EnCoach Odoo 19 custom modules

Full backend implementation with custom Odoo modules:
- encoach_api: Core API, user management, JWT auth
- encoach_exam: Exam generation (reading, writing, listening, speaking)
- encoach_evaluate: AI-powered evaluation (writing, speaking)
- encoach_training: Training tips and walkthrough
- encoach_storage: File storage management
- encoach_payment: Stripe, PayPal, Paymob integration
- encoach_mail: Email notifications

Made-with: Cursor

encoach_training/services/__init__.py

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+from . import training_service

encoach_training/services/training_service.py

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+import json
+import logging
+import pickle
+
+import numpy as np
+
+from odoo.addons.encoach_ai.models.constants import GPT_MODELS, TEMPERATURE
+from odoo.addons.encoach_ai.services.openai_service import EncoachOpenAIService
+
+_logger = logging.getLogger(__name__)
+
+TIPS_PROMPT = (
+    "You are an IELTS tutor. Based on the context, the question, the "
+    "student's wrong answer, and the correct answer, provide a concise, "
+    "helpful tip to help the student improve. Use the following reference "
+    "tips to inform your response but adapt them to the specific situation.\n\n"
+    "Reference tips:\n{tips}\n\n"
+    "Context: {context}\n"
+    "Question: {question}\n"
+    "Student's answer: {answer}\n"
+    "Correct answer: {correct_answer}\n\n"
+    "Provide your response as JSON: "
+    '{{"tips": "your personalized tip text"}}'
+)
+
+TRAINING_PROMPT = (
+    "You are an IELTS training advisor. Analyze the student's exam history "
+    "and generate personalized training recommendations.\n\n"
+    "Student stats:\n{stats}\n\n"
+    "Generate a JSON response with:\n"
+    '{{"exams": [recommended practice exams], '
+    '"tips": [personalized tips], '
+    '"weakAreas": [identified weak areas with suggestions]}}'
+)
+
+
+class EncoachTrainingService:
+
+    def __init__(self, env):
+        self.env = env
+        self.ai = EncoachOpenAIService(env)
+        self._faiss_index = None
+        self._tip_ids = None
+
+    def fetch_tips(self, context, question, answer, correct_answer):
+        """Retrieve relevant tips using FAISS + generate personalized tip via GPT."""
+        query = f"{context} {question} {answer}"
+        similar_tips = self._query_faiss(query, category=None, top_k=5)
+
+        tips_text = "\n".join(
+            f"- [{t.category}] {t.content}" for t in similar_tips
+        )
+
+        prompt = TIPS_PROMPT.format(
+            tips=tips_text,
+            context=context,
+            question=question,
+            answer=answer,
+            correct_answer=correct_answer,
+        )
+
+        result = self.ai.prediction(
+            model=GPT_MODELS["grading"],
+            messages=[
+                {"role": "system", "content": "You are a helpful IELTS tutor. Output JSON."},
+                {"role": "user", "content": prompt},
+            ],
+            temperature=TEMPERATURE["tips"],
+            check_blacklisted=False,
+        )
+        return result
+
+    def get_training_content(self, user_id, stats):
+        """Generate full training content for a user based on their exam stats."""
+        prompt = TRAINING_PROMPT.format(stats=json.dumps(stats))
+
+        result = self.ai.prediction(
+            model=GPT_MODELS["grading"],
+            messages=[
+                {"role": "system", "content": "You are an IELTS training advisor. Output JSON."},
+                {"role": "user", "content": prompt},
+            ],
+            temperature=TEMPERATURE["tips"],
+            check_blacklisted=False,
+        )
+
+        if result:
+            training = self.env["encoach.training"].sudo().create({
+                "user_id": user_id,
+                "exams": result.get("exams"),
+                "tips": result.get("tips"),
+                "weak_areas": result.get("weakAreas"),
+            })
+            return training
+        return None
+
+    def _query_faiss(self, query, category=None, top_k=5):
+        """Search FAISS index for similar training tips.
+
+        Falls back to keyword search if FAISS is not available.
+        """
+        try:
+            import faiss
+            from sentence_transformers import SentenceTransformer
+        except ImportError:
+            _logger.warning("FAISS/sentence-transformers not installed, falling back to keyword search")
+            return self._keyword_fallback(query, category, top_k)
+
+        self._ensure_faiss_index()
+        if self._faiss_index is None:
+            return self._keyword_fallback(query, category, top_k)
+
+        model = SentenceTransformer("all-MiniLM-L6-v2")
+        query_vec = model.encode([query]).astype(np.float32)
+
+        distances, indices = self._faiss_index.search(query_vec, top_k * 2)
+
+        tip_ids = [self._tip_ids[i] for i in indices[0] if i < len(self._tip_ids)]
+        domain = [("id", "in", tip_ids)]
+        if category:
+            domain.append(("category", "=", category))
+
+        tips = self.env["encoach.training.tip"].sudo().search(domain, limit=top_k)
+        return tips
+
+    def _ensure_faiss_index(self):
+        """Build or load the FAISS index from stored tip embeddings."""
+        if self._faiss_index is not None:
+            return
+
+        try:
+            import faiss
+        except ImportError:
+            return
+
+        tips = self.env["encoach.training.tip"].sudo().search(
+            [("embedding", "!=", False)]
+        )
+        if not tips:
+            return
+
+        vectors = []
+        ids = []
+        for tip in tips:
+            vec = pickle.loads(tip.embedding)
+            vectors.append(vec)
+            ids.append(tip.id)
+
+        matrix = np.array(vectors, dtype=np.float32)
+
+        dim = matrix.shape[1]
+        index = faiss.IndexFlatL2(dim)
+        index.add(matrix)
+
+        self._faiss_index = index
+        self._tip_ids = ids
+
+    def _keyword_fallback(self, query, category, top_k):
+        """Simple keyword-based search when FAISS is unavailable."""
+        domain = []
+        if category:
+            domain.append(("category", "=", category))
+
+        tips = self.env["encoach.training.tip"].sudo().search(domain, limit=200)
+
+        query_words = set(query.lower().split())
+        scored = []
+        for tip in tips:
+            content_words = set(tip.content.lower().split())
+            overlap = len(query_words & content_words)
+            if overlap > 0:
+                scored.append((overlap, tip))
+
+        scored.sort(key=lambda x: x[0], reverse=True)
+        return self.env["encoach.training.tip"].browse(
+            [t.id for _, t in scored[:top_k]]
+        )