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Tecton MCP Server

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examples_spark.parquet57.2 kB
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#�f�_v2� �p�(9�bz�:v��l2k�����=\ O�K��yu9V��},(=�;%�has_go�!nNew4211N�sqlm�:s�(:i�67�JdU~B�.-Q�B%��9] )�Y<"m�$b FXIV)'�2�.�6� #$~) �Z�2�R$R��K�F3�3%~n)  ݒ;f7�6�fI5]a8!8~4$�j Z�(�� �.g�8|���V�X�)�i 6P�  32 ��clE 8.= -V %��&��a8Lg2{ Item�'an -e��=ntR�Av���$N��" �KB� j�A%�$- ���V} 2 �S&��m�-�.{7B|"7�����fS�recsy�t2�~M2$�_�)Q�3�_7�"� -� >n��. �f�=.]"Unique]�F ��� Qon�M �=[6�v�= P� �F�N�\W"ts.��F>FB�*[R��j9��]SS �g's(6:�4-NdM 0, a�s�4 E { M&"J]M@F ��2Art��%^-�:.xs�NcS�2�art���)Z�an�%�%.��Rary� ="�"" g�ZS5se Sary%�� Fx(SQLvjis would!T'=|R�umn T primZ k�� -key�?I_(=����Qߑ�=�6�v �-�=��eEe-�&IQN=v�I�62g"^��J�6�+BY.��]mT, An,l\Q��l���C�?��F��>�Oͱ��͍ѝ'͗b��4yz �.Y�Q^Q 7, 3�7�U�&'>L&��I�6���L.5 ����a�vv3)�1u�J2��v.v76����.u'"�k.�*ҡ��PM}�@1, 3,�VFi/ !aMi�B��ao�&N6V;D�/laF}$@F"(:V �sm� "A t��f �Ge/�g"� *�!� ��J `",>�"&�!��a��< MF"u �� ���`� �]�25a-2In�1lts", IntegerType(), True), HStructField("type",ing210]) Xdf = spark.read.schema( �<).parquet("s3://tecton.ai.public/tutorials/recsys-demo/train. ;" ph8df.withColumn("�Pdf.ts.cast(TimestampT�) :preturn df sessions_batch = BSource(�name="6(")"4_config="data_sB _function 0Downer='demo-user@t�',lXtags={'release': 'produC8'} ) �from ;X import FeatureService "recsys.f s. �articleKВM -BM0approx_count_ b4s recommender;_s � =:�=�'j8-Jdescrip!|='- -4 for computing� and� 1&+mak(uaVs i*=[ �59,NV�])�onlin5Prving_enabled=False )��,�6(� import tecton from tecton import RequestSource, Attribute, realtime_feature_view from tecton.types import String, Float64, Field from Search.features.product_attributes import product_title request_schema = [ Field('search_term', String), Field('product_uid', String) ] search_query = RequestSource(schema=request_schema) @realtime_feature_view( description='''Jaccard similarity between the tokenized input query and the product title, computed in real-time''', sources=[search_query, product_title], features=[ Attribute('jaccard_similarity_query_token_title_token', Float64) ], mode='python' ) def search_query_product_similarity(search_query, product_title): def jaccard(list1, list2): intersection = len(list(set(list1).intersection(list2))) union = (len(list1) + len(list2)) - intersection return float(intersection) / union #Normalizing and tokenizing search query search_term = search_query.get('search_term') search_term = search_term.lower() tokenized_query = search_term.split(' ') #Normalizing and tokenizing product title product_title = product_title.get('product_title') product_title = product_title.lower() product_title_tokenized = product_title.split(' ') #Compute Jaccard similarity jaccard_similarity = jaccard(tokenized_query, product_title_tokenized) return { 'jaccard_similarity_query_token_title_token': jaccard_similarity }� from Dynamic_pricing.data_sources.completed_rides import completed_rides_batch from Dynamic_pricing.entities import origin_zipcode from tecton import batch_feature_view, Aggregate from datetime import timedelta from tecton.types import Field, Int64 @batch_feature_view( description='''Standard deviation of ride durations from the given zipcode over a series of time windows, updated daily.''', sources=[completed_rides_batch], entities=[origin_zipcode], mode='spark_sql', aggregation_interval=timedelta(days=1), # This feature will be updated daily features=[ Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=10)), Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=30)), Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=60)), Aggregate(input_column=Field('duration', Int64), function='mean', time_window=timedelta(days=60)) ], timestamp_field='timestamp' ) def ride_durations(completed_rides_batch): return f''' SELECT origin_zipcode, duration, timestamp FROM {completed_rides_batch} '''���1@  ���@  ��p@$�DB�X,��b�X, ��h8��D$J�R�X,����d2��F��h6� ���t:�N���|>� �B��H$�D"�h4�F�шD*�J���d2�N' �B�P�T*�R�V+��j�^�� ��b�XL&��f3�F��l6��f��n7 �\N���v;����|�������!�H$�DŢ�x<�H$�D"�I�r�\2�Lf��t:��'�F�R�t:�R�ժ�z�b�٬V��r��n�����a�X,��c2� &΍ 5code�����&��&��6(� import tecton from tecton import RequestSource, Attribute, realtime_feature_view from tecton.types import String, Float64, Field from Search.features.product_attributes import product_title request_schema = [ Field('search_term', String), Field('product_uid', String) ] search_query = RequestSource(schema=request_schema) @realtime_feature_view( description='''Jaccard similarity between the tokenized input query and the product title, computed in real-time''', sources=[search_query, product_title], features=[ Attribute('jaccard_similarity_query_token_title_token', Float64) ], mode='python' ) def search_query_product_similarity(search_query, product_title): def