Bases: MultiModalEmbedding
CLIP embedding models for encoding text and image for Multi-Modal purpose.
This class provides an interface to generate embeddings using a model
deployed in OpenAI CLIP. At the initialization it requires a model name
of CLIP.
Note
Requires clip package to be available in the PYTHONPATH. It can be installed with
pip install git+https://github.com/openai/CLIP.git.
Source code in llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py
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142 | class ClipEmbedding(MultiModalEmbedding):
"""CLIP embedding models for encoding text and image for Multi-Modal purpose.
This class provides an interface to generate embeddings using a model
deployed in OpenAI CLIP. At the initialization it requires a model name
of CLIP.
Note:
Requires `clip` package to be available in the PYTHONPATH. It can be installed with
`pip install git+https://github.com/openai/CLIP.git`.
"""
embed_batch_size: int = Field(default=DEFAULT_EMBED_BATCH_SIZE, gt=0)
_clip: Any = PrivateAttr()
_model: Any = PrivateAttr()
_preprocess: Any = PrivateAttr()
_device: Any = PrivateAttr()
@classmethod
def class_name(cls) -> str:
return "ClipEmbedding"
def __init__(
self,
*,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
model_name: str = DEFAULT_CLIP_MODEL,
**kwargs: Any,
):
"""Initializes the ClipEmbedding class.
During the initialization the `clip` package is imported.
Args:
embed_batch_size (int, optional): The batch size for embedding generation. Defaults to 10,
must be > 0 and <= 100.
model_name (str): The model name of Clip model.
Raises:
ImportError: If the `clip` package is not available in the PYTHONPATH.
ValueError: If the model cannot be fetched from Open AI. or if the embed_batch_size
is not in the range (0, 100].
"""
if embed_batch_size <= 0:
raise ValueError(f"Embed batch size {embed_batch_size} must be > 0.")
try:
import clip
import torch
except ImportError:
raise ImportError(
"ClipEmbedding requires `pip install git+https://github.com/openai/CLIP.git` and torch."
)
super().__init__(
embed_batch_size=embed_batch_size, model_name=model_name, **kwargs
)
try:
self._device = "cuda" if torch.cuda.is_available() else "cpu"
if self.model_name not in AVAILABLE_CLIP_MODELS:
raise ValueError(
f"Model name {self.model_name} is not available in CLIP."
)
self._model, self._preprocess = clip.load(
self.model_name, device=self._device
)
except Exception as e:
logger.error("Error while loading clip model.")
raise ValueError("Unable to fetch the requested embeddings model") from e
# TEXT EMBEDDINGS
async def _aget_query_embedding(self, query: str) -> Embedding:
return self._get_query_embedding(query)
def _get_text_embedding(self, text: str) -> Embedding:
return self._get_text_embeddings([text])[0]
def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]:
results = []
for text in texts:
try:
import clip
except ImportError:
raise ImportError(
"ClipEmbedding requires `pip install git+https://github.com/openai/CLIP.git` and torch."
)
text_embedding = self._model.encode_text(
clip.tokenize(text).to(self._device)
)
results.append(text_embedding.tolist()[0])
return results
def _get_query_embedding(self, query: str) -> Embedding:
return self._get_text_embedding(query)
# IMAGE EMBEDDINGS
async def _aget_image_embedding(self, img_file_path: ImageType) -> Embedding:
return self._get_image_embedding(img_file_path)
def _get_image_embedding(self, img_file_path: ImageType) -> Embedding:
import torch
with torch.no_grad():
image = (
self._preprocess(Image.open(img_file_path))
.unsqueeze(0)
.to(self._device)
)
return self._model.encode_image(image).tolist()[0]
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