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各章引用网址列表
第1章 在WSL2上搭建GPU Linux Server深度学习环境
1、数里乾坤——用R与Python玩转大数据
https://zhuanlan.zhihu.com/p/640437583
2、优刻得
https://www.ucloud.cn/
3、GPU算力查询网址
https://developer.nvidia.com/cuda-gpus
4、拯救者Y9000X
https://activity.lenovo.com.cn/xiaofei/zjz/hdy.html
5、Tensorflow Linux Tested build configurations
https://tensorflow.google.cn/install/source?hl=en#linux
6、Tensorflow Winodws Tested build configurations
https://tensorflow.google.cn/install/source_windows?hl=en#gpu
7、Docker安装运行报错wsl问题排查方案
https://www.cnblogs.com/bokemoqi/p/17926296.html
8、Windows10/11家庭版开启Hyper-V虚拟机功能详解
https://zhuanlan.zhihu.com/p/667571538
9、WSL Github主页
https://github.com/microsoft/WSL
10、wsl.2.4.11.0.x64 下载地址
https://github.com/microsoft/WSL/releases/download/2.4.11/wsl.2.4.11.0.x64.msi
11、WSL 使用教程
https://www.jianshu.com/p/39a5b2e002b6
12、Getting Started with CUDA on WSL 2
https://docs.nvidia.com/cuda/wsl-user-guide/index.html#getting-started-with-cuda-on-wsl-2
13、How to Install CUDA on Ubuntu 22.04 | Step-by-Step
https://www.cherryservers.com/blog/install-cuda-ubuntu
14、How to Install NVIDIA Drivers on Ubuntu 24.04
https://linuxconfig.org/how-to-install-nvidia-drivers-on-ubuntu-24-04
15、CUDA 12.3下载地址
https://developer.nvidia.com/cuda-12-3-2-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local
16、Tensorflow Tested build configurations
https://tensorflow.google.cn/install/source?hl=en#linux
17、cuDNN Support Matrix
https://docs.nvidia.com/deeplearning/cudnn/latest/reference/support-matrix.html#support-matrix
18、Ubuntu 20.04(linux) cuda(12)+cudnn的deb方式安装以及验证
https://blog.csdn.net/qq_32033383/article/details/135015041
19、cuDNN9.4下载地址
https://developer.nvidia.com/cudnn-9-4-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_network
20、下载Linux版64-Bit (x86) Installer17
https://repo.anaconda.com/archive/Anaconda3-2024.02-1-Linux-x86_64.sh
21、PyTorch主页
https://pytorch.org/
22、Jupyter Lab设置切换虚拟环境
https://blog.csdn.net/CUFEECR/article/details/123987150
23、在MNIST上用Pytorch跑跑GPU
https://blog.csdn.net/song5bai/article/details/116358451
24、HanLP
https://github.com/hankcs/HanLP
25、何晗
https://github.com/hankcs
26、自然语义科技有限公司
https://www.hanlp.com/
27、HanLP 安装文档
https://hanlp.hankcs.com/docs/install.html
28、HanLP Demo
https://github.com/hankcs/HanLP/tree/doc-zh/plugins/hanlp_demo/hanlp_demo/zh
29、HanLP Demo API
https://hanlp.hankcs.com/docs/
30、HanLP多语种分句模型
https://github.com/hankcs/HanLP/blob/master/plugins/hanlp_demo/hanlp_demo/sent_split.py
31、HanLP基于规则的分句函数
https://github.com/hankcs/HanLP/blob/master/hanlp/utils/rules.py#L19
32、Nvidia GeForce RTX 2060
https://www.nvidia.cn/geforce/graphics-cards/rtx-2060/
33、Nvidia A100
https://images.nvidia.cn/aem-dam/en-zz/Solutions/data-center/a100/nvidia-a100-datasheet-nvidia-a4-2188504-r5-zhCN.pdf
34、Nvidia Tesla T4
https://www.nvidia.cn/content/dam/en-zz/zh_cn/Solutions/Data-Center/tesla-t4/nvidia-t4-datasheet-a4-nvidia-772234-r14-lr-cn.pdf
35、Neo4j Graph Data Science Compatibility matrix
https://github.com/neo4j/graph-data-science
36、微云数聚
https://we-yun.com/
37、Neo4j Community Linux Tarball Installation
https://neo4j.com/docs/operations-manual/current/installation/linux/tarball/#installation-linux-tarball-service
