1、代码#!/usr/bin/env python # -*- coding: UTF-8 -*- from langchain_chroma import Chroma from meta_data import docs, metadata_field_info # 自我问询rag self-rag from base_llm import llm, embeddings_model from langchain_classic.retrievers.self_query.base import SelfQueryRetriever #from langchain_classic.retrievers.self_query.chroma import ChromaTranslator # 文档内容描述指导LLM理解文档内容 document_content_description Brief description of technical articles # 创建向量数据库 vectorstore Chroma.from_documents(docs, embeddings_model) SelfQueryRetriever.from_llm 问题:作者A发布的论文2025的 发送给大模型 意图识别 { query: 发布的论文 filter: {year:2025, author:A} } 2.元数据过滤 本地条件判断 3. 语义搜索 在过滤完之后的数据中进行相识度对比 retriever SelfQueryRetriever.from_llm( llm, vectorstore, document_content_description, metadata_field_info, #translatorChromaTranslator(), # 明确指定 enable_limitTrue ) print(retriever.invoke(作者A发布的一篇论文))引用的文件base_llm.py代码如下#!/usr/bin/env python # -*- coding: UTF-8 -*- from dotenv import load_dotenv from langchain_huggingface import HuggingFaceEmbeddings from langchain_openai import ChatOpenAI import os load_dotenv() llm ChatOpenAI(api_keyos.getenv(DASHSCOPE_API_KEY), base_urlos.getenv(DASHSCOPE_BASE_URL), model_nameqwen3.7-plus) # 本地embedding模型地址 embedding_model_path rD:\LLM\Local_model\maidalun\bce-embedding-base_v1 # 初始化嵌入模型用于文本向量化 embeddings_model HuggingFaceEmbeddings( model_nameembedding_model_path )引用的文件meta_data.py代码如下#!/usr/bin/env python # -*- coding: UTF-8 -*- from langchain_core.documents import Document from langchain_classic.chains.query_constructor.schema import AttributeInfo docs [ Document( page_content作者A团队开发出基于深度学习的图像识别系统在复杂场景下的识别准确率提升250%, metadata{year: 2025, rating: 9.3, genre: AI, author: A}, ), Document( page_content物联网技术成功应用于智能农业监控作者B主导的项目实现农作物产量提升20%, metadata{year: 2024, rating: 9.5, genre: IoT, author: B}, ), Document( page_content边缘计算平台实现实时数据处理突破作者C构建的新型架构支持千万级并发计算, metadata{year: 2023, rating: 8.8, genre: Edge Computing, author: C}, ), Document( page_content机器学习模型预测2025年股市趋势作者A团队构建的模型准确率超95%, metadata{year: 2024, rating: 9.0, genre: Machine Learning, author: A}, ), Document( page_content基于人工智能的心脏病诊断系统在临床应用中达到顶级专家水平作者B获医疗科技创新奖, metadata{year: 2025, rating: 7.2, genre: AI, author: B}, ), Document( page_content区块链技术在供应链管理中取得突破作者C设计的新型协议提升供应链透明度30%, metadata{year: 2024, rating: 8.9, genre: Blockchain, author: C}, ), Document( page_content云计算平台实现能效优化作者A研发的智能调度系统使数据中心能耗降低50%, metadata{year: 2024, rating: 8.6, genre: Cloud, author: A}, ), Document( page_content大数据分析助力环保监测作者B团队实现污染源识别准确率提升30%, metadata{year: 2025, rating: 7.5, genre: Big Data, author: B}, ) ] # 元数据字段定义指导LLM如何解析查询条件 工具 metadata_field_info [ AttributeInfo( namegenre, descriptionTechnical domain of the article, options: [AI, Blockchain, Cloud, Big Data], typestring, ), AttributeInfo( nameyear, descriptionPublication year of the article, typeinteger, ), AttributeInfo( nameauthor, descriptionAuthors name who signed the article, typestring, ), AttributeInfo( namerating, descriptionTechnical value assessment score (1-10 scale), typefloat ) ]2、执行报错了报错内容如下C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Scripts\python.exe C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_core\utils\pydantic.py:41: UserWarning: Core Pydantic V1 functionality isnt compatible with Python 3.14 or greater. from pydantic.v1 import BaseModel as BaseModelV1 Loading weights: 100%|██████████| 199/199 [00:0000:00, 65081.21it/s] Traceback (most recent call last): File C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py, line 