I am using this code below where every time i am getting this issue even my query_vector is of of 100 dimension and all are float values. I am getting problem at this position results = list(self.collection.aggregate(pipeline)) def _get_relevant_documents(self, query: str) -> List[Document]: query_vector = self.embedding_model.embed_query(query) print("query_vector",type(query_vector)) if hasattr(query_vector, "tolist"): query_vector = query_vector.tolist() print("query_vector2",type(query_vector)) query_vector1 = [float(x) for x in query_vector] query_vector = query_vector1[:100] cosmos_search_stage = { "$search": { "cosmosSearch": { "vector": { "path": "embedding", "queryVector": query_vector, "k": self.top_k, "similarity": "COS" } } } } if self.keyword: cosmos_search_stage["$search"]["cosmosSearch"]["text"] = { "path": "description", "query": self.keyword } pipeline = [cosmos_search_stage] results = list(self.collection.aggregate(pipeline)) Continue reading...