.rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of documents and
the query for a document as similar as possible.
To learn more, check out: https://docs.aleph-alpha.com/docs/tasks/semantic_embed/
Example
from aleph_alpha import AlephAlphaAsymmetricSemanticEmbedding
embeddings = AlephAlphaSymmetricSemanticEmbedding()
document = "This is a content of the document"
query = "What is the content of the document?"
doc_result = embeddings.embed_documents([document])
query_result = embeddings.embed_query(query)
field aleph_alpha_api_key: Optional[str] = None#
API key for Aleph Alpha API.
field compress_to_size: Optional[int] = 128#
Should the returned embeddings come back as an original 5120-dim vector,
or should it be compressed to 128-dim.
field contextual_control_threshold: Optional[int] = None#
Attention control parameters only apply to those tokens that have
explicitly been set in the request.
field control_log_additive: Optional[bool] = True#
Apply controls on prompt items by adding the log(control_factor)
to attention scores.
field hosting: Optional[str] = 'https://api.aleph-alpha.com'#
Optional parameter that specifies which datacenters may process the request.
field model: Optional[str] = 'luminous-base'#
Model name to use.
field normalize: Optional[bool] = True#
Should returned embeddings be normalized
embed_documents(texts: List[str]) → List[List[float]][source]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html