From f2f714748f11c5915c4b9d4cb9090a1fe9059b29 Mon Sep 17 00:00:00 2001 From: HuangHai <10402852@qq.com> Date: Sun, 6 Jul 2025 12:42:01 +0800 Subject: [PATCH] 'commit' --- dsRagAnything/EndToEnd.py | 11 +- dsRagAnything/load_existing_lightrag.py | 109 ++++++++++++++++++ .../kv_store_llm_response_cache.json | 22 ++++ 3 files changed, 137 insertions(+), 5 deletions(-) create mode 100644 dsRagAnything/load_existing_lightrag.py diff --git a/dsRagAnything/EndToEnd.py b/dsRagAnything/EndToEnd.py index 8075ad80..bd74ed47 100644 --- a/dsRagAnything/EndToEnd.py +++ b/dsRagAnything/EndToEnd.py @@ -102,16 +102,17 @@ async def main(): parse_method="auto" ) + print("Processing complete.") # 调用LightRAG的代码，包括:1、创建索引，2、查询知识库，不应该放在这个模块中完成，这里只需要完成到文本解析完成即可。 # Query the processed content # Pure text query - for basic knowledge base search - # text_result = await rag.aquery( - # "这个文档是做什么用的?", - # mode="hybrid" - # ) - # print("Text query result:", text_result) + text_result = await rag.aquery( + "这个文档是做什么用的?", + mode="hybrid" + ) + print("Text query result:", text_result) # # Multimodal query with specific multimodal content # multimodal_result = await rag.aquery_with_multimodal( diff --git a/dsRagAnything/load_existing_lightrag.py b/dsRagAnything/load_existing_lightrag.py new file mode 100644 index 00000000..a08cf3fc --- /dev/null +++ b/dsRagAnything/load_existing_lightrag.py @@ -0,0 +1,109 @@ +import asyncio + +from lightrag.kg.shared_storage import initialize_pipeline_status +from raganything import RAGAnything +from lightrag import LightRAG +from lightrag.llm.openai import openai_complete_if_cache, openai_embed +from lightrag.utils import EmbeddingFunc +import os + +async def load_existing_lightrag(): + # 设置 API 配置 + api_key = "sk-44ae895eeb614aa1a9c6460579e322f1" + base_url = "https://api.deepseek.com" # Optional + + # 首先，创建或加载已存在的 LightRAG 实例 + lightrag_working_dir = "./rag_storage" + + # 检查是否存在之前的 LightRAG 实例 + if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir): + print("✅ 发现已存在的 LightRAG 实例，正在加载...") + else: + print("❌ 未找到已存在的 LightRAG 实例，将创建新实例") + + # 使用您的配置创建/加载 LightRAG 实例 + lightrag_instance = LightRAG( + working_dir=lightrag_working_dir, + llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( + "deepseek-chat", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ), + embedding_func=EmbeddingFunc( + embedding_dim=1024, + max_token_size=8192, + func=lambda texts: openai_embed( + texts, + model="BAAI/bge-m3", + api_key="sk-pbqibyjwhrgmnlsmdygplahextfaclgnedetybccknxojlyl", + base_url="https://api.siliconflow.cn/v1", + ), + ) + ) + + # 初始化存储（如果有现有数据，这将加载它们） + await lightrag_instance.initialize_storages() + await initialize_pipeline_status() + + # 定义视觉模型函数用于图像处理 + def vision_model_func( + prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs + ): + if image_data: + return openai_complete_if_cache( + "GLM-4.1V-9B-Thinking", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} + if system_prompt + else None, + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_data}" + }, + }, + ], + } + if image_data + else {"role": "user", "content": prompt}, + ], + api_key="sk-pbqibyjwhrgmnlsmdygplahextfaclgnedetybccknxojlyl", + base_url="https://api.siliconflow.cn/v1/chat/completions", + **kwargs, + ) + else: + return lightrag_instance.llm_model_func(prompt, system_prompt, history_messages, **kwargs) + + # 现在使用已存在的 LightRAG 实例初始化 RAGAnything + rag = RAGAnything( + lightrag=lightrag_instance, # 传入已存在的 LightRAG 实例 + vision_model_func=vision_model_func, + # 注意：working_dir、llm_model_func、embedding_func 等都从 lightrag_instance 继承 + ) + + # 查询已存在的知识库 + result = await rag.aquery( + "这个 LightRAG 实例中处理了哪些数据？", + mode="hybrid" + ) + print("查询结果:", result) + + # 向已存在的 LightRAG 实例添加新的多模态文档 + # await rag.process_document_complete( + # file_path="path/to/new/multimodal_document.pdf", + # output_dir="./output" + # ) + +if __name__ == "__main__": + asyncio.run(load_existing_lightrag()) \ No newline at end of file diff --git a/dsRagAnything/rag_storage/kv_store_llm_response_cache.json b/dsRagAnything/rag_storage/kv_store_llm_response_cache.json index dc613ce5..06e63796 100644 --- a/dsRagAnything/rag_storage/kv_store_llm_response_cache.json +++ b/dsRagAnything/rag_storage/kv_store_llm_response_cache.json @@ -140,5 +140,27 @@ "embedding_max": null, "original_prompt": "[{\"role\": \"user\", \"content\": \"---Goal---\\nGiven a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.\\nUse English as output language.\\n\\n---Steps---\\n1. Identify all entities. For each identified entity, extract the following information:\\n- entity_name: Name of the entity, use same language as input text. If English, capitalized the name.\\n- entity_type: One of the following types: [organization,person,geo,event,category]\\n- entity_description: Comprehensive description of the entity's attributes and activities\\nFormat each entity as (\\\"entity\\\"<|><|><|>)\\n\\n2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.\\nFor each pair of related entities, extract the following information:\\n- source_entity: name of the source entity, as identified in step 1\\n- target_entity: name of the target entity, as identified in step 1\\n- relationship_description: explanation as to why you think the source entity and the target entity are related to each other\\n- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity\\n- relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details\\nFormat each relationship as (\\\"relationship\\\"<|><|><|><|><|>)\\n\\n3. Identify high-level key words that summarize the main concepts, themes, or topics of the entire text. These should capture the overarching ideas present in the document.\\nFormat the content-level key words as (\\\"content_keywords\\\"<|>)\\n\\n4. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **##** as the list delimiter.\\n\\n5. When finished, output <|COMPLETE|>\\n\\n######################\\n---Examples---\\n######################\\nExample 1:\\n\\nEntity_types: [person, technology, mission, organization, location]\\nText:\\n```\\nwhile Alex clenched his jaw, the buzz of frustration dull against the backdrop of Taylor's authoritarian certainty. It was this competitive undercurrent that kept him alert, the sense that his and Jordan's shared commitment to discovery was an unspoken rebellion against Cruz's narrowing vision of control and order.