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04703f2b3a
Co-authored-by: dong <dongzhancai@iie2.com> Co-authored-by: Zhancai Dong <dongzhancai1@jd.com> Co-authored-by: aries_ckt <916701291@qq.com>
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
import asyncio
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import os
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from dbgpt.configs.model_config import ROOT_PATH
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from dbgpt.model.proxy.llms.chatgpt import OpenAILLMClient
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from dbgpt.rag import ChunkParameters
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from dbgpt.rag.assembler import EmbeddingAssembler
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from dbgpt.rag.knowledge import KnowledgeFactory
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from dbgpt.storage.knowledge_graph.knowledge_graph import BuiltinKnowledgeGraphConfig
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from dbgpt.storage.vector_store.base import VectorStoreConfig
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from dbgpt.storage.vector_store.connector import VectorStoreConnector
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"""GraphRAG example.
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pre-requirements:
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* Set LLM config (url/sk) in `.env`.
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* Setup/startup TuGraph from: https://github.com/TuGraph-family/tugraph-db
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* Config TuGraph following the format below.
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```
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GRAPH_STORE_TYPE=TuGraph
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TUGRAPH_HOST=127.0.0.1
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TUGRAPH_PORT=7687
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TUGRAPH_USERNAME=admin
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TUGRAPH_PASSWORD=73@TuGraph
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```
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Examples:
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..code-block:: shell
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python examples/rag/graph_rag_example.py
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"""
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def _create_kg_connector():
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"""Create knowledge graph connector."""
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return VectorStoreConnector(
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vector_store_type="KnowledgeGraph",
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vector_store_config=VectorStoreConfig(
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name="graph_rag_test",
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embedding_fn=None,
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llm_client=OpenAILLMClient(),
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model_name="gpt-3.5-turbo",
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),
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)
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async def main():
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file_path = os.path.join(ROOT_PATH, "examples/test_files/tranformers_story.md")
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knowledge = KnowledgeFactory.from_file_path(file_path)
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vector_connector = _create_kg_connector()
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chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
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# get embedding assembler
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assembler = EmbeddingAssembler.load_from_knowledge(
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knowledge=knowledge,
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chunk_parameters=chunk_parameters,
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vector_store_connector=vector_connector,
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)
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assembler.persist()
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# get embeddings retriever
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retriever = assembler.as_retriever(3)
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chunks = await retriever.aretrieve_with_scores(
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"What actions has Megatron taken ?", score_threshold=0.3
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)
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print(f"embedding rag example results:{chunks}")
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vector_connector.delete_vector_name("graph_rag_test")
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if __name__ == "__main__":
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asyncio.run(main())
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