[RS-101] - Integrated Astradb into Ecom Review Sense

src/reviewsense_ecom/core/config.py

+
+from functools import lru_cache
+
+from pydantic.v1 import BaseSettings
+
+
+class Settings(BaseSettings):
+    """Application configuration settings"""
+    ASTRA_DB_API_ENDPOINT: str = "https://0c52512b-0c65-4e70-ac47-71edf5244a82-us-east-2.apps.astra.datastax.com"
+    ASTRA_DB_APPLICATION_TOKEN: str = "AstraCS:EvXpFFafufegdQJvhqlYxmxt:ef86b5996013b12140b69254bd554d7e8e10eb5a7137859b9c432f92a5a3b65c"
+    ASTRA_DB_NAMESPACE: str = "default_keyspace"
+    
+    # Default settings
+    EMBEDDING_MODEL: str = "sentence-transformers/all-mpnet-base-v2"
+    
+    class Config:
+        env_file = ".env"
+        env_file_encoding = "utf-8"
+        extra = "allow"  # Allow extra fields
+
+@lru_cache()
+def get_settings():
+    """
+    Cached settings retrieval to optimize performance
+    
+    Returns:
+        Settings: Configured application settings
+    """
+    return Settings()

src/reviewsense_ecom/mongo/mongo_db_config.py

@@ -6,6 +6,7 @@ from pymongo import MongoClient
 from src.reviewsense_ecom.model.FeatureSentiment import FeatureSentiment
 from src.reviewsense_ecom.model.Product import Product
 from src.reviewsense_ecom.model.ProductReview import ProductReview
+from src.reviewsense_ecom.service.review_adder import ReviewAdder
 
 
 # def get_db_connection(collection_name: str):   #LOCAL DB
@@ -134,7 +135,14 @@ def add_review_features(input_data, reviews_by_feature):
     inserted_result = collection.insert_one(new_review.__dict__)
 
     if inserted_result.inserted_id:
-        print(f"Review successfully added with ID: {inserted_result.inserted_id}")
+        print(f"Review successfully added Into MongoDB with ID: {inserted_result.inserted_id}")
+    # ✅ Add review to vector DB
+    try:
+        review_adder = ReviewAdder()
+        vector_id = review_adder.add_review_vector(input_data.product_id, input_data.new_review)
+        print(f"Review also added to Vector DB with ID: {vector_id} ")
+    except Exception as e:
+        print(f"Failed to add review to vector DB: {e}")
 
     # Return the newly inserted review object
     return new_review.__dict__

src/reviewsense_ecom/service/retrieval.py

+# src/reviewsense/core/database.py
+from functools import lru_cache
+
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_astradb import AstraDBVectorStore
+from src.reviewsense_ecom.core.config import get_settings
+
+
+@lru_cache(maxsize=1)
+def get_vector_store(
+    collection_name: str = "customer_reviews",
+    embedding_model: str = "sentence-transformers/all-mpnet-base-v2"
+):
+    """
+    Create a singleton vector store instance
+    
+    Args:
+        collection_name (str): Database collection name
+        embedding_model (str): Embedding model to use
+    
+    Returns:
+        AstraDBVectorStore: Configured vector store instance
+    """
+    settings = get_settings()
+    
+    # Initialize embeddings
+    embeddings = HuggingFaceInferenceAPIEmbeddings(api_key="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", model_name="BAAI/bge-base-en-v1.5")
+    
+    # Create and return vector store
+    return AstraDBVectorStore(
+        collection_name=collection_name,
+        embedding=embeddings,
+        api_endpoint="https://9706ee0b-b3e8-4ee2-bb26-45e8a0db1586-us-east-2.apps.astra.datastax.com",
+        token="AstraCS:OEbpcggZbUjOvRiesULZkTnf:9d215d8d32c2ffb09081212208e474f81a0bcdde45e7b683cd67f4a1a936a8bf",
+        namespace="default_keyspace",
+    )
\ No newline at end of file

src/reviewsense_ecom/service/review_adder.py

+# services/review_adder.py
+
+from langchain_community.docstore.document import Document
+from .retrieval import get_vector_store
+
+class ReviewAdder:
+    """Class for adding reviews to the vector store"""
+
+    def __init__(self):
+        self.vector_store = get_vector_store()
+
+    def add_review_vector(self, product_id: str, review: str) -> str:
+        review_document = Document(page_content=review, metadata={"title": product_id})
+        return self.vector_store.add_documents([review_document])[0]