pyvespa

import time from pyvespa.application import ApplicationPackage from pyvespa.schema import Schema, Document, Field from pyvespa.clients.vespa_docker import VespaDocker from pyvespa.clients.vespa import Vespa # 1. Define your application schema app_package = ApplicationPackage( name='my_app', schema=Schema( name='my_document', document=Document( fields=[ Field(name='id', type='string', indexing=['attribute', 'summary']), Field(name='title', type='string', indexing=['index', 'summary'], index='enable-bm25'), Field(name='body', type='string', indexing=['index', 'summary'], index='enable-bm25') ] ) ) ) # 2. Deploy to local Docker instance # Ensure Docker daemon is running and has at least 6GB memory allocated vespa_docker = VespaDocker(port=8080, container_memory='6G') try: app = vespa_docker.deploy(application_package=app_package) print("Vespa application deployed successfully to Docker.") # 3. Feed documents docs_to_feed = [ {"id": "doc:1", "title": "The Quick Brown Fox", "body": "Jumps over the lazy dog."}, {"id": "doc:2", "title": "Lazy Dog Sits", "body": "The quick brown fox watches."} ] app.feed_iterable(docs_to_feed) print("Documents fed.") # Wait a bit for indexing to complete time.sleep(5) # 4. Query data query_result = app.query(yql='select * from sources * where userQuery();', query='fox') print("Query Results:") for hit in query_result.hits: print(f" ID: {hit['id']}, Title: {hit['fields']['title']}, Body: {hit['fields']['body']}") finally: # 5. Shut down Vespa Docker instance vespa_docker.stop() print("Vespa Docker instance stopped.")