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import os
import json
from transformers import pipeline
from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex, LLMPredictor, OpenAI
_index = None
def query_symptoms_tool(prompt_json: str):
# parse “prompt_json” into Python dict and call your existing query_symptoms()
data = json.loads(prompt_json)
return query_symptoms(data["raw_input"])
def get_llm_predictor():
"""
Return an LLMPredictor configured for local GPU (transformers) if USE_LOCAL_GPU=1,
otherwise uses OpenAI.
"""
if os.getenv("USE_LOCAL_GPU") == "1":
# Local GPU inference using GPT-2 as an example
local_pipe = pipeline("text-generation", model="gpt2", device=0)
return LLMPredictor(llm=local_pipe)
# Default to OpenAI provider
return LLMPredictor(llm=OpenAI(temperature=0))
def build_index(data_path="data/icd10cm_tabular_2025"): # noqa: C901
"""
Build (or retrieve cached) GPTVectorStoreIndex from ICD documents.
"""
global _index
if _index is None:
# Load documents from the ICD data directory
docs = SimpleDirectoryReader(data_path).load_data()
# Initialize the index with chosen LLM predictor
predictor = get_llm_predictor()
_index = GPTVectorStoreIndex.from_documents(docs, llm_predictor=predictor)
return _index
def query_symptoms(prompt: str, top_k: int = 5):
"""
Query the index for the given symptom prompt and return the result.
"""
idx = build_index()
# Create a query engine with the same predictor
predictor = get_llm_predictor()
query_engine = idx.as_query_engine(similarity_top_k=top_k, llm_predictor=predictor)
return query_engine.query(prompt)
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