Refactor MongoDBVectorStore and add delete_index method

application/vectorstore/mongodb.py

@@ -5,21 +5,22 @@ from application.vectorstore.document_class import Document
 class MongoDBVectorStore(BaseVectorStore):
     def __init__(
         self,
+        path: str = "",
+        embeddings_key: str = "embeddings",
         collection: str = "documents",
-        index_name: str = "default",
+        index_name: str = "vector_search_index",
         text_key: str = "text",
         embedding_key: str = "embedding",
-        embedding_api_key: str = "embedding_api_key",
-        path: str = "",
+        database: str = "docsgpt",
     ):
-        self._collection = collection
-        self._embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embedding_api_key)
         self._index_name = index_name
         self._text_key = text_key
         self._embedding_key = embedding_key
+        self._embeddings_key = embeddings_key
         self._mongo_uri = settings.MONGO_URI
-        self._path = path
-        # import pymongo
+        self._path = path.replace("application/indexes/", "").rstrip("/")
+        self._embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+
         try:
             import pymongo
         except ImportError:
@@ -27,30 +28,40 @@ class MongoDBVectorStore(BaseVectorStore):
                 "Could not import pymongo python package. "
                 "Please install it with `pip install pymongo`."
             )
+
         self._client = pymongo.MongoClient(self._mongo_uri)
+        self._database = self._client[database]
+        self._collection = self._database[collection]
+
         
     def search(self, question, k=2, *args, **kwargs):
-        query_vector = self._embeddings.embed_query(question)
-        
+        query_vector = self._embedding.embed_query(question)
+
         pipeline = [
             {
                 "$vectorSearch": {
                     "queryVector": query_vector, 
                     "path": self._embedding_key,
                     "limit": k, 
-                    "index": self._index_name
+                    "numCandidates": k * 10, 
+                    "index": self._index_name,
+                    "filter": {
+                        "store": {"$eq": self._path}
+                    }
                 }
             }
         ]
-        
-        cursor = self._client._collection.aggregate(pipeline)
+
+        cursor = self._collection.aggregate(pipeline)
         
         results = []
         for doc in cursor:
             text = doc[self._text_key]
+            doc.pop("_id")
+            doc.pop(self._text_key)
+            doc.pop(self._embedding_key)
             metadata = doc
             results.append(Document(text, metadata))
-            
         return results
     
     def _insert_texts(self, texts, metadatas):
@@ -62,7 +73,7 @@ class MongoDBVectorStore(BaseVectorStore):
             for t, m, embedding in zip(texts, metadatas, embeddings)
         ]
         # insert the documents in MongoDB Atlas
-        insert_result = self.client._collection.insert_many(to_insert)
+        insert_result = self._collection.insert_many(to_insert)
         return insert_result.inserted_ids
     
     def add_texts(self,
@@ -75,6 +86,26 @@ class MongoDBVectorStore(BaseVectorStore):
         **kwargs,):
 
 
+        #dims = self._embedding.client[1].word_embedding_dimension
+        # # check if index exists
+        # if create_index_if_not_exists:
+        #     # check if index exists
+        #     info = self._collection.index_information()
+        #     if self._index_name not in info:
+        #         index_mongo = {
+        #         "fields": [{
+        #             "type": "vector",
+        #             "path": self._embedding_key,
+        #             "numDimensions": dims,
+        #             "similarity": "cosine",
+        #         },
+        #         {
+        #             "type": "filter",
+        #             "path": "store"
+        #         }]
+        #         }
+        #         self._collection.create_index(self._index_name, index_mongo)
+
         batch_size = 100
         _metadatas = metadatas or ({} for _ in texts)
         texts_batch = []
@@ -89,4 +120,7 @@ class MongoDBVectorStore(BaseVectorStore):
                 metadatas_batch = []
         if texts_batch:
             result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
-        return result_ids
+        return result_ids
+    
+    def delete_index(self, *args, **kwargs):
+        self._collection.delete_many({"store": self._path})

application/vectorstore/vector_creator.py

@@ -1,11 +1,13 @@
 from application.vectorstore.faiss import FaissStore
 from application.vectorstore.elasticsearch import ElasticsearchStore
+from application.vectorstore.mongodb import MongoDBVectorStore
 
 
 class VectorCreator:
     vectorstores = {
         'faiss': FaissStore,
-        'elasticsearch':ElasticsearchStore
+        'elasticsearch':ElasticsearchStore,
+        'mongodb': MongoDBVectorStore,
     }
 
     @classmethod