"""Ollama chat models."""
import json
from operator import itemgetter
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Final,
Iterator,
List,
Literal,
Mapping,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
from uuid import uuid4
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
from langchain_core.exceptions import OutputParserException
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,
BaseMessageChunk,
HumanMessage,
SystemMessage,
ToolCall,
ToolMessage,
)
from langchain_core.messages.ai import UsageMetadata
from langchain_core.messages.tool import tool_call
from langchain_core.output_parsers import (
JsonOutputKeyToolsParser,
JsonOutputParser,
PydanticOutputParser,
PydanticToolsParser,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import (
convert_to_json_schema,
convert_to_openai_tool,
)
from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
from ollama import AsyncClient, Client, Message, Options
from pydantic import BaseModel, PrivateAttr, model_validator
from pydantic.json_schema import JsonSchemaValue
from pydantic.v1 import BaseModel as BaseModelV1
from typing_extensions import Self, is_typeddict
DEFAULT_THINK_TOKEN_START: Final[str] = ""
DEFAULT_THINK_TOKEN_END: Final[str] = ""
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 _parse_json_string(
json_string: str, raw_tool_call: dict[str, Any], skip: bool
) -> Any:
"""Attempt to parse a JSON string for tool calling.
Args:
json_string: JSON string to parse.
skip: Whether to ignore parsing errors and return the value anyways.
raw_tool_call: Raw tool call to include in error message.
Returns:
The parsed JSON string.
Raises:
OutputParserException: If the JSON string wrong invalid and skip=False.
"""
try:
return json.loads(json_string)
except json.JSONDecodeError as e:
if skip:
return json_string
msg = (
f"Function {raw_tool_call['function']['name']} arguments:\n\n"
f"{raw_tool_call['function']['arguments']}\n\nare not valid JSON. "
f"Received JSONDecodeError {e}"
)
raise OutputParserException(msg) from e
except TypeError as e:
if skip:
return json_string
msg = (
f"Function {raw_tool_call['function']['name']} arguments:\n\n"
f"{raw_tool_call['function']['arguments']}\n\nare not a string or a "
f"dictionary. Received TypeError {e}"
)
raise OutputParserException(msg) from e
def _parse_arguments_from_tool_call(
raw_tool_call: dict[str, Any],
) -> Optional[dict[str, Any]]:
"""Parse arguments by trying to parse any shallowly nested string-encoded JSON.
Band-aid fix for issue in Ollama with inconsistent tool call argument structure.
Should be removed/changed if fixed upstream.
See https://github.com/ollama/ollama/issues/6155
"""
if "function" not in raw_tool_call:
return None
arguments = raw_tool_call["function"]["arguments"]
parsed_arguments: dict = {}
if isinstance(arguments, dict):
for key, value in arguments.items():
if isinstance(value, str):
parsed_value = _parse_json_string(
value, skip=True, raw_tool_call=raw_tool_call
)
if isinstance(parsed_value, (dict, list)):
parsed_arguments[key] = parsed_value
else:
parsed_arguments[key] = value
else:
parsed_arguments[key] = value
else:
parsed_arguments = _parse_json_string(
arguments, skip=False, raw_tool_call=raw_tool_call
)
return parsed_arguments
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 raw_tool_calls := response["message"].get("tool_calls"):
for tc in raw_tool_calls:
tool_calls.append(
tool_call(
id=str(uuid4()),
name=tc["function"]["name"],
args=_parse_arguments_from_tool_call(tc) or {},
)
)
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"],
},
}
def _is_pydantic_class(obj: Any) -> bool:
return isinstance(obj, type) and is_basemodel_subclass(obj)
class ChatOllama(BaseChatModel):
r"""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.text(), end="")
.. 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:
.. 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."""
extract_reasoning: Optional[Union[bool, Tuple[str, str]]] = False
"""Whether to extract the reasoning tokens in think blocks.
Extracts `chunk.content` to `chunk.additional_kwargs.reasoning_content`.
If a tuple is supplied, they are assumed to be the (start, end) tokens.
If `extract_reasoning=True`, the tokens will default to (, ).
"""
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: Optional[Union[Literal["", "json"], JsonSchemaValue]] = None
"""Specify the format of the output (options: "json", JSON schema)."""
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) # type: ignore
"""
The client to use for making requests.
"""
_async_client: AsyncClient = PrivateAttr(default=None) # type: ignore
"""
The async client to use for making requests.
"""
def _chat_params(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Dict[str, Any]:
ollama_messages = self._convert_messages_to_ollama_messages(messages)
if self.stop is not None and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
elif 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,
},
)
params = {
"messages": ollama_messages,
"stream": kwargs.pop("stream", True),
"model": kwargs.pop("model", self.model),
"format": kwargs.pop("format", self.format),
"options": Options(**options_dict),
"keep_alive": kwargs.pop("keep_alive", self.keep_alive),
**kwargs,
}
if tools := kwargs.get("tools"):
params["tools"] = tools
return params
@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, 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
def _extract_reasoning(
self, message_chunk: BaseMessageChunk, is_thinking: bool
) -> Tuple[BaseMessageChunk, bool]:
"""Mutate a message chunk to extract reasoning content."""
if not self.extract_reasoning:
return message_chunk, is_thinking
elif self.extract_reasoning is True:
start_token = DEFAULT_THINK_TOKEN_START
end_token = DEFAULT_THINK_TOKEN_END
else:
start_token, end_token = cast(tuple, self.extract_reasoning)
if start_token in message_chunk.content:
is_thinking = True
content = message_chunk.content
if is_thinking:
message_chunk.additional_kwargs["reasoning_content"] = content
message_chunk.content = ""
if end_token in content:
is_thinking = False
return message_chunk, is_thinking
async def _acreate_chat_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
chat_params = self._chat_params(messages, stop, **kwargs)
if chat_params["stream"]:
async for part in await self._async_client.chat(**chat_params):
yield part
else:
yield await self._async_client.chat(**chat_params)
def _create_chat_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Iterator[Union[Mapping[str, Any], str]]:
chat_params = self._chat_params(messages, stop, **kwargs)
if chat_params["stream"]:
yield from self._client.chat(**chat_params)
else:
yield self._client.chat(**chat_params)
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 chunk in self._iterate_over_stream(messages, stop, **kwargs):
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 chunk in self._aiterate_over_stream(messages, stop, **kwargs):
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,
additional_kwargs=final_chunk.message.additional_kwargs,
),
generation_info=generation_info,
)
return ChatResult(generations=[chat_generation])
def _iterate_over_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
is_thinking = False
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 self.extract_reasoning:
message, is_thinking = self._extract_reasoning(
chunk.message, is_thinking
)
chunk.message = message
yield chunk
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
for chunk in self._iterate_over_stream(messages, stop, **kwargs):
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
verbose=self.verbose,
)
yield chunk
async def _aiterate_over_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
is_thinking = False
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 self.extract_reasoning:
message, is_thinking = self._extract_reasoning(
chunk.message, is_thinking
)
chunk.message = message
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 chunk in self._aiterate_over_stream(messages, stop, **kwargs):
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,
additional_kwargs=final_chunk.message.additional_kwargs,
),
generation_info=generation_info,
)
return ChatResult(generations=[chat_generation])
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "chat-ollama"
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
*,
tool_choice: Optional[Union[dict, str, Literal["auto", "any"], bool]] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind tool-like objects to this chat model.
Assumes model is compatible with OpenAI tool-calling API.
Args:
tools: A list of tool definitions to bind to this chat model.
Supports any tool definition handled by
:meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: If provided, which tool for model to call. **This parameter
is currently ignored as it is not supported by Ollama.**
kwargs: Any additional parameters are passed directly to
``self.bind(**kwargs)``.
