This will help you get started with MistralAI embedding models using LangChain. For detailed documentation onMistralAIEmbeddings features and configuration options, please refer to theAPI reference.

Overview​

Integration details​

Setup​

To access MistralAI embedding models you'll need to create a/an MistralAI account, get an API key, and install thelangchain-mistralai integration package.

Credentials​

Head tohttps://console.mistral.ai/ to sign up to MistralAI and generate an API key. Once you've done this set the MISTRALAI_API_KEY environment variable:

import getpass
import os

ifnot os.getenv("MISTRALAI_API_KEY"):
    os.environ["MISTRALAI_API_KEY"]= getpass.getpass("Enter your MistralAI API key: ")

To enable automated tracing of your model calls, set yourLangSmith API key:

# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation​

The LangChain MistralAI integration lives in thelangchain-mistralai package:

%pip install-qU langchain-mistralai

Instantiation​

Now we can instantiate our model object and generate chat completions:

from langchain_mistralaiimport MistralAIEmbeddings

embeddings= MistralAIEmbeddings(
    model="mistral-embed",
)

Indexing and Retrieval​

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see ourRAG tutorials.

Below, see how to index and retrieve data using theembeddings object we initialized above. In this example, we will index and retrieve a sample document in theInMemoryVectorStore.

# Create a vector store with a sample text
from langchain_core.vectorstoresimport InMemoryVectorStore

text="LangChain is the framework for building context-aware reasoning applications"

vectorstore= InMemoryVectorStore.from_texts(
[text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever= vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents= retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

'LangChain is the framework for building context-aware reasoning applications'

Direct Usage​

Under the hood, the vectorstore and retriever implementations are callingembeddings.embed_documents(...) andembeddings.embed_query(...) to create embeddings for the text(s) used infrom_texts and retrievalinvoke operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

Embed single texts​

You can embed single texts or documents withembed_query:

single_vector= embeddings.embed_query(text)
print(str(single_vector)[:100])# Show the first 100 characters of the vector

[-0.04443359375, 0.01885986328125, 0.018035888671875, -0.00864410400390625, 0.049652099609375, -0.00

Embed multiple texts​

You can embed multiple texts withembed_documents:

text2=(
"LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors= embeddings.embed_documents([text, text2])
for vectorin two_vectors:
print(str(vector)[:100])# Show the first 100 characters of the vector

[-0.04443359375, 0.01885986328125, 0.0180511474609375, -0.0086517333984375, 0.049652099609375, -0.00
[-0.02032470703125, 0.02606201171875, 0.051605224609375, -0.0281982421875, 0.055755615234375, 0.0019

API Reference​

For detailed documentation onMistralAIEmbeddings features and configuration options, please refer to theAPI reference.

Related​

• Embedding modelconceptual guide
• Embedding modelhow-to guides