feat(ChatKnowledge): ChatKnowledge Support Keyword Retrieve (#1624)

Co-authored-by: Fangyin Cheng <staneyffer@gmail.com>

examples/rag/cross_encoder_rerank_example.py

@@ -17,24 +17,21 @@ from dbgpt.rag.assembler import EmbeddingAssembler
 from dbgpt.rag.embedding import DefaultEmbeddingFactory
 from dbgpt.rag.knowledge import KnowledgeFactory
 from dbgpt.rag.retriever.rerank import CrossEncoderRanker
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 
 def _create_vector_connector():
     """Create vector connector."""
-    print(f"persist_path:{os.path.join(PILOT_PATH, 'data')}")
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="example_cross_encoder_rerank",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="embedding_rag_test",
         embedding_fn=DefaultEmbeddingFactory(
             default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
+    return ChromaStore(config)
+
 
 async def main():
     file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
@@ -45,7 +42,7 @@ async def main():
     assembler = EmbeddingAssembler.load_from_knowledge(
         knowledge=knowledge,
         chunk_parameters=chunk_parameters,
-        vector_store_connector=vector_connector,
+        index_store=vector_connector,
     )
     assembler.persist()
     # get embeddings retriever
@@ -57,7 +54,7 @@ async def main():
     print("before rerank results:\n")
     for i, chunk in enumerate(chunks):
         print(f"----{i+1}.chunk content:{chunk.content}\n score:{chunk.score}")
-    # cross-encoder rerank
+    # cross-encoder rerankpython
     cross_encoder_model = os.path.join(MODEL_PATH, "bge-reranker-base")
     rerank = CrossEncoderRanker(topk=3, model=cross_encoder_model)
     new_chunks = rerank.rank(chunks, query=query)

examples/rag/db_schema_rag_example.py

@@ -4,8 +4,7 @@ from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH
 from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnector
 from dbgpt.rag.assembler import DBSchemaAssembler
 from dbgpt.rag.embedding import DefaultEmbeddingFactory
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 """DB struct rag example.
     pre-requirements:
@@ -46,27 +45,27 @@ def _create_temporary_connection():
 
 def _create_vector_connector():
     """Create vector connector."""
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="db_schema_vector_store_name",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="dbschema_rag_test",
         embedding_fn=DefaultEmbeddingFactory(
             default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
+    return ChromaStore(config)
+
 
 if __name__ == "__main__":
     connection = _create_temporary_connection()
-    vector_connector = _create_vector_connector()
+    index_store = _create_vector_connector()
     assembler = DBSchemaAssembler.load_from_connection(
         connector=connection,
-        vector_store_connector=vector_connector,
+        index_store=index_store,
     )
     assembler.persist()
     # get db schema retriever
     retriever = assembler.as_retriever(top_k=1)
     chunks = retriever.retrieve("show columns from user")
     print(f"db schema rag example results:{[chunk.content for chunk in chunks]}")
+    index_store.delete_vector_name("dbschema_rag_test")

examples/rag/embedding_rag_example.py

@@ -6,8 +6,7 @@ from dbgpt.rag import ChunkParameters
 from dbgpt.rag.assembler import EmbeddingAssembler
 from dbgpt.rag.embedding import DefaultEmbeddingFactory
 from dbgpt.rag.knowledge import KnowledgeFactory
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 """Embedding rag example.
     pre-requirements:
@@ -24,28 +23,27 @@ from dbgpt.storage.vector_store.connector import VectorStoreConnector
 
 def _create_vector_connector():
     """Create vector connector."""
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="db_schema_vector_store_name",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="embedding_rag_test",
         embedding_fn=DefaultEmbeddingFactory(
             default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
+    return ChromaStore(config)
+
 
 async def main():
     file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
     knowledge = KnowledgeFactory.from_file_path(file_path)
-    vector_connector = _create_vector_connector()
+    vector_store = _create_vector_connector()
     chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
     # get embedding assembler
     assembler = EmbeddingAssembler.load_from_knowledge(
         knowledge=knowledge,
         chunk_parameters=chunk_parameters,
-        vector_store_connector=vector_connector,
+        index_store=vector_store,
     )
     assembler.persist()
     # get embeddings retriever

examples/rag/graph_rag_example.py

@@ -6,9 +6,11 @@ from dbgpt.model.proxy.llms.chatgpt import OpenAILLMClient
 from dbgpt.rag import ChunkParameters
 from dbgpt.rag.assembler import EmbeddingAssembler
 from dbgpt.rag.knowledge import KnowledgeFactory
-from dbgpt.storage.knowledge_graph.knowledge_graph import BuiltinKnowledgeGraphConfig
-from dbgpt.storage.vector_store.base import VectorStoreConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.rag.retriever import RetrieverStrategy
+from dbgpt.storage.knowledge_graph.knowledge_graph import (
+    BuiltinKnowledgeGraph,
+    BuiltinKnowledgeGraphConfig,
+)
 
