Skip to main content

Pydantic models for the Reasoner API data formats

Project description

Reasoner-Pydantic

Test status via GitHub Actions ℹ️

Pydantic models for the Reasoner API data formats.

These models are very handy when setting up a Reasoner API with FastAPI.

Example usage

from reasoner_pydantic import (
    Query,
    Message,
    QNode,
    KnowledgeGraph,
    Node,
    Result,
    NodeBinding,
)


def add_result_to_query(query_dict):
    query = Query.parse_obj(query_dict)
    message: Message = query.message

    # get query graph node
    qnode_id = next(iter(message.query_graph.nodes))

    # add knowledge graph node
    knode = Node.parse_obj({"categories": ["biolink:FooBar"]})
    knode_id = "foo:bar"
    message.knowledge_graph.nodes[knode_id] = knode

    # add result
    result: Result = Result.parse_obj(
        {
            "node_bindings": {qnode_id: [{"id": knode_id}]}
        }
    )

    message.results.add(result)

    return message.json()


add_result_to_query({
    "message": {
        "query_graph": {"nodes": {"n0": {}}, "edges": {}},
        "knowledge_graph": {"nodes": {}, "edges": {}},
        "results" : []
    }
})

Validation Usage

Because of performance concerns, as well as how types are implemented in Python, there is no assignment validation enforced on these models. For example:

from reasoner_pydantic import KnowledgeGraph

# This will not throw an error
kg = KnowledgeGraph(nodes = "hi")

This is especially important to keep in mind when constructing objects that use containers. This library uses custom container types: HashableMapping, HashableSequence, HashableSet:

from reasoner_pydantic import KnowledgeGraph, Node, CURIE

# This is not correct and will not throw an error, but will cause problems later
kg = KnowledgeGraph(nodes = {})

# Instead, if you would like to build models this way, use a typed container constructor
kg = KnowledgeGraph(nodes = HashableMapping[CURIE, Node](__root__ = {}))

For this reason, we recommend one of the following options:

  1. Use parse_obj exclusively for constructing models. This will perform validation for you. This option is best if performance is not important.
  2. Use a static type checker to ensure that models are being constructed correctly. Constructing objects this way is more performant, and the static type checker will ensure that it is done correctly. We recommend using pyright in your editor.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

reasoner-pydantic-4.0.0.tar.gz (11.8 kB view details)

Uploaded Source

Built Distribution

reasoner_pydantic-4.0.0-py3-none-any.whl (14.5 kB view details)

Uploaded Python 3

File details

Details for the file reasoner-pydantic-4.0.0.tar.gz.

File metadata

  • Download URL: reasoner-pydantic-4.0.0.tar.gz
  • Upload date:
  • Size: 11.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for reasoner-pydantic-4.0.0.tar.gz
Algorithm Hash digest
SHA256 2b047ff1478e369254644c6626a86bcf8fde9bcd26e3048fa5c02849347699b3
MD5 d74a0be7479a588a533fdded2dee4cbd
BLAKE2b-256 bf3db8ad454fb6d64f8d0cf63327ff67310803212e1878c82e7a4c837be8eb1e

See more details on using hashes here.

File details

Details for the file reasoner_pydantic-4.0.0-py3-none-any.whl.

File metadata

File hashes

Hashes for reasoner_pydantic-4.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 786a631e14420bcd5d4a1d11e3d3fba4571ebd47bfc3852377955ede74457530
MD5 ed618d87f389bbf0941d35c6c793ceb7
BLAKE2b-256 f1842711e70622c07b398562937b1a8294e76876d4ddf67ebc70a253163534cf

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page