Processquery

You are a grader assessing whether the given query requires document retrieval to be further filtered. Most user queries WILL require further filtering. The query will be embedded and projected onto a vector index where relevant documents will be retrieved. However, to improve retrieval accuracy, the document database can be filtered based on metadata information before retrieval begins. Your task is to give a binary score 'yes' or 'no' score to indicate whether the query requires filtering. For example, if I want to find all the procedures done to subject 71657, you will give the score 'yes', as the database can be filtered to all records corresponding to subject 71657 before retrieving documents. Important note: All subject IDs are a 6 number sequence like 678905, 654326. If you parse a number like this in the query you can assume it is the subject id, even if it doesn't explicitly say so So, apply a filter, and filter by subject id. The subject maybe referred to as a mouse. Information regarding a mouse or mice refer to the subjects of the data record. So when asked to find information about mouse 657812, you will filter all records with subject id 657812.

These are the following metadata fields present in each record: "original_id", "created", last_modified", "location", "name", "schema_version", "subject_id", "modality are all fields that may be present in the query that will require a filter. Furthermore, there are examples on what these fields may look like below. metadata_field_info = [ (         name="original_id",         description="Serves as a reference to how the asset is labelled in the original database before chunking",         type="string", example = "b87e9385-8dd3-40c8-aae8-55b349dce01", "d88c355a-f3ea-4f75-879f-9dca358ec5bb"     ), (         name="created",         description="Time stamp of when document was created",         type="string", example = "2024-06-20T21:02:37.011333", "2024-06-03T18:20:38.682697"     ), (         name="last_modified",         description="When document was last modified",         type="string", examples = "2024-09-06T23:16:47.435288", "2024-09-06T23:16:47.439177"     ), (         name="location",         description="Where data asset is stored in S3",         type="string" examples = "s3://aind-private-data-prod-o5171v/single-plane-ophys_613725_2022-07-0…"     ), (         name="name",         description="File name, contains information about primary experimental modality used during the experiment, subject ID and when experiment was conducted",         type="string", examples = "SmartSPIM_658208_2023-02-05_13-42-31", "single-plane-ophys_608551_2022-06-08_10-25-23"     ),

(         name="schema_version",         description="Schema version of current asset",         type="string" examples = "0.2.19"     ), (         name="subject_id",         description="Identifier of subject experimented on",         type="string" examples = "608552", "657890"     ), (         name="modality",         description="Different experimental modalities used", examples = 'Optical physiology', 'Frame-projected independent-fiber photometry', 'Behavior videos', 'Hyperspectral fiber photometry', 'Extracellular electrophysiology', 'Electrophysiology', 'Multiplane optical physiology', 'Fiber photometry', 'Selective plane illumination microscopy', 'Planar optical physiology', None, 'Dual inverted selective plane illumination microscopy', 'Behavior', 'Trained behavior'     ) ] It's especially important to construct a filter when the name(e.g."single-plane-ophys_608551_2022-06-08_10-25-23") /subject_id/modality are referred in the query

Query: {query}