39, in module retriever SelfQueryRetriever.from_llm( llm, ...4 lines... enable_limitTrue ) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 374, in from_llm structured_query_translator _get_builtin_translator(vectorstore) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 71, in _get_builtin_translator from langchain_community.vectorstores import ( ...17 lines... ) ImportError: cannot import name DatabricksVectorSearch from langchain_community.vectorstores (C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_community\vectorstores\__init__.py) Process finished with exit code 1在deepseek上面搜索方案DeepSeek使用了这种方案执行还是报一样的错C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Scripts\python.exe C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_core\utils\pydantic.py:41: UserWarning: Core Pydantic V1 functionality isnt compatible with Python 3.14 or greater. from pydantic.v1 import BaseModel as BaseModelV1 Loading weights: 100%|██████████| 199/199 [00:0000:00, 49983.02it/s] Traceback (most recent call last): File C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py, line 39, in module retriever SelfQueryRetriever.from_llm( llm, ...4 lines... enable_limitTrue ) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 374, in from_llm structured_query_translator _get_builtin_translator(vectorstore) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 71, in _get_builtin_translator from langchain_community.vectorstores import ( ...17 lines... ) ImportError: cannot import name DatabricksVectorSearch from langchain_community.vectorstores (C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_community\vectorstores\__init__.py) Process finished with exit code 1
元数据索引有关的错
1、代码#!/usr/bin/env python # -*- coding: UTF-8 -*- from langchain_chroma import Chroma from meta_data import docs, metadata_field_info # 自我问询rag self-rag from base_llm import llm, embeddings_model from langchain_classic.retrievers.self_query.base import SelfQueryRetriever #from langchain_classic.retrievers.self_query.chroma import ChromaTranslator # 文档内容描述指导LLM理解文档内容 document_content_description Brief description of technical articles # 创建向量数据库 vectorstore Chroma.from_documents(docs, embeddings_model) SelfQueryRetriever.from_llm 问题:作者A发布的论文2025的 发送给大模型 意图识别 { query: 发布的论文 filter: {year:2025, author:A} } 2.元数据过滤 本地条件判断 3. 语义搜索 在过滤完之后的数据中进行相识度对比 retriever SelfQueryRetriever.from_llm( llm, vectorstore, document_content_description, metadata_field_info, #translatorChromaTranslator(), # 明确指定 enable_limitTrue ) print(retriever.invoke(作者A发布的一篇论文))引用的文件base_llm.py代码如下#!/usr/bin/env python # -*- coding: UTF-8 -*- from dotenv import load_dotenv from langchain_huggingface import HuggingFaceEmbeddings from langchain_openai import ChatOpenAI import os load_dotenv() llm ChatOpenAI(api_keyos.getenv(DASHSCOPE_API_KEY), base_urlos.getenv(DASHSCOPE_BASE_URL), model_nameqwen3.7-plus) # 本地embedding模型地址 embedding_model_path rD:\LLM\Local_model\maidalun\bce-embedding-base_v1 # 初始化嵌入模型用于文本向量化 embeddings_model HuggingFaceEmbeddings( model_nameembedding_model_path )引用的文件meta_data.py代码如下#!