Embedding Visualization Embedding Atlas
vector-searchvector-databaseretrieval-augmented-generationllm-frameworksoperationsfunction-callingweaviate-recipesintegrationsPythongenerative-aiapple
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Embedding visualization using Embedding Atlas and Weaviate
This notebook shows you how to visualize the data stored within a Weaviate vector database using the open source Embedding Atlas by Apple.
Installation
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Name: weaviate-client Version: 4.16.5.dev3+gbdd43c4b8 Summary: A python native Weaviate client Home-page: https://github.com/weaviate/weaviate-python-client Author: Weaviate Author-email: hello@weaviate.io, License: BSD 3-clause Location: /usr/local/lib/python3.11/dist-packages Requires: authlib, deprecation, grpcio, grpcio-health-checking, httpx, pydantic, validators Required-by:
Connect to Weaviate
You can connect to Weaviate in different ways.
- Embedded Weaviate: Experimental in-memory option without API keys for quick experimentation.
- Weaviate Cloud (WCD): Managed services for development and production environments.
- Local Weaviate instance e.g. deployed with Docker
You can get 14 days of free access to Weaviate Cloud's Sandbox by creating an account in Weaviate Cloud (WCD), where you can also obtain your API key. No name, no credit card required.
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INFO:weaviate-client:Binary /root/.cache/weaviate-embedded did not exist. Downloading binary from https://github.com/weaviate/weaviate/releases/download/v1.30.5/weaviate-v1.30.5-Linux-amd64.tar.gz INFO:weaviate-client:Started /root/.cache/weaviate-embedded: process ID 1726
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<weaviate.collections.collection.sync.Collection at 0x7e70fdee8b10>
Import the data
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Batch import finished. Checking object count... Imported 1000 objects.
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Successfully processed: JeopardyQuestion
Usage
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Want to scale this notebook?
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