\\n\\nThen Taylor did something unexpected. They paused beside Jordan and, for a moment, observed the device with something akin to reverence. \\\"If this tech can be understood...\\\" Taylor said, their voice quieter, \\\"It could change the game for us. For all of us.\\\"\\n\\nThe underlying dismissal earlier seemed to falter, replaced by a glimpse of reluctant respect for the gravity of what lay in their hands. Jordan looked up, and for a fleeting heartbeat, their eyes locked with Taylor's, a wordless clash of wills softening into an uneasy truce.\\n\\nIt was a small transformation, barely perceptible, but one that Alex noted with an inward nod. They had all been brought here by different paths\\n```\\n\\nOutput:\\n(\\\"entity\\\"<|>\\\"Alex\\\"<|>\\\"person\\\"<|>\\\"Alex is a character who experiences frustration and is observant of the dynamics among other characters.\\\")##\\n(\\\"entity\\\"<|>\\\"Taylor\\\"<|>\\\"person\\\"<|>\\\"Taylor is portrayed with authoritarian certainty and shows a moment of reverence towards a device, indicating a change in perspective.\\\")##\\n(\\\"entity\\\"<|>\\\"Jordan\\\"<|>\\\"person\\\"<|>\\\"Jordan shares a commitment to discovery and has a significant interaction with Taylor regarding a device.\\\")##\\n(\\\"entity\\\"<|>\\\"Cruz\\\"<|>\\\"person\\\"<|>\\\"Cruz is associated with a vision of control and order, influencing the dynamics among other characters.\\\")##\\n(\\\"entity\\\"<|>\\\"The Device\\\"<|>\\\"technology\\\"<|>\\\"The Device is central to the story, with potential game-changing implications, and is revered by Taylor.\\\")##\\n(\\\"relationship\\\"<|>\\\"Alex\\\"<|>\\\"Taylor\\\"<|>\\\"Alex is affected by Taylor's authoritarian certainty and observes changes in Taylor's attitude towards the device.\\\"<|>\\\"power dynamics, perspective shift\\\"<|>7)##\\n(\\\"relationship\\\"<|>\\\"Alex\\\"<|>\\\"Jordan\\\"<|>\\\"Alex and Jordan share a commitment to discovery, which contrasts with Cruz's vision.\\\"<|>\\\"shared goals, rebellion\\\"<|>6)##\\n(\\\"relationship\\\"<|>\\\"Taylor\\\"<|>\\\"Jordan\\\"<|>\\\"Taylor and Jordan interact directly regarding the device, leading to a moment of mutual respect and an uneasy truce.\\\"<|>\\\"conflict resolution, mutual respect\\\"<|>8)##\\n(\\\"relationship\\\"<|>\\\"Jordan\\\"<|>\\\"Cruz\\\"<|>\\\"Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order.\\\"<|>\\\"ideological conflict, rebellion\\\"<|>5)##\\n(\\\"relationship\\\"<|>\\\"Taylor\\\"<|>\\\"The Device\\\"<|>\\\"Taylor shows reverence towards the device, indicating its importance and potential impact.\\\"<|>\\\"reverence, technological significance\\\"<|>9)##\\n(\\\"content_keywords\\\"<|>\\\"power dynamics, ideological conflict, discovery, rebellion\\\")<|COMPLETE|>\\n#############################\\nExample 2:\\n\\nEntity_types: [company, index, commodity, market_trend, economic_policy, biological]\\nText:\\n```\\nStock markets faced a sharp downturn today as tech giants saw significant declines, with the Global Tech Index dropping by 3.4% in midday trading. Analysts attribute the selloff to investor concerns over rising interest rates and regulatory uncertainty.