[WIP] Similarity search with vector search

mp_api/client/routes/materials/similarity.py

@@ -1,16 +1,40 @@
 from __future__ import annotations
 
-from emmet.core.similarity import SimilarityDoc
+from typing import TYPE_CHECKING
 
-from mp_api.client.core import BaseRester
+from emmet.core.mpid import MPID, AlphaID
+from emmet.core.similarity import (
+    CrystalNNSimilarity,
+    SimilarityDoc,
+    SimilarityEntry,
+    _vector_to_hex_and_norm,
+)
+from pymatgen.core import Composition, Structure
+
+from mp_api.client.core import BaseRester, MPRestError
 from mp_api.client.core.utils import validate_ids
 
+if TYPE_CHECKING:
+    import numpy as np
+    from emmet.core.similarity import SimilarityScorer
+
+# This limit seems to be associated with MongoDB vector search
+MAX_VECTOR_SEARCH_RESULTS = 10_000
+
 
 class SimilarityRester(BaseRester):
     suffix = "materials/similarity"
     document_model = SimilarityDoc  # type: ignore
     primary_key = "material_id"
 
+    _fingerprinter: SimilarityScorer | None = None
+
+    def fingerprint_structure(self, structure: Structure) -> np.ndarray:
+        """Get the fingerprint of a user-submitted structures."""
+        if self._fingerprinter is None:
+            self._fingerprinter = CrystalNNSimilarity()
+        return self._fingerprinter._featurize_structure(structure)
+
     def search(
         self,
         material_ids: str | list[str] | None = None,
@@ -53,3 +77,83 @@ def search(
             fields=fields,
             **query_params,
         )
+
+    def find_similar(
+        self,
+        structure_or_mpid: Structure | str | MPID | AlphaID,
+        top: int | None = 50,
+        num_chunks: int | None = None,
+        chunk_size: int | None = 1000,
+    ) -> list[SimilarityEntry] | list[dict]:
+        """Find structures most similar to a user-submitted structure.
+
+        Arguments:
+            structure_or_mpid : pymatgen .Structure, or str, MPID, AlphaID
+                If a .Structure, the feature vector is computed on the fly
+                If a str, MPID, or AlphaID, attempts to retrieve a pre-computed
+                feature vector using the input as a material ID
+            top : int
+                The number of most similar materials to return, defaults to 50.
+                Setting to None will return the maximum possible number of
+                most similar materials..
+            num_chunks (int or None): Maximum number of chunks of data to yield. None will yield all possible.
+            chunk_size (int or None): Number of data entries per chunk.
+                The chunk_size is also used to limit the number of responses returned.
+
+        Returns:
+            ([SimilarityEntry] | [dict]) List of SimilarityEntry documents
+            (if `use_document_model`) or dict (otherwise) listing
+            structures most similar to the input structure.
+        """
+        if isinstance(structure_or_mpid, str | MPID | AlphaID):
+            fmt_idx = AlphaID(structure_or_mpid).string
+
+            docs = self.search(material_ids=[fmt_idx], fields=["feature_vector"])
+            if not docs:
+                raise MPRestError(f"No similarity data available for {fmt_idx}")
+            feature_vector = docs[0]["feature_vector"]
+
+        elif isinstance(structure_or_mpid, Structure):
+            feature_vector = self.fingerprint_structure(structure_or_mpid)
+
+        else:
+            raise ValueError("Please submit a pymatgen Structure or MP ID.")
+
+        top = top or MAX_VECTOR_SEARCH_RESULTS
+        if not isinstance(top, int) or top < 1:
+            raise ValueError(
+                f"Invalid number of possible top matches specified = {top}."
+                "Please specify a positive integer or `None` to return all results."
+            )
+
+        vector_hex, vector_norm = _vector_to_hex_and_norm(feature_vector)
+        result = self._query_resource(
+            criteria={
+                "feature_vector_hex": vector_hex,
+                "feature_vector_norm": vector_norm,
+                "_limit": top,
+            },
+            suburl="match",
+            use_document_model=False,  # Return type is not exactly a SimilarityDoc, closer to SimilarityEntry
+            chunk_size=chunk_size,
+            num_chunks=num_chunks,
+        ).get("data", None)
+
+        if result is None:
+            raise MPRestError(
+                "Could not find any structures similar to the input structure."
+            )
+
+        sim_docs = [
+            {
+                "formula": entry["formula_pretty"],
+                "task_id": entry["material_id"],
+                "nelements": len(Composition(entry["formula_pretty"]).elements),
+                "dissimilarity": 100 * (1.0 - entry["score"]),
+            }
+            for entry in result
+        ]
+
+        if self.use_document_model:
+            return [SimilarityEntry(**doc) for doc in sim_docs]
+        return sim_docs

pyproject.toml

@@ -25,15 +25,15 @@ dependencies = [
   "typing-extensions>=3.7.4.1",
   "requests>=2.23.0",
   "monty>=2024.12.10",
-  "emmet-core>=0.85.1rc0",
+  "emmet-core>=0.86.2rc1",
   "smart_open",
   "boto3",
   "orjson >= 3.10,<4",
 ]
 dynamic = ["version"]
 
 [project.optional-dependencies]
-all = ["emmet-core[all]>=0.85.1rc0", "custodian", "mpcontribs-client>=5.10"]
+all = ["emmet-core[all]>=0.86.2rc1", "custodian", "mpcontribs-client>=5.10"]
 test = [
   "pre-commit",
   "pytest",

requirements/requirements-ubuntu-latest_py3.11.txt

@@ -24,7 +24,7 @@ contourpy==1.3.3
     # via matplotlib
 cycler==0.12.1
     # via matplotlib
-emmet-core==0.86.0
+emmet-core==0.86.2rc1
     # via mp-api (pyproject.toml)
 fonttools==4.60.1
     # via matplotlib

requirements/requirements-ubuntu-latest_py3.11_extras.txt

@@ -62,7 +62,7 @@ dnspython==2.8.0
     #   pymongo
 docutils==0.21.2
     # via sphinx
-emmet-core[all]==0.86.0
+emmet-core[all]==0.86.2rc1
     # via mp-api (pyproject.toml)
 execnet==2.1.1
     # via pytest-xdist

requirements/requirements-ubuntu-latest_py3.12.txt

@@ -24,7 +24,7 @@ contourpy==1.3.3
     # via matplotlib
 cycler==0.12.1
     # via matplotlib
-emmet-core==0.86.0
+emmet-core==0.86.2rc1
     # via mp-api (pyproject.toml)
 fonttools==4.60.1
     # via matplotlib

requirements/requirements-ubuntu-latest_py3.12_extras.txt

@@ -62,7 +62,7 @@ dnspython==2.8.0
     #   pymongo
 docutils==0.21.2
     # via sphinx
-emmet-core[all]==0.86.0
+emmet-core[all]==0.86.2rc1
     # via mp-api (pyproject.toml)
 execnet==2.1.1
     # via pytest-xdist