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-5.0.2.tar.gz (13.0 kB view details)

Uploaded Source

Built Distribution

reasoner_pydantic-5.0.2-py3-none-any.whl (15.7 kB view details)

Uploaded Python 3

File details

Details for the file reasoner-pydantic-5.0.2.tar.gz.

File metadata

  • Download URL: reasoner-pydantic-5.0.2.tar.gz
  • Upload date:
  • Size: 13.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.19

File hashes

Hashes for reasoner-pydantic-5.0.2.tar.gz
Algorithm Hash digest
SHA256 5b8acecb0d0a8101ededabf8966db6de7b565c3d477531b66857334ebccd3b8e
MD5 3d29765e11b10e6dae700803122879f8
BLAKE2b-256 caa0c2ee487cbb158aca07a2167b4f9a9087113f0e66002b7174e8de49f1340c

See more details on using hashes here.

File details

Details for the file reasoner_pydantic-5.0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for reasoner_pydantic-5.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 bc95a286a1ddde3f15639cc1f0d7a7d6d878b799f80ebe29219dab9e5c5ad092
MD5 fe644f9a11e5b8662918ba612a6f43cd
BLAKE2b-256 77f42e41e36dc935c9c0bcf8a31e9358390956e5d947b1abd80f164483df86d5

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