mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-14 23:26:34 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
237 lines
7.6 KiB
Python
237 lines
7.6 KiB
Python
"""Test ChatMistral chat model."""
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import json
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from typing import Any, Optional
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessageChunk,
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HumanMessage,
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)
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from langchain_core.pydantic_v1 import BaseModel
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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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full: Optional[BaseMessageChunk] = None
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chunks_with_token_counts = 0
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async for token in llm.astream("I'm Pickle Rick"):
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assert isinstance(token, AIMessageChunk)
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assert isinstance(token.content, str)
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full = token if full is None else full + token
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if token.usage_metadata is not None:
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chunks_with_token_counts += 1
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if chunks_with_token_counts != 1:
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raise AssertionError(
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"Expected exactly one chunk with token counts. "
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"AIMessageChunk aggregation adds counts. Check that "
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"this is behaving properly."
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)
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assert isinstance(full, AIMessageChunk)
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assert full.usage_metadata is not None
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assert full.usage_metadata["input_tokens"] > 0
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assert full.usage_metadata["output_tokens"] > 0
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assert (
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full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
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== full.usage_metadata["total_tokens"]
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)
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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_chat_mistralai_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatMistralAI(max_tokens=10)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model
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def test_chat_mistralai_streaming_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatMistralAI(max_tokens=10, streaming=True)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model
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def test_chat_mistralai_llm_output_contains_token_usage() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatMistralAI(max_tokens=10)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert "token_usage" in llm_result.llm_output
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token_usage = llm_result.llm_output["token_usage"]
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assert "prompt_tokens" in token_usage
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assert "completion_tokens" in token_usage
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assert "total_tokens" in token_usage
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def test_chat_mistralai_streaming_llm_output_not_contain_token_usage() -> None:
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"""Mistral currently doesn't return token usage when streaming."""
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chat = ChatMistralAI(max_tokens=10, streaming=True)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert "token_usage" in llm_result.llm_output
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token_usage = llm_result.llm_output["token_usage"]
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assert not token_usage
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def test_structured_output() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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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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def test_streaming_structured_output() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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class Person(BaseModel):
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name: str
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age: int
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structured_llm = llm.with_structured_output(Person)
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strm = structured_llm.stream("Erick, 27 years old")
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chunk_num = 0
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for chunk in strm:
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assert chunk_num == 0, "should only have one chunk with model"
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assert isinstance(chunk, Person)
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assert chunk.name == "Erick"
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assert chunk.age == 27
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chunk_num += 1
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def test_tool_call() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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class Person(BaseModel):
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name: str
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age: int
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tool_llm = llm.bind_tools([Person])
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result = tool_llm.invoke("Erick, 27 years old")
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assert isinstance(result, AIMessage)
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assert len(result.tool_calls) == 1
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tool_call = result.tool_calls[0]
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assert tool_call["name"] == "Person"
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assert tool_call["args"] == {"name": "Erick", "age": 27}
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def test_streaming_tool_call() -> None:
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
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class Person(BaseModel):
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name: str
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age: int
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tool_llm = llm.bind_tools([Person])
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# where it calls the tool
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strm = tool_llm.stream("Erick, 27 years old")
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additional_kwargs = None
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for chunk in strm:
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assert isinstance(chunk, AIMessageChunk)
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assert chunk.content == ""
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additional_kwargs = chunk.additional_kwargs
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assert additional_kwargs is not None
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assert "tool_calls" in additional_kwargs
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assert len(additional_kwargs["tool_calls"]) == 1
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assert additional_kwargs["tool_calls"][0]["function"]["name"] == "Person"
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assert json.loads(additional_kwargs["tool_calls"][0]["function"]["arguments"]) == {
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"name": "Erick",
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"age": 27,
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}
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assert isinstance(chunk, AIMessageChunk)
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assert len(chunk.tool_call_chunks) == 1
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tool_call_chunk = chunk.tool_call_chunks[0]
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assert tool_call_chunk["name"] == "Person"
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assert tool_call_chunk["args"] == '{"name": "Erick", "age": 27}'
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# where it doesn't call the tool
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strm = tool_llm.stream("What is 2+2?")
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acc: Any = None
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for chunk in strm:
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assert isinstance(chunk, AIMessageChunk)
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acc = chunk if acc is None else acc + chunk
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assert acc.content != ""
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assert "tool_calls" not in acc.additional_kwargs
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