Use this Python client to embed documents with VectorFlow and check on the status of those embeddings.
The client has 2 methods for uploading documents to embed and 2 for checking statuses, listed below. All four methods return a python response object from the python requests library. You must parse the response using the .json() method.
from client.vectorflow import Vectorflow
vectorflow = Vectorflow()
vectorflow.embedding_api_key = "YOUR_OPEN_AI_KEY"
paths = ['./src/api/tests/fixtures/test_pdf.pdf', './src/api/tests/fixtures/test_medium_text.txt']
response = vectorflow.upload(paths)
filepath = './src/api/tests/fixtures/test_medium_text.txt'
response = vectorflow.embed(filepath)
response = vectorflow.get_job_statuses(jobs_ids)
response = vectorflow.get_job_status(job_id)
By default, this will set up vectorflow to embed files locally. It assumes you follow the default configuration in the VectorFlow repository's setup.sh which runs a collection of docker images locally using docker compose that will embed the documents with Open AI's ADA model and upload it to a local qdrant instance.
For more granular control over the chunking, embedding and vector DB configurations, override default values on the Vectorflow class or on its embeddings_metadata and vector_db_metadata fields.