 """GraphRAG example.
     pre-requirements:
@@ -31,9 +33,8 @@ from dbgpt.storage.vector_store.connector import VectorStoreConnector
 
 def _create_kg_connector():
     """Create knowledge graph connector."""
-    return VectorStoreConnector(
-        vector_store_type="KnowledgeGraph",
-        vector_store_config=VectorStoreConfig(
+    return BuiltinKnowledgeGraph(
+        config=BuiltinKnowledgeGraphConfig(
             name="graph_rag_test",
             embedding_fn=None,
             llm_client=OpenAILLMClient(),
@@ -45,22 +46,23 @@ def _create_kg_connector():
 async def main():
     file_path = os.path.join(ROOT_PATH, "examples/test_files/tranformers_story.md")
     knowledge = KnowledgeFactory.from_file_path(file_path)
-    vector_connector = _create_kg_connector()
+    graph_store = _create_kg_connector()
     chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
     # get embedding assembler
-    assembler = EmbeddingAssembler.load_from_knowledge(
+    assembler = await EmbeddingAssembler.aload_from_knowledge(
         knowledge=knowledge,
         chunk_parameters=chunk_parameters,
-        vector_store_connector=vector_connector,
+        index_store=graph_store,
+        retrieve_strategy=RetrieverStrategy.GRAPH,
     )
-    assembler.persist()
+    await assembler.apersist()
     # get embeddings retriever
     retriever = assembler.as_retriever(3)
     chunks = await retriever.aretrieve_with_scores(
         "What actions has Megatron taken ?", score_threshold=0.3
     )
     print(f"embedding rag example results:{chunks}")
-    vector_connector.delete_vector_name("graph_rag_test")
+    graph_store.delete_vector_name("graph_rag_test")
 
 
 if __name__ == "__main__":

examples/rag/keyword_rag_example.py

@@ -0,0 +1,55 @@
+import asyncio
+import os
+
+from dbgpt.configs.model_config import ROOT_PATH
+from dbgpt.rag import ChunkParameters
+from dbgpt.rag.assembler import EmbeddingAssembler
+from dbgpt.rag.knowledge import KnowledgeFactory
+from dbgpt.storage.full_text.elasticsearch import (
+    ElasticDocumentConfig,
+    ElasticDocumentStore,
+)
+
+"""Keyword rag example.
+    pre-requirements:
+    set your Elasticsearch environment.
+
+    Examples:
+        ..code-block:: shell
+            python examples/rag/keyword_rag_example.py
+"""
+
+
+def _create_es_connector():
+    """Create es connector."""
+    config = ElasticDocumentConfig(
+        name="keyword_rag_test",
+        uri="localhost",
+        port="9200",
+        user="elastic",
+        password="dbgpt",
+    )
+
+    return ElasticDocumentStore(config)
+
+
+async def main():
+    file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
+    knowledge = KnowledgeFactory.from_file_path(file_path)
+    keyword_store = _create_es_connector()
+    chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
+    # get embedding assembler
+    assembler = EmbeddingAssembler.load_from_knowledge(
+        knowledge=knowledge,
+        chunk_parameters=chunk_parameters,
+        index_store=keyword_store,
+    )
+    assembler.persist()
+    # get embeddings retriever
+    retriever = assembler.as_retriever(3)
+    chunks = await retriever.aretrieve_with_scores("what is awel talk about", 0.3)
+    print(f"keyword rag example results:{chunks}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/rag/metadata_filter_example.py

@@ -14,35 +14,33 @@ from dbgpt.rag import ChunkParameters
 from dbgpt.rag.assembler import EmbeddingAssembler
 from dbgpt.rag.embedding import DefaultEmbeddingFactory
 from dbgpt.rag.knowledge import KnowledgeFactory
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 from dbgpt.storage.vector_store.filters import MetadataFilter, MetadataFilters
 
 
 def _create_vector_connector():
     """Create vector connector."""
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="example_metadata_filter_name",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="metadata_rag_test",
         embedding_fn=DefaultEmbeddingFactory(
             default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
+    return ChromaStore(config)
+
 
