Merge pull request '增加新的前端子模块' (#4) from dev into main

Reviewed-on: #4

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 = "^3.9.1"
+nltk = "^3.8.2"
 jieba = "^0.42.1"
 
 #arize-phoenix = "^4.12.0"
@@ -35,7 +35,6 @@ 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"
 
@@ -63,12 +62,6 @@ 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

@@ -1,81 +0,0 @@
-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}")