# langchain-mistralai
This package contains the LangChain integrations for [MistralAI](https://docs.mistral.ai) through their [mistralai](https://pypi.org/project/mistralai/) SDK.
## Installation
```bash
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.
```bash
export MISTRAL_API_KEY=your-api-key
```
Then initialize
```python
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:
```python
# 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"])`
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"description": "# langchain-mistralai\n\nThis package contains the LangChain integrations for [MistralAI](https://docs.mistral.ai) through their [mistralai](https://pypi.org/project/mistralai/) SDK.\n\n## Installation\n\n```bash\npip install -U langchain-mistralai\n```\n\n## Chat Models\n\nThis package contains the `ChatMistralAI` class, which is the recommended way to interface with MistralAI models.\n\nTo use, install the requirements, and configure your environment.\n\n```bash\nexport MISTRAL_API_KEY=your-api-key\n```\n\nThen initialize\n\n```python\nfrom langchain_core.messages import HumanMessage\nfrom langchain_mistralai.chat_models import ChatMistralAI\n\nchat = ChatMistralAI(model=\"mistral-small\")\nmessages = [HumanMessage(content=\"say a brief hello\")]\nchat.invoke(messages)\n```\n\n`ChatMistralAI` also supports async and streaming functionality:\n\n```python\n# For async...\nawait chat.ainvoke(messages)\n\n# For streaming...\nfor chunk in chat.stream(messages):\n print(chunk.content, end=\"\", flush=True)\n```\n\n## Embeddings\n\nWith `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available.\n\n### Choose model\n\n`embedding.model = 'mistral-embed'`\n\n### Simple query\n\n`res_query = embedding.embed_query(\"The test information\")`\n\n### Documents\n\n`res_document = embedding.embed_documents([\"test1\", \"another test\"])`\n",
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