第2章 微软GraphRAG
1、bgm-3 embedding模型
https://ollama.com/library/bge-m3
2、all-minilm embedding模型
https://ollama.com/library/all-minilm
3、Visualizing and Debugging Your Knowledge Graph
https://github.com/microsoft/graphrag/blob/main/docs/visualization_guide.md
4、《悟空传》的前7章
https://dushu.baidu.com/pc/detail?gid=4305630473
5、《解决tiktoken库调用get_encoding时SSL超时》
https://blog.csdn.net/yufanwenshu/article/details/142290067
6、Auto Prompt Tuning
https://microsoft.github.io/graphrag/prompt_tuning/auto_prompt_tuning/
7、GraphRAG自动Prompt Tuning
https://mp.weixin.qq.com/s/69MeYny5nZmfjS1b4QUvTg
8、开发GraphRAG（知识图谱检索增强生成）应用
https://zhuanlan.zhihu.com/p/704919102
9、ms_graphrag_import.ipynb
https://github.com/microsoft/graphrag/blob/main/examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb
10、高级API源码
https://github.com/microsoft/graphrag/blob/main/graphrag/api/query.py
11、命令行查询工具源码
https://github.com/microsoft/graphrag/blob/main/graphrag/cli/query.py
12、local_search.ipynb
https://github.com/microsoft/graphrag/blob/main/docs/examples_notebooks/local_search.ipynb
13、Ollama主页
https://github.com/ollama/ollama
14、Qwen2.5
https://ollama.com/library/qwen2.5:7b
第3章 Neo4j GraphRAG
1、Neo4j Knowledge Graph Builder主页
https://github.com/neo4j-labs/llm-graph-builder
2、Neo4j GrapRAG的生态
https://neo4j.com/labs/genai-ecosystem/
3、Neo4j Knowledge Graph Builder简介
https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/
4、NeoConverse简介
https://neo4j.com/labs/genai-ecosystem/neoconverse/
5、NeoConverse主页
https://github.com/neo4j-labs/neoconverse
6、GenAI Stack简介
https://neo4j.com/labs/genai-ecosystem/genai-stack/
7、Get Started With GraphRAG: Neo4j’s Ecosystem Tools
https://neo4j.com/developer-blog/graphrag-ecosystem-tools/
8、LLM Knowledge Graph Builder: From Zero to GraphRAG in Five Minutes
https://neo4j.com/developer-blog/graphrag-llm-knowledge-graph-builder/
9、LangChain Neo4j Integration
https://neo4j.com/labs/genai-ecosystem/langchain/
10、LlamaIndex Neo4j Integration
https://neo4j.com/labs/genai-ecosystem/llamaindex/
11、Langchain4j Neo4j Integration
https://neo4j.com/labs/genai-ecosystem/langchain4j/
12、Docker
https://www.docker.com/
13、Docker Desktop
https://www.docker.com/products/docker-desktop/
14、Install Docker Engine on Ubuntu
https://docs.docker.com/engine/install/ubuntu/
15、Overview of installing Docker Compose
https://docs.docker.com/compose/install/
16、Install the Compose plugin
https://docs.docker.com/compose/install/linux/#install-using-the-repository
17、目前国内可用Docker镜像源汇总
https://cloud.tencent.com/developer/article/2459822
18、Daemon proxy configuration
https://docs.docker.com/engine/daemon/proxy/#systemd-unit-file
19、LangChain
https://python.langchain.com/
20、LangSmith
https://smith.langchain.com/
21、Issue#839《Does NOT work with Neo4J Community Edition
https://github.com/neo4j-labs/llm-graph-builder/issues/839
22、HuggingFace主页
https://huggingface.co/
23、Embedding model all-MiniLM-L6-v2
https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
24、Embedding model BAAI/bge-m3
https://huggingface.co/BAAI/bge-m3
25、LLMGraphTransformer文档
https://python.langchain.com/api_reference/experimental/graph_transformers/langchain_experimental.graph_transformers.llm.LLMGraphTransformer.html
26、LLMGraphTransformer源码
