Fix/micro learning

src/app/api/api_v1/endpoints/micro_learning.py

@@ -30,10 +30,13 @@
 async def get_subject_list(
     lang: str | None = None, sp: SearchService = Depends(get_search_service)
 ) -> list[str]:
-    collection_info, model_id = await collection_and_model_id_according_lang(
+    collection_info, models_ids = await collection_and_model_id_according_lang(
         sp=sp, lang=lang
     )
-    ret = [md.title for md in get_subjects(embedding_model_id=model_id)]
+    ret = [
+        md.title
+        for md in get_subjects(embedding_models_ids=[model.id for model in models_ids])
+    ]
     if len(ret) == 0:
         raise HTTPException(status_code=404, detail="No subjects found.")
     return ret
@@ -50,21 +53,21 @@ async def get_full_journey(
     lang: str | None = None,
     sp: SearchService = Depends(get_search_service),
 ):
-    collection_info, model_id = await collection_and_model_id_according_lang(
+    collection_info, models_id = await collection_and_model_id_according_lang(
         sp=sp, lang=lang
     )
-
+    emb_models_ids = [model.id for model in models_id]
     journey_part = [i.lower() for i in JourneySectionType]
     sdg_meta_documents = get_context_documents(
-        journey_part=journey_part, sdg=sdg, embedding_model_id=model_id
+        journey_part=journey_part, sdg=sdg, embedding_models_ids=emb_models_ids
     )
     if not sdg_meta_documents:
         raise HTTPException(
             status_code=404, detail=f"SDG '{sdg}' not found in meta documents."
         )
 
     subject_meta_document: ContextDocument | None = get_subject(
-        subject=subject, embedding_model_id=model_id
+        subject=subject, embedding_models_ids=emb_models_ids
     )
 
     if not subject_meta_document:

src/app/services/helpers.py

@@ -1,4 +1,3 @@
-import uuid
 from functools import cache
 from typing import Any, List
 
@@ -8,6 +7,7 @@
 from json_repair import JSONReturnType
 from langdetect import detect_langs
 from qdrant_client.http.models import models
+from welearn_database.data.models import EmbeddingModel
 
 from src.app.models.collections import Collection
 from src.app.models.documents import JourneySectionType
@@ -184,7 +184,7 @@ def choose_readability_according_journey_section_type(
 @log_time_and_error_sync
 async def collection_and_model_id_according_lang(
     lang: str | None, sp
-) -> tuple[Collection, uuid]:
+) -> tuple[Collection, list[EmbeddingModel]]:
     """
     Get the collection info and model id according to the language.
     Args:
@@ -203,9 +203,9 @@ async def collection_and_model_id_according_lang(
         raise HTTPException(
             status_code=404, detail=f"No collection found for language '{lang}'."
         )
-    model_id = get_embeddings_model_id_according_name(collection_info.model)
-    if not model_id:
+    emb_models = get_embeddings_model_id_according_name(collection_info.model)
+    if not emb_models:
         raise ValueError(
             f"Embedding model '{collection_info.model}' not found in the database."
         )
-    return collection_info, model_id
+    return collection_info, emb_models

src/app/services/search.py

@@ -25,7 +25,10 @@
 from src.app.services.data_quality import DataQualityChecker
 from src.app.services.exceptions import CollectionNotFoundError, ModelNotFoundError
 from src.app.services.helpers import convert_embedding_bytes
-from src.app.services.sql_db.queries import get_subject
+from src.app.services.sql_db.queries import (
+    get_embeddings_model_id_according_name,
+    get_subject,
+)
 from src.app.utils.decorators import log_time_and_error, log_time_and_error_sync
 from src.app.utils.logger import logger as logger_utils
 
@@ -253,7 +256,9 @@ async def search_handler(
         assert isinstance(qp.query, str)
 
         collection = await self.get_collection_by_language(lang="mul")
-        subject_vector = await run_in_threadpool(get_subject_vector, qp.subject)
+        subject_vector = await run_in_threadpool(
+            get_subject_vector, qp.subject, collection.model
+        )
         embedding = await self.get_query_embed(
             model=collection.model,
             query=qp.query,
@@ -443,7 +448,7 @@ def concatenate_same_doc_id_slices(
 
 
 @log_time_and_error_sync
-def get_subject_vector(subject: str | None) -> list[float] | None:
+def get_subject_vector(subject: str | None, model_name: str) -> list[float] | None:
     """
     Get the subject vector from the database.
     Args:
@@ -455,7 +460,15 @@ def get_subject_vector(subject: str | None) -> list[float] | None:
     if not subject:
         return None
 
-    subject_from_db = get_subject(subject=subject)
+    emb_models = get_embeddings_model_id_according_name(model_name)
+    embedding_model_ids = [m.id for m in emb_models if m is not None]
+    if not embedding_model_ids:
+        return None
+
+    subject_from_db = get_subject(
+        subject=subject,
+        embedding_models_ids=embedding_model_ids,
+    )
     if not subject_from_db:
         return None
 

src/app/services/sql_db/queries.py

@@ -34,7 +34,6 @@
 from src.app.services.constants import APP_NAME
 from src.app.services.sql_db.sql_service import session_maker
 