 async def main():
     file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
     knowledge = KnowledgeFactory.from_file_path(file_path)
-    vector_connector = _create_vector_connector()
+    vector_store = _create_vector_connector()
     chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_MARKDOWN_HEADER")
     # get embedding assembler
     assembler = EmbeddingAssembler.load_from_knowledge(
         knowledge=knowledge,
         chunk_parameters=chunk_parameters,
-        vector_store_connector=vector_connector,
+        index_store=vector_store,
     )
     assembler.persist()
     # get embeddings retriever
@@ -54,6 +52,7 @@ async def main():
         "what is awel talk about", 0.0, filters
     )
     print(f"embedding rag example results:{chunks}")
+    vector_store.delete_vector_name("metadata_rag_test")
 
 
 if __name__ == "__main__":

examples/rag/rag_embedding_api_example.py

@@ -31,8 +31,7 @@ from dbgpt.rag import ChunkParameters
 from dbgpt.rag.assembler import EmbeddingAssembler
 from dbgpt.rag.embedding import OpenAPIEmbeddings
 from dbgpt.rag.knowledge import KnowledgeFactory
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 
 def _create_embeddings(
@@ -54,33 +53,32 @@ def _create_embeddings(
 
 def _create_vector_connector():
     """Create vector connector."""
-
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="example_embedding_api_vector_store_name",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="embedding_api_rag_test",
         embedding_fn=_create_embeddings(),
     )
 
+    return ChromaStore(config)
+
 
 async def main():
     file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
     knowledge = KnowledgeFactory.from_file_path(file_path)
-    vector_connector = _create_vector_connector()
+    vector_store = _create_vector_connector()
     chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
     # get embedding assembler
     assembler = EmbeddingAssembler.load_from_knowledge(
         knowledge=knowledge,
         chunk_parameters=chunk_parameters,
-        vector_store_connector=vector_connector,
+        index_store=vector_store,
     )
     assembler.persist()
     # get embeddings retriever
     retriever = assembler.as_retriever(3)
     chunks = await retriever.aretrieve_with_scores("what is awel talk about", 0.3)
     print(f"embedding rag example results:{chunks}")
+    vector_store.delete_vector_name("embedding_api_rag_test")
 
 
 if __name__ == "__main__":

examples/rag/retriever_evaluation_example.py

@@ -15,8 +15,7 @@ from dbgpt.rag.evaluation.retriever import (
 )
 from dbgpt.rag.knowledge import KnowledgeFactory
 from dbgpt.rag.operators import EmbeddingRetrieverOperator
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 
 def _create_embeddings(
@@ -28,19 +27,16 @@ def _create_embeddings(
     ).create()
 
 
-def _create_vector_connector(
-    embeddings: Embeddings, space_name: str = "retriever_evaluation_example"
-) -> VectorStoreConnector:
+def _create_vector_connector(embeddings: Embeddings):
     """Create vector connector."""
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name=space_name,
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="embedding_rag_test",
         embedding_fn=embeddings,
     )
 
+    return ChromaStore(config)
+
 
 async def main():
     file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
@@ -52,7 +48,7 @@ async def main():
     assembler = EmbeddingAssembler.load_from_knowledge(
         knowledge=knowledge,
         chunk_parameters=chunk_parameters,
-        vector_store_connector=vector_connector,
+        index_store=vector_connector,
     )
     assembler.persist()
 

examples/rag/simple_dbschema_retriever_example.py

@@ -11,7 +11,7 @@
     retriever_task = DBSchemaRetrieverOperator(
         connector=_create_temporary_connection()
         top_k=1,
-        vector_store_connector=vector_store_connector
+        index_store=vector_store_connector
     )
     ```
 
@@ -27,31 +27,29 @@ from typing import Dict, List
 
 from dbgpt._private.config import Config
 from dbgpt._private.pydantic import BaseModel, Field
-from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, PILOT_PATH
+from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH
 from dbgpt.core import Chunk
-from dbgpt.core.awel import DAG, HttpTrigger, InputOperator, JoinOperator, MapOperator
+from dbgpt.core.awel import DAG, HttpTrigger, JoinOperator, MapOperator
 from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnector
 from dbgpt.rag.embedding import DefaultEmbeddingFactory
 from dbgpt.rag.operators import DBSchemaAssemblerOperator, DBSchemaRetrieverOperator
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 CFG = Config()
 
 
 def _create_vector_connector():
     """Create vector connector."""
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="vector_name",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=os.path.join(PILOT_PATH, "data"),
+        name="vector_name",
         embedding_fn=DefaultEmbeddingFactory(
-            default_model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
+            default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
+    return ChromaStore(config)
+
 
