mirror of https://github.com/hwchase17/langchain synced 2024-11-04 06:00:26 +00:00

mistralai[minor]: Add embeddings (#15282 )

- **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.

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Co-authored-by: Erick Friis <erick@langchain.dev>

2024-01-16 17:48:37 -08:00

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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"])

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