Files
encoach_be_odoo19/encoach_training/models/training_tip.py
Talal Sharabi c919e83526 Add AI stack configuration: ELAI avatars, system params, training tips import
- Add ELAI avatar seed data (7 avatars with codes, URLs, voice configs)
  from the original backend's avatars.json
- Add missing system parameters: encoach.aws_region (eu-west-1),
  encoach.whisper_workers (4)
- Add training tips import script with pathways_2_rw.json data source
- Add action_compute_embeddings() method to training tip model for
  computing sentence-transformer embeddings on demand

Made-with: Cursor
2026-03-15 01:23:56 +04:00

49 lines
1.5 KiB
Python

import logging
import pickle
from odoo import api, models, fields
_logger = logging.getLogger(__name__)
class EncoachTrainingTip(models.Model):
_name = "encoach.training.tip"
_description = "EnCoach Training Tip"
tip_id = fields.Char(required=True, index=True)
category = fields.Selection(
[
("ct_focus", "CT Focus"),
("language_for_writing", "Language for Writing"),
("reading_skill", "Reading Skill"),
("strategy", "Strategy"),
("writing_skill", "Writing Skill"),
("word_link", "Word Link"),
("word_partners", "Word Partners"),
],
string="Category",
)
content = fields.Text(required=True)
embedding = fields.Binary(string="FAISS Embedding Vector")
def action_compute_embeddings(self):
"""Compute sentence-transformer embeddings for tips missing them."""
try:
from sentence_transformers import SentenceTransformer
import numpy as np
except ImportError:
_logger.error("sentence-transformers not installed; cannot compute embeddings")
return
tips = self.search([("embedding", "=", False)])
if not tips:
_logger.info("All tips already have embeddings")
return
model = SentenceTransformer("all-MiniLM-L6-v2")
for tip in tips:
vec = model.encode([tip.content]).astype(np.float32)[0]
tip.embedding = pickle.dumps(vec)
_logger.info("Computed embeddings for %d tips", len(tips))