Python API Quickstart

This page assumes you already have Conservator CLI installed. If you do not, follow the Installation instructions first.

This guide will show you how to perform basic operations using the Python API. Please see the CLI Quickstart to get started using the CLI.

If you’re looking for more detailed information on how Conservator CLI is structured, view the Advanced Python API Guide and API Reference.

Config and Getting Connected

Typically, you won’t have to manually worry about loading different Config objects, and can just use the default for connections.

Conservator CLI makes getting connected with the default configuration pretty easy:

>>> from FLIR.conservator.conservator import Conservator
>>> conservator = Conservator.default()
>>> print(conservator)
<Conservator at>

If someone doesn’t have a config already, this will prompt them for their info, and save it in a file for the future. Now that you have an instance of Conservator, you are free to start running queries.

Basic Queries

Let’s start with something simple. Listing the names of all the projects on conservator:

>>> all_projects = conservator.projects.all()
>>> all_projects.include_field("name")
>>> for project in all_projects:
...     print(

Running this will demonstrate a few things. First off, using all() doesn’t immediately execute the query. It returns a PaginatedQuery, which lets you specify which fields you want to include in the results. By default, it will request all fields of its type (in this case, Project). Since we only want to see the name, we call include_field() with it specified.

Once we’ve set up the fields we want to return from our query, we need to actually send it to Conservator and iterate through the results. This is done automatically when you attempt to iterate through a PaginatedQuery using for … in (or anything else that calls __iter__).

The Project instances returned from the iteration will contain the fields we included.

Querying for all Fields

Let’s say we were interested in all of the fields in Project. We can repeat above, without including include_field().

>>> all_projects = conservator.projects.all()
>>> for project in all_projects:
...     print(project)

This will print a few errors, then the projects (which will be a lot of text).

The errors are expected, because we’re requesting every possible field. A few fields may have been deprecated, or be undefined in the Conservator database. When we tried running the query, the server returned an error. Luckily, Conservator CLI was able to catch it, and find the problematic field. It tried the request again, excluding that field.

Once the request went through, your results were returned. Conservator has a FieldsManager to keep track of the problematic fields in past requests, and to exclude them in future ones.

Specifying Fields

The ability to specify fields is a powerful feature of GraphQL, the API framework used by Conservator. In many API requests in Conservator CLI, you will have to specify which fields you are interested in. Usually, these are provided using a FieldsRequest. These let you include or exclude fields in your request.

A PaginatedQuery has an internal FieldsRequest that it maintains and uses when executing the actual query.

See the documentation on FieldsRequest for more information on including and excluding fields, subfields, etc.

Other types of Queries

You can do more than just list all Projects on conservator.

Conservator also provides utilities for querying Collections, Datasets and Videos.

Each query endpoint can list all of its type (as used above), or perform searches using Conservator’s Advanced Search feature.

For example, if we wanted to print the names of all datasets that contains the word ADAS, we could do the following:

>>> adas_datasets ="ADAS")
>>> adas_datasets.include_field("name")
>>> for ds in adas_datasets:
...     print(

Sometimes you’ll only want (or expect) a single result. You can short-circuit the full query using first():

>>> adas_datasets ="ADAS")
>>> adas_datasets.include_field("name")
>>> dataset = adas_datasets.first()
>>> print(

Another frequent use is counting the number of results. This can be done with count_all() for all instances, or count() for a specific search text:

>>> adas_projects_count = conservator.projects.count("ADAS")
>>> print(adas_projects_count)

Populating Fields Later

Sometimes you’ll need to add fields to an object after your initial request. For instance, assume you queried for a Project’s id:

>>> adas_datasets ="ADAS")
>>> adas_datasets.include_field("id")
>>> dataset = adas_datasets.first()
>>> print(

But later want to print its name. You can fetch the name field using populate():

>>> from FLIR.conservator.fields_request import FieldsRequest
>>> fields = FieldsRequest()
>>> fields.include_field("name")
>>> dataset.populate(fields)
>>> print(

If for some reason you have an ID, but don’t have an instance of the correct type to use populate(), you can create one with from_id(), and then populate the fields:

>>> collection = conservator.collections.from_id("some_collection_id")
>>> fields = FieldsRequest()
>>> fields.include_field("path")
>>> collection.populate(fields)
>>> print(collection.path)

You can also call populate() with no argument to populate all fields:

>>> collection = conservator.collections.from_id("some_collection_id")
>>> collection.populate()
>>> print(collection.path)

Next Steps

Take a look at the Advanced Python API Guide and API Reference for more info on the structure of Conservator CLI.

You may also want to check out the examples directory.