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https://github.com/NirDiamant/RAG_Techniques.git
synced 2025-04-07 00:48:52 +03:00
fix chunk size utilization
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@@ -7,6 +7,7 @@ from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
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from llama_index.core.prompts import PromptTemplate
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from llama_index.core.evaluation import DatasetGenerator, FaithfulnessEvaluator, RelevancyEvaluator
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from llama_index.llms.openai import OpenAI
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from llama_index.core.node_parser import SentenceSplitter
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# Apply asyncio fix for Jupyter notebooks
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nest_asyncio.apply()
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@@ -44,7 +45,8 @@ def evaluate_response_time_and_accuracy(chunk_size, eval_questions, eval_documen
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Settings.llm = llm
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# Create vector index
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vector_index = VectorStoreIndex.from_documents(eval_documents)
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splitter = SentenceSplitter(chunk_size=chunk_size)
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vector_index = VectorStoreIndex.from_documents(eval_documents, transformations=[splitter])
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# Build query engine
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query_engine = vector_index.as_query_engine(similarity_top_k=5)
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