From 03a40ca1a53f7cae814df6cae3ecb9f67fa8e9e5 Mon Sep 17 00:00:00 2001
From: KingSkyLi <15566300566@163.com>
Date: Fri, 30 Aug 2024 15:01:42 +0800
Subject: [PATCH] delete graph_rag_summary;

---
 examples/rag/graph_rag_summary_example.py | 79 -----------------------
 1 file changed, 79 deletions(-)
 delete mode 100644 examples/rag/graph_rag_summary_example.py

diff --git a/examples/rag/graph_rag_summary_example.py b/examples/rag/graph_rag_summary_example.py
deleted file mode 100644
index 2ddb5d70d..000000000
--- a/examples/rag/graph_rag_summary_example.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import asyncio
-import os
-
-from dbgpt.configs.model_config import ROOT_PATH, MODEL_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.rag.embedding import DefaultEmbeddingFactory
-from dbgpt.rag.retriever import RetrieverStrategy
-from dbgpt.storage.knowledge_graph.community_summary import (
-    CommunitySummaryKnowledgeGraph,
-    CommunitySummaryKnowledgeGraphConfig,
-)
-from dbgpt.storage.knowledge_graph.knowledge_graph import BuiltinKnowledgeGraphConfig
-
-
-
-"""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
-    GRAPH_COMMUNITY_SUMMARY_ENABLED=True
-    TUGRAPH_PLUGIN_NAMES=leiden
-    ```
-    Examples:
-        ..code-block:: shell
-            python examples/rag/graph_rag_summary_example.py
-"""
-
-
-def _create_kg_connector():
-    """Create knowledge graph connector."""
-    return CommunitySummaryKnowledgeGraph(
-        config=CommunitySummaryKnowledgeGraphConfig(
-            name="graph_rag_summary_test",
-            embedding_fn=DefaultEmbeddingFactory(
-            default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
-        ).create(),
-            llm_client=OpenAILLMClient(
-                api_base=os.getenv('PROXY_SERVER_URL'),
-                api_key=os.getenv('PROXY_API_KEY'),
-            ),
-            model_name="gpt-3.5-turbo",
-        ),
-    )
-
-
-async def main():
-    file_path = os.path.join(ROOT_PATH, "examples/test_files/graph_rag_mini.md")
-    knowledge = KnowledgeFactory.from_file_path(file_path)
-    graph_store = _create_kg_connector()
-    chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
-    # get embedding assembler
-    assembler = await EmbeddingAssembler.aload_from_knowledge(
-        knowledge=knowledge,
-        chunk_parameters=chunk_parameters,
-        index_store=graph_store,
-        retrieve_strategy=RetrieverStrategy.GRAPH,
-    )
-    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}")
-    graph_store.delete_vector_name("graph_rag_summary_test")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())