fix: Use Qdrant wrapper

course/module-5/.env.example

@@ -1,4 +1,12 @@
+OPENAI_API_KEY = "str"
 HUGGINGFACE_ACCESS_TOKEN = "str"
+
 COMET_API_KEY = "str"
 COMET_WORKSPACE = "str"
-COMET_PROJECT = "scrabble"
+COMET_PROJECT = "llm-twin-course"
+
+QWAK_DEPLOYMENT_MODEL_ID = "str"
+QWAK_DEPLOYMENT_MODEL_API = "str"
+
+QDRANT_CLOUD_URL = "str"
+QDRANT_APIKEY = "str"

course/module-5/db/qdrant.py

@@ -1,17 +1,18 @@
-from qdrant_client import QdrantClient
+import logger_utils
+from qdrant_client import QdrantClient, models
 from qdrant_client.http.exceptions import UnexpectedResponse
 from qdrant_client.http.models import Batch, Distance, VectorParams
 
-import logger_utils
+
 from settings import settings
 
 logger = logger_utils.get_logger(__name__)
 
 
 class QdrantDatabaseConnector:
-    _instance: QdrantClient = None
-
-    def __init__(self):
+    _instance: QdrantClient | None = None
+    
+    def __init__(self) -> None:
         if self._instance is None:
             try:
                 if settings.USE_QDRANT_CLOUD:
@@ -24,12 +25,12 @@ def __init__(self):
                         host=settings.QDRANT_DATABASE_HOST,
                         port=settings.QDRANT_DATABASE_PORT,
                     )
-
             except UnexpectedResponse:
                 logger.exception(
-                    "Couldn't connect to the database.",
+                    "Couldn't connect to Qdrant.",
                     host=settings.QDRANT_DATABASE_HOST,
                     port=settings.QDRANT_DATABASE_PORT,
+                    url=settings.QDRANT_CLOUD_URL,
                 )
 
                 raise
@@ -57,6 +58,14 @@ def write_data(self, collection_name: str, points: Batch):
             logger.exception("An error occurred while inserting data.")
 
             raise
+
+    def search(self, collection_name: str, query_vector: list, query_filter: models.Filter, limit: int) -> list:
+        return self._instance.search(
+            collection_name=collection_name,
+            query_vector=query_vector,
+            query_filter=query_filter,
+            limit=limit,
+        )
 
     def scroll(self, collection_name: str, limit: int):
         return self._instance.scroll(collection_name=collection_name, limit=limit)
@@ -66,6 +75,3 @@ def close(self):
             self._instance.close()
 
             logger.info("Connected to database has been closed.")
-
-
-connection = QdrantDatabaseConnector()

course/module-5/finetuning_model/dataset_client.py

@@ -47,7 +47,10 @@ def split_data(self, artifact_name: str) -> tuple:
             return training_file_path, validation_file_path
         except Exception as e:
             logging.error(f"Error splitting data: {str(e)}")
+
+            raise
 
-    def download_dataset(self, file_name: str):
+    def download_dataset(self, file_name: str) -> tuple:
         self.get_artifact(file_name)
+
         return self.split_data(file_name)

course/module-5/inference.py

@@ -11,7 +11,8 @@
 class ModelInference:
     def __init__(self) -> None:
         self.qwak_client = RealTimeClient(
-            model_id=settings.MODEL_ID, model_api=settings.MODEL_API
+            model_id=settings.QWAK_DEPLOYMENT_MODEL_ID,
+            model_api=settings.QWAK_DEPLOYMENT_MODEL_API,
         )
         self.template = InferenceTemplate()
         self.prompt_monitoring_manager = PromptMonitoringManager()

course/module-5/rag/retriever.py

@@ -1,13 +1,13 @@
 import concurrent.futures
 
-from qdrant_client import QdrantClient, models
-from sentence_transformers.SentenceTransformer import SentenceTransformer
-
 import logger_utils
 import utils
+from db.qdrant import QdrantDatabaseConnector
+from qdrant_client import models
 from rag.query_expanison import QueryExpansion
 from rag.reranking import Reranker
 from rag.self_query import SelfQuery
+from sentence_transformers.SentenceTransformer import SentenceTransformer
 from settings import settings
 
 logger = logger_utils.get_logger(__name__)
@@ -18,11 +18,8 @@ class VectorRetriever:
     Class for retrieving vectors from a Vector store in a RAG system using query expansion and Multitenancy search.
     """
 
-    def __init__(self, query: str):
-        self._client = QdrantClient(
-            host=settings.QDRANT_DATABASE_HOST,
-            port=settings.QDRANT_DATABASE_PORT,
-        )
+    def __init__(self, query: str) -> None:
+        self._client = QdrantDatabaseConnector()
         self.query = query
         self._embedder = SentenceTransformer(settings.EMBEDDING_MODEL_ID)
         self._query_expander = QueryExpansion()
@@ -33,7 +30,9 @@ def _search_single_query(
         self, generated_query: str, metadata_filter_value: str, k: int
     ):
         assert k > 3, "k should be greater than 3"
+
         query_vector = self._embedder.encode(generated_query).tolist()
+
         vectors = [
             self._client.search(
                 collection_name="vector_posts",

course/module-5/settings.py

@@ -4,20 +4,16 @@
 class AppSettings(BaseSettings):
     model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
 
-    # Embeddings config
+     # Embeddings config
     EMBEDDING_MODEL_ID: str = "sentence-transformers/all-MiniLM-L6-v2"
     EMBEDDING_MODEL_MAX_INPUT_LENGTH: int = 256
     EMBEDDING_SIZE: int = 384
     EMBEDDING_MODEL_DEVICE: str = "cpu"
-
+    
     # OpenAI config
     OPENAI_MODEL_ID: str = "gpt-4-1106-preview"
     OPENAI_API_KEY: str | None = None
 
-    # MongoDB config
-    MONGO_DATABASE_HOST: str = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
-    MONGO_DATABASE_NAME: str = "scrabble"
-
     # QdrantDB config
     QDRANT_DATABASE_HOST: str = "localhost"
     QDRANT_DATABASE_PORT: int = 6333
@@ -34,16 +30,16 @@ class AppSettings(BaseSettings):
     RABBITMQ_PORT: int = 5673
 
     # CometML config
-    COMET_API_KEY: str | None = None
-    COMET_WORKSPACE: str = "vladadu"
-    COMET_PROJECT: str = "llm-twin"
+    COMET_API_KEY: str
+    COMET_WORKSPACE: str
+    COMET_PROJECT: str = "llm-twin-course"
 
     # LLM Model config
     TOKENIZERS_PARALLELISM: str = "false"
     HUGGINGFACE_ACCESS_TOKEN: str | None = None
     MODEL_TYPE: str = "mistralai/Mistral-7B-Instruct-v0.1"
-    MODEL_ID: str = "copywriter_model"
-    MODEL_API: str = (
+    QWAK_DEPLOYMENT_MODEL_ID: str = "copywriter_model"
+    QWAK_DEPLOYMENT_MODEL_API: str = (
         "https://models.llm-twin.qwak.ai/v1/copywriter_model/default/predict"
     )