\\n\\nAmong the hardest hit, Nexon Technologies saw its stock plummet by 7.8% after reporting lower-than-expected quarterly earnings. In contrast, Omega Energy posted a modest 2.1% gain, driven by rising oil prices.\\n\\nMeanwhile, commodity markets reflected a mixed sentiment. Gold futures rose by 1.5%, reaching $2,080 per ounce, as investors sought safe-haven assets. Crude oil prices continued their rally, climbing to $87.60 per barrel, supported by supply constraints and strong demand.\\n\\nFinancial experts are closely watching the Federal Reserve's next move, as speculation grows over potential rate hikes. The upcoming policy announcement is expected to influence investor confidence and overall market stability.\\n```\\n\\nOutput:\\n(\\\"entity\\\"<|>\\\"Global Tech Index\\\"<|>\\\"index\\\"<|>\\\"The Global Tech Index tracks the performance of major technology stocks and experienced a 3.4% decline today.\\\")##\\n(\\\"entity\\\"<|>\\\"Nexon Technologies\\\"<|>\\\"company\\\"<|>\\\"Nexon Technologies is a tech company that saw its stock decline by 7.8% after disappointing earnings.\\\")##\\n(\\\"entity\\\"<|>\\\"Omega Energy\\\"<|>\\\"company\\\"<|>\\\"Omega Energy is an energy company that gained 2.1% in stock value due to rising oil prices.\\\")##\\n(\\\"entity\\\"<|>\\\"Gold Futures\\\"<|>\\\"commodity\\\"<|>\\\"Gold futures rose by 1.5%, indicating increased investor interest in safe-haven assets.\\\")##\\n(\\\"entity\\\"<|>\\\"Crude Oil\\\"<|>\\\"commodity\\\"<|>\\\"Crude oil prices rose to $87.60 per barrel due to supply constraints and strong demand.\\\")##\\n(\\\"entity\\\"<|>\\\"Market Selloff\\\"<|>\\\"market_trend\\\"<|>\\\"Market selloff refers to the significant decline in stock values due to investor concerns over interest rates and regulations.\\\")##\\n(\\\"entity\\\"<|>\\\"Federal Reserve Policy Announcement\\\"<|>\\\"economic_policy\\\"<|>\\\"The Federal Reserve's upcoming policy announcement is expected to impact investor confidence and market stability.\\\")##\\n(\\\"relationship\\\"<|>\\\"Global Tech Index\\\"<|>\\\"Market Selloff\\\"<|>\\\"The decline in the Global Tech Index is part of the broader market selloff driven by investor concerns.\\\"<|>\\\"market performance, investor sentiment\\\"<|>9)##\\n(\\\"relationship\\\"<|>\\\"Nexon Technologies\\\"<|>\\\"Global Tech Index\\\"<|>\\\"Nexon Technologies' stock decline contributed to the overall drop in the Global Tech Index.\\\"<|>\\\"company impact, index movement\\\"<|>8)##\\n(\\\"relationship\\\"<|>\\\"Gold Futures\\\"<|>\\\"Market Selloff\\\"<|>\\\"Gold prices rose as investors sought safe-haven assets during the market selloff.\\\"<|>\\\"market reaction, safe-haven investment\\\"<|>10)##\\n(\\\"relationship\\\"<|>\\\"Federal Reserve Policy Announcement\\\"<|>\\\"Market Selloff\\\"<|>\\\"Speculation over Federal Reserve policy changes contributed to market volatility and investor selloff.\\\"<|>\\\"interest rate impact, financial regulation\\\"<|>7)##\\n(\\\"content_keywords\\\"<|>\\\"market downturn, investor sentiment, commodities, Federal Reserve, stock performance\\\")<|COMPLETE|>\\n#############################\\nExample 3:\\n\\nEntity_types: [economic_policy, athlete, event, location, record, organization, equipment]\\nText:\\n```\\nAt the World Athletics Championship in Tokyo, Noah Carter broke the 100m sprint record using cutting-edge carbon-fiber spikes.