Python API

Python API#

APIManager#

APIManager loads one or more RAMOSE spec files and routes API calls to the matching operation. Spec files can use .hf, .yaml, or .yml.

from ramose import APIManager

am = APIManager(["meta_v1.hf", "index_v2.yaml"])

To override the SPARQL endpoint defined in the spec files (useful for staging or testing):

am = APIManager(["meta_v1.hf"], endpoint_override="http://localhost:9999/sparql")

Caching#

Result caching is enabled by passing cache_dir:

am = APIManager(["meta_v1.hf"], cache_dir=".cache", cache_ttl=86400)

cache_dir sets the directory for the SQLite-backed cache store. cache_ttl sets the default TTL in seconds (default: 86400). Pass cache_dir=None to disable caching.

get_op(url)#

Returns an Operation for the given call URL, or a (status_code, message, content_type) tuple if no operation matches.

from ramose import Operation

op = am.get_op("/v1/metadata/doi:10.1162/qss_a_00292")

if isinstance(op, Operation):
    status, body, content_type, headers = op.exec()
else:
    status, message, content_type = op

Operation#

Represents a single API operation ready to execute.

exec(method, content_type)#

Runs the full pipeline and returns (http_status_code, response_body, content_type, headers).

status, body, content_type, headers = op.exec(
    method="get",
    content_type="text/csv",
)

Both arguments are optional. Defaults: method="get", content_type="application/json".

The headers dict contains HTTP headers that should be forwarded to the client. When pagination is active (the request URL includes page and page_size parameters), it includes a Link header with rel="next", rel="prev", rel="first", and rel="last" URLs following RFC 8288.

op = am.get_op("/v1/author/orcid:0000-0002-8420-0696?page=2&page_size=10")
status, body, content_type, headers = op.exec()
print(headers.get("Link"))

Pipeline#

The execution follows these steps in order:

  1. Extract parameters from the URL path

  2. Run #preprocess functions on parameters

  3. Check the result cache; on hit, skip to step 8

  4. Execute the SPARQL query (single or multi-source)

  5. Run #postprocess functions on results

  6. Apply query string filters (require, filter, sort)

  7. Cache the processed result (if caching is enabled)

  8. Apply pagination slicing (if page_size is present; page requires page_size)

  9. Convert to the requested output format

Error codes#

Code

Meaning

200

Success

400

Invalid parameter or malformed multi-source query

422

Invalid built-in query parameter (format, json, require, filter, sort, page, page_size)

404

No matching operation

405

HTTP method not allowed

408

SPARQL or SPARQL Anything timeout after all read attempts fail

500

Unexpected error

502

SPARQL endpoint returned an error or all SPARQL/SPARQL Anything connection attempts failed