| Name | langchain-mistralai JSON |
| Version |
0.2.7
JSON |
| download |
| home_page | None |
| Summary | An integration package connecting Mistral and LangChain |
| upload_time | 2025-02-20 17:43:38 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# 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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