client to use for making requests.
"""
_async_client: AsyncClient = PrivateAttr(default=None)
"""
The async client to use for making requests.
"""
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Ollama."""
return {
"model": self.model,
"format": self.format,
"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,
"tfs_z": self.tfs_z,
"top_k": self.top_k,
"top_p": self.top_p,
},
"keep_alive": self.keep_alive,
}
@model_validator(mode="after")
def _set_clients(self) -> Self:
"""Set clients to use for ollama."""
client_kwargs = self.client_kwargs or {}
self._client = Client(host=self.base_url, **client_kwargs)
self._async_client = AsyncClient(host=self.base_url, **client_kwargs)
return self
def _convert_messages_to_ollama_messages(
self, messages: List[BaseMessage]
) -> Sequence[Message]:
ollama_messages: List = []
for message in messages:
role: Literal["user", "assistant", "system", "tool"]
tool_call_id: Optional[str] = None
tool_calls: Optional[List[Dict[str, Any]]] = None
if isinstance(message, HumanMessage):
role = "user"
elif isinstance(message, AIMessage):
role = "assistant"
tool_calls = (
[
_lc_tool_call_to_openai_tool_call(tool_call)
for tool_call in message.tool_calls
]
if message.tool_calls
else None
)
elif isinstance(message, SystemMessage):
role = "system"
elif isinstance(message, ToolMessage):
role = "tool"
tool_call_id = message.tool_call_id
else:
raise ValueError("Received unsupported message type for Ollama.")
content = ""
images = []
if isinstance(message.content, str):
content = message.content
else:
for content_part in cast(List[Dict], message.content):
if content_part.get("type") == "text":
content += f"\n{content_part['text']}"
elif content_part.get("type") == "tool_use":
continue
elif content_part.get("type") == "image_url":
image_url = None
temp_image_url = content_part.get("image_url")
if isinstance(temp_image_url, str):
image_url = temp_image_url
elif (
isinstance(temp_image_url, dict)
and "url" in temp_image_url
and isinstance(temp_image_url["url"], str)
):
image_url = temp_image_url["url"]
else:
raise ValueError(
"Only string image_url or dict with string 'url' "
"inside content parts are supported."
)
image_url_components = image_url.split(",")
# Support data:image/jpeg;base64,<image> format
# and base64 strings
if len(image_url_components) > 1:
images.append(image_url_components[1])
else:
images.append(image_url_components[0])
else:
raise ValueError(
"Unsupported message content type. "
"Must either have type 'text' or type 'image_url' "
"with a string 'image_url' field."
)
# Should convert to ollama.Message once role includes tool, and tool_call_id is in Message # noqa: E501
msg: dict = {
"role": role,
"content": content,
"images": images,
}
if tool_calls:
msg["tool_calls"] = tool_calls # type: ignore
if tool_call_id:
msg["tool_call_id"] = tool_call_id
ollama_messages.append(msg)
return ollama_messages
async def _acreate_chat_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
ollama_messages = self._convert_messages_to_ollama_messages(messages)
stop = stop if stop is not None else self.stop
params = self._default_params
for key in self._default_params:
if key in kwargs:
params[key] = kwargs[key]
params["options"]["stop"] = stop
if "tools" in kwargs:
yield await self._async_client.chat(
model=params["model"],
messages=ollama_messages,
stream=False,
options=Options(**params["options"]),
keep_alive=params["keep_alive"],
format=params["format"],
tools=kwargs["tools"],
) # type:ignore
else:
async for part in await self._async_client.chat(
model=params["model"],
messages=ollama_messages,
stream=True,
options=Options(**params["options"]),
keep_alive=params["keep_alive"],
format=params["format"],
): # type:ignore
yield part
def _create_chat_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Iterator[Union[Mapping[str, Any], str]]:
ollama_messages = self._convert_messages_to_ollama_messages(messages)
stop = stop if stop is not None else self.stop
params = self._default_params
for key in self._default_params:
if key in kwargs:
params[key] = kwargs[key]
params["options"]["stop"] = stop
if "tools" in kwargs:
yield self._client.chat(
