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Connects directly to the Python Bridge, to send text or sections of the interpretation graph to be process by ML algorithms in Python |
Operates On: Lexical Items with TOKEN and TEXT_BLOCK.
Saga_is_recognizer
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The difference between this and the Python Model Recognizer Stage is that this stage requires a trigger flag to start processing the text. |
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Saga_config_stage | ||
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"dependencyTags": [], "modelName": "bert-base-nli-stsb-mean-tokens", "modelVersion": "1", "modelMethod": "predict", "normalizeTags": false, "hostname": "localhost", "port": 5000, |
The output of the Watcher Stage is at the metadata of the vertex flagged as the trigger, for this example it is the EOF but it could be configured to work with TEXT_BLOCK_SPLIT or any other flag.
Saga_graph |
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V--------------[abraham lincoln likes macaroni and cheese]--------------------V <=== EOF, at this vertex's metadata is the embedded vector
^--[abraham]--V--[lincoln]--V--[likes]--V--[macaroni]--V--[and]--V--[cheese]--^
^---{place}---^ ^----{food}----^ ^---{food}---^
^----------{person}---------^ ^-----------------{food}--------------^ |
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No vertices are created in this stage |
Description of resource.
Saga_json | ||
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"_id" : "KGAAJGsBemSwA0nZTLXA", "tag": "recipe", "pattern": "("how many"|"how much") {ingredient} ", "confAdjust": 0.95 . . . additional fields as needed go here . . . |
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These will all be added to the interpretation graph with the SEMANTIC_TAG flag.
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Tags are hierarchical representations of the same intent. For example, {city} → {administrative-area} → {geographical-area} |
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