minor fix

memory_scope/models/llama_index_embedding_model.py

@@ -39,7 +39,6 @@ class LlamaIndexEmbeddingModel(BaseModel):
             model_response.details = "empty embeddings"
             model_response.status = False
             return model_response
-
         if len(embeddings) == 1:
             # return list[float]
             embeddings = embeddings[0]

memory_scope/storage/llama_index_es_memory_store.py

@@ -21,7 +21,7 @@ class LlamaIndexEsMemoryStore(BaseMemoryStore):
                  use_hybrid: bool = True,
                  emb_dims: int = 1536,
                  **kwargs):
-
+        self.index_name = index_name
         self.emb_dims = emb_dims
         self.embedding_model: BaseModel = embedding_model
         self.es_store = SyncElasticsearchStore(index_name=index_name,
@@ -32,21 +32,26 @@ class LlamaIndexEsMemoryStore(BaseMemoryStore):
         #  adding the following lines of code, the display of deprecated information is suppressed.
         
         self.index = VectorStoreIndex.from_vector_store(vector_store=self.es_store,
-                                                            embed_model=self.embedding_model.model)
+                                                        embed_model=self.embedding_model.model)
 
-        self.index.build_index_from_nodes([TextNode(text="text")])
         self.logger = Logger.get_logger()
 
     def retrieve_memories(self,
                           query: Optional[str] = None,
                           top_k: int = 3,
                           filter_dict: Dict[str, List[str]] | Dict[str, str] = None) -> List[MemoryNode]:
+        # if index is not created, return []
+        exists = self.es_store._store.client.indices.exists(index=self.index_name)
+        if not exists:
+            return []
+        
         if filter_dict is None:
             filter_dict = {}
 
         es_filter = _to_elasticsearch_filter(filter_dict)
-        retriever = self.index.as_retriever(vector_store_kwargs={"es_filter": es_filter}, similarity_top_k=top_k,
-                                            sparse_top_k=top_k)
+        retriever = self.index.as_retriever(vector_store_kwargs={"es_filter": es_filter, "fields": ['embedding']}, 
+                                            similarity_top_k=top_k,
+                                            sparse_top_k=top_k, )
         if query is None:
             query = QueryBundle(query_str='**--**',
                                 embedding=self.dummy_query_vector())
@@ -124,11 +129,14 @@ class LlamaIndexEsMemoryStore(BaseMemoryStore):
 
         Returns:
             TextNode: The converted TextNode with content and metadata from the MemoryNode.
-        """        
+        """ 
+        embedding = memory_node.vector
+        if not embedding:
+            embedding = None
         return TextNode(id_=memory_node.memory_id,
                         text=memory_node.content,
-                        embedding=memory_node.vector,
-                        metadata=memory_node.model_dump(exclude={"content"}))
+                        embedding=embedding,
+                        metadata=memory_node.model_dump(exclude={"content", "vector"}))
 
     @staticmethod
     def _text_node_2_memory_node(text_node: NodeWithScore) -> MemoryNode:
@@ -141,4 +149,8 @@ class LlamaIndexEsMemoryStore(BaseMemoryStore):
         Returns:
             MemoryNode: The converted MemoryNode with text and metadata from the NodeWithScore.
         """
-        return MemoryNode(content=text_node.text, vector=text_node.embedding, **text_node.metadata)
+        embedding = text_node.embedding
+        print("textnode embedding", embedding)
+        if not embedding:
+            embedding = []
+        return MemoryNode(content=text_node.text, vector=embedding, **text_node.metadata)

memory_scope/storage/llama_index_sync_elasticsearch.py

@@ -169,6 +169,7 @@ class _AsyncDenseVectorStrategy(AsyncDenseVectorStrategy):
                 query_body["rank"] = {"rrf": self.rrf}
             elif isinstance(self.rrf, bool) and self.rrf is True:
                 query_body["rank"] = {"rrf": {"window_size": top_k}}
+
         return query_body
 
     def es_query(
@@ -590,6 +591,7 @@ class SyncElasticsearchStore(BasePydanticVectorStore):
                 Callable[[Dict, Union[VectorStoreQuery, None]], Dict]
             ] = None,
             es_filter: Optional[List[Dict]] = None,
+            fields: List[str] = [],
             **kwargs: Any,
     ) -> VectorStoreQueryResult:
         """
@@ -629,15 +631,15 @@ class SyncElasticsearchStore(BasePydanticVectorStore):
             num_candidates=num_candidates,  # query.similarity_top_k * 10,
             filter=filter,
             custom_query=custom_query,
+            fields=fields,
         )
-
         top_k_nodes = []
         top_k_ids = []
         top_k_scores = []
-        print("hits:", len(hits))
         for hit in hits:
             source = hit["_source"]
             metadata = source.get("metadata", None)
+            embedding = source.get("embedding", None)
             text = source.get(self.text_field, None)
             node_id = hit["_id"]
 
@@ -645,6 +647,7 @@ class SyncElasticsearchStore(BasePydanticVectorStore):
                 # Attempt to parse metadata using the standard method
                 node = metadata_dict_to_node(metadata)
                 node.text = text
+                node.embedding = embedding
             except Exception:
                 # Legacy support for old metadata format
                 logger.warning(
@@ -662,6 +665,7 @@ class SyncElasticsearchStore(BasePydanticVectorStore):
                     text=text,
                     metadata=metadata,
                     id_=node_id,
+                    embedding=embedding,
                     start_char_idx=start_char_idx,
                     end_char_idx=end_char_idx,
                     relationships=relationships,
@@ -682,5 +686,6 @@ class SyncElasticsearchStore(BasePydanticVectorStore):
         return VectorStoreQueryResult(
             nodes=top_k_nodes,
             ids=top_k_ids,
-            similarities=_to_llama_similarities(top_k_scores),
+            # similarities=_to_llama_similarities(top_k_scores),
+            similarities=top_k_scores
         )