更新了依赖包

backend/pyproject.toml

@@ -17,7 +17,7 @@ aiostream = "^0.6.2"
 llama-index = "0.10.63"
 cachetools = "^5.3.3"
 protobuf = "4.25.4"
-nltk = "^3.8.2"
+nltk = "^3.9.1"
 jieba = "^0.42.1"
 
 #arize-phoenix = "^4.12.0"
@@ -35,6 +35,7 @@ chroma="^0.2.0"
 llama-index-vector-stores-chroma = "^0.1.10"
 llama-index-readers-json = "^0.1.5"
 llama-index-retrievers-bm25 = "^0.2.2"
+llama-index-experimental = "^0.2.0"
 
 duckduckgo_search = "^6.2.6"
 
@@ -62,6 +63,12 @@ version = "^0.8"
 version = "0.0.7"
 
 
+
+[[tool.poetry.source]]
+name = "mirrors"
+url = "https://pypi.tuna.tsinghua.edu.cn/simple/"
+priority = "default"
+
 [build-system]
 requires = [ "poetry-core" ]
 build-backend = "poetry.core.masonry.api"

backend/test1/evaluator.py

@@ -0,0 +1,81 @@
+from app.observability import init_observability
+from app.settings import init_settings
+from dotenv import load_dotenv
+
+import nest_asyncio
+nest_asyncio.apply()
+
+load_dotenv()
+
+
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import (
+    VectorStoreIndex,
+    SimpleDirectoryReader,
+    Response,
+)
+from llama_index.core.evaluation import (
+    FaithfulnessEvaluator, 
+    DatasetGenerator, 
+    CorrectnessEvaluator, 
+    SemanticSimilarityEvaluator,)
+
+
+
+init_settings()
+init_observability()
+
+faith_evaluator_qwen = FaithfulnessEvaluator() #诚实度评测
+corr_evaluator_qwen = CorrectnessEvaluator() #准确率评测
+Seman_evaluator_qwen = SemanticSimilarityEvaluator()#嵌入相似度评估
+
+documents = SimpleDirectoryReader("D:/LLM_model/text2sql/zjdataai-app-test/backend/data-test").load_data()
+
+splitter = SentenceSplitter(chunk_size=512)
+
+
+vector_index = VectorStoreIndex.from_documents(
+    documents, transformations=[splitter],
+)
+
+
+# # 运行评估
+# query_engine = vector_index.as_query_engine()
+# response_vector = query_engine.query("工程监理费的金额是多少？")
+# eval_result = evaluator_qwen.evaluate_response(response=response_vector)
+
+# print(response_vector)
+# print(eval_result)
+
+
+question_generator = DatasetGenerator.from_documents(documents)
+eval_questions = question_generator.generate_questions_from_nodes(5)
+print(eval_questions)
+
+import asyncio
+
+async def evaluate_query_engine_async(query_engine, questions):
+    c = [query_engine.aquery(q) for q in questions]
+    gathering_future = asyncio.gather(*c)
+    results = await gathering_future
+    #print(results)
+    
+    total_correct = 0
+    for r in results:
+        eval_result = (
+            1 if faith_evaluator_qwen.evaluate_response(response=r).passing else 0
+        )
+        total_correct += eval_result
+
+    return total_correct, len(results)
+
+def evaluate_query_engine(query_engine, questions):
+    loop = asyncio.get_event_loop()
+    correct, total = loop.run_until_complete(evaluate_query_engine_async(query_engine, questions))
+    return correct, total
+
+# 使用 evaluate_query_engine 函数
+vector_query_engine = vector_index.as_query_engine()
+correct, total = evaluate_query_engine(vector_query_engine, eval_questions[:5])
+
+print(f"score: {correct}/{total}")