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biiiiiiiiiiiiiiiigggggggg pass
This commit is contained in:
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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237
libs/partners/mistralai/langchain_mistralai/chat_models.py
View File

@@ -808,113 +808,109 @@ class ChatMistralAI(BaseChatModel):
- `'parsing_error'`: BaseException | None
Example: schema=Pydantic class, method="function_calling", include_raw=False:
.. code-block:: python
```python
from typing import Optional
from typing import Optional
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel, Field
from langchain_mistralai import ChatMistralAI
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
# If we provide default values and/or descriptions for fields, these will be passed
# to the model. This is an important part of improving a model's ability to
# correctly return structured outputs.
justification: str | None = Field(
default=None, description="A justification for the answer."
)
llm = ChatMistralAI(model="mistral-large-latest", 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: str
# If we provide default values and/or descriptions for fields, these will be passed
# to the model. This is an important part of improving a model's ability to
# correctly return structured outputs.
justification: str | None = Field(
default=None, description="A justification for the answer."
)
# -> 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 = ChatMistralAI(model="mistral-large-latest", 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.'
# )
```
Example: schema=Pydantic class, method="function_calling", include_raw=True:
.. code-block:: python
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
```python
from langchain_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, include_raw=True
)
llm = ChatMistralAI(model="mistral-large-latest", 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
# }
```
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_mistralai import ChatMistralAI
from langchain_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
llm = ChatMistralAI(model="mistral-large-latest", 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.'
# }
```
Example: schema=OpenAI function schema, method="function_calling", include_raw=False:
.. code-block:: python
```python
from langchain_mistralai import ChatMistralAI
from langchain_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)
@@ -926,53 +922,56 @@ class ChatMistralAI(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.'
# }
```
Example: schema=Pydantic class, method="json_mode", include_raw=True:
.. code-block::
```python
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
from langchain_mistralai import ChatMistralAI
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
class AnswerWithJustification(BaseModel):
answer: str
justification: str
llm = ChatMistralAI(model="mistral-large-latest", 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 = ChatMistralAI(model="mistral-large-latest", 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
# }
```
Example: schema=None, method="json_mode", include_raw=True:
.. code-block::
```python
structured_llm = llm.with_structured_output(
method="json_mode", include_raw=True
)
structured_llm = llm.with_structured_output(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': {
# '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
# }
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': {
# '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)

84
libs/partners/mistralai/langchain_mistralai/embeddings.py
View File

@@ -44,10 +44,10 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
Install `langchain_mistralai` and set environment variable
`MISTRAL_API_KEY`.
.. code-block:: bash
pip install -U langchain_mistralai
export MISTRAL_API_KEY="your-api-key"
```bash
pip install -U langchain_mistralai
export MISTRAL_API_KEY="your-api-key"
```
Key init args — completion params:
model: str
@@ -71,57 +71,53 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
Instantiate:
.. code-block:: python
```python
from __module_name__ import MistralAIEmbeddings
from __module_name__ import MistralAIEmbeddings
embed = MistralAIEmbeddings(
model="mistral-embed",
# api_key="...",
# other params...
)
embed = MistralAIEmbeddings(
model="mistral-embed",
# api_key="...",
# other params...
)
```
Embed single text:
.. code-block:: python
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
input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])
```
```python
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
```
Embed multiple text:
.. code-block:: 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])
.. code-block:: python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
```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])
```
```python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
```
Async:
.. code-block:: python
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]
```python
vector = await embed.aembed_query(input_text)
print(vector[:3])
# multiple:
# await embed.aembed_documents(input_texts)
```
```python
[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
```
"""
# The type for client and async_client is ignored because the type is not