feat(RAG):add cross-encoder rerank (#1442)

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

examples/rag/cross_encoder_rerank_example.py

@@ -0,0 +1,70 @@
+"""This example demonstrates how to use the cross-encoder reranker
+to rerank the retrieved chunks.
+The cross-encoder reranker is a neural network model that takes a query
+and a chunk as input and outputs a score that represents the relevance of the chunk
+to the query.
+
+Download pretrained cross-encoder models can be found at https://huggingface.co/models.
+Example:
+    python examples/rag/cross_encoder_rerank_example.py
+"""
+import asyncio
+import os
+
+from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH, ROOT_PATH
+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.rag.retriever.rerank import CrossEncoderRanker
+from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
+from dbgpt.storage.vector_store.connector import VectorStoreConnector
+
+
+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"),
+        ),
+        embedding_fn=DefaultEmbeddingFactory(
+            default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
+        ).create(),
+    )
+
+
+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()
+    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,
+    )
+    assembler.persist()
+    # get embeddings retriever
+    retriever = assembler.as_retriever(3)
+    # create metadata filter
+    query = "what is awel talk about"
+    chunks = await retriever.aretrieve_with_scores(query, 0.3)
+
+    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_model = os.path.join(MODEL_PATH, "bge-reranker-base")
+    rerank = CrossEncoderRanker(topk=3, model=cross_encoder_model)
+    new_chunks = rerank.rank(chunks, query=query)
+    print("after cross-encoder rerank results:\n")
+    for i, chunk in enumerate(new_chunks):
+        print(f"----{i+1}.chunk content:{chunk.content}\n score:{chunk.score}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())