Source code for langchain_experimental.open_clip.open_clip

from typing import Any, Dict, List

from langchain.pydantic_v1 import BaseModel, root_validator
from langchain_core.embeddings import Embeddings


[docs]class OpenCLIPEmbeddings(BaseModel, Embeddings): """OpenCLIP Embeddings model.""" model: Any preprocess: Any tokenizer: Any # Select model: https://github.com/mlfoundations/open_clip model_name: str = "ViT-H-14" checkpoint: str = "laion2b_s32b_b79k" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that open_clip and torch libraries are installed.""" try: import open_clip # Fall back to class defaults if not provided model_name = values.get("model_name", cls.__fields__["model_name"].default) checkpoint = values.get("checkpoint", cls.__fields__["checkpoint"].default) # Load model model, _, preprocess = open_clip.create_model_and_transforms( model_name=model_name, pretrained=checkpoint ) tokenizer = open_clip.get_tokenizer(model_name) values["model"] = model values["preprocess"] = preprocess values["tokenizer"] = tokenizer except ImportError: raise ImportError( "Please ensure both open_clip and torch libraries are installed. " "pip install open_clip_torch torch" ) return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: text_features = [] for text in texts: # Tokenize the text tokenized_text = self.tokenizer(text) # Encode the text to get the embeddings embeddings_tensor = self.model.encode_text(tokenized_text) # Normalize the embeddings norm = embeddings_tensor.norm(p=2, dim=1, keepdim=True) normalized_embeddings_tensor = embeddings_tensor.div(norm) # Convert normalized tensor to list and add to the text_features list embeddings_list = normalized_embeddings_tensor.squeeze(0).tolist() text_features.append(embeddings_list) return text_features
[docs] def embed_query(self, text: str) -> List[float]: return self.embed_documents([text])[0]
[docs] def embed_image(self, uris: List[str]) -> List[List[float]]: try: from PIL import Image as _PILImage except ImportError: raise ImportError("Please install the PIL library: pip install pillow") # Open images directly as PIL images pil_images = [_PILImage.open(uri) for uri in uris] image_features = [] for pil_image in pil_images: # Preprocess the image for the model preprocessed_image = self.preprocess(pil_image).unsqueeze(0) # Encode the image to get the embeddings embeddings_tensor = self.model.encode_image(preprocessed_image) # Normalize the embeddings tensor norm = embeddings_tensor.norm(p=2, dim=1, keepdim=True) normalized_embeddings_tensor = embeddings_tensor.div(norm) # Convert tensor to list and add to the image_features list embeddings_list = normalized_embeddings_tensor.squeeze(0).tolist() image_features.append(embeddings_list) return image_features