feat: add GraphRAG framework and integrate TuGraph (#1506)

Co-authored-by: KingSkyLi <15566300566@163.com>
Co-authored-by: aries_ckt <916701291@qq.com>
Co-authored-by: Fangyin Cheng <staneyffer@gmail.com>

examples/rag/graph_rag_example.py

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+import asyncio
+import os
+
+from dbgpt.configs.model_config import ROOT_PATH
+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.vector_store.base import VectorStoreConfig
+from dbgpt.storage.vector_store.connector import VectorStoreConnector
+
+"""GraphRAG example.
+    pre-requirements:
+    * Set LLM config (url/sk) in `.env`.
+    * Setup/startup TuGraph from: https://github.com/TuGraph-family/tugraph-db
+    * Config TuGraph following the format below. 
+    ```
+    GRAPH_STORE_TYPE=TuGraph
+    TUGRAPH_HOST=127.0.0.1
+    TUGRAPH_PORT=7687
+    TUGRAPH_USERNAME=admin
+    TUGRAPH_PASSWORD=73@TuGraph
+    ```
+
+    Examples:
+        ..code-block:: shell
+            python examples/rag/graph_rag_example.py
+"""
+
+
+def _create_kg_connector():
+    """Create knowledge graph connector."""
+    return VectorStoreConnector(
+        vector_store_type="KnowledgeGraph",
+        vector_store_config=VectorStoreConfig(
+            name="graph_rag_test",
+            embedding_fn=None,
+            llm_client=OpenAILLMClient(),
+            model_name="gpt-3.5-turbo",
+        ),
+    )
+
+
+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()
+    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,
+    )
+    assembler.persist()
+    # 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")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())