mirror of
https://github.com/hwchase17/langchain.git
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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()
```
langchain-mistralai
This package contains the LangChain integrations for MistralAI through their mistralai SDK.
Installation
pip install -U langchain-mistralai
Chat Models
This package contains the ChatMistralAI class, which is the recommended way to interface with MistralAI models.
To use, install the requirements, and configure your environment.
export MISTRAL_API_KEY=your-api-key
Then initialize
from langchain_core.messages import HumanMessage
from langchain_mistralai.chat_models import ChatMistralAI
chat = ChatMistralAI(model="mistral-small")
messages = [HumanMessage(content="say a brief hello")]
chat.invoke(messages)
ChatMistralAI also supports async and streaming functionality:
# For async...
await chat.ainvoke(messages)
# For streaming...
for chunk in chat.stream(messages):
print(chunk.content, end="", flush=True)
Embeddings
With MistralAIEmbeddings, you can directly use the default model 'mistral-embed', or set a different one if available.
Choose model
embedding.model = 'mistral-embed'
Simple query
res_query = embedding.embed_query("The test information")
Documents
res_document = embedding.embed_documents(["test1", "another test"])