Restructured code.

kairon/api/models.py

@@ -364,23 +364,6 @@ def validate_request_method(cls, v, values, **kwargs):
         return v.upper()
 
 
-class QueryConfig(BaseModel):
-    type: DbQueryValueType
-    value: DbActionOperationType
-
-    @root_validator
-    def check(cls, values):
-        from kairon.shared.utils import Utility
-
-        if Utility.check_empty_string(values.get('type')):
-            raise ValueError("type cannot be empty")
-
-        if Utility.check_empty_string(values.get('value')):
-            raise ValueError("value cannot be empty")
-
-        return values
-
-
 class PayloadConfig(BaseModel):
     type: DbQueryValueType
     value: Any

tests/unit_test/llm_test.py

@@ -369,72 +369,6 @@ async def test_gpt3_faq_embedding_train_int(self, aioresponses):
                             'payload': expected_payload
                             }]}
 
-    # @pytest.mark.asyncio
-    # async def test_gpt3_faq_embedding_train_payload_json_no_metadata(self, aioresponses):
-    #     bot = "test_embed_faq_json_no_metadata"
-    #     user = "test"
-    #     value = "nupurkhare"
-    #     CognitionSchema(
-    #         metadata=[],
-    #         bot=bot, user=user).save()
-    #     test_content = CognitionData(
-    #         data={"name": "Nupur", "age": 25, "city": "Bengaluru"},
-    #         content_type="json",
-    #         bot=bot, user=user).save()
-    #     secret = BotSecrets(secret_type=BotSecretType.gpt_key.value, value=value, bot=bot, user=user).save()
-    #
-    #     embedding = list(np.random.random(GPT3FAQEmbedding.__embedding__))
-    #     request_header = {"Authorization": "Bearer nupurkhare"}
-    #
-    #     aioresponses.add(
-    #         url="https://api.openai.com/v1/embeddings",
-    #         method="POST",
-    #         status=200,
-    #         payload={'data': [{'embedding': embedding}]}
-    #     )
-    #
-    #     with mock.patch.dict(Utility.environment, {'llm': {"faq": "GPT3_FAQ_EMBED", 'api_key': secret}}):
-    #         gpt3 = GPT3FAQEmbedding(test_content.bot, LLMSettings(provider="openai").to_mongo().to_dict())
-    #
-    #         aioresponses.add(
-    #             url=urljoin(Utility.environment['vector']['db'], f"/collections"),
-    #             method="GET",
-    #             payload={"time": 0, "status": "ok", "result": {"collections": []}})
-    #
-    #
-    #         aioresponses.add(
-    #             method="DELETE",
-    #             url=urljoin(Utility.environment['vector']['db'], f"/collections/{gpt3.bot}{gpt3.suffix}"),
-    #         )
-    #
-    #         aioresponses.add(
-    #             url=urljoin(Utility.environment['vector']['db'], f"/collections/{gpt3.bot}{gpt3.suffix}"),
-    #             method="PUT",
-    #             status=200
-    #         )
-    #
-    #         aioresponses.add(
-    #             url=urljoin(Utility.environment['vector']['db'], f"/collections/{gpt3.bot}{gpt3.suffix}/points"),
-    #             method="PUT",
-    #             payload={"result": {"operation_id": 0, "status": "acknowledged"}, "status": "ok", "time": 0.003612634}
-    #         )
-    #
-    #         response = await gpt3.train()
-    #         assert response['faq'] == 1
-    #
-    #         assert list(aioresponses.requests.values())[1][0].kwargs['json'] == {'name': gpt3.bot + gpt3.suffix,
-    #                                                                              'vectors': gpt3.vector_config}
-    #         assert list(aioresponses.requests.values())[2][0].kwargs['json'] == {"model": "text-embedding-ada-002",
-    #                                                                              "input": json.dumps(test_content.data)}
-    #         assert list(aioresponses.requests.values())[2][0].kwargs['headers'] == request_header
-    #         expected_payload = test_content.data
-    #         expected_payload['collection_name'] = f"{gpt3.bot}{gpt3.suffix}"
-    #         assert list(aioresponses.requests.values())[3][0].kwargs['json'] == {
-    #             'points': [{'id': test_content.vector_id,
-    #                         'vector': embedding,
-    #                         'payload': expected_payload
-    #                         }]}
-
     def test_gpt3_faq_embedding_train_failure(self):
         with pytest.raises(AppException, match=f"Bot secret '{BotSecretType.gpt_key.value}' not configured!"):
             GPT3FAQEmbedding('test_failure', LLMSettings(provider="openai").to_mongo().to_dict())