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These are set in Github workflows, but forgot to add them to most makefiles for convenience when developing locally. `uv run` will automatically sync the lock file. Because many of our development dependencies are local installs, it will pick up version changes and update the lock file. Passing `--frozen` or setting this environment variable disables the behavior. |
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| LICENSE | ||
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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"])