-model_id_cache: dict[str, UUID] = {}
 model_id_lock = Lock()
 
 
@@ -152,10 +151,13 @@ def register_endpoint(endpoint, session_id, http_code):
         session.commit()
 
 
-def get_subject(subject: str, embedding_model_id: UUID) -> ContextDocument | None:
+def get_subject(
+    subject: str, embedding_models_ids: list[UUID]
+) -> ContextDocument | None:
     """
     Get the subject meta document from the database.
     Args:
+        embedding_models_ids: Database IDs of embeddings models used for vectorize documents
         subject: The subject to get.
 
     Returns: The subject meta document.
@@ -167,14 +169,14 @@ def get_subject(subject: str, embedding_model_id: UUID) -> ContextDocument | Non
             .filter(
                 ContextDocument.context_type == ContextType.SUBJECT.value.lower(),
                 ContextDocument.title == subject,
-                ContextDocument.embedding_model_id == embedding_model_id,
+                ContextDocument.embedding_model_id.in_(embedding_models_ids),
             )
             .first()
         )
     return subject_meta_document
 
 
-def get_subjects(embedding_model_id: UUID) -> list[ContextDocument]:
+def get_subjects(embedding_models_ids: list[UUID]) -> list[ContextDocument]:
     """
     Get all the subject meta documents from the database.
     Returns: List of subject meta documents.
@@ -184,20 +186,21 @@ def get_subjects(embedding_model_id: UUID) -> list[ContextDocument]:
             session.query(ContextDocument)
             .filter(
                 ContextDocument.context_type == ContextType.SUBJECT.value.lower(),
-                ContextDocument.embedding_model_id == embedding_model_id,
+                ContextDocument.embedding_model_id.in_(embedding_models_ids),
             )
             .all()
         )
     return sdg_meta_documents
 
 
 def get_context_documents(
-    journey_part: JourneySection, sdg: int, embedding_model_id: UUID
+    journey_part: list[JourneySection], sdg: int, embedding_models_ids: list[UUID]
 ):
     """
     Get the context documents from the database.
 
     Args:
+        embedding_models_ids: Database IDs of embeddings models used for vectorize documents
         journey_part: The journey part to get the context documents for.
         sdg: The SDG to get the context documents for.
     Returns: List of context documents.
@@ -208,14 +211,16 @@ def get_context_documents(
             .filter(
                 ContextDocument.context_type.in_(journey_part),
                 ContextDocument.sdg_related.contains([sdg]),
-                ContextDocument.embedding_model_id == embedding_model_id,
+                ContextDocument.embedding_model_id.in_(embedding_models_ids),
             )
             .all()
         )
     return sdg_meta_documents
 
 
-def get_embeddings_model_id_according_name(model_name: str) -> UUID | None:
+def get_embeddings_model_id_according_name(
+    model_name: str,
+) -> list[EmbeddingModel | None]:
     """
     Get the embeddings model ID according to its name.
 
@@ -225,17 +230,12 @@ def get_embeddings_model_id_according_name(model_name: str) -> UUID | None:
     Returns:
         The ID of the embeddings model if found, otherwise None.
     """
-    with model_id_lock:
-        if model_name in model_id_cache:
-            return model_id_cache[model_name]
-
     with session_maker() as session:
-        model = (
+        return (
             session.query(EmbeddingModel)
             .filter(EmbeddingModel.title == model_name)
-            .first()
+            .all()
         )
-        return model.id if model else None
 
 
 def write_new_data_quality_error(

src/app/services/tutor/agents.py

@@ -1,5 +1,5 @@
-import time
 import json
+import time
 from pathlib import Path
 
 from langchain_core.language_models import BaseChatModel

src/app/tests/api/api_v1/test_micro_learning.py

@@ -1,8 +1,9 @@
 import unittest
+import uuid
 from unittest import mock
 
 from fastapi.testclient import TestClient
-from welearn_database.data.models import ContextDocument
+from welearn_database.data.models import ContextDocument, EmbeddingModel
 
 from src.app.core.config import settings
 from src.app.models.collections import Collection
@@ -38,7 +39,7 @@ async def test_get_full_journey(
         with TestClient(app) as client:
             mock_collection_and_model_id_according_lang.return_value = (
                 Collection(name="test_collection", lang="en", model="test_model"),
-                "model_id",
+                [EmbeddingModel(id=uuid.uuid4(), title="test_model", lang="en")],
             )
             # Mock data
             mock_get_context_docs.return_value = [
@@ -114,7 +115,7 @@ async def test_get_subject_list(
             ]
             mock_collection_and_model_id_according_lang.return_value = (
                 None,
-                "model_id",
+                [],
             )
 
             # API call