TogetherEmbeddings

Setup:

Install langchain_together and set environment variable TOGETHER_API_KEY.

.. code-block:: bash

pip install -U langchain_together
export TOGETHER_API_KEY="your-api-key"

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

Key init args — client params: api_key: Optional[SecretStr]

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

Instantiate:

.. code-block:: python

from module_name import TogetherEmbeddings

embed = TogetherEmbeddings( model="togethercomputer/m2-bert-80M-8k-retrieval", # 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 texts:

.. 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]