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style: .. code-block:: admonition translations (#33400)

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biiiiiiiiiiiiiiiigggggggg pass
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Mason Daugherty
2025-10-09 16:52:58 -04:00
committed by GitHub
parent 50445d4a27
commit 6fc21afbc9
199 changed files with 10133 additions and 10940 deletions
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libs/partners/ollama/langchain_ollama/chat_models.py
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@@ -261,10 +261,10 @@ class ChatOllama(BaseChatModel):
Install `langchain-ollama` and download any models you want to use from ollama.
.. code-block:: bash
ollama pull gpt-oss:20b
pip install -U langchain-ollama
```bash
ollama pull gpt-oss:20b
pip install -U langchain-ollama
```
Key init args — completion params:
model: str
@@ -291,193 +291,191 @@ class ChatOllama(BaseChatModel):
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
```python
from langchain_ollama import ChatOllama
from langchain_ollama import ChatOllama
llm = ChatOllama(
model="gpt-oss:20b",
validate_model_on_init=True,
temperature=0.8,
num_predict=256,
# other params ...
)
llm = ChatOllama(
model="gpt-oss:20b",
validate_model_on_init=True,
temperature=0.8,
num_predict=256,
# other params ...
)
```
Invoke:
.. code-block:: python
```python
messages = [
("system", "You are a helpful translator. Translate the user sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
```
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')
```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
```python
for chunk in llm.stream("Return the words Hello World!"):
print(chunk.text, end="")
```
for chunk in llm.stream("Return the words Hello World!"):
print(chunk.text, end="")
```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
```python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
```
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",
)
```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
```python
await llm.ainvoke("Hello how are you!")
```
await llm.ainvoke("Hello how are you!")
```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
```python
async for chunk in llm.astream("Say hello world!"):
print(chunk.content)
```
```python
HEL
LO
WORLD
!
```
```python
messages = [("human", "Say hello world!"), ("human", "Say goodbye world!")]
await llm.abatch(messages)
```
```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?",
content="HELLO, WORLD!",
response_metadata={
"model": "llama3",
"created_at": "2024-07-04T03:52:08.165478Z",
"created_at": "2024-07-04T03:55:07.315396Z",
"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,
"total_duration": 1696745458,
"load_duration": 1505000,
"prompt_eval_count": 8,
"prompt_eval_duration": 111627000,
"eval_count": 6,
"eval_duration": 185181000,
},
id="run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0",
)
.. code-block:: python
async for chunk in llm.astream("Say hello world!"):
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",
),
]
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
```python
json_llm = ChatOllama(format="json")
llm.invoke(
"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."
).content
```
json_llm = ChatOllama(format="json")
llm.invoke(
"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."
).content
.. code-block:: python
'{"location": "Pune, India", "time_of_day": "morning"}'
```python
'{"location": "Pune, India", "time_of_day": "morning"}'
```
Tool Calling:
.. code-block:: python
from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field
```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")
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
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",
}
]
```python
[
{
"name": "Multiply",
"args": {"a": 45, "b": 67},
"id": "420c3f3b-df10-4188-945f-eb3abdb40622",
"type": "tool_call",
}
]
```
Thinking / Reasoning:
You can enable reasoning mode for models that support it by setting
@@ -494,30 +492,30 @@ class ChatOllama(BaseChatModel):
!!! note
This feature is only available for [models that support reasoning](https://ollama.com/search?c=thinking).
.. code-block:: python
```python
from langchain_ollama import ChatOllama
from langchain_ollama import ChatOllama
llm = ChatOllama(
model="deepseek-r1:8b",
validate_model_on_init=True,
reasoning=True,
)
llm = ChatOllama(
model="deepseek-r1:8b",
validate_model_on_init=True,
reasoning=True,
)
llm.invoke("how many r in the word strawberry?")
llm.invoke("how many r in the word strawberry?")
