Source code for langchain_community.embeddings.awa
from typing import Any, Dict, List
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator
[docs]class AwaEmbeddings(BaseModel, Embeddings):
"""Embedding documents and queries with Awa DB.
Attributes:
client: The AwaEmbedding client.
model: The name of the model used for embedding.
Default is "all-mpnet-base-v2".
"""
client: Any #: :meta private:
model: str = "all-mpnet-base-v2"
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that awadb library is installed."""
try:
from awadb import AwaEmbedding
except ImportError as exc:
raise ImportError(
"Could not import awadb library. "
"Please install it with `pip install awadb`"
) from exc
values["client"] = AwaEmbedding()
return values
[docs] def set_model(self, model_name: str) -> None:
"""Set the model used for embedding.
The default model used is all-mpnet-base-v2
Args:
model_name: A string which represents the name of model.
"""
self.model = model_name
self.client.model_name = model_name
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of documents using AwaEmbedding.
Args:
texts: The list of texts need to be embedded
Returns:
List of embeddings, one for each text.
"""
return self.client.EmbeddingBatch(texts)
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using AwaEmbedding.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.client.Embedding(text)