File size: 12,414 Bytes
bcceea4 1ccdc0b bcceea4 d1f5505 1ccdc0b bcceea4 7dd2c52 bcceea4 7dd2c52 bcceea4 d62c791 bcceea4 1ccdc0b bcceea4 1ccdc0b d62c791 bcceea4 904dfee bcceea4 58d2397 904dfee 58d2397 bcceea4 58d2397 bcceea4 d62c791 bcceea4 59cb995 d62c791 59cb995 9083f85 d62c791 9083f85 61b12b4 d62c791 61b12b4 d62c791 61b12b4 526a8e8 67a1218 526a8e8 67a1218 526a8e8 67a1218 526a8e8 67a1218 526a8e8 d62c791 bcceea4 1ccdc0b fe7e528 bcceea4 fe7e528 1ccdc0b bcceea4 d62c791 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 | import sys, os, re
from concurrent.futures import ThreadPoolExecutor, as_completed
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from langchain_groq import ChatGroq
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from retrieval.pubmed import fetch_pubmed
def get_llm():
from langchain_openai import ChatOpenAI
return ChatOpenAI(
model="Qwen/Qwen2.5-72B-Instruct",
temperature=0,
openai_api_key=os.environ.get("VLLM_API_KEY", "EMPTY"),
openai_api_base=os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1"),
)
def llm_invoke_with_retry(llm, prompt, max_retries=5):
import time
for attempt in range(max_retries):
try:
return llm.invoke(prompt)
except Exception as e:
if "429" in str(e) or "rate_limit" in str(e).lower():
wait = 10 * (attempt + 1)
print(f"[ARIA] Rate limit hit, waiting {wait}s (attempt {attempt+1}/{max_retries})")
time.sleep(wait)
else:
raise
raise RuntimeError("Max retries exceeded on rate limit")
@tool
def PubMedSearch(query: str) -> str:
"""Searches PubMed for biomedical literature abstracts."""
results = fetch_pubmed(query, max_results=5)
if not results:
return "No abstracts found for this query."
out = []
for r in results:
out.append("[PMID " + r["pmid"] + "]\n" + r["abstract"])
return "\n\n".join(out)
def run_query_architect(user_question):
llm = get_llm()
prompt = (
"You are a biomedical librarian expert in PubMed search strategy.\n"
"Given this clinical question, generate exactly 5 distinct PubMed search queries using "
"MeSH terminology and clinical keywords to maximise literature coverage.\n"
"Return ONLY a numbered list 1-5, one query per line, no explanations.\n\n"
"Question: " + user_question
)
response = llm_invoke_with_retry(llm, prompt)
raw_lines = response.content.strip().split("\n")
queries = []
for line in raw_lines:
clean = re.sub(r"^[\d]+[\.)\s]+", "", line.strip())
if clean:
queries.append(clean)
return queries[:5]
def run_literature_scout(queries):
from retrieval.pubmed import fetch_europepmc
all_papers = {}
import time
for q in queries:
time.sleep(0.4)
for r in fetch_pubmed(q, max_results=5):
if r["pmid"] not in all_papers:
all_papers[r["pmid"]] = r
time.sleep(0.4)
for r in fetch_europepmc(q, max_results=3):
if r["pmid"] not in all_papers:
all_papers[r["pmid"]] = r
return all_papers
def run_evidence_synthesiser(user_question, papers):
llm = get_llm()
parts = []
for pmid, p in list(papers.items())[:6]:
title = p.get("title", "N/A")
abstract = p["abstract"]
parts.append("[PMID " + pmid + "]\nTitle: " + title + "\n" + abstract)
corpus = "\n\n".join(parts)
prompt = (
"You are a senior biomedical researcher writing a structured evidence synthesis.\n"
"Answer the clinical question using ONLY this structure:\n\n"
"## Background\n"
"Brief context (2-3 sentences).\n\n"
"## Key Findings\n"
"Most important findings with PMID citations inline.\n\n"
"## Level of Evidence\n"
"Rate: Strong / Moderate / Preliminary. Justify briefly.\n\n"
"## Conflicting Evidence\n"
"Any contradictions across studies.\n\n"
"## Research Gaps\n"
"What the literature does not answer.\n\n"
"## Clinical Implications\n"
"What this means for practice or future research.\n\n"
"Clinical Question: " + user_question + "\n\n"
"Retrieved Literature:\n" + corpus + "\n\n"
"Be precise and cite PMIDs throughout."
