mirror of
https://github.com/hwchase17/langchain
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- **Description:** Adds MistralAIEmbeddings class for embeddings, using the new official API. - **Dependencies:** mistralai - **Tag maintainer**: @efriis, @hwchase17 - **Twitter handle:** @LMS_David_RS Create `integrations/text_embedding/mistralai.ipynb`: an example notebook for MistralAIEmbeddings class Modify `embeddings/__init__.py`: Import the class Create `embeddings/mistralai.py`: The embedding class Create `integration_tests/embeddings/test_mistralai.py`: The test file. --------- Co-authored-by: Erick Friis <erick@langchain.dev>
57 lines
1.3 KiB
Markdown
57 lines
1.3 KiB
Markdown
# langchain-mistralai
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This package contains the LangChain integrations for [MistralAI](https://docs.mistral.ai) through their [mistralai](https://pypi.org/project/mistralai/) SDK.
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## Installation
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```bash
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pip install -U langchain-mistralai
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```
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## Chat Models
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This package contains the `ChatMistralAI` class, which is the recommended way to interface with MistralAI models.
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To use, install the requirements, and configure your environment.
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```bash
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export MISTRAL_API_KEY=your-api-key
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```
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Then initialize
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```python
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from langchain_core.messages import HumanMessage
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from langchain_mistralai.chat_models import ChatMistralAI
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chat = ChatMistralAI(model="mistral-small")
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messages = [HumanMessage(content="say a brief hello")]
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chat.invoke(messages)
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```
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`ChatMistralAI` also supports async and streaming functionality:
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```python
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# For async...
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await chat.ainvoke(messages)
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# For streaming...
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for chunk in chat.stream(messages):
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print(chunk.content, end="", flush=True)
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```
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## Embeddings
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With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available.
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### Choose model
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`embedding.model = 'mistral-embed'`
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### Simple query
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`res_query = embedding.embed_query("The test information")`
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### Documents
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`res_document = embedding.embed_documents(["test1", "another test"])` |