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-4.1.2.tar.gz (12.5 kB view details)

Uploaded Source

Built Distribution

reasoner_pydantic-4.1.2-py3-none-any.whl (15.3 kB view details)

Uploaded Python 3

File details

Details for the file reasoner-pydantic-4.1.2.tar.gz.

File metadata

  • Download URL: reasoner-pydantic-4.1.2.tar.gz
  • Upload date:
  • Size: 12.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for reasoner-pydantic-4.1.2.tar.gz
Algorithm Hash digest
SHA256 1936f4fae4bcb614858163f906f7a699a74da58af0dfe3acffe39ddd526b6383
MD5 60d1fb41815a22b1ec9efc5a204d3c98
BLAKE2b-256 0fc7357aba594f0cbadac7814b6eeb74685d67e006b0fb702813bd4f8ada42ec

See more details on using hashes here.

File details

Details for the file reasoner_pydantic-4.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for reasoner_pydantic-4.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 126504cc292b9554660e78ef974bb935d760633f3baf555f6e1923977676b675
MD5 792f14bd2b020ba6c4751f241872a652
BLAKE2b-256 e81a586cbe77586a1c9b825c1bce472306a6cf769b78ba257aef859a94ef4c0e

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