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
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144 | 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"
is_local_path = os.path.exists(self.model_name)
if not is_local_path and 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]
|