镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
69 行
1.9 KiB
Python
69 行
1.9 KiB
Python
def validate_path():
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import os, sys
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os.path.dirname(__file__)
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root_dir_assume = os.path.abspath(os.path.dirname(__file__) + "/..")
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os.chdir(root_dir_assume)
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sys.path.append(root_dir_assume)
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validate_path() # validate path so you can run from base directory
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# """
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# Test 1
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# """
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# from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
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# from shared_utils.connect_void_terminal import get_chat_default_kwargs
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# oaiem = OpenAiEmbeddingModel()
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# chat_kwargs = get_chat_default_kwargs()
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# llm_kwargs = chat_kwargs['llm_kwargs']
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# llm_kwargs.update({
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# 'llm_model': "text-embedding-3-small"
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# })
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# res = oaiem.compute_embedding("你好", llm_kwargs)
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# print(res)
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"""
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Test 2
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"""
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from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
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from shared_utils.connect_void_terminal import get_chat_default_kwargs
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
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from llama_index.core.ingestion import run_transformations
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chat_kwargs = get_chat_default_kwargs()
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llm_kwargs = chat_kwargs['llm_kwargs']
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llm_kwargs.update({
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'llm_model': "text-embedding-3-small"
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})
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embed_model = OpenAiEmbeddingModel(llm_kwargs)
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## dir
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documents = SimpleDirectoryReader("private_upload/rag_test/").load_data()
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## single files
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# from llama_index.core import Document
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# text_list = [text1, text2, ...]
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# documents = [Document(text=t) for t in text_list]
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vsi = GptacVectorStoreIndex.default_vector_store(embed_model=embed_model)
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documents_nodes = run_transformations(
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documents, # type: ignore
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vsi._transformations,
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show_progress=True
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)
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index = vsi.insert_nodes(documents_nodes)
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query_engine = index.as_query_engine()
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response = query_engine.query("Some question about the data should go here")
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print(response)
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