\\n```\\n\\nOutput:\\n(\\\"entity\\\"<|>\\\"World Athletics Championship\\\"<|>\\\"event\\\"<|>\\\"The World Athletics Championship is a global sports competition featuring top athletes in track and field.\\\")##\\n(\\\"entity\\\"<|>\\\"Tokyo\\\"<|>\\\"location\\\"<|>\\\"Tokyo is the host city of the World Athletics Championship.\\\")##\\n(\\\"entity\\\"<|>\\\"Noah Carter\\\"<|>\\\"athlete\\\"<|>\\\"Noah Carter is a sprinter who set a new record in the 100m sprint at the World Athletics Championship.\\\")##\\n(\\\"entity\\\"<|>\\\"100m Sprint Record\\\"<|>\\\"record\\\"<|>\\\"The 100m sprint record is a benchmark in athletics, recently broken by Noah Carter.\\\")##\\n(\\\"entity\\\"<|>\\\"Carbon-Fiber Spikes\\\"<|>\\\"equipment\\\"<|>\\\"Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction.\\\")##\\n(\\\"entity\\\"<|>\\\"World Athletics Federation\\\"<|>\\\"organization\\\"<|>\\\"The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations.\\\")##\\n(\\\"relationship\\\"<|>\\\"World Athletics Championship\\\"<|>\\\"Tokyo\\\"<|>\\\"The World Athletics Championship is being hosted in Tokyo.\\\"<|>\\\"event location, international competition\\\"<|>8)##\\n(\\\"relationship\\\"<|>\\\"Noah Carter\\\"<|>\\\"100m Sprint Record\\\"<|>\\\"Noah Carter set a new 100m sprint record at the championship.\\\"<|>\\\"athlete achievement, record-breaking\\\"<|>10)##\\n(\\\"relationship\\\"<|>\\\"Noah Carter\\\"<|>\\\"Carbon-Fiber Spikes\\\"<|>\\\"Noah Carter used carbon-fiber spikes to enhance performance during the race.\\\"<|>\\\"athletic equipment, performance boost\\\"<|>7)##\\n(\\\"relationship\\\"<|>\\\"World Athletics Federation\\\"<|>\\\"100m Sprint Record\\\"<|>\\\"The World Athletics Federation is responsible for validating and recognizing new sprint records.\\\"<|>\\\"sports regulation, record certification\\\"<|>9)##\\n(\\\"content_keywords\\\"<|>\\\"athletics, sprinting, record-breaking, sports technology, competition\\\")<|COMPLETE|>\\n#############################\\n\\n#############################\\n---Real Data---\\n######################\\nEntity_types: [organization,person,geo,event,category]\\nText:\\n驿来特平台安全\\n\\n平台硬件\\n\\n目前，微信公众号管理功能模块上线了运维监控功能，可以实现域名、账户余额、数据库实例或MongoDb实例、Redis实例的CPU磁盘情况等情况的监控，同时，也可以提示到期时间等功能，对于未尽的监控事宜，可以考虑要求增加功能即可完成监控。经观察，上线一个月多的时间内，Mysql 数据库的磁盘由原来的 40GB，增加到 46GB，增长过快，按这个速度，一年后就是将近 100G，而100GB 的 Mysql 数据库在性能上是无法正常工作的，亟需解决主数据库磁盘增长过快的问题，不能等到问题发生时才关注。解决思路如下：\\n\\n1、 哪个表增长过快？ \\n2、 是否可以考虑迁移到历史库？ \\n这个工作虽然无法带来业务上的增长，但可以保证业务系统健康的运行，也是非常重要的工作，需要未雨绸缪。\\n\\n二、 防护措施\\n\\n1.网络安全层面：云防火墙，通过 ACL 访问控制策略，对于需要接入的平台允许放行，对于没有访问需求的 IP 进行阻断。实现精细的访问控制策略。通过云防火墙的内置入侵防御模块，对于网络攻击、病毒传播都有较好的防护效果。\\n\\n2.应用层面：Web 应用防火墙，通过 CNAME 的接入方式将域名接入WAF，可以隐藏您的网站源 IP，对于所有请求进行分析和识别，将具备攻击行为的请求进行拦截。利用区域封禁功能拦截境外不需要访问的 IP；扫描防护攻击将一些恶意扫描拦截；BOT 管理可以拦截爬虫、IDC 等机器行为。\\n\\n3.主机层面：云安全中心，使用云安全中心对主机进行防护、可以针对病毒、漏洞、入侵攻击、恶意行为、暴力破解等行为进行识别和防御。还支持主机防勒索和数据库防勒索功能。\\n\\n三、 维护制度\\n\\n1、 数据库只进行了备份，未按等保要求进行定期的恢复实验不能保证备份的东西我们就一定能恢复，需要制订制度由专人定期（比如每月 1 次）进行全量恢复实验，增量的采用 RedoLog 日志进行恢复实验，保证随时可以更新到出故障的最后一刻。