mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
c4599444ee
```python
from langchain.agents import tool
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
return len(word)
tools = [get_word_length]
llm_with_tools = llm.bind_tools(tools)
llm_with_tools.invoke("how long is the word chrysanthemum")
```
currently raises
```
AttributeError: 'dict' object has no attribute 'model_dump'
```
Same with `.with_structured_output`
```python
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
"""An answer to the user question along with justification for the answer."""
answer: str
justification: str
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")
```
This appears to fix.
85 lines
2.4 KiB
Python
85 lines
2.4 KiB
Python
"""Test ChatMistral chat model."""
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from langchain_mistralai.chat_models import ChatMistralAI
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def test_stream() -> None:
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"""Test streaming tokens from ChatMistralAI."""
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llm = ChatMistralAI()
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for token in llm.stream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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async def test_astream() -> None:
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"""Test streaming tokens from ChatMistralAI."""
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llm = ChatMistralAI()
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async for token in llm.astream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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async def test_abatch() -> None:
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"""Test streaming tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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async def test_abatch_tags() -> None:
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"""Test batch tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = await llm.abatch(
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["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
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)
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for token in result:
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assert isinstance(token.content, str)
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def test_batch() -> None:
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"""Test batch tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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async def test_ainvoke() -> None:
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"""Test invoke tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
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assert isinstance(result.content, str)
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def test_invoke() -> None:
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"""Test invoke tokens from ChatMistralAI"""
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llm = ChatMistralAI()
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result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
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assert isinstance(result.content, str)
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def test_structred_output() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
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schema = {
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"title": "AnswerWithJustification",
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"description": (
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"An answer to the user question along with justification for the answer."
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),
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"type": "object",
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"properties": {
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"answer": {"title": "Answer", "type": "string"},
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"justification": {"title": "Justification", "type": "string"},
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},
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"required": ["answer", "justification"],
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}
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structured_llm = llm.with_structured_output(schema)
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result = structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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assert isinstance(result, dict)
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