)
response = llm_invoke_with_retry(llm, prompt)
return response.content
def run_citation_builder(papers):
result_lines = []
for i, (pmid, p) in enumerate(papers.items(), 1):
title = p.get("title", "Title unavailable")
authors = p.get("authors", "Authors unavailable")
journal = p.get("journal", "Journal unavailable")
year = p.get("year", "n.d.")
result_lines.append(
str(i) + ". " + authors + " (" + year + "). " + title + ". " + journal + ". PMID: " + pmid
)
return "\n".join(result_lines)
def run_confidence_scorer(synthesis):
llm = get_llm()
prompt = (
"You are a critical appraiser of biomedical evidence.\n"
"Given this synthesis, score each section for evidence quality.\n"
"Return ONLY valid JSON, no markdown, no explanation:\n"
"{\n"
' "Background": {"score": 8, "rationale": "one sentence"},\n'
' "Key Findings": {"score": 7, "rationale": "one sentence"},\n'
' "Level of Evidence": {"score": 6, "rationale": "one sentence"},\n'
' "Conflicting Evidence": {"score": 5, "rationale": "one sentence"},\n'
' "Research Gaps": {"score": 7, "rationale": "one sentence"},\n'
' "Clinical Implications": {"score": 6, "rationale": "one sentence"}\n'
"}\n\n"
"Scores: 8-10 = strong evidence, 5-7 = moderate, 1-4 = weak/preliminary.\n\n"
"Synthesis:\n" + synthesis
)
response = llm_invoke_with_retry(llm, prompt)
import json
text = response.content.strip()
text = text.replace("```json", "").replace("```", "").strip()
return json.loads(text)
def run_selective_review(user_question, selected_papers):
llm = get_llm()
parts = []
for pmid, p in selected_papers.items():
title = p.get("title", "N/A")
abstract = p.get("abstract", "")[:400]
authors = p.get("authors", "")
year = p.get("year", "")
parts.append("[PMID " + pmid + "] " + authors + " (" + year + "). " + title + "\n" + abstract)
corpus = "\n\n".join(parts)
prompt = (
"You are an academic writer producing a literature review paragraph.\n"
"Write a single cohesive academic paragraph (200-300 words) that synthesises "
"the following selected papers in relation to this question.\n"
"Cite papers inline by PMID in parentheses e.g. (PMID: 12345678).\n"
"Write in formal academic prose. No bullet points. No headings.\n\n"
"Question: " + user_question + "\n\n"
"Selected Papers:\n" + corpus
)
response = llm_invoke_with_retry(llm, prompt)
return response.content
def run_predictive_model(user_question, synthesis):
llm = get_llm()
prompt = (
"You are a biomedical futurist analyzing research trends.\n"
"Based on this evidence synthesis, provide two short forecasts:\n\n"
"## Constructive Forecast\n"
"2-3 sentences: What directions does the current evidence suggest the field is moving toward? "
"What findings are likely to be confirmed or expanded in future research?\n\n"
"## Destructive Forecast\n"
"2-3 sentences: Which current assumptions, treatments, or paradigms does the evidence suggest "
"may be challenged, overturned, or significantly revised in coming years?\n\n"
"IMPORTANT: Always produce both sections even if evidence is limited. Never ask for more input.\n"
"Be specific and grounded in the evidence. No speculation beyond what the data implies.\n\n"
"Clinical Question: " + user_question + "\n\n"
"Synthesis:\n" + synthesis
)
response = llm_invoke_with_retry(llm, prompt)
return response.content
def run_table_extractor(user_question, synthesis, papers):
llm = get_llm()
paper_list = []
for pmid, p in list(papers.items())[:10]:
authors = p.get("authors", "")
first_author = authors.split(",")[0].strip() if authors else "Unknown"
paper_list.append("PMID " + pmid + ": " + first_author + " - " + p.get("title", "N/A") + " (" + p.get("year", "") + ")")
