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Synthesis endpoint now uses aggregation resource
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,169 @@ | ||
| from typing import Optional, List, Dict, Union, Any | ||
| from fastapi import Query | ||
| from pymatgen.core import Composition | ||
| from maggma.api.query_operator import QueryOperator | ||
| from maggma.api.utils import STORE_PARAMS | ||
| from mp_api.routes.synthesis.models import SynthesisTypeEnum, OperationTypeEnum | ||
| from mp_api.routes.synthesis.utils import mask_highlights, mask_paragraphs | ||
|
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|
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| class SynthesisSearchQuery(QueryOperator): | ||
| """ | ||
| Method to generate a synthesis text search query | ||
| """ | ||
|
|
||
| def query( | ||
| self, | ||
| keywords: Optional[str] = Query( | ||
| None, | ||
| description="String keywords to search synthesis paragraph text with.", | ||
| ), | ||
| synthesis_type: Optional[List[SynthesisTypeEnum]] = Query( | ||
| None, description="Type of synthesis to include." | ||
| ), | ||
| target_formula: Optional[str] = Query( | ||
| None, description="Chemical formula of the target material." | ||
| ), | ||
| precursor_formula: Optional[str] = Query( | ||
| None, description="Chemical formula of the precursor material." | ||
| ), | ||
| operations: Optional[List[OperationTypeEnum]] = Query( | ||
| None, description="List of operations that syntheses must have." | ||
| ), | ||
| condition_heating_temperature_min: Optional[float] = Query( | ||
| None, description="Minimal heating temperature." | ||
| ), | ||
| condition_heating_temperature_max: Optional[float] = Query( | ||
| None, description="Maximal heating temperature." | ||
| ), | ||
| condition_heating_time_min: Optional[float] = Query( | ||
| None, description="Minimal heating time." | ||
| ), | ||
| condition_heating_time_max: Optional[float] = Query( | ||
| None, description="Maximal heating time." | ||
| ), | ||
| condition_heating_atmosphere: Optional[List[str]] = Query( | ||
| None, description='Required heating atmosphere, such as "air", "argon".' | ||
| ), | ||
| condition_mixing_device: Optional[List[str]] = Query( | ||
| None, description='Required mixing device, such as "zirconia", "Al2O3".' | ||
| ), | ||
| condition_mixing_media: Optional[List[str]] = Query( | ||
| None, description='Required mixing media, such as "alcohol", "water".' | ||
| ), | ||
| skip: int = Query(0, description="Number of entries to skip in the search"), | ||
| limit: int = Query( | ||
| 10, | ||
| description="Max number of entries to return in a single query. Limited to 10.", | ||
| ), | ||
| ): | ||
| project_dict: Dict[str, Union[Dict, int]] = { | ||
| "_id": 0, | ||
| "doi": 1, | ||
| "synthesis_type": 1, | ||
| "reaction": 1, | ||
| "reaction_string": 1, | ||
| "operations": 1, | ||
| "target": 1, | ||
| "targets_formula": 1, | ||
| "targets_formula_s": 1, | ||
| "precursors": 1, | ||
| "precursors_formula_s": 1, | ||
| "paragraph_string": 1, | ||
| } | ||
|
|
||
| pipeline: List[Any] = [] | ||
| if keywords: | ||
| pipeline.append( | ||
| { | ||
| "$search": { | ||
| # NOTICE to MongoDB admin: | ||
| # This MongoDB Atlas index should be created with: | ||
| # {"mappings": { "dynamic": false, | ||
| # "fields": { | ||
| # "paragraph_string": {"type": "string"} | ||
| # }}} | ||
| "index": "synth_descriptions", | ||
| "search": {"query": keywords, "path": "paragraph_string",}, | ||
| "highlight": { | ||
| "path": "paragraph_string", | ||
| # "maxNumPassages": 1 | ||
| }, | ||
| } | ||
| } | ||
| ) | ||
| project_dict.update( | ||
| { | ||
| "search_score": {"$meta": "searchScore"}, | ||
| "highlights": {"$meta": "searchHighlights"}, | ||
| } | ||
| ) | ||
| pipeline.append({"$project": project_dict}) | ||
|
|
||
| crit: Dict[str, Any] = {} | ||
| if synthesis_type: | ||
| crit["synthesis_type"] = {"$in": synthesis_type} | ||
| if target_formula: | ||
| reduced_formula = Composition( | ||
| target_formula | ||
| ).get_reduced_formula_and_factor()[0] | ||
| crit["targets_formula_s"] = reduced_formula | ||
| if precursor_formula: | ||
| reduced_formula = Composition( | ||
| precursor_formula | ||
| ).get_reduced_formula_and_factor()[0] | ||
| crit["precursors_formula_s"] = reduced_formula | ||
| if operations: | ||
| crit["operations.type"] = {"$all": operations} | ||
| if condition_heating_temperature_min: | ||
| crit["operations.conditions.heating_temperature.values"] = { | ||
| "$gte": condition_heating_temperature_min | ||
| } | ||
| if condition_heating_temperature_max: | ||
| crit["operations.conditions.heating_temperature.values"] = { | ||
| "$lte": condition_heating_temperature_max | ||
| } | ||
| if condition_heating_time_min: | ||
| crit["operations.conditions.heating_time.values"] = { | ||
| "$gte": condition_heating_time_min | ||
| } | ||
| if condition_heating_time_max: | ||
| crit["operations.conditions.heating_time.values"] = { | ||
| "$lte": condition_heating_time_max | ||
| } | ||
| if condition_heating_atmosphere: | ||
| crit["operations.conditions.heating_atmosphere"] = { | ||
| "$all": condition_heating_atmosphere | ||
| } | ||
| if condition_mixing_device: | ||
| crit["operations.conditions.mixing_device"] = { | ||
| "$all": condition_mixing_device | ||
| } | ||
| if condition_mixing_media: | ||
| crit["operations.conditions.mixing_media"] = { | ||
| "$all": condition_mixing_media | ||
| } | ||
|
|
||
| if crit: | ||
| pipeline.append({"$match": crit}) | ||
|
|
||
| # total = next( | ||
| # self.store._collection.aggregate( | ||
| # pipeline + [{"$count": "total"}], allowDiskUse=True | ||
| # ) | ||
| # )["total"] | ||
|
|
||
| pipeline.extend( | ||
| [{"$sort": {"search_score": -1}}, {"$skip": skip}, {"$limit": limit}] | ||
| ) | ||
|
|
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| return {"pipeline": pipeline} | ||
|
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| def post_process(self, docs): | ||
|
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| for doc in docs: | ||
| mask_highlights(doc) | ||
| mask_paragraphs(doc) | ||
|
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| return docs | ||
|
|
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