jaccard(list1, list2): intersection = len(list(set(list1).intersection(list2))) union = (len(list1) + len(list2)) - intersection return float(intersection) / union #Normalizing and tokenizing search query search_term = search_query.get('search_term') search_term = search_term.lower() tokenized_query = search_term.split(' ') #Normalizing and tokenizing product title product_title = product_title.get('product_title') product_title = product_title.lower() product_title_tokenized = product_title.split(' ') #Compute Jaccard similarity jaccard_similarity = jaccard(tokenized_query, product_title_tokenized) return { 'jaccard_similarity_query_token_title_token': jaccard_similarity }� from Dynamic_pricing.data_sources.completed_rides import completed_rides_batch from Dynamic_pricing.entities import origin_zipcode from tecton import batch_feature_view, Aggregate from datetime import timedelta from tecton.types import Field, Int64 @batch_feature_view( description='''Standard deviation of ride durations from the given zipcode over a series of time windows, updated daily.''', sources=[completed_rides_batch], entities=[origin_zipcode], mode='spark_sql', aggregation_interval=timedelta(days=1), # This feature will be updated daily features=[ Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=10)), Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=30)), Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=60)), Aggregate(input_column=Field('duration', Int64), function='mean', time_window=timedelta(days=60)) ], timestamp_field='timestamp' ) def ride_durations(completed_rides_batch): return f''' SELECT origin_zipcode, duration, timestamp FROM {completed_rides_batch} ''',<5schema %text%L %code%L�,&�� 5text�����&ʌ&6([Example of transformation. Create new column by splitting the string in an existing column.fExample of Aggregate. 50th percentile (median) of transaction amounts for a user over the past 30 days,&΍ 5code�����&��&��6(� import tecton from tecton import RequestSource, Attribute, realtime_feature_view from tecton.types import String, Float64, Field from Search.features.product_attributes import product_title request_schema = [ Field('search_term', String), Field('product_uid', String) ] search_query = RequestSource(schema=request_schema) @realtime_feature_view( description='''Jaccard similarity between the tokenized input query and the product title, computed in real-time''', sources=[search_query, product_title], features=[ Attribute('jaccard_similarity_query_token_title_token', Float64) ], mode='python' ) def search_query_product_similarity(search_query, product_title): def jaccard(list1, list2): intersection = len(list(set(list1).intersection(list2))) union = (len(list1) + len(list2)) - intersection return float(intersection) / union #Normalizing and tokenizing search query search_term = search_query.get('search_term') search_term = search_term.lower() tokenized_query = search_term.split(' ') #Normalizing and tokenizing product title product_title = product_title.get('product_title') product_title = product_title.lower() product_title_tokenized = product_title.split(' ') #Compute Jaccard similarity jaccard_similarity = jaccard(tokenized_query, product_title_tokenized) return { 'jaccard_similarity_query_token_title_token': jaccard_similarity }� from Dynamic_pricing.data_sources.completed_rides import completed_rides_batch from Dynamic_pricing.entities import origin_zipcode from tecton import batch_feature_view, Aggregate from datetime import timedelta from tecton.types import Field, Int64 @batch_feature_view( description='''Standard deviation of ride durations from the given zipcode over a series of time windows, updated daily.''', sources=[completed_rides_batch], entities=[origin_zipcode], mode='spark_sql', aggregation_interval=timedelta(days=1), # This feature will be updated daily features=[ Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=10)), Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=30)), Aggregate(input_column=Field('duration', Int64), function='stddev_samp', time_window=timedelta(days=60)), Aggregate(input_column=Field('duration', Int64), function='mean', time_window=timedelta(days=60)) ], timestamp_field='timestamp' ) def ride_durations(completed_rides_batch): return f''' SELECT origin_zipcode, duration, timestamp FROM {completed_rides_batch} ''',��&ĉ,pandas�{"index_columns": [], "column_indexes": [], "columns": [{"name": "text", "field_name": "text", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "code", "field_name": "code", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}], "creator": {"library": "pyarrow", "version": "15.0.2"}, "pandas_version": "2.2.3"} ARROW:schema�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 parquet-cpp-arrow version 15.0.2,PAR1

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