https://github.com/langchain-ai/langchain-experimental/blob/main/libs/experimental/langchain_experimental/graph_transformers/llm.py
27、Leiden社区发现算法
https://neo4j.com/docs/graph-data-science/2.12/algorithms/leiden/
28、SLLPA(Speaker-Listener Label Propagation)社区发现算法
https://neo4j.com/docs/graph-data-science/2.12/algorithms/sllpa/
29、Neo4j Knowledge Graph Builder集成测试程序
https://github.com/neo4j-labs/llm-graph-builder/blob/main/backend/test_integrationqa.py
30、Neo4j Knowledge Graph Builder backend requirements.txt
https://github.com/neo4j-labs/llm-graph-builder/blob/main/backend/requirements.txt
31、Neo4j Knowledge Graph Builder backend API
https://github.com/neo4j-labs/llm-graph-builder/blob/main/docs/backend/backend_docs.adoc
32、Answer relevancy metric is worse in languages other than English
https://github.com/neo4j-labs/llm-graph-builder/issues/998
33、Optimization for loading big embedding models into GPU
https://github.com/neo4j-labs/llm-graph-builder/issues/1036
34、Optimization for building more efficient backend image requires less disk space and time
https://github.com/neo4j-labs/llm-graph-builder/issues/1037
第4章 开发GraphRAG应用
1、微软GraphRAG论文
https://arxiv.org/abs/2404.16130
2、Tomaz Bratanic
https://bratanic-tomaz.medium.com/
3、Implementing ‘From Local to Global’ GraphRAG with Neo4j and LangChain: Constructing the Graph
https://medium.com/neo4j/implementing-from-local-to-global-graphrag-with-neo4j-and-langchain-constructing-the-graph-73924cc5bab4
4、《用Neo4j与LangChain实现从局部到全局的RAG：建立知识图谱》
https://zhuanlan.zhihu.com/p/709060837
5、Jupyter Notebook的源码文件
https://github.com/tomasonjo/blogs/blob/master/llm/ms_graphrag.ipynb
6、LangChain
https://www.langchain.com/langchain
7、LlamaIndex
https://www.llamaindex.ai/
8、《LlamaIndex 或 LangChain,哪个更适合作为RAG框架？》
https://blog.csdn.net/CSDNDN/article/details/139596103
9、LangSmith
https://www.langchain.com/langsmith
10、LangGraph
https://www.langchain.com/langgraph
11、Neo4j Cummunity的汉化版
https://we-yun.com/blog/prod-56.html
12、《开发GraphRAG（知识图谱检索增强生成）应用》
https://zhuanlan.zhihu.com/p/704919102
13、HanLP
https://github.com/hankcs/HanLP
14、《揭秘大模型提升秘诀：RAG系统中的文本分块策略》
https://mp.weixin.qq.com/s/HaRKys1A98cF9N1dSoEmGw
15、Neo4jGraph文档
https://python.langchain.com/v0.2/api_reference/community/graphs/langchain_community.graphs.neo4j_graph.Neo4jGraph.html
16、Neo4j KGBuilder make_relationships.py源码
https://github.com/neo4j-labs/llm-graph-builder/blob/main/backend/src/make_relationships.py
17、Neo4j KGBuilder的llm.py源码
https://github.com/neo4j-labs/llm-graph-builder/blob/main/backend/src/llm.py
18、LLMGraphTransformer文档
https://python.langchain.com/api_reference/experimental/graph_transformers/langchain_experimental.graph_transformers.llm.LLMGraphTransformer.html
19、LLMGraphTransformer源码
https://python.langchain.com/api_reference/_modules/langchain_experimental/graph_transformers/llm.html
20、GraphDocument文档
https://python.langchain.com/api_reference/community/graphs/langchain_community.graphs.graph_document.GraphDocument.html
21、Relationship文档
https://python.langchain.com/api_reference/community/graphs/langchain_community.graphs.graph_document.Relationship.html#langchain_community.graphs.graph_document.Relationship
22、Neo4j KGBuilder合并结点的实现