# or, on an invocation basis:
# or, on an invocation basis:
llm.invoke("how many r in the word strawberry?", reasoning=True)
# or llm.stream("how many r in the word strawberry?", reasoning=True)
llm.invoke("how many r in the word strawberry?", reasoning=True)
# or llm.stream("how many r in the word strawberry?", reasoning=True)
# If not provided, the invocation will default to the ChatOllama reasoning
# param provided (None by default).
```
# If not provided, the invocation will default to the ChatOllama reasoning
# param provided (None by default).
.. code-block:: python
AIMessage(content='The word "strawberry" contains **three \'r\' letters**. Here\'s a breakdown for clarity:\n\n- The spelling of "strawberry" has two parts ... be 3.\n\nTo be thorough, let\'s confirm with an online source or common knowledge.\n\nI can recall that "strawberry" has: s-t-r-a-w-b-e-r-r-y — yes, three r\'s.\n\nPerhaps it\'s misspelled by some, but standard is correct.\n\nSo I think the response should be 3.\n'}, response_metadata={'model': 'deepseek-r1:8b', 'created_at': '2025-07-08T19:33:55.891269Z', 'done': True, 'done_reason': 'stop', 'total_duration': 98232561292, 'load_duration': 28036792, 'prompt_eval_count': 10, 'prompt_eval_duration': 40171834, 'eval_count': 3615, 'eval_duration': 98163832416, 'model_name': 'deepseek-r1:8b'}, id='run--18f8269f-6a35-4a7c-826d-b89d52c753b3-0', usage_metadata={'input_tokens': 10, 'output_tokens': 3615, 'total_tokens': 3625})
```python
AIMessage(content='The word "strawberry" contains **three \'r\' letters**. Here\'s a breakdown for clarity:\n\n- The spelling of "strawberry" has two parts ... be 3.\n\nTo be thorough, let\'s confirm with an online source or common knowledge.\n\nI can recall that "strawberry" has: s-t-r-a-w-b-e-r-r-y — yes, three r\'s.\n\nPerhaps it\'s misspelled by some, but standard is correct.\n\nSo I think the response should be 3.\n'}, response_metadata={'model': 'deepseek-r1:8b', 'created_at': '2025-07-08T19:33:55.891269Z', 'done': True, 'done_reason': 'stop', 'total_duration': 98232561292, 'load_duration': 28036792, 'prompt_eval_count': 10, 'prompt_eval_duration': 40171834, 'eval_count': 3615, 'eval_duration': 98163832416, 'model_name': 'deepseek-r1:8b'}, id='run--18f8269f-6a35-4a7c-826d-b89d52c753b3-0', usage_metadata={'input_tokens': 10, 'output_tokens': 3615, 'total_tokens': 3625})
```
""" # noqa: E501, pylint: disable=line-too-long
model: str
@@ -1312,140 +1310,136 @@ class ChatOllama(BaseChatModel):
??? note "Example: `schema=Pydantic` class, `method='json_schema'`, `include_raw=False`"
.. code-block:: python
```python
from typing import Optional
from typing import Optional
from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field
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.'''
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str | None = Field(
default=...,
description="A justification for the answer.",
)
answer: str
justification: str | None = Field(
default=...,
description="A justification for the answer.",
)
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
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")
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.'
# )
# -> 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.'
# )
```
??? note "Example: `schema=Pydantic` class, `method='json_schema'`, `include_raw=True`"
.. code-block:: python
from langchain_ollama import ChatOllama
from pydantic import BaseModel
```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.'''
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
answer: str
justification: str
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification,
include_raw=True,
)
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
# }
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
# }
```
??? note "Example: `schema=Pydantic` class, `method='function_calling'`, `include_raw=False`"
.. code-block:: python
```python
from typing import Optional
from typing import Optional
from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field
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.'''
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str | None = 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",
answer: str
justification: str | None = Field(
default=...,
description="A justification for the answer.",
)
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.'
# )
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.'
# )
```
??? note "Example: `schema=TypedDict` class, `method='function_calling'`, `include_raw=False`"
.. code-block:: python
```python
from typing_extensions import Annotated, TypedDict
# 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
from langchain_ollama import ChatOllama
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[str | None, None, "A justification for the answer."]
answer: str
justification: Annotated[str | None, None, "A justification for the answer."]
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
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.'
# }
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.'
# }
```
??? note "Example: `schema=OpenAI` function schema, `method='function_calling'`, `include_raw=False`"
.. code-block:: python
```python
from langchain_ollama import ChatOllama
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']
}
}
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)
@@ -1457,35 +1451,36 @@ class ChatOllama(BaseChatModel):
# '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.'
# }
```
??? note "Example: `schema=Pydantic` class, `method='json_mode'`, `include_raw=True`"
.. code-block::
```python
from langchain_ollama import ChatOllama
from pydantic import BaseModel
from langchain_ollama import ChatOllama
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
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
# }
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
_ = kwargs.pop("strict", None)

97
libs/partners/ollama/langchain_ollama/embeddings.py
View File

@@ -27,9 +27,9 @@ class OllamaEmbeddings(BaseModel, Embeddings):
For example, to pull the llama3 model:
.. code-block:: bash
ollama pull llama3
```bash
ollama pull llama3
```
This will download the default tagged version of the model.
