Delete backend

backend/llm_utils.py

@@ -1,34 +0,0 @@
-
-"""Utilities for loading the ZeroSearch simulation model and performing simulated searches."""
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import functools
-
-MODEL_NAME = "sunhaonlp/SearchSimulation_14B"
-
-@functools.lru_cache(maxsize=1)
-def _load_search_pipe():
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_NAME,
-        trust_remote_code=True,
-        device_map="auto"
-    )
-    return pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-        do_sample=False,
-        temperature=0.0,
-    )
-
-def simulate_search(query: str, k: int = 5):
-    """Generate *k* synthetic documents for *query*."""
-    pipe = _load_search_pipe()
-    prompt = f"SearchSimulation:\nQuery: {query}\nDocuments:"
-    outputs = pipe(prompt, num_return_sequences=k)
-    docs = []
-    for o in outputs:
-        text = o["generated_text"]
-        docs.append(text.split("Documents:")[-1].strip())
-    return docs

backend/main.py

@@ -1,52 +0,0 @@
-
-from fastapi import FastAPI
-from pydantic import BaseModel
-from .llm_utils import simulate_search
-from .umls_linker import link_umls
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import functools
-
-ANSWER_MODEL = "sunhaonlp/SearchSimulation_14B"
-
-@functools.lru_cache(maxsize=1)
-def _load_answer_pipe():
-    tokenizer = AutoTokenizer.from_pretrained(ANSWER_MODEL)
-    model = AutoModelForCausalLM.from_pretrained(
-        ANSWER_MODEL,
-        trust_remote_code=True,
-        device_map="auto"
-    )
-    return pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=256,
-        do_sample=False,
-        temperature=0.0,
-    )
-
-class Query(BaseModel):
-    question: str
-
-app = FastAPI(
-    title="ZeroSearch Medical Q&A API",
-    description="Ask clinical questions; get answers with UMLS links, no external search APIs.",
-    version="0.1.0",
-)
-
-@app.post("/ask")
-def ask(query: Query):
-    docs = simulate_search(query.question, k=5)
-    context = "\n\n".join(docs)
-    prompt = (
-        "Answer the medical question strictly based on the provided context.\n\n"
-        f"Context:\n{context}\n\n"
-        f"Question: {query.question}\nAnswer:"
-    )
-    answer_pipe = _load_answer_pipe()
-    answer = (
-        answer_pipe(prompt, num_return_sequences=1)[0]["generated_text"]
-        .split("Answer:")[-1].strip()
-    )
-    umls = link_umls(answer)
-    return {"answer": answer, "docs": docs, "umls": umls}

backend/umls_linker.py

@@ -1,19 +0,0 @@
-
-"""Simple UMLS linker using SciSpacy."""
-import spacy
-from scispacy.linking import UmlsEntityLinker
-
-nlp = spacy.load("en_core_sci_lg")
-linker = UmlsEntityLinker(resolve_abbreviations=True, disambiguate=True)
-nlp.add_pipe(linker)
-
-def link_umls(text: str):
-    doc = nlp(text)
-    results = []
-    for ent in doc.ents:
-        for cui, score in ent._.kb_ents:
-            results.append(
-                {"text": ent.text, "cui": cui, "score": score}
-            )
-            break  # take top candidate
-    return results