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Code Issues Packages Projects Releases Wiki Activity

style: llm -> model (#33423)

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This commit is contained in:
Mason Daugherty
2025-10-10 13:19:13 -04:00
committed by GitHub
parent dd994b9d7f
commit 291a9fcea1
67 changed files with 586 additions and 572 deletions
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52
libs/partners/groq/langchain_groq/chat_models.py
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@@ -124,7 +124,7 @@ class ChatGroq(BaseChatModel):
```python
from langchain_groq import ChatGroq
llm = ChatGroq(
model = ChatGroq(
model="llama-3.1-8b-instant",
temperature=0.0,
max_retries=2,
@@ -138,7 +138,7 @@ class ChatGroq(BaseChatModel):
("system", "You are a helpful translator. Translate the user sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
model.invoke(messages)
```
```python
AIMessage(content='The English sentence "I love programming" can
@@ -155,7 +155,7 @@ class ChatGroq(BaseChatModel):
Stream:
```python
# Streaming `text` for each content chunk received
for chunk in llm.stream(messages):
for chunk in model.stream(messages):
print(chunk.text, end="")
```
@@ -173,7 +173,7 @@ class ChatGroq(BaseChatModel):
```python
# Reconstructing a full response
stream = llm.stream(messages)
stream = model.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
@@ -195,7 +195,7 @@ class ChatGroq(BaseChatModel):
Async:
```python
await llm.ainvoke(messages)
await model.ainvoke(messages)
```
```python
@@ -228,7 +228,7 @@ class ChatGroq(BaseChatModel):
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
model_with_tools = llm.bind_tools([GetWeather, GetPopulation])
model_with_tools = model.bind_tools([GetWeather, GetPopulation])
ai_msg = model_with_tools.invoke("What is the population of NY?")
ai_msg.tool_calls
```
@@ -260,7 +260,7 @@ class ChatGroq(BaseChatModel):
rating: int | None = Field(description="How funny the joke is, from 1 to 10")
structured_model = llm.with_structured_output(Joke)
structured_model = model.with_structured_output(Joke)
structured_model.invoke("Tell me a joke about cats")
```
@@ -276,7 +276,7 @@ class ChatGroq(BaseChatModel):
Response metadata:
```python
ai_msg = llm.invoke(messages)
ai_msg = model.invoke(messages)
ai_msg.response_metadata
```
@@ -921,10 +921,10 @@ class ChatGroq(BaseChatModel):
justification: str | None = Field(default=None, description="A justification for the answer.")
llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
@@ -945,13 +945,13 @@ class ChatGroq(BaseChatModel):
justification: str
llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(
model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(
AnswerWithJustification,
include_raw=True,
)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
structured_model.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.'),
@@ -973,10 +973,10 @@ class ChatGroq(BaseChatModel):
justification: Annotated[str | None, None, "A justification for the answer."]
llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
structured_model.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.'
@@ -999,10 +999,10 @@ class ChatGroq(BaseChatModel):
'required': ['answer']
}
llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)
model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(oai_schema)
structured_llm.invoke(
structured_model.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
@@ -1029,13 +1029,13 @@ class ChatGroq(BaseChatModel):
justification: str | None = Field(default=None, description="A justification for the answer.")
llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(
model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(
AnswerWithJustification,
method="json_schema",
)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
@@ -1054,12 +1054,12 @@ class ChatGroq(BaseChatModel):
justification: str
llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(
model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_model = model.with_structured_output(
AnswerWithJustification, method="json_mode", include_raw=True
)
structured_llm.invoke(
structured_model.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?"