langchain_nomic.embeddings
.NomicEmbeddings¶
- class langchain_nomic.embeddings.NomicEmbeddings(*, model: str, nomic_api_key: Optional[str] = None, dimensionality: Optional[int] = None)[source]¶
NomicEmbeddings embedding model.
Example
from langchain_nomic import NomicEmbeddings model = NomicEmbeddings()
Initialize NomicEmbeddings model.
- Parameters
model (str) – model name
nomic_api_key (Optional[str]) – optionally, set the Nomic API key. Uses the NOMIC_API_KEY environment variable by default.
dimensionality (Optional[int]) –
Methods
__init__
(*, model[, nomic_api_key, ...])Initialize NomicEmbeddings model.
aembed_documents
(texts)Asynchronous Embed search docs.
aembed_query
(text)Asynchronous Embed query text.
embed
(texts, *, task_type)Embed texts.
embed_documents
(texts)Embed search docs.
embed_query
(text)Embed query text.
- __init__(*, model: str, nomic_api_key: Optional[str] = None, dimensionality: Optional[int] = None)[source]¶
Initialize NomicEmbeddings model.
- Parameters
model (str) – model name
nomic_api_key (Optional[str]) – optionally, set the Nomic API key. Uses the NOMIC_API_KEY environment variable by default.
dimensionality (Optional[int]) –
- async aembed_documents(texts: List[str]) List[List[float]] ¶
Asynchronous Embed search docs.
- Parameters
texts (List[str]) –
- Return type
List[List[float]]
- async aembed_query(text: str) List[float] ¶
Asynchronous Embed query text.
- Parameters
text (str) –
- Return type
List[float]
- embed(texts: List[str], *, task_type: str) List[List[float]] [source]¶
Embed texts.
- Parameters
texts (List[str]) – list of texts to embed
task_type (str) – the task type to use when embedding. One of search_query, search_document, classification, clustering
- Return type
List[List[float]]