MistralAIEmbeddings

Setup:

Install langchain_mistralai and set environment variable MISTRAL_API_KEY.

pip install -U langchain_mistralai
export MISTRAL_API_KEY="your-api-key"

Key init args — completion params: model: Name of MistralAI model to use.

Key init args — client params: api_key: The API key for the MistralAI API. If not provided, it will be read from the environment variable MISTRAL_API_KEY. max_concurrent_requests: int max_retries: The number of times to retry a request if it fails. timeout: The number of seconds to wait for a response before timing out. wait_time: The number of seconds to wait before retrying a request in case of 429 error. max_concurrent_requests: The maximum number of concurrent requests to make to the Mistral API.

See full list of supported init args and their descriptions in the params section.

Instantiate:

from __module_name__ import MistralAIEmbeddings

embed = MistralAIEmbeddings(
    model="mistral-embed",
    # api_key="...",
    # other params...
)

Embed single text:

input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])

[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Embed multiple text:

input_texts = ["Document 1...", "Document 2..."]
vectors = embed.embed_documents(input_texts)
print(len(vectors))
# The first 3 coordinates for the first vector
print(vectors[0][:3])

2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Async:

vector = await embed.aembed_query(input_text)
print(vector[:3])

# multiple:
# await embed.aembed_documents(input_texts)

[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]