Move some vars to globals (#11017)

### What problem does this PR solve?

As title.

### Type of change

- [x] Refactoring

---------

Signed-off-by: Jin Hai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
2025-11-05 14:14:38 +08:00
committed by GitHub
parent cf9611c96f
commit 1a9215bc6f
35 changed files with 185 additions and 164 deletions

View File

@ -20,10 +20,10 @@ import time
import argparse
from collections import defaultdict
from common import globals
from common.constants import LLMType
from api.db.services.llm_service import LLMBundle
from api.db.services.knowledgebase_service import KnowledgebaseService
from api import settings
from common.misc_utils import get_uuid
from rag.nlp import tokenize, search
from ranx import evaluate
@ -52,7 +52,7 @@ class Benchmark:
run = defaultdict(dict)
query_list = list(qrels.keys())
for query in query_list:
ranks = settings.retriever.retrieval(query, self.embd_mdl, self.tenant_id, [self.kb.id], 1, 30,
ranks = globals.retriever.retrieval(query, self.embd_mdl, self.tenant_id, [self.kb.id], 1, 30,
0.0, self.vector_similarity_weight)
if len(ranks["chunks"]) == 0:
print(f"deleted query: {query}")
@ -77,9 +77,9 @@ class Benchmark:
def init_index(self, vector_size: int):
if self.initialized_index:
return
if settings.docStoreConn.indexExist(self.index_name, self.kb_id):
settings.docStoreConn.deleteIdx(self.index_name, self.kb_id)
settings.docStoreConn.createIdx(self.index_name, self.kb_id, vector_size)
if globals.docStoreConn.indexExist(self.index_name, self.kb_id):
globals.docStoreConn.deleteIdx(self.index_name, self.kb_id)
globals.docStoreConn.createIdx(self.index_name, self.kb_id, vector_size)
self.initialized_index = True
def ms_marco_index(self, file_path, index_name):
@ -114,13 +114,13 @@ class Benchmark:
docs_count += len(docs)
docs, vector_size = self.embedding(docs)
self.init_index(vector_size)
settings.docStoreConn.insert(docs, self.index_name, self.kb_id)
globals.docStoreConn.insert(docs, self.index_name, self.kb_id)
docs = []
if docs:
docs, vector_size = self.embedding(docs)
self.init_index(vector_size)
settings.docStoreConn.insert(docs, self.index_name, self.kb_id)
globals.docStoreConn.insert(docs, self.index_name, self.kb_id)
return qrels, texts
def trivia_qa_index(self, file_path, index_name):
@ -155,12 +155,12 @@ class Benchmark:
docs_count += len(docs)
docs, vector_size = self.embedding(docs)
self.init_index(vector_size)
settings.docStoreConn.insert(docs,self.index_name)
globals.docStoreConn.insert(docs,self.index_name)
docs = []
docs, vector_size = self.embedding(docs)
self.init_index(vector_size)
settings.docStoreConn.insert(docs, self.index_name)
globals.docStoreConn.insert(docs, self.index_name)
return qrels, texts
def miracl_index(self, file_path, corpus_path, index_name):
@ -210,12 +210,12 @@ class Benchmark:
docs_count += len(docs)
docs, vector_size = self.embedding(docs)
self.init_index(vector_size)
settings.docStoreConn.insert(docs, self.index_name)
globals.docStoreConn.insert(docs, self.index_name)
docs = []
docs, vector_size = self.embedding(docs)
self.init_index(vector_size)
settings.docStoreConn.insert(docs, self.index_name)
globals.docStoreConn.insert(docs, self.index_name)
return qrels, texts
def save_results(self, qrels, run, texts, dataset, file_path):