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added ability to develop on my local rtx2060
Browse files- requirements.txt +2 -1
- utils/llama_index_utils.py +33 -6
requirements.txt
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gradio
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llama-index==0.6.9
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openai==0.27.0
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gradio[full]
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llama-index==0.6.9
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openai==0.27.0
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transformers
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utils/llama_index_utils.py
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_index = None
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def
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global _index
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if _index is None:
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docs = SimpleDirectoryReader(data_path).load_data()
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return _index
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def query_symptoms(prompt: str, top_k: int = 5):
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idx = build_index()
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import os
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from transformers import pipeline
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from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex, LLMPredictor, OpenAI
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_index = None
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def get_llm_predictor():
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"""
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Return an LLMPredictor configured for local GPU (transformers) if USE_LOCAL_GPU=1,
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otherwise uses OpenAI.
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"""
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if os.getenv("USE_LOCAL_GPU") == "1":
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# Local GPU inference using GPT-2 as an example
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local_pipe = pipeline("text-generation", model="gpt2", device=0)
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return LLMPredictor(llm=local_pipe)
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# Default to OpenAI provider
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return LLMPredictor(llm=OpenAI(temperature=0))
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def build_index(data_path="data/icd10cm_tabular_2025"): # noqa: C901
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"""
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Build (or retrieve cached) GPTVectorStoreIndex from ICD documents.
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"""
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global _index
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if _index is None:
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# Load documents from the ICD data directory
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docs = SimpleDirectoryReader(data_path).load_data()
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# Initialize the index with chosen LLM predictor
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predictor = get_llm_predictor()
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_index = GPTVectorStoreIndex.from_documents(docs, llm_predictor=predictor)
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return _index
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def query_symptoms(prompt: str, top_k: int = 5):
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"""
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Query the index for the given symptom prompt and return the result.
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"""
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idx = build_index()
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# Create a query engine with the same predictor
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predictor = get_llm_predictor()
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query_engine = idx.as_query_engine(similarity_top_k=top_k, llm_predictor=predictor)
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return query_engine.query(prompt)
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