refactor: remove unused abstract method and improve retrievers

application/vectorstore/base.py

@@ -1,20 +1,28 @@
-from abc import ABC, abstractmethod
 import os
-from sentence_transformers import SentenceTransformer
+from abc import ABC, abstractmethod
+
 from langchain_openai import OpenAIEmbeddings
+from sentence_transformers import SentenceTransformer
+
 from application.core.settings import settings
 
+
 class EmbeddingsWrapper:
     def __init__(self, model_name, *args, **kwargs):
-        self.model = SentenceTransformer(model_name, config_kwargs={'allow_dangerous_deserialization': True}, *args, **kwargs)
+        self.model = SentenceTransformer(
+            model_name,
+            config_kwargs={"allow_dangerous_deserialization": True},
+            *args,
+            **kwargs
+        )
         self.dimension = self.model.get_sentence_embedding_dimension()
 
     def embed_query(self, query: str):
         return self.model.encode(query).tolist()
-    
+
     def embed_documents(self, documents: list):
         return self.model.encode(documents).tolist()
-    
+
     def __call__(self, text):
         if isinstance(text, str):
             return self.embed_query(text)
@@ -24,15 +32,14 @@ class EmbeddingsWrapper:
             raise ValueError("Input must be a string or a list of strings")
 
 
-
 class EmbeddingsSingleton:
     _instances = {}
 
     @staticmethod
     def get_instance(embeddings_name, *args, **kwargs):
         if embeddings_name not in EmbeddingsSingleton._instances:
-            EmbeddingsSingleton._instances[embeddings_name] = EmbeddingsSingleton._create_instance(
-                embeddings_name, *args, **kwargs
+            EmbeddingsSingleton._instances[embeddings_name] = (
+                EmbeddingsSingleton._create_instance(embeddings_name, *args, **kwargs)
             )
         return EmbeddingsSingleton._instances[embeddings_name]
 
@@ -40,9 +47,15 @@ class EmbeddingsSingleton:
     def _create_instance(embeddings_name, *args, **kwargs):
         embeddings_factory = {
             "openai_text-embedding-ada-002": OpenAIEmbeddings,
-            "huggingface_sentence-transformers/all-mpnet-base-v2": lambda: EmbeddingsWrapper("sentence-transformers/all-mpnet-base-v2"),
-            "huggingface_sentence-transformers-all-mpnet-base-v2": lambda: EmbeddingsWrapper("sentence-transformers/all-mpnet-base-v2"),
-            "huggingface_hkunlp/instructor-large": lambda: EmbeddingsWrapper("hkunlp/instructor-large"),
+            "huggingface_sentence-transformers/all-mpnet-base-v2": lambda: EmbeddingsWrapper(
+                "sentence-transformers/all-mpnet-base-v2"
+            ),
+            "huggingface_sentence-transformers-all-mpnet-base-v2": lambda: EmbeddingsWrapper(
+                "sentence-transformers/all-mpnet-base-v2"
+            ),
+            "huggingface_hkunlp/instructor-large": lambda: EmbeddingsWrapper(
+                "hkunlp/instructor-large"
+            ),
         }
 
         if embeddings_name in embeddings_factory:
@@ -50,34 +63,63 @@ class EmbeddingsSingleton:
         else:
             return EmbeddingsWrapper(embeddings_name, *args, **kwargs)
 
+
 class BaseVectorStore(ABC):
     def __init__(self):
         pass
 
     @abstractmethod
     def search(self, *args, **kwargs):
+        """Search for similar documents/chunks in the vectorstore"""
+        pass
+
+    @abstractmethod
+    def add_texts(self, texts, metadatas=None, *args, **kwargs):
+        """Add texts with their embeddings to the vectorstore"""
+        pass
+
+    def delete_index(self, *args, **kwargs):
+        """Delete the entire index/collection"""
+        pass
+
+    def save_local(self, *args, **kwargs):
+        """Save vectorstore to local storage"""
+        pass
+
+    def get_chunks(self, *args, **kwargs):
+        """Get all chunks from the vectorstore"""
+        pass
+
+    def add_chunk(self, text, metadata=None, *args, **kwargs):
+        """Add a single chunk to the vectorstore"""
+        pass
+
+    def delete_chunk(self, chunk_id, *args, **kwargs):
+        """Delete a specific chunk from the vectorstore"""
         pass
 
     def is_azure_configured(self):
-        return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
+        return (
+            settings.OPENAI_API_BASE
+            and settings.OPENAI_API_VERSION
+            and settings.AZURE_DEPLOYMENT_NAME
+        )
 
     def _get_embeddings(self, embeddings_name, embeddings_key=None):
         if embeddings_name == "openai_text-embedding-ada-002":
             if self.is_azure_configured():
                 os.environ["OPENAI_API_TYPE"] = "azure"
                 embedding_instance = EmbeddingsSingleton.get_instance(
-                    embeddings_name,
-                    model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME
+                    embeddings_name, model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME
                 )
             else:
                 embedding_instance = EmbeddingsSingleton.get_instance(
-                    embeddings_name,
-                    openai_api_key=embeddings_key
+                    embeddings_name, openai_api_key=embeddings_key
                 )
         elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
             if os.path.exists("./models/all-mpnet-base-v2"):
                 embedding_instance = EmbeddingsSingleton.get_instance(
-                    embeddings_name = "./models/all-mpnet-base-v2",
+                    embeddings_name="./models/all-mpnet-base-v2",
                 )
             else:
                 embedding_instance = EmbeddingsSingleton.get_instance(
@@ -87,4 +129,3 @@ class BaseVectorStore(ABC):
             embedding_instance = EmbeddingsSingleton.get_instance(embeddings_name)
 
         return embedding_instance
-