实验完成后，需要实验人员签字留痕。 \\n2、 购买阿里云提供的漏洞扫描服务对软件系统进行定期扫描一来是由于系统在不断的增加代码，带来更多风险，二来是 Java 或系统的缺陷会随时间的增长而更多的暴露出来，\\n\\n漏洞库也在不断的更新，建议每 3 个月增加一次扫描工作。3、 进行空难性故障恢复演练，梳理出异常情况下快速恢复服务的方法和路径。\\n######################\\nOutput:\"}, {\"role\": \"assistant\", \"content\": \"(\\\"entity\\\"<|>\\\"驿来特平台\\\"<|>\\\"organization\\\"<|>\\\"A platform with security and maintenance features, including monitoring for domains, account balances, database instances, and more.\\\")##\\n(\\\"entity\\\"<|>\\\"微信公众号管理功能模块\\\"<|>\\\"category\\\"<|>\\\"A module within the platform that includes operation and maintenance monitoring capabilities.\\\")##\\n(\\\"entity\\\"<|>\\\"Mysql 数据库\\\"<|>\\\"category\\\"<|>\\\"A database system experiencing rapid disk growth, increasing from 40GB to 46GB in a month.\\\")##\\n(\\\"entity\\\"<|>\\\"云防火墙\\\"<|>\\\"category\\\"<|>\\\"A network security measure using ACL access control policies and built-in intrusion prevention modules.\\\")##\\n(\\\"entity\\\"<|>\\\"Web 应用防火墙\\\"<|>\\\"category\\\"<|>\\\"An application-level firewall that hides source IPs, analyzes requests, and blocks malicious activities.\\\")##\\n(\\\"entity\\\"<|>\\\"云安全中心\\\"<|>\\\"category\\\"<|>\\\"A host-level security solution protecting against viruses, vulnerabilities, and malicious attacks.\\\")##\\n(\\\"entity\\\"<|>\\\"阿里云\\\"<|>\\\"organization\\\"<|>\\\"A cloud service provider offering vulnerability scanning services.\\\")##\\n(\\\"entity\\\"<|>\\\"RedoLog 日志\\\"<|>\\\"category\\\"<|>\\\"A logging mechanism used for incremental recovery experiments.\\\")##\\n(\\\"relationship\\\"<|>\\\"驿来特平台\\\"<|>\\\"微信公众号管理功能模块\\\"<|>\\\"The platform includes the WeChat public account management module for monitoring purposes.\\\"<|>\\\"platform features, monitoring\\\"<|>8)##\\n(\\\"relationship\\\"<|>\\\"驿来特平台\\\"<|>\\\"云防火墙\\\"<|>\\\"The platform uses cloud firewall for network security and access control.\\\"<|>\\\"security measures, network protection\\\"<|>9)##\\n(\\\"relationship\\\"<|>\\\"驿来特平台\\\"<|>\\\"Web 应用防火墙\\\"<|>\\\"The platform employs a web application firewall for application-level security.\\\"<|>\\\"application security, threat prevention\\\"<|>9)##\\n(\\\"relationship\\\"<|>\\\"驿来特平台\\\"<|>\\\"云安全中心\\\"<|>\\\"The platform utilizes a cloud security center for host-level protection.\\\"<|>\\\"host security, threat detection\\\"<|>9)##\\n(\\\"relationship\\\"<|>\\\"驿来特平台\\\"<|>\\\"阿里云\\\"<|>\\\"The platform uses Alibaba Cloud's services for vulnerability scanning.\\\"<|>\\\"third-party services, security enhancement\\\"<|>7)##\\n(\\\"relationship\\\"<|>\\\"Mysql 数据库\\\"<|>\\\"RedoLog 日志\\\"<|>\\\"The MySQL database uses RedoLog logs for incremental recovery experiments.\\\"<|>\\\"database recovery, logging\\\"<|>8)##\\n(\\\"content_keywords\\\"<|>\\\"platform security, database management, network protection, application security, maintenance protocols\\\")<|COMPLETE|>\"}]\nMANY entities and relationships were missed in the last extraction.\n\n---Remember Steps---\n\n1. Identify all entities. For each identified entity, extract the following information:\n- entity_name: Name of the entity, use same language as input text. If English, capitalized the name.