Vector Embeddings Formatter
Paste raw vector embeddings and convert them into ready-to-use snippets for JSON, Python, PostgreSQL (pgvector), Pinecone, or MongoDB. Speeds up the step between generating embeddings and inserting them into your vector database.
1. Tạo embeddings từ Python/API
2. Paste vector vào tool này
3. Nhập tên file/ID
4. Copy format mong muốn và save vào DB
# Python với CLIP
from PIL import Image
import torch
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
image = Image.open("image.jpg")
inputs = processor(images=image, return_tensors="pt")
image_features = model.get_image_features(**inputs)
embeddings = image_features[0].tolist()
print(embeddings) # Copy vào tool nàyNhập Vector Embeddings
1. Tạo embeddings từ Python/API
Công cụ liên quan
Tất cả công cụAbout Vector Embeddings Formatter
Convert raw vector embeddings into the exact syntax your vector database or ML pipeline expects. Paste an array of floats, then copy the formatted output for JSON, Python, PostgreSQL (pgvector), Pinecone, or MongoDB in one click.
Key Features
- -Paste embeddings as JSON arrays or comma-separated values
- -Export to JSON, Python list, pgvector SQL, Pinecone, or MongoDB
- -Attach a file name or ID to each vector for database rows
- -Automatic vector dimension detection
- -One-click copy of formatted output
- -Runs entirely in your browser — embeddings are not uploaded
Use Cases
pgvector Inserts
Format embeddings as PostgreSQL pgvector literals ready to drop into INSERT or UPDATE statements.
Pinecone Upserts
Generate Pinecone upsert payloads with matching id and values fields for your index.
MongoDB Atlas Vector Search
Produce MongoDB documents with an embeddings field ready for Atlas Vector Search indexing.
Python Prototyping
Copy embeddings as a Python list for quick use in notebooks, scripts, and ML prototyping.