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. Full guide →
1. Generate embeddings from Python/API
2. Paste vector into this tool
3. Enter file name/ID
4. Copy desired format and save to DB
# Python with 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 to this toolInput Vector Embeddings
1. Generate embeddings from Python/API
Format embeddings for AI stacks and databases
What is this tool?
Turns embedding arrays into JSON, Python literals, SQL friendly to pgvector, Pinecone-ready payloads, or MongoDB vector syntax — bridging notebooks and production schemas.
Useful when you copy vectors from Python APIs and need exact syntax for migrations or tests.
Related Tools
All toolsAbout 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.