Typically, the default points to the latest, smallest sized-parameter model.
@@ -42,26 +42,26 @@ class OllamaEmbeddings(BaseModel, Embeddings):
To view pulled models:
.. code-block:: bash
ollama list
```bash
ollama list
```
To start serving:
.. code-block:: bash
ollama serve
```bash
ollama serve
```
View the Ollama documentation for more commands.
.. code-block:: bash
```bash
ollama help
```
ollama help
Install the langchain-ollama integration package:
.. code-block:: bash
pip install -U langchain_ollama
Install the `langchain-ollama` integration package:
```bash
pip install -U langchain_ollama
```
Key init args — completion params:
model: str
@@ -72,50 +72,49 @@ class OllamaEmbeddings(BaseModel, Embeddings):
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
```python
from langchain_ollama import OllamaEmbeddings
from langchain_ollama import OllamaEmbeddings
embed = OllamaEmbeddings(model="llama3")
embed = OllamaEmbeddings(model="llama3")
```
Embed single text:
.. code-block:: python
```python
input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])
```
input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])
.. code-block:: python
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
```python
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
```
Embed multiple texts:
.. code-block:: python
```python
input_texts = ["Document 1...", "Document 2..."]
vectors = embed.embed_documents(input_texts)
print(len(vectors))
# The first 3 coordinates for the first vector
print(vectors[0][:3])
```
input_texts = ["Document 1...", "Document 2..."]
vectors = embed.embed_documents(input_texts)
print(len(vectors))
# The first 3 coordinates for the first vector
print(vectors[0][:3])
.. code-block:: python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
```python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
```
Async:
.. code-block:: python
```python
vector = await embed.aembed_query(input_text)
print(vector[:3])
vector = await embed.aembed_query(input_text)
print(vector[:3])
# multiple:
# await embed.aembed_documents(input_texts)
.. code-block:: python
[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
# multiple:
# await embed.aembed_documents(input_texts)
```
```python
[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
```
""" # noqa: E501
model: str

85
libs/partners/ollama/langchain_ollama/llms.py
View File

@@ -24,17 +24,17 @@ class OllamaLLM(BaseLLM):
Setup:
Install `langchain-ollama` and install/run the Ollama server locally:
.. code-block:: bash
pip install -U langchain-ollama
# Visit https://ollama.com/download to download and install Ollama
# (Linux users): start the server with `ollama serve`
```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:
.. code-block:: bash
ollama pull llama3.1
```bash
ollama pull llama3.1
```
Key init args — generation params:
model: str
@@ -63,50 +63,47 @@ class OllamaLLM(BaseLLM):
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
```python
from langchain_ollama import OllamaLLM
from langchain_ollama import OllamaLLM
llm = OllamaLLM(
model="llama3.1",
temperature=0.7,
num_predict=256,
# base_url="http://localhost:11434",
# other params...
)
llm = OllamaLLM(
model="llama3.1",
temperature=0.7,
num_predict=256,
# base_url="http://localhost:11434",
# other params...
)
```
Invoke:
.. code-block:: python
input_text = "The meaning of life is "
response = llm.invoke(input_text)
print(response)
.. code-block::
"a philosophical question that has been contemplated by humans for
centuries..."
```python
input_text = "The meaning of life is "
response = llm.invoke(input_text)
print(response)
```
```txt
"a philosophical question that has been contemplated by humans for
centuries..."
```
Stream:
.. code-block:: python
for chunk in llm.stream(input_text):
print(chunk, end="")
.. code-block::
a philosophical question that has been contemplated by humans for
centuries...
```python
for chunk in llm.stream(input_text):
print(chunk, end="")
```
```txt
a philosophical question that has been contemplated by humans for
centuries...
```
Async:
.. code-block:: python
response = await llm.ainvoke(input_text)
# stream:
# async for chunk in llm.astream(input_text):
# print(chunk, end="")
```python
response = await llm.ainvoke(input_text)
# stream:
# async for chunk in llm.astream(input_text):
# print(chunk, end="")
```
"""
model: str