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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
🧠 Text2Frappe - Stage 3 Query Generator (FLAN-T5-BASE)
This model is the third stage in the Text2Frappe pipeline, which enables natural language interface to ERPNext by converting questions into executable database queries.
🎯 Task
Text2Text Generation – Prompt-based query formulation.
Given:
- A detected ERPNext Doctype (from Stage 1),
- A natural language question,
- A list of selected relevant fields (from Stage 2),
this model generates a valid Frappe ORM-style query (e.g., frappe.get_all(...)) to retrieve the required data.
🧩 Input Format
Inputs are JSON-style strings containing:
doctype: the ERPNext document type.question: the user's question in natural language.fields: a list of relevant field names predicted by Stage 2.
📥 Example Input
{
"doctype": "Purchase Invoice Advance",
"question": "List the reference types used in advance payments made this month.",
"fields": ["reference_type"]
}
📤 Example Output
frappe.get_all('Purchase Invoice Advance', filters={'posting_date': ['between', ['2024-04-01', '2024-04-30']]}, fields=['reference_type'])
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