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import faiss
from fastapi import FastAPI
import torch
import pandas as pd
from collections import defaultdict
import pandas as pd
import jsonlines
from transformers import AutoModel, AutoTokenizer
import uvicorn
import asyncio
from pydantic import BaseModel
from typing import List, Optional
import re
import json
import asyncio
import argparse
app = FastAPI()
model_name = "openbmb/MiniCPM-Embedding-Light"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
co = faiss.GpuMultipleClonerOptions()
co.shard = True
co.useFloat16 = True
faiss_index_path = "./index/index_abstract.faiss" # Replace with your FAISS index path"
faiss_index = faiss.read_index(faiss_index_path)
faiss_index = faiss.index_cpu_to_all_gpus(faiss_index,co=co)
corpus_path = "./data/arxiv.jsonl"
with jsonlines.open(corpus_path) as f:
paper_data = list(f)
paper_dict = {}
item_key = "text"
index_path = "./index/str_int_ids_abstract.csv"
index_df = pd.read_csv(index_path,converters={0: lambda x: str(x),1: lambda x: int(x)})
index_df.columns = ["str_id", "int_id"]
index_dict = index_df.set_index("int_id")["str_id"].to_dict()
for item in paper_data:
paper_dict[item["bibkey"]] = item[item_key]
class QueryRequest(BaseModel):
queries: List[str]
topk: Optional[int] = None
return_scores: bool = False
class MessageRequest(BaseModel):
tool_calls: List
topk: Optional[int] = 10
@app.post("/")
async def search_text_batch(request:MessageRequest):
tool_calls = request.tool_calls
topk = request.topk
results = []
finalize_indices = []
search_engine_indices = []
for i in range(len(tool_calls)):
try:
tool_calls[i]["name"]
except KeyError:
finalize_indices.append(i)
continue
if tool_calls[i]["name"] == "search_engine":
search_engine_indices.append(i)
elif tool_calls[i]["name"] == "finalize":
finalize_indices.append(i)
else:
finalize_indices.append(i)
tasks = []
for i in range(len(tool_calls)):
if i in search_engine_indices:
tasks.append(call_search_engine(tool_calls[i], topk))
search_task_results = await asyncio.gather(*tasks)
num_search = 0
num_finalize = 0
for i in range(len(tool_calls)):
if i in finalize_indices:
search_keywords, bibkeys,abstracts, done, score = "",[], [], True, 0.0
num_finalize += 1
elif i in search_engine_indices:
search_keywords, bibkeys, abstracts, done, score = search_task_results[num_search]
num_search += 1
titles = []
for abstract in abstracts:
try:
title = abstract.split("\n")[1]
title = title.split(":")[1].strip()
titles.append(title)
except:
titles.append("")
results.append({ "search_keywords":search_keywords, "summarys":abstracts, "done":done, "score":score, "titles":titles, "bibkeys":bibkeys})
return results
def extract_tool_call(text: str):
text = text.strip()
pattern = r"<tool_call>(.*?)</tool_call>"
match = re.search(pattern, text, re.DOTALL)
if not match:
return None
tool_text = match.group(1)
try:
tool_call = json.loads(tool_text)
except json.JSONDecodeError:
return None
return tool_call if isinstance(tool_call, dict) else None
def get_response(queries,ref):
text_raw = paper_dict[str(ref)]
text_raw = tokenizer(text_raw, max_length=8192, truncation=True)
text_raw = tokenizer.decode(text_raw["input_ids"])
response = text_raw
response = f"bibkey: {str(ref)}\n"+response
return response
async def call_search_engine(tool_call, topk=10):
try:
queries = tool_call["arguments"]["query"]
if isinstance(queries, str):
queries = [queries]
else:
queries = list(queries)
if len(queries) == 0:
return "", [], [], False, 0.0
results = defaultdict(dict)
query_embedding_to_text,_ = model.encode_query(queries, max_length=512, show_progress_bar=False)
_,results = faiss_index.search(query_embedding_to_text, topk)
result2query = {}
merge_rrf = defaultdict(float)
for i in range(len(results)):
for j in range(len(results[i])):
merge_rrf[results[i][j]] += 1/(j+1)
result2query[results[i][j]] = queries[i]
results = sorted(merge_rrf.items(), key=lambda x: x[1], reverse=True)
results = [x[0] for x in results][:topk]
# new_queries = [result2query[result] for result in results]
queries = ",".join(queries)
# bibkeys = [str(results[i]) for i in range(len(results))]
bibkeys = [str(index_dict[results[i]]) for i in range(len(results))]
response = [f"bibkey: {bibkey}\n{paper_dict[bibkey]}" for bibkey in bibkeys]
return queries,bibkeys , response, False, 0.0
except Exception as e:
print(f"Error in call_search_engine: {e}")
return "",[], [], False, 0.0
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run the FastAPI application.")
parser.add_argument("--port", type=int, default=8400, help="Port to run the FastAPI application on.")
args = parser.parse_args()
uvicorn.run(app, host="0.0.0.0", port=args.port) |