model=params["model"],
messages=ollama_messages,
stream=False,
options=Options(**params["options"]),
keep_alive=params["keep_alive"],
format=params["format"],
tools=kwargs["tools"],
)
else:
yield from self._client.chat(
model=params["model"],
messages=ollama_messages,
stream=True,
options=Options(**params["options"]),
keep_alive=params["keep_alive"],
format=params["format"],
)
def _chat_stream_with_aggregation(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
verbose: bool = False,
**kwargs: Any,
) -> ChatGenerationChunk:
final_chunk = None
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
if not isinstance(stream_resp, str):
chunk = ChatGenerationChunk(
message=AIMessageChunk(
content=(
stream_resp["message"]["content"]
if "message" in stream_resp
and "content" in stream_resp["message"]
else ""
),
usage_metadata=_get_usage_metadata_from_generation_info(
stream_resp
),
tool_calls=_get_tool_calls_from_response(stream_resp),
),
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:
raise ValueError("No data received from Ollama stream.")
return final_chunk
async def _achat_stream_with_aggregation(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
verbose: bool = False,
**kwargs: Any,
) -> ChatGenerationChunk:
final_chunk = None
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
if not isinstance(stream_resp, str):
chunk = ChatGenerationChunk(
message=AIMessageChunk(
content=(
stream_resp["message"]["content"]
if "message" in stream_resp
and "content" in stream_resp["message"]
else ""
),
usage_metadata=_get_usage_metadata_from_generation_info(
stream_resp
),
tool_calls=_get_tool_calls_from_response(stream_resp),
),
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:
raise ValueError("No data received from Ollama stream.")
return final_chunk
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="ollama",
ls_model_name=self.model,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_stop := stop or params.get("stop", None) or self.stop:
ls_params["ls_stop"] = ls_stop
return ls_params
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
final_chunk = self._chat_stream_with_aggregation(
messages, stop, run_manager, verbose=self.verbose, **kwargs
)
generation_info = final_chunk.generation_info
chat_generation = ChatGeneration(
message=AIMessage(
content=final_chunk.text,
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
),
generation_info=generation_info,
)
return ChatResult(generations=[chat_generation])
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
if not isinstance(stream_resp, str):
chunk = ChatGenerationChunk(
message=AIMessageChunk(
content=(
stream_resp["message"]["content"]
if "message" in stream_resp
and "content" in stream_resp["message"]
else ""
),
usage_metadata=_get_usage_metadata_from_generation_info(
stream_resp
),
tool_calls=_get_tool_calls_from_response(stream_resp),
),
generation_info=(
dict(stream_resp) if stream_resp.get("done") is True else None
),
)
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
verbose=self.verbose,
)
yield chunk
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
if not isinstance(stream_resp, str):
chunk = ChatGenerationChunk(
message=AIMessageChunk(
content=(
stream_resp["message"]["content"]
if "message" in stream_resp
and "content" in stream_resp["message"]
else ""
),
usage_metadata=_get_usage_metadata_from_generation_info(
stream_resp
),
tool_calls=_get_tool_calls_from_response(stream_resp),
),
generation_info=(
dict(stream_resp) if stream_resp.get("done") is True else None
),
)
if run_manager:
await run_manager.on_llm_new_token(
chunk.text,
verbose=self.verbose,
)
yield chunk
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
final_chunk = await self._achat_stream_with_aggregation(
messages, stop, run_manager, verbose=self.verbose, **kwargs
)
generation_info = final_chunk.generation_info
chat_generation = ChatGeneration(
message=AIMessage(
content=final_chunk.text,
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
),
generation_info=generation_info,
)
return ChatResult(generations=[chat_generation])
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "chat-ollama"
