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83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
import asyncio
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import os
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from typing import Optional
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from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH, ROOT_PATH
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from dbgpt.core import Embeddings
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from dbgpt.rag.chunk_manager import ChunkParameters
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from dbgpt.rag.embedding import DefaultEmbeddingFactory
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from dbgpt.rag.evaluation import RetrieverEvaluator
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from dbgpt.rag.knowledge import KnowledgeFactory
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from dbgpt.rag.operators import EmbeddingRetrieverOperator
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from dbgpt.serve.rag.assembler.embedding import EmbeddingAssembler
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from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
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from dbgpt.storage.vector_store.connector import VectorStoreConnector
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def _create_embeddings(
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model_name: Optional[str] = "text2vec-large-chinese",
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) -> Embeddings:
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"""Create embeddings."""
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return DefaultEmbeddingFactory(
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default_model_name=os.path.join(MODEL_PATH, model_name),
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).create()
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def _create_vector_connector(
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embeddings: Embeddings, space_name: str = "retriever_evaluation_example"
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) -> VectorStoreConnector:
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"""Create vector connector."""
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return VectorStoreConnector.from_default(
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"Chroma",
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vector_store_config=ChromaVectorConfig(
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name=space_name,
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persist_path=os.path.join(PILOT_PATH, "data"),
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),
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embedding_fn=embeddings,
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)
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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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embeddings = _create_embeddings()
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vector_connector = _create_vector_connector(embeddings)
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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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dataset = [
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{
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"query": "what is awel talk about",
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"contexts": [
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"Through the AWEL API, you can focus on the development"
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" of business logic for LLMs applications without paying "
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"attention to cumbersome model and environment details."
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],
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},
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]
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evaluator = RetrieverEvaluator(
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operator_cls=EmbeddingRetrieverOperator,
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embeddings=embeddings,
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operator_kwargs={
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"top_k": 5,
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"vector_store_connector": vector_connector,
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},
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)
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results = await evaluator.evaluate(dataset)
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for result in results:
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for metric in result:
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print("Metric:", metric.metric_name)
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print("Question:", metric.query)
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print("Score:", metric.score)
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print(f"Results:\n{results}")
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if __name__ == "__main__":
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asyncio.run(main())
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