ai21[patch]: Update API reference documentation (#25302)

Issue: https://github.com/langchain-ai/langchain/issues/24856
pull/25313/head
Eugene Yurtsev 1 month ago committed by GitHub
parent 53ee5770d3
commit ccff1ba8b8
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@ -16,9 +16,17 @@ class AI21Base(BaseModel):
client: Any = Field(default=None, exclude=True) #: :meta private:
api_key: Optional[SecretStr] = None
"""API key for AI21 API."""
api_host: Optional[str] = None
"""Host URL"""
timeout_sec: Optional[float] = None
"""Timeout in seconds.
If not set, it will default to the value of the environment
variable `AI21_TIMEOUT_SEC` or 300 seconds.
"""
num_retries: Optional[int] = None
"""Maximum number of retries for API requests before giving up."""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:

@ -15,18 +15,64 @@ def _split_texts_into_batches(texts: List[str], batch_size: int) -> Iterator[Lis
class AI21Embeddings(Embeddings, AI21Base):
"""AI21 embedding model.
"""AI21 embedding model integration.
To use, you should have the 'AI21_API_KEY' environment variable set
or pass as a named parameter to the constructor.
Install ``langchain_ai21`` and set environment variable ``AI21_API_KEY``.
Example:
.. code-block:: bash
pip install -U langchain_ai21
export AI21_API_KEY="your-api-key"
Key init args client params:
api_key: Optional[SecretStr]
batch_size: int
The number of texts that will be sent to the API in each batch.
Use larger batch sizes if working with many short texts. This will reduce
the number of API calls made, and can improve the time it takes to embed
a large number of texts.
num_retries: Optional[int]
Maximum number of retries for API requests before giving up.
timeout_sec: Optional[float]
Timeout in seconds for API requests. If not set, it will default to the
value of the environment variable `AI21_TIMEOUT_SEC` or 300 seconds.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_ai21 import AI21Embeddings
embeddings = AI21Embeddings()
query_result = embeddings.embed_query("Hello embeddings world!")
embed = AI21Embeddings(
# api_key="...",
# batch_size=128,
)
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]
"""
batch_size: int = _DEFAULT_BATCH_SIZE

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