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107 | class FastEmbedEmbedding(BaseEmbedding):
"""
Qdrant FastEmbedding models.
FastEmbed is a lightweight, fast, Python library built for embedding generation.
See more documentation at:
* https://github.com/qdrant/fastembed/
* https://qdrant.github.io/fastembed/.
To use this class, you must install the `fastembed` Python package.
`pip install fastembed`
Example:
from llama_index.embeddings.fastembed import FastEmbedEmbedding
fastembed = FastEmbedEmbedding()
"""
model_name: str = Field(
"BAAI/bge-small-en-v1.5",
description="Name of the FastEmbedding model to use.\n"
"Defaults to 'BAAI/bge-small-en-v1.5'.\n"
"Find the list of supported models at "
"https://qdrant.github.io/fastembed/examples/Supported_Models/",
)
max_length: int = Field(
512,
description="The maximum number of tokens. Defaults to 512.\n"
"Unknown behavior for values > 512.",
)
cache_dir: Optional[str] = Field(
None,
description="The path to the cache directory.\n"
"Defaults to `local_cache` in the parent directory",
)
threads: Optional[int] = Field(
None,
description="The number of threads single onnxruntime session can use.\n"
"Defaults to None",
)
doc_embed_type: Literal["default", "passage"] = Field(
"default",
description="Type of embedding method to use for documents.\n"
"Available options are 'default' and 'passage'.",
)
providers: Optional[List[str]] = Field(
default=None,
description="The ONNX providers to use for the embedding model.",
)
_model: Any = PrivateAttr()
@classmethod
def class_name(self) -> str:
return "FastEmbedEmbedding"
def __init__(
self,
model_name: Optional[str] = "BAAI/bge-small-en-v1.5",
max_length: Optional[int] = 512,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
doc_embed_type: Literal["default", "passage"] = "default",
providers: Optional[List[str]] = None,
):
super().__init__(
model_name=model_name,
max_length=max_length,
threads=threads,
doc_embed_type=doc_embed_type,
providers=providers,
)
self._model = TextEmbedding(
model_name=model_name,
max_length=max_length,
cache_dir=cache_dir,
threads=threads,
providers=providers,
)
def _get_text_embedding(self, text: str) -> List[float]:
embeddings: List[np.ndarray]
if self.doc_embed_type == "passage":
embeddings = list(self._model.passage_embed(text))
else:
embeddings = list(self._model.embed(text))
return embeddings[0].tolist()
def _get_query_embedding(self, query: str) -> List[float]:
query_embeddings: np.ndarray = next(self._model.query_embed(query))
return query_embeddings.tolist()
async def _aget_query_embedding(self, query: str) -> List[float]:
return self._get_query_embedding(query)
|
