chore: import upstream snapshot with attribution

examples/rag/embedding_rag_example.py

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+import asyncio
+import os
+
+from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH, ROOT_PATH
+from dbgpt.rag.embedding import DefaultEmbeddingFactory
+from dbgpt_ext.rag import ChunkParameters
+from dbgpt_ext.rag.assembler import EmbeddingAssembler
+from dbgpt_ext.rag.knowledge import KnowledgeFactory
+from dbgpt_ext.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
+
+"""Embedding rag example.
+    pre-requirements:
+    set your embedding model path in your example code.
+    ```
+    embedding_model_path = "{your_embedding_model_path}"
+    ```
+
+    Examples:
+        ..code-block:: shell
+            python examples/rag/embedding_rag_example.py
+"""
+
+
+def _create_vector_connector():
+    """Create vector connector."""
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+    )
+
+    return ChromaStore(
+        config,
+        name="embedding_rag_test",
+        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_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,
+        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}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())