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- from GPTSimpleVectorIndex to GPTVectorIndex
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@ -5,7 +5,7 @@ import gradio as gr
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from langchain.agents import AgentType, initialize_agent, load_tools
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from llama_index import (
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Document,
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GPTSimpleVectorIndex,
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GPTVectorStoreIndex,
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LangchainEmbedding,
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LLMPredictor,
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ServiceContext,
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@ -33,7 +33,7 @@ def knowledged_qa_demo(text_list):
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service_context = ServiceContext.from_defaults(
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llm_predictor=llm_predictor, embed_model=embed_model
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)
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index = GPTSimpleVectorIndex.from_documents(
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index = GPTVectorStoreIndex.from_documents(
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documents, service_context=service_context
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)
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return index
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@ -4,7 +4,7 @@
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import logging
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import sys
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from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader
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from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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@ -13,7 +13,7 @@ logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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documents = SimpleDirectoryReader("data").load_data()
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# split the document to chunk, max token size=500, convert chunk to vector
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index = GPTSimpleVectorIndex(documents)
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index = GPTVectorStoreIndex(documents)
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# save index
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index.save_to_disk("index.json")
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@ -6,7 +6,7 @@ from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.llms.base import LLM
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from llama_index import (
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GPTListIndex,
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GPTSimpleVectorIndex,
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GPTVectorStoreIndex,
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LangchainEmbedding,
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LLMPredictor,
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PromptHelper,
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