 def _create_temporary_connection():
     """Create a temporary database connection for testing."""
@@ -104,17 +102,17 @@ with DAG("simple_rag_db_schema_example") as dag:
     )
     request_handle_task = RequestHandleOperator()
     query_operator = MapOperator(lambda request: request["query"])
-    vector_store_connector = _create_vector_connector()
+    index_store = _create_vector_connector()
     connector = _create_temporary_connection()
     assembler_task = DBSchemaAssemblerOperator(
         connector=connector,
-        vector_store_connector=vector_store_connector,
+        index_store=index_store,
     )
     join_operator = JoinOperator(combine_function=_join_fn)
     retriever_task = DBSchemaRetrieverOperator(
         connector=_create_temporary_connection(),
         top_k=1,
-        vector_store_connector=vector_store_connector,
+        index_store=index_store,
     )
     result_parse_task = MapOperator(lambda chunks: [chunk.content for chunk in chunks])
     trigger >> assembler_task >> join_operator

examples/rag/simple_rag_embedding_example.py

@@ -16,30 +16,28 @@ from typing import Dict, List
 
 from dbgpt._private.config import Config
 from dbgpt._private.pydantic import BaseModel, Field
-from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, PILOT_PATH
+from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, MODEL_PATH, PILOT_PATH
 from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
 from dbgpt.rag.embedding import DefaultEmbeddingFactory
 from dbgpt.rag.knowledge import KnowledgeType
 from dbgpt.rag.operators import EmbeddingAssemblerOperator, KnowledgeOperator
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 CFG = Config()
 
 
-def _create_vector_connector() -> VectorStoreConnector:
+def _create_vector_connector():
     """Create vector connector."""
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="vector_name",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="embedding_rag_test",
         embedding_fn=DefaultEmbeddingFactory(
-            default_model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
+            default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
+    return ChromaStore(config)
+
 
 class TriggerReqBody(BaseModel):
     url: str = Field(..., description="url")
@@ -75,10 +73,10 @@ with DAG("simple_sdk_rag_embedding_example") as dag:
     )
     request_handle_task = RequestHandleOperator()
     knowledge_operator = KnowledgeOperator(knowledge_type=KnowledgeType.URL.name)
-    vector_connector = _create_vector_connector()
+    vector_store = _create_vector_connector()
     url_parser_operator = MapOperator(map_function=lambda x: x["url"])
     embedding_operator = EmbeddingAssemblerOperator(
-        vector_store_connector=vector_connector,
+        index_store=vector_store,
     )
     output_task = ResultOperator()
     (

examples/rag/simple_rag_retriever_example.py

@@ -31,7 +31,7 @@ from typing import Dict, List
 
 from dbgpt._private.config import Config
 from dbgpt._private.pydantic import BaseModel, Field
-from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, PILOT_PATH
+from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH
 from dbgpt.core import Chunk
 from dbgpt.core.awel import DAG, HttpTrigger, JoinOperator, MapOperator
 from dbgpt.model.proxy import OpenAILLMClient
@@ -41,8 +41,7 @@ from dbgpt.rag.operators import (
     QueryRewriteOperator,
     RerankOperator,
 )
-from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
-from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
 
 CFG = Config()
 
@@ -78,21 +77,19 @@ def _context_join_fn(context_dict: Dict, chunks: List[Chunk]) -> Dict:
 
 def _create_vector_connector():
     """Create vector connector."""
-    model_name = os.getenv("EMBEDDING_MODEL", "text2vec")
-    return VectorStoreConnector.from_default(
-        "Chroma",
-        vector_store_config=ChromaVectorConfig(
-            name="vector_name",
-            persist_path=os.path.join(PILOT_PATH, "data"),
-        ),
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+        name="embedding_rag_test",
         embedding_fn=DefaultEmbeddingFactory(
-            default_model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
+            default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
+    return ChromaStore(config)
+
 
 with DAG("simple_sdk_rag_retriever_example") as dag:
-    vector_connector = _create_vector_connector()
+    vector_store = _create_vector_connector()
     trigger = HttpTrigger(
         "/examples/rag/retrieve", methods="POST", request_body=TriggerReqBody
     )
@@ -102,11 +99,11 @@ with DAG("simple_sdk_rag_retriever_example") as dag:
     rewrite_operator = QueryRewriteOperator(llm_client=OpenAILLMClient())
     retriever_context_operator = EmbeddingRetrieverOperator(
         top_k=3,
-        vector_store_connector=vector_connector,
+        index_store=vector_store,
     )
     retriever_operator = EmbeddingRetrieverOperator(
         top_k=3,
-        vector_store_connector=vector_connector,
+        index_store=vector_store,
     )
     rerank_operator = RerankOperator()
     model_parse_task = MapOperator(lambda out: out.to_dict())