From 328882505e6011dd60b2f503f8fb92aed84dbba5 Mon Sep 17 00:00:00 2001 From: HuangHai <10402852@qq.com> Date: Tue, 19 Aug 2025 10:41:58 +0800 Subject: [PATCH] 'commit' --- dsSchoolBuddy/ElasticSearch/T2_Vector.py | 9 +- dsSchoolBuddy/ElasticSearch/T3_InsertData.py | 2 - .../ElasticSearch/T7_XiangLiangQuery.py | 3 +- .../ElasticSearch/Utils/EsSearchUtil.py | 262 +++++++++++------- .../ElasticSearch/Utils/VectorDBUtil.py | 125 +++++++++ .../ElasticSearch/Utils/VectorUtil.py | 115 -------- .../__pycache__/EsSearchUtil.cpython-310.pyc | Bin 11475 -> 12197 bytes .../__pycache__/VectorDBUtil.cpython-310.pyc | Bin 0 -> 4136 bytes 8 files changed, 294 insertions(+), 222 deletions(-) create mode 100644 dsSchoolBuddy/ElasticSearch/Utils/VectorDBUtil.py delete mode 100644 dsSchoolBuddy/ElasticSearch/Utils/VectorUtil.py create mode 100644 dsSchoolBuddy/ElasticSearch/Utils/__pycache__/VectorDBUtil.cpython-310.pyc diff --git a/dsSchoolBuddy/ElasticSearch/T2_Vector.py b/dsSchoolBuddy/ElasticSearch/T2_Vector.py index 774e3599..c805b7c3 100644 --- a/dsSchoolBuddy/ElasticSearch/T2_Vector.py +++ b/dsSchoolBuddy/ElasticSearch/T2_Vector.py @@ -1,5 +1,5 @@ # pip install pydantic requests -from ElasticSearch.Utils.VectorUtil import text_to_vector_db, query_vector_db +from ElasticSearch.Utils.VectorDBUtil import VectorDBUtil def main(): @@ -16,12 +16,15 @@ def main(): 随着建筑技术的发展，高性能混凝土、自密实混凝土、再生骨料混凝土等新型混凝土不断涌现，为土木工程领域提供了更多的选择。""" + # 创建工具实例 + vector_util = VectorDBUtil() + # 调用文本入库功能 - vector_store, doc_count, split_count = text_to_vector_db(long_text) + vector_util.text_to_vector_db(long_text) # 调用文本查询功能 query = "混凝土" - reranked_results = query_vector_db(vector_store, query, k=4) + reranked_results = vector_util.query_vector_db(query, k=4) # 打印所有查询结果及其可信度 print("最终查询结果：") diff --git a/dsSchoolBuddy/ElasticSearch/T3_InsertData.py b/dsSchoolBuddy/ElasticSearch/T3_InsertData.py index 63985aa7..a5c0e76d 100644 --- a/dsSchoolBuddy/ElasticSearch/T3_InsertData.py +++ b/dsSchoolBuddy/ElasticSearch/T3_InsertData.py @@ -1,5 +1,3 @@ -import warnings - from Config import Config from ElasticSearch.Utils.EsSearchUtil import EsSearchUtil diff --git a/dsSchoolBuddy/ElasticSearch/T7_XiangLiangQuery.py b/dsSchoolBuddy/ElasticSearch/T7_XiangLiangQuery.py index 7e9cfc90..f91115bb 100644 --- a/dsSchoolBuddy/ElasticSearch/T7_XiangLiangQuery.py +++ b/dsSchoolBuddy/ElasticSearch/T7_XiangLiangQuery.py @@ -32,8 +32,7 @@ if __name__ == "__main__": print(f"3. 前3维向量值: {query_embedding[:3]}") print("4. 正在执行Elasticsearch向量搜索...") - vector_results = search_util.search_by_vector(query_embedding, k=5) - vector_hits = vector_results['hits']['hits'] + vector_hits = search_util.search_by_vector(query_embedding, k=5) print(f"5. 向量搜索结果数量: {len(vector_hits)}") # 向量结果重排 diff --git a/dsSchoolBuddy/ElasticSearch/Utils/EsSearchUtil.py b/dsSchoolBuddy/ElasticSearch/Utils/EsSearchUtil.py index 55b40a26..717cb22b 100644 --- a/dsSchoolBuddy/ElasticSearch/Utils/EsSearchUtil.py +++ b/dsSchoolBuddy/ElasticSearch/Utils/EsSearchUtil.py @@ -185,33 +185,33 @@ class EsSearchUtil: # 2. 从连接池获取连接 conn = search_util.es_pool.get_connection() - # 3. 检查索引是否存在，不存在则创建 + # # 3. 