mistralai[patch]: Docs Update APIReference for MistralAIEmbeddings (#25294)

Update API Reference for MistralAI embeddings

Issue: https://github.com/langchain-ai/langchain/issues/24856
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Eugene Yurtsev 2024-08-12 11:25:37 -04:00 committed by GitHub
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@ -32,20 +32,82 @@ class DummyTokenizer:
class MistralAIEmbeddings(BaseModel, Embeddings):
"""MistralAI embedding models.
"""MistralAI embedding model integration.
To use, set the environment variable `MISTRAL_API_KEY` is set with your API key or
pass it as a named parameter to the constructor.
Setup:
Install ``langchain_mistralai`` and set environment variable
``MISTRAL_API_KEY``.
Example:
.. code-block:: bash
pip install -U langchain_mistralai
export MISTRAL_API_KEY="your-api-key"
Key init args completion params:
model: str
Name of MistralAI model to use.
Key init args client params:
api_key: Optional[SecretStr]
The API key for the MistralAI API. If not provided, it will be read from the
environment variable `MISTRAL_API_KEY`.
max_retries: int
The number of times to retry a request if it fails.
timeout: int
The number of seconds to wait for a response before timing out.
max_concurrent_requests: int
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:
.. code-block:: python
from langchain_mistralai import MistralAIEmbeddings
from __module_name__ import MistralAIEmbeddings
mistral = MistralAIEmbeddings(
embed = MistralAIEmbeddings(
model="mistral-embed",
api_key="my-api-key"
# api_key="...",
# other params...
)
Embed single text:
.. code-block:: python
input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])
.. code-block:: python
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
Embed multiple text:
.. code-block:: python
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])
.. code-block:: python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
Async:
.. code-block:: python
vector = await embed.aembed_query(input_text)
print(vector[:3])
# multiple:
# await embed.aembed_documents(input_texts)
.. code-block:: python
[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
"""
client: httpx.Client = Field(default=None) #: :meta private: