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)