papers_str = "\n".join(paper_list)
prompt = (
"You are a biomedical data extractor.\n"
"From this synthesis and paper list, extract a comparison table.\n"
"Return ONLY valid JSON, no markdown, no explanation.\n"
"Format:\n"
"{\n"
' "title": "Comparison of Methods/Treatments",\n'
' "columns": ["Study (PMID)", "Method/Treatment", "Key Metric", "Outcome", "Year"],\n'
' "rows": [\n'
' ["Smith et al. (PMID: 12345)", "CNN", "Accuracy: 95%", "Positive", "2024"],\n'
' ...\n'
' ]\n'
"}\n\n"
"Rules:\n"
"- Maximum 8 rows, NO duplicate rows\n"
"- Each study should appear at most once\n"
"- Use the actual first author surname from the paper list, never write 'Author'\n"
"- Only include rows where you have concrete data from the synthesis\n"
"- If no specific metrics exist, use concise descriptive outcomes\n"
"- Never invent data\n\n"
"Clinical Question: " + user_question + "\n\n"
"Papers:\n" + papers_str + "\n\n"
"Synthesis:\n" + synthesis[:1500]
)
response = llm.invoke(prompt)
import json
text = response.content.strip().replace("```json", "").replace("```", "").strip()
return json.loads(text)
def run_prisma_filter(user_question, papers):
llm = get_llm()
import json
paper_list = []
for pmid, p in papers.items():
paper_list.append(
"PMID " + pmid + ": " + p.get("title", "N/A") + "\n" +
p.get("abstract", "")[:200]
)
corpus = "\n\n".join(paper_list)
prompt = (
"You are a systematic review methodologist applying PRISMA screening criteria.\n"
"For each paper, decide if it should be INCLUDED or EXCLUDED for answering this clinical question.\n"
"Return ONLY valid JSON, no markdown, no explanation.\n"
"Format:\n"
"{\n"
' "decisions": [\n'
' {"pmid": "12345678", "decision": "included", "reason": "one sentence"},\n'
' {"pmid": "87654321", "decision": "excluded", "reason": "one sentence"}\n'
' ]\n'
"}\n\n"
"Inclusion criteria: directly relevant to the clinical question, has empirical data or clinical findings.\n"
"Exclusion criteria: off-topic, editorial, commentary without data, animal studies if human data exists.\n\n"
"Clinical Question: " + user_question + "\n\n"
"Papers:\n" + corpus
)
response = llm_invoke_with_retry(llm, prompt)
text = response.content.strip().replace("```json", "").replace("```", "").strip()
data = json.loads(text)
result = {}
for d in data["decisions"]:
pmid = d["pmid"]
if pmid in papers:
result[pmid] = {
**papers[pmid],
"included": d["decision"] == "included",
"reason": d["reason"]
}
for pmid in papers:
if pmid not in result:
result[pmid] = {**papers[pmid], "included": True, "reason": "Not reviewed"}
return result
def run_pipeline(user_question):
print("[1/4] Query Architect: generating search queries...")
queries = run_query_architect(user_question)
print(" Generated " + str(len(queries)) + " queries")
print("[2/4] Literature Scout: fetching PubMed in parallel...")
papers = run_literature_scout(queries)
print(" Retrieved " + str(len(papers)) + " unique papers")
print("[3/4] Evidence Synthesiser: building structured synthesis...")
synthesis = run_evidence_synthesiser(user_question, papers)
print("[4/4] Citation Builder: formatting references...")
citations = run_citation_builder(papers)
return {
"question": user_question,
"queries": queries,
"paper_count": len(papers),
"synthesis": synthesis,
"citations": citations,
"papers": papers
}
def build_agent():
llm = get_llm()
return create_react_agent(llm, [PubMedSearch])
if __name__ == "__main__":
question = "What are the most effective ML methods for epilepsy seizure detection from EEG signals?"
result = run_pipeline(question)
print("\n=== SYNTHESIS ===")
print(result["synthesis"])
print("\n=== REFERENCES ===")
print(result["citations"]) |