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55 lines
1.5 KiB
Python
55 lines
1.5 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_ext.rag import ChunkParameters
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from dbgpt_ext.rag.assembler import EmbeddingAssembler
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from dbgpt_ext.rag.knowledge import KnowledgeFactory
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from dbgpt_ext.storage.full_text.elasticsearch import (
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ElasticDocumentStore,
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ElasticsearchStoreConfig,
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)
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"""Keyword rag example.
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pre-requirements:
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set your Elasticsearch environment.
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Examples:
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..code-block:: shell
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python examples/rag/keyword_rag_example.py
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"""
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def _create_es_connector():
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"""Create es connector."""
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config = ElasticsearchStoreConfig(
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uri="localhost",
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port="9200",
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user="elastic",
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password="dbgpt",
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)
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return ElasticDocumentStore(config, name="keyword_rag_test")
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async def main():
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file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
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knowledge = KnowledgeFactory.from_file_path(file_path)
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keyword_store = _create_es_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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index_store=keyword_store,
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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("what is awel talk about", 0.3)
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print(f"keyword rag example results:{chunks}")
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if __name__ == "__main__":
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asyncio.run(main())
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