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embedder.py
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from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Callable, List, Optional
if TYPE_CHECKING:
import numpy as np
import pandas as pd
@dataclass
class EmbeddingConfig:
batch_size: int = 64
show_progress: bool = True
class BaseEmbedder(ABC):
"""
Abstract base class for embedding generation.
Supports multiple modalities via routing.
Users can register custom modality handlers.
"""
def __init__(self, config: Optional[EmbeddingConfig] = None):
self.config = config or EmbeddingConfig()
# Registry: modality -> embedding function
self._modality_handlers: dict[str, Callable[[List[Any]], "np.ndarray"]] = {}
# Register default modalities (subclass can override)
self._register_default_modalities()
def _register_default_modalities(self) -> None:
"""Override in subclass to register default modality handlers."""
pass
def register_modality(
self,
modality: str,
handler: Callable[[List[Any]], "np.ndarray"],
) -> None:
"""
Register a handler for a modality.
Args:
modality: Name of modality ("text", "image", "video", etc.)
handler: Function that takes list of inputs and returns embeddings.
"""
self._modality_handlers[modality] = handler
@property
def supported_modalities(self) -> List[str]:
"""Return list of supported modalities."""
return list(self._modality_handlers.keys())
def get_embedding_dim(self, modality: str) -> Optional[int]:
"""
Return the embedding dimension for a given modality.
Subclasses should override this to return the actual dimension
so that auto-generated FeatureView schemas use the correct vector_length.
Args:
modality: The modality to query (e.g. "text", "image").
Returns:
The embedding dimension, or None if unknown.
"""
return None
@abstractmethod
def embed(self, inputs: List[Any], modality: str) -> "np.ndarray":
"""
Generate embeddings for inputs of a given modality.
Args:
inputs: List of inputs.
modality: Type of content ("text", "image", "video", etc.)
Returns:
numpy array of shape (len(inputs), embedding_dim)
"""
pass
def embed_dataframe(
self,
df: pd.DataFrame,
column_mapping: dict[str, tuple[str, str]],
) -> pd.DataFrame:
"""
Add embeddings for multiple columns with modality routing.
Args:
df: Input DataFrame.
column_mapping: Dict mapping source_column -> (modality, output_column).
Example: {
"text": ("text", "text_embedding"),
"image_path": ("image", "image_embedding"),
"video_path": ("video", "video_embedding"),
}
"""
df = df.copy()
for source_column, (modality, output_column) in column_mapping.items():
inputs = df[source_column].tolist()
embeddings = self.embed(inputs, modality)
df[output_column] = pd.Series(
[emb.tolist() for emb in embeddings], dtype=object, index=df.index
)
return df
class MultiModalEmbedder(BaseEmbedder):
"""
Multi-modal embedder with built-in support for common modalities.
Supports: text, image, video (extensible)
"""
def __init__(
self,
text_model: str = "all-MiniLM-L6-v2",
image_model: str = "openai/clip-vit-base-patch32",
config: Optional[EmbeddingConfig] = None,
):
self.text_model_name = text_model
self.image_model_name = image_model
# Lazy-loaded models
self._text_model = None
self._image_model = None
self._image_processor = None
super().__init__(config)
def _register_default_modalities(self) -> None:
"""Register built-in modality handlers."""
self.register_modality("text", self._embed_text)
self.register_modality("image", self._embed_image)
# Future: add more as needed
# self.register_modality("video", self._embed_video)
# self.register_modality("audio", self._embed_audio)
def embed(self, inputs: List[Any], modality: str) -> "np.ndarray":
"""Route to appropriate handler based on modality."""
if modality not in self._modality_handlers:
raise ValueError(
f"Unsupported modality: '{modality}'. "
f"Supported: {self.supported_modalities}"
)
handler = self._modality_handlers[modality]
return handler(inputs)
def get_embedding_dim(self, modality: str) -> Optional[int]:
"""
Return the embedding dimension for a given modality.
For "text", this queries the SentenceTransformer model's dimension
(which triggers lazy model loading).
Args:
modality: The modality to query (e.g. "text", "image").
Returns:
The embedding dimension, or None if unknown.
"""
if modality == "text":
return self.text_model.get_sentence_embedding_dimension()
elif modality == "image":
return self.image_model.config.vision_config.hidden_size
return None
# Text Embedding
@property
def text_model(self):
if self._text_model is None:
from sentence_transformers import SentenceTransformer
self._text_model = SentenceTransformer(self.text_model_name)
return self._text_model
def _embed_text(self, inputs: List[str]) -> "np.ndarray":
return self.text_model.encode(
inputs,
batch_size=self.config.batch_size,
show_progress_bar=self.config.show_progress,
)
# Image Embedding
@property
def image_model(self):
if self._image_model is None:
from transformers import CLIPModel
self._image_model = CLIPModel.from_pretrained(self.image_model_name)
return self._image_model
@property
def image_processor(self):
if self._image_processor is None:
from transformers import CLIPProcessor
self._image_processor = CLIPProcessor.from_pretrained(self.image_model_name)
return self._image_processor
def _embed_image(self, inputs: List[Any]) -> "np.ndarray":
from pathlib import Path
import numpy as np
from PIL import Image
all_embeddings: List["np.ndarray"] = []
batch_size = self.config.batch_size
for start in range(0, len(inputs), batch_size):
batch = inputs[start : start + batch_size]
images = []
opened: List[Image.Image] = []
try:
for inp in batch:
if isinstance(
inp, (str, Path)
): # If the input string path is too large that It gives error and we could not open the image.
img = Image.open(inp)
opened.append(img)
images.append(img)
else:
images.append(inp)
processed = self.image_processor(images=images, return_tensors="pt")
finally:
for opened_img in opened:
opened_img.close()
embeddings = self.image_model.get_image_features(**processed)
embeddings = embeddings / embeddings.norm(p=2, dim=-1, keepdim=True)
all_embeddings.append(embeddings.detach().numpy())
return np.concatenate(all_embeddings, axis=0)