https://github.com/neo4j-labs/llm-graph-builder/blob/main/backend/src/make_relationships.py#L15
23、Neo4jGraph源码
https://python.langchain.com/v0.2/api_reference/_modules/langchain_community/graphs/neo4j_graph.html#Neo4jGraph.add_graph_documents
24、Hugginface
https://link.zhihu.com/?target=https%3A//huggingface.co/models
25、《如何选择RAG的Embedding模型？》
https://techdiylife.github.io/blog/blog.html?category1=c02&blogid=0047
26、《中文Embedding模型优劣数据评测分析报告》
https://zhuanlan.zhihu.com/p/679166797
27、北京智源研究院
https://www.baai.ac.cn/
28、BAAI/bge-m3模型
https://huggingface.co/BAAI/bge-m3
29、BAAI/bge-m3模型Github主页
https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md
30、KNN算法
https://neo4j.com/docs/graph-data-science/2.12/algorithms/knn/
31、WCC算法
https://neo4j.com/docs/graph-data-science/2.12/algorithms/wcc/
32、《如何从模型中返回结构化数据》
https://python.langchain.ac.cn/docs/how_to/structured_output/
33、apoc.refactor.mergeNodes的文档
https://neo4j.com/labs/apoc/4.0/overview/apoc.refactor/apoc.refactor.mergeNodes
34、Leiden算法
https://neo4j.com/docs/graph-data-science/2.12/algorithms/leiden/
35、SLLPA(Speaker-Listener Label Propagation)算法
https://neo4j.com/docs/graph-data-science/2.12/algorithms/sllpa/
36、《将微软GraphRAG集成到Neo4j中》
https://zhuanlan.zhihu.com/p/713201715
37、Integrating Microsoft GraphRAG into Neo4j
https://towardsdatascience.com/integrating-microsoft-graphrag-into-neo4j-e0d4fa00714c
38、《Implementing RAG: How to Write a Graph Retrieval Query in LangChain》
https://neo4j.com/developer-blog/rag-graph-retrieval-query-langchain/
39、Neo4j向量索引的实例化
https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.neo4j_vector.Neo4jVector.html#langchain_community.vectorstores.neo4j_vector.Neo4jVector.from_existing_index
40、Neo4jVector.similarity_search文档
https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.neo4j_vector.Neo4jVector.html#langchain_community.vectorstores.neo4j_vector.Neo4jVector.similarity_search
41、Customize response with retrieval query
https://python.langchain.com/docs/integrations/vectorstores/neo4jvector/#customize-response-with-retrieval-query
第5章 Agent开发
1、Conversational RAG
https://python.langchain.com/v0.2/docs/tutorials/qa_chat_history/
2、Agentic RAG
https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/
3、Neo4jVector的文档
https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.neo4j_vector.Neo4jVector.html#langchain_community.vectorstores.neo4j_vector.Neo4jVector.as_retriever
4、LangSmith
https://smith.langchain.com/
5、函数create_react_agent() API
https://langchain-ai.github.io/langgraph/reference/prebuilt/
6、Build an Agent
https://python.langchain.com/docs/tutorials/agents/
7、LangGraph
https://langchain-ai.github.io/langgraph/
8、Graph Definitions
https://langchain-ai.github.io/langgraph/reference/graphs/
9、LanagGraph Prebuilt tools_condition
https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.tools_condition
10、How to manage conversation history
https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/
11、How to control graph recursion limit
https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/
12、How to create tools
https://python.langchain.com/docs/how_to/custom_tools/
13、Tool Calling with LangChain
https://blog.langchain.dev/tool-calling-with-langchain/
14、