Part 7/11:
Automating with Python: Building a Microservice
Rather than relying solely on command-line curl commands, the next step was to develop a Python client to automate interactions. This involved:
Installing the
quadrant-clientPython library.Instantiating a client object pointing to the local server.
Programmatically creating collections and adding vectors.
Embedding large datasets using sentence transformers for semantic encoding.
Problems arose around data formatting—specifically, ensuring the payloads matched what the Python client expected (dictionaries vs. JSON strings). Some initial errors were encountered due to missing imports, but straightforward fixes like importing the correct modules and abiding by the API specifications resolved the issues.