/usr/bin/env python # -*- coding: UTF-8 -*- from langchain_core.documents import Document from langchain_classic.chains.query_constructor.schema import AttributeInfo docs [ Document( page_content作者A团队开发出基于深度学习的图像识别系统在复杂场景下的识别准确率提升250%, metadata{year: 2025, rating: 9.3, genre: AI, author: A}, ), Document( page_content物联网技术成功应用于智能农业监控作者B主导的项目实现农作物产量提升20%, metadata{year: 2024, rating: 9.5, genre: IoT, author: B}, ), Document( page_content边缘计算平台实现实时数据处理突破作者C构建的新型架构支持千万级并发计算, metadata{year: 2023, rating: 8.8, genre: Edge Computing, author: C}, ), Document( page_content机器学习模型预测2025年股市趋势作者A团队构建的模型准确率超95%, metadata{year: 2024, rating: 9.0, genre: Machine Learning, author: A}, ), Document( page_content基于人工智能的心脏病诊断系统在临床应用中达到顶级专家水平作者B获医疗科技创新奖, metadata{year: 2025, rating: 7.2, genre: AI, author: B}, ), Document( page_content区块链技术在供应链管理中取得突破作者C设计的新型协议提升供应链透明度30%, metadata{year: 2024, rating: 8.9, genre: Blockchain, author: C}, ), Document( page_content云计算平台实现能效优化作者A研发的智能调度系统使数据中心能耗降低50%, metadata{year: 2024, rating: 8.6, genre: Cloud, author: A}, ), Document( page_content大数据分析助力环保监测作者B团队实现污染源识别准确率提升30%, metadata{year: 2025, rating: 7.5, genre: Big Data, author: B}, ) ] # 元数据字段定义指导LLM如何解析查询条件 工具 metadata_field_info [ AttributeInfo( namegenre, descriptionTechnical domain of the article, options: [AI, Blockchain, Cloud, Big Data], typestring, ), AttributeInfo( nameyear, descriptionPublication year of the article, typeinteger, ), AttributeInfo( nameauthor, descriptionAuthors name who signed the article, typestring, ), AttributeInfo( namerating, descriptionTechnical value assessment score (1-10 scale), typefloat ) ]2、执行报错了报错内容如下C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Scripts\python.exe C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_core\utils\pydantic.py:41: UserWarning: Core Pydantic V1 functionality isnt compatible with Python 3.14 or greater. from pydantic.v1 import BaseModel as BaseModelV1 Loading weights: 100%|██████████| 199/199 [00:0000:00, 65081.21it/s] Traceback (most recent call last): File C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py, line 39, in module retriever SelfQueryRetriever.from_llm( llm, ...4 lines... enable_limitTrue ) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 374, in from_llm structured_query_translator _get_builtin_translator(vectorstore) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 71, in _get_builtin_translator from langchain_community.vectorstores import ( ...17 lines... ) ImportError: cannot import name DatabricksVectorSearch from langchain_community.vectorstores (C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_community\vectorstores\__init__.py) Process finished with exit code 1在deepseek上面搜索方案DeepSeek使用了这种方案执行还是报一样的错C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Scripts\python.exe C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_core\utils\pydantic.py:41: UserWarning: Core Pydantic V1 functionality isnt compatible with Python 3.14 or greater. from pydantic.v1 import BaseModel as BaseModelV1 Loading weights: 100%|██████████| 199/199 [00:0000:00, 49983.02it/s] Traceback (most recent call last): File C:\Users\lenovo\PycharmProjects\PythonProject\rag\day08\02-元数据索引.py, line 39, in module retriever SelfQueryRetriever.from_llm( llm, ...4 lines... enable_limitTrue ) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 374, in from_llm structured_query_translator _get_builtin_translator(vectorstore) File C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_classic\retrievers\self_query\base.py, line 71, in _get_builtin_translator from langchain_community.vectorstores import ( ...17 lines... ) ImportError: cannot import name DatabricksVectorSearch from langchain_community.vectorstores (C:\Users\lenovo\PycharmProjects\PythonProject\.venv\Lib\site-packages\langchain_community\vectorstores\__init__.py) Process finished with exit code 1