\n- entity_type: One of the following types: [organization,person,geo,event,category]\n- entity_description: Comprehensive description of the entity's attributes and activities\nFormat each entity as (\"entity\"<|><|><|>)\n\n2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.\nFor each pair of related entities, extract the following information:\n- source_entity: name of the source entity, as identified in step 1\n- target_entity: name of the target entity, as identified in step 1\n- relationship_description: explanation as to why you think the source entity and the target entity are related to each other\n- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity\n- relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details\nFormat each relationship as (\"relationship\"<|><|><|><|><|>)\n\n3. Identify high-level key words that summarize the main concepts, themes, or topics of the entire text. These should capture the overarching ideas present in the document.\nFormat the content-level key words as (\"content_keywords\"<|>)\n\n4. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **##** as the list delimiter.\n\n5. When finished, output <|COMPLETE|>\n\n---Output---\n\nAdd them below using the same format:" } + }, + "hybrid": { + "8675f7b54039e4722cf84dc472e485bf": { + "return": "{\"high_level_keywords\": [\"LightRAG\", \"\\u6570\\u636e\\u5904\\u7406\", \"\\u5b9e\\u4f8b\"], \"low_level_keywords\": [\"\\u6570\\u636e\\u5185\\u5bb9\", \"\\u6570\\u636e\\u7c7b\\u578b\", \"\\u6570\\u636e\\u6765\\u6e90\"]}", + "cache_type": "keywords", + "chunk_id": null, + "embedding": null, + "embedding_shape": null, + "embedding_min": null, + "embedding_max": null, + "original_prompt": "这个 LightRAG 实例中处理了哪些数据？" + }, + "6914052924b05ffac19d395b96a15767": { + "return": "根据提供的知识图谱和文档片段，这个LightRAG实例处理了以下主要类型的数据：\n\n### 1. 蓝桥杯竞赛数据\n- **参赛者信息**：包含选手黄琬乔的个人资料（姓名、身份证号220105201212060427）、准考证号2305C10579，以及出生日期（2012-12-06）等身份验证信息\n- **比赛详情**：第十四届蓝桥杯大赛青少组C++初级组的比赛时间（2023年5月14日10:00-11:30）、官方网址（http://kao.lanqiaoqingshao.cn）和登录凭证（账号Ianqiao/密码542t96）\n- **技术规范**：包括编程环境要求（C++5.1.1、Python3.7、Scratch 3.0等）、设备监控规则（微考试助手工具）和防作弊措施\n\n### 2. 驿来特平台安全数据\n- **系统监控指标**：MySQL数据库磁盘增长情况（40GB→46GB/月）、Redis/MongoDB实例的CPU/磁盘状态\n- **安全防护措施**：\n - 网络层：云防火墙的ACL访问控制策略和入侵防御模块\n - 应用层：WAF的区域封禁功能和BOT管理\n - 主机层：云安全中心的防勒索功能\n- **运维制度**：包括全量恢复实验、漏洞扫描（阿里云服务）和故障恢复演练\n\n### 3. 编程算法数据\n- **鸽巢原理应用**：涉及模运算（modular arithmetic）的C++代码实现，包含数组操作（r[], s[]）和数学证明\n- **HDU 1205题目**：处理序列求和（a_1到a_m）与模数m的关系，演示如何通过余数分布（Ramsey理论）解决问题\n\n### 数据处理特征\n- **强验证性数据**：如身份证号隐含的出生日期验证、准考证号的日期编码（2305=2023年5月）\n- **时序敏感数据**：数据库磁盘增长监控、比赛时间约束\n- **结构化与非结构化混合**：既有表格化的参赛信息（DC1），也有代码片段（DC4）和安全策略文本（DC3）\n\n### References\n1. [KG] 黄琬乔2023蓝桥杯省赛准考证.pdf (实体1-16,64)\n2. [DC] 黄琬乔2023蓝桥杯省赛准考证.pdf (文档1-2)\n3. [KG] 驿来特平台安全.docx (实体19-32,35,37,43-44,48-49,53)\n4. [DC] 驿来特平台安全.docx (文档3)\n5. [KG] 鸽巢原理.md (实体33-34,38-42,45,47,50-52,57-60)", + "cache_type": "query", + "chunk_id": null, + "embedding": null, + "embedding_shape": null, + "embedding_min": null, + "embedding_max": null, + "original_prompt": "这个 LightRAG 实例中处理了哪些数据？" + } } } \ No newline at end of file