检查索引是否存在，不存在则创建 index_name = Config.ES_CONFIG['index_name'] - if not conn.indices.exists(index=index_name): - # 定义mapping结构 - mapping = { - "mappings": { - "properties": { - "embedding": { - "type": "dense_vector", - "dims": 1024, # 根据实际embedding维度调整 - "index": True, - "similarity": "l2_norm" - }, - "user_input": {"type": "text"}, - "tags": { - "type": "object", - "properties": { - "tags": {"type": "keyword"}, - "full_content": {"type": "text"} - } - }, - "timestamp": {"type": "date"} - } - } - } - conn.indices.create(index=index_name, body=mapping) - print(f"索引 '{index_name}' 创建成功") + # if not conn.indices.exists(index=index_name): + # # 定义mapping结构 + # mapping = { + # "mappings": { + # "properties": { + # "embedding": { + # "type": "dense_vector", + # "dims": Config.EMBED_DIM, # 根据实际embedding维度调整 + # "index": True, + # "similarity": "l2_norm" + # }, + # "user_input": {"type": "text"}, + # "tags": { + # "type": "object", + # "properties": { + # "tags": {"type": "keyword"}, + # "full_content": {"type": "text"} + # } + # }, + # "timestamp": {"type": "date"} + # } + # } + # } + # conn.indices.create(index=index_name, body=mapping) + # print(f"索引 '{index_name}' 创建成功") # 4. 切割文本 text_chunks = self.split_text_into_chunks(long_text) @@ -285,108 +285,128 @@ class EsSearchUtil: query_embedding = embeddings.embed_query(query) return query_embedding - def rerank_results(self, query: str, results: List[Dict]) -> List[Tuple[Dict, float]]: + def rerank_results(self, query: str, results: list) -> list: """ - 对搜索结果进行重排 + 使用重排模型对搜索结果进行重排 参数: query: 查询文本 results: 搜索结果列表 返回: - list: 重排后的结果列表，每个元素是(文档, 分数)元组 + list: 重排后的结果列表，每个元素是(文档对象, 分数)的元组 """ - if len(results) <= 1: - return [(doc, 1.0) for doc in results] - - # 准备重排请求数据 - rerank_data = { - "model": Config.RERANK_MODEL, - "query": query, - "documents": [doc['_source']['user_input'] for doc in results], - "top_n": len(results) - } - - # 调用API进行重排 - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {Config.RERANK_BINDING_API_KEY}" - } + if not results: + print("警告: 没有搜索结果可供重排") + return [] try: + # 准备重排请求数据 + # 确保doc是字典并包含'_source'和'user_input'字段 + documents = [] + valid_results = [] + for i, doc in enumerate(results): + if isinstance(doc, dict) and '_source' in doc and 'user_input' in doc['_source']: + documents.append(doc['_source']['user_input']) + valid_results.append(doc) + else: + print(f"警告: 结果项 {i} 格式不正确，跳过该结果") + print(f"结果项内容: {doc}") + + if not documents: + print("警告: 没有有效的文档可供重排") + # 返回原始结果，但转换为(结果, 分数)的元组格式 + return [(doc, doc.get('_score', 0.0)) for doc in results] + + rerank_data = { + "model": Config.RERANK_MODEL, + "query": query, + "documents": documents, + "top_n": len(documents) + } + + # 调用重排API + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {Config.RERANK_BINDING_API_KEY}" + } + response = requests.post(Config.RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data)) - response.raise_for_status() + response.raise_for_status() # 检查请求是否成功 rerank_result = response.json() # 处理重排结果 - reranked_docs_with_scores = [] + reranked_results = [] if "results" in rerank_result: for item in rerank_result["results"]: - # 尝试获取index和relevance_score字段 doc_idx = item.get("index") score = item.get("relevance_score", 0.0) - - # 如果找不到，尝试fallback到document和score字段 - if doc_idx is None: - doc_idx = item.get("document") - if score == 0.0: - score = item.get("score", 0.0) + if 0 <= doc_idx < len(valid_results): + result = valid_results[doc_idx] + reranked_results.append((result, score)) + else: + print("警告: 无法识别重排API响应格式") + # 返回原始结果，但转换为(结果, 分数)的元组格式 + reranked_results = [(doc, doc.get('_score', 0.0)) for doc in valid_results] - if doc_idx is not None and 0 <= doc_idx < len(results): - reranked_docs_with_scores.append((results[doc_idx], score)) - logger.debug(f"重排结果: 文档索引={doc_idx}, 分数={score}") - else: - logger.warning(f"重排结果项索引无效: {doc_idx}") + print(f"重排后结果数量：{len(reranked_results)}") + return reranked_results - # 如果没有有效的重排结果，返回原始结果 - if not reranked_docs_with_scores: - logger.warning("没有获取到有效的重排结果，返回原始结果") - return [(doc, 1.0) for doc in results] - - return reranked_docs_with_scores except Exception as e: - logger.error(f"重排失败: {str(e)}") - return [(doc, 1.0) for doc in results] + print(f"重排失败: {e}") + print("将使用原始搜索结果") + # 返回原始结果，但转换为(结果, 分数)的元组格式 + return [(doc, doc.get('_score', 0.0)) for doc in results] - def search_by_vector(self, query_embedding: list, k: int = 10) -> dict: + def search_by_vector(self, query_embedding: list, k: int = 10) -> list: """ - 在Elasticsearch中按向量搜索 + 根据向量进行相似性搜索 参数: query_embedding: 查询向量 - k: 返回结果数量 + k: 返回的结果数量 返回: - dict: 搜索结果 + list: 搜索结果列表 """ - # 从连接池获取连接 - conn = self.es_pool.get_connection() try: - # 构建向量搜索查询 - query = { - "query": { - "script_score": { - "query": { - "bool": { - "should": [], - "minimum_should_match": 0 - } - }, - "script": { - "source": "double score = cosineSimilarity(params.query_vector, 'embedding'); return score >= 0 ? score : 0", - "params": {"query_vector": query_embedding} - } - } - }, - "size": k - } + # 从连接池获取连接 + conn = self.es_pool.get_connection() + index_name = Config.ES_CONFIG['index_name'] + + # 执行向量搜索 + response = conn.search( + index=index_name, + body={ + "query": { + "script_score": { + "query": {"match_all": {}}, + "script": { + "source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0", + "params": { + "query_vector": query_embedding + } + } + } + }, + "size": k + } + ) + + # 提取结果 + # 确保我们提取的是 hits.hits 部分 + if 'hits' in response and 'hits' in response['hits']: + results = response['hits']['hits'] + print(f"向量搜索结果数量: {len(results)}") + return results + else: + print("警告: 向量搜索响应格式不正确") + print(f"响应内容: {response}") + return [] - # 执行查询 - response = conn.search(index=self.es_config['index_name'], body=query) - return response except Exception as e: - logger.error(f"向量搜索失败: {str(e)}") - raise + print(f"向量搜索失败: {e}") + return [] finally: # 释放连接回连接池 self.es_pool.release_connection(conn) @@ -404,11 +424,53 @@ class EsSearchUtil: return print(f"找到 {len(results)} 条结果:\n") - for i, (result, score) in enumerate(results, 1): + for i, item in enumerate(results, 1): print(f"结果 {i}:") - print(f"内容: {result['_source']['user_input']}") - if show_score: - print(f"分数: {score:.4f}") + try: + # 检查item是否为元组格式 (result, score) + if isinstance(item, tuple): + if len(item) >= 2: + result, score = item[0], item[1] + else: + result, score = item[0], 0.0 + else: + # 如果不是元组，假设item就是result + result = item + score = result.get('_score', 0.0) + + # 确保result是字典类型 + if not isinstance(result, dict): + print(f"警告: 结果项 {i} 不是字典类型，跳过显示") + print(f"结果项内容: {result}") + print("---") + continue + + # 尝试获取user_input内容 + if '_source' in result and 'user_input' in result['_source']: + content = result['_source']['user_input'] + print(f"内容: {content}") + elif 'user_input' in result: + content = result['user_input'] + print(f"内容: {content}") + else: + print(f"警告: 结果项 {i} 缺少'user_input'字段") + print(f"结果项内容: {result}") + print("---") + continue + + # 显示分数 + if show_score: + print(f"分数: {score:.4f}") + + # 如果有标签信息，也显示出来 + if '_source' in result and 'tags' in result['_source']: + tags = result['_source']['tags'] + if isinstance(tags, dict) and 'tags' in tags: + print(f"标签: {tags['tags']}") + + except Exception as e: + print(f"处理结果项 {i} 时出错: {str(e)}") + print(f"结果项内容: {item}") print("---") def merge_results(self, keyword_results: List[Tuple[Dict, float]], vector_results: List[Tuple[Dict, float]]) -> List[Tuple[Dict, float, str]]: diff --git a/dsSchoolBuddy/ElasticSearch/Utils/VectorDBUtil.py b/dsSchoolBuddy/ElasticSearch/Utils/VectorDBUtil.py new file mode 100644 index 00000000..2cb6ccd9 --- /dev/null +++ b/dsSchoolBuddy/ElasticSearch/Utils/VectorDBUtil.py @@ -0,0 +1,125 @@ +# pip install pydantic requests +from langchain_core.documents import Document +from langchain_core.vectorstores import InMemoryVectorStore +from langchain_openai import OpenAIEmbeddings +from langchain_text_splitters import RecursiveCharacterTextSplitter +from pydantic import SecretStr +import requests +import json +from Config.Config import ( + EMBED_MODEL_NAME, EMBED_BASE_URL, EMBED_API_KEY, + RERANK_MODEL, RERANK_BASE_URL, RERANK_BINDING_API_KEY +) + + +class VectorDBUtil: + """向量数据库工具类，提供文本向量化存储和查询功能""" + + def __init__(self): + """初始化向量数据库工具""" + # 初始化嵌入模型 + self.embeddings = OpenAIEmbeddings( + model=EMBED_MODEL_NAME, + base_url=EMBED_BASE_URL, + api_key=SecretStr(EMBED_API_KEY) # 包装成 SecretStr 类型 + ) + # 初始化向量存储 + self.vector_store = None + + def text_to_vector_db(self, text: str, chunk_size: int = 200, chunk_overlap: int = 0) -> tuple: + """ + 将文本存入向量数据库 + + 参数: + text: 要入库的文本 + chunk_size: 文本分割块大小 + chunk_overlap: 文本块重叠大小 + + 返回: + tuple: (向量存储对象, 文档数量, 分割后的文档块数量) + """ + # 创建文档对象 + docs = [Document(page_content=text, metadata={"source": "simulated_document"})] + doc_count = len(docs) + print(f"文档数量：{doc_count} 个") + + # 切割文档 + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True + ) + all_splits = text_splitter.split_documents(docs) + split_count = len(all_splits) + print(f"切割后的文档块数量：{split_count}") + + # 向量存储 + self.vector_store = InMemoryVectorStore(self.embeddings) + ids = self.vector_store.add_documents(documents=all_splits) + + return self.vector_store, doc_count, split_count + + def query_vector_db(self, query: str, k: int = 4) -> list: + """ + 从向量数据库查询文本 + + 参数: + query: 查询字符串 + k: 要返回的结果数量 + + 返回: + list: 重排后的结果列表，每个元素是(文档对象, 可信度分数)的元组 + """ + if not self.vector_store: + print("错误: 向量数据库未初始化，请先调用text_to_vector_db方法") + return [] + + # 向量查询 - 获取更多结果用于重排 + results = self.vector_store.similarity_search(query, k=k) + print(f"向量搜索结果数量：{len(results)}") + + # 存储重排后的文档和分数 + reranked_docs_with_scores = [] + + # 调用重排模型 + if len(results) > 1: + # 准备重排请求数据 + rerank_data = { + "model": RERANK_MODEL, + "query": query, + "documents": [doc.page_content for doc in results], + "top_n": len(results) + } + + # 调用SiliconFlow API进行重排 + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {RERANK_BINDING_API_KEY}" + } + + try: + response = requests.post(RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data)) + response.raise_for_status() # 检查请求是否成功 + rerank_result = response.json() + + # 处理重排结果，提取relevance_score + if "results" in rerank_result: + for item in rerank_result["results"]: + doc_idx = item.get("index") + score = item.get("relevance_score", 0.0) + if 0 <= doc_idx < len(results): + reranked_docs_with_scores.append((results[doc_idx], score)) + else: + print("警告: 无法识别重排API响应格式") + reranked_docs_with_scores = [(doc, 0.0) for doc in results] + + print(f"重排后结果数量：{len(reranked_docs_with_scores)}") + except Exception as e: + print(f"重排模型调用失败: {e}") + print("将使用原始搜索结果") + reranked_docs_with_scores = [(doc, 0.0) for doc in results] + else: + # 只有一个结果，无需重排 + reranked_docs_with_scores = [(doc, 1.0) for doc in results] # 单个结果可信度设为1.0 + + return reranked_docs_with_scores + + diff --git a/dsSchoolBuddy/ElasticSearch/Utils/VectorUtil.py b/dsSchoolBuddy/ElasticSearch/Utils/VectorUtil.py deleted file mode 100644 index fb1a42c9..00000000 --- a/dsSchoolBuddy/ElasticSearch/Utils/VectorUtil.py +++ /dev/null @@ -1,115 +0,0 @@ -# pip install pydantic requests -from langchain_core.documents import Document -from langchain_core.vectorstores import InMemoryVectorStore -from langchain_openai import OpenAIEmbeddings -from langchain_text_splitters import RecursiveCharacterTextSplitter -from pydantic import SecretStr -import requests -import json -from Config.Config import ( - EMBED_MODEL_NAME, EMBED_BASE_URL, EMBED_API_KEY, - RERANK_MODEL, RERANK_BASE_URL, RERANK_BINDING_API_KEY -) - -def text_to_vector_db(text: str, chunk_size: int = 200, chunk_overlap: int = 0) -> tuple: - """ - 将文本存入向量数据库 - - 参数: - text: 要入库的文本 - chunk_size: 文本分割块大小 - chunk_overlap: 文本块重叠大小 - - 返回: - tuple: (向量存储对象, 文档数量, 分割后的文档块数量) - """ - # 创建文档对象 - docs = [Document(page_content=text, metadata={"source": "simulated_document"})] - doc_count = len(docs) - print(f"文档数量：{doc_count} 个") - - # 切割文档 - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True - ) - all_splits = text_splitter.split_documents(docs) - split_count = len(all_splits) - print(f"切割后的文档块数量：{split_count}") - - # 嵌入模型 - embeddings = OpenAIEmbeddings( - model=EMBED_MODEL_NAME, - base_url=EMBED_BASE_URL, - api_key=SecretStr(EMBED_API_KEY) # 包装成 SecretStr 类型 - ) - - # 向量存储 - vector_store = InMemoryVectorStore(embeddings) - ids = vector_store.add_documents(documents=all_splits) - - return vector_store, doc_count, split_count - - -def query_vector_db(vector_store: InMemoryVectorStore, query: str, k: int = 4) -> list: - """ - 从向量数据库查询文本 - - 参数: - vector_store: 向量存储对象 - query: 查询字符串 - k: 要返回的结果数量 - - 返回: - list: 重排后的结果列表，每个元素是(文档对象, 可信度分数)的元组 - """ - # 向量查询 - 获取更多结果用于重排 - results = vector_store.similarity_search(query, k=k) - print(f"向量搜索结果数量：{len(results)}") - - # 存储重排后的文档和分数 - reranked_docs_with_scores = [] - - # 调用重排模型 - if len(results) > 1: - # 准备重排请求数据 - rerank_data = { - "model": RERANK_MODEL, - "query": query, - "documents": [doc.page_content for doc in results], - "top_n": len(results) - } - - # 调用SiliconFlow API进行重排 - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {RERANK_BINDING_API_KEY}" - } - - try: - response = requests.post(RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data)) - response.raise_for_status() # 检查请求是否成功 - rerank_result = response.json() - - # 处理重排结果，提取relevance_score - if "results" in rerank_result: - for item in rerank_result["results"]: - doc_idx = item.get("index") - score = item.get("relevance_score", 0.0) - if 0 <= doc_idx < len(results): - reranked_docs_with_scores.append((results[doc_idx], score)) - else: - print("警告: 无法识别重排API响应格式") - reranked_docs_with_scores = [(doc, 0.0) for doc in results] - - print(f"重排后结果数量：{len(reranked_docs_with_scores)}") - except Exception as e: - print(f"重排模型调用失败: {e}") - print("将使用原始搜索结果") - reranked_docs_with_scores = [(doc, 0.0) for doc in results] - else: - # 只有一个结果，无需重排 - reranked_docs_with_scores = [(doc, 1.0) for doc in results] # 单个结果可信度设为1.0 - - return reranked_docs_with_scores - - diff --git a/dsSchoolBuddy/ElasticSearch/Utils/__pycache__/EsSearchUtil.cpython-310.pyc b/dsSchoolBuddy/ElasticSearch/Utils/__pycache__/EsSearchUtil.cpython-310.pyc index f9158876c10cc14d8c160cf2c688b2e329422c67..baa3d6795d21468c4adf7c1e9841ea383bf3936c 100644 GIT binary patch delta 5148 zcmZ`-Yj6|S72dnMTCG-38$Wr9M+}0H010jCqzO=ygak+$NC728Rdm-zMwaZW6=1Wn zXlx!Sm`9d`1Upm;(2%se;5u$CvYGzKOp~;oBtv@6t}F{m z<=M0Mo_p@O=iWWvIp_M*O{YVigxjrf@Ds0}+;U*mMb8l7|HB!-COhXks84mYN^~ln 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