""" # noqa: E501
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
return super().bind(tools=formatted_tools, **kwargs)
def with_structured_output(
self,
schema: Union[Dict, type],
*,
method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
schema:
The output schema. Can be passed in as:
- a Pydantic class,
- a JSON schema
- a TypedDict class
- an OpenAI function/tool schema.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
method: The method for steering model generation, one of:
- "json_schema":
Uses Ollama's structured output API: https://ollama.com/blog/structured-outputs
- "function_calling":
Uses Ollama's tool-calling API
- "json_mode":
Specifies ``format="json"``. Note that if using JSON mode then you
must include instructions for formatting the output into the
desired schema into the model call.
include_raw:
If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
kwargs: Additional keyword args aren't supported.
Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
| If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
| If ``include_raw`` is True, then Runnable outputs a dict with keys:
- "raw": BaseMessage
- "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- "parsing_error": Optional[BaseException]
.. versionchanged:: 0.2.2
Added support for structured output API via ``format`` parameter.
.. versionchanged:: 0.3.0
Updated default ``method`` to ``"json_schema"``.
.. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=False
.. code-block:: python
from typing import Optional
from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Optional[str] = Field(
default=..., description="A justification for the answer."
)
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
.. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=True
.. code-block:: python
from langchain_ollama import ChatOllama
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, include_raw=True
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False
.. code-block:: python
from typing import Optional
from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Optional[str] = Field(
default=..., description="A justification for the answer."
)
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, method="function_calling"
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
.. dropdown:: Example: schema=TypedDict class, method="function_calling", include_raw=False
.. code-block:: python
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict
from langchain_ollama import ChatOllama
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[
Optional[str], None, "A justification for the answer."
]
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
.. dropdown:: Example: schema=OpenAI function schema, method="function_calling", include_raw=False
.. code-block:: python
from langchain_ollama import ChatOllama
oai_schema = {
'name': 'AnswerWithJustification',
'description': 'An answer to the user question along with justification for the answer.',
'parameters': {
'type': 'object',
'properties': {
'answer': {'type': 'string'},
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
},
'required': ['answer']
}
}
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
.. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True
.. code-block::
from langchain_ollama import ChatOllama
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification,
method="json_mode",
include_raw=True
)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
"What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
# 'raw': AIMessage(content='{\\n "answer": "They are both the same weight.",\\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
# 'parsing_error': None
# }
""" # noqa: E501, D301
_ = kwargs.pop("strict", None)
if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}")
is_pydantic_schema = _is_pydantic_class(schema)
if method == "function_calling":
if schema is None:
raise ValueError(
"schema must be specified when method is not 'json_mode'. "
"Received None."
)
formatted_tool = convert_to_openai_tool(schema)
tool_name = formatted_tool["function"]["name"]
llm = self.bind_tools(
[schema],
tool_choice=tool_name,
ls_structured_output_format={
"kwargs": {"method": method},
"schema": formatted_tool,
},
)
if is_pydantic_schema:
output_parser: Runnable = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True,
)
else:
output_parser = JsonOutputKeyToolsParser(
key_name=tool_name, first_tool_only=True
)
elif method == "json_mode":
llm = self.bind(
format="json",
ls_structured_output_format={
"kwargs": {"method": method},
"schema": schema,
},
)
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema
else JsonOutputParser()
)
elif method == "json_schema":
if schema is None:
raise ValueError(
"schema must be specified when method is not 'json_mode'. "
"Received None."
)
if is_pydantic_schema:
schema = cast(TypeBaseModel, schema)
if issubclass(schema, BaseModelV1):
response_format = schema.schema()
else:
response_format = schema.model_json_schema()
llm = self.bind(
format=response_format,
ls_structured_output_format={
"kwargs": {"method": method},
"schema": schema,
},
)
output_parser = PydanticOutputParser(pydantic_object=schema)
else:
if is_typeddict(schema):
response_format = convert_to_json_schema(schema)
if "required" not in response_format:
response_format["required"] = list(
response_format["properties"].keys()
)
else:
# is JSON schema
response_format = cast(dict, schema)
llm = self.bind(
format=response_format,
ls_structured_output_format={
"kwargs": {"method": method},
"schema": response_format,
},
)
output_parser = JsonOutputParser()
else:
raise ValueError(
f"Unrecognized method argument. Expected one of 'function_calling', "
f"'json_schema', or 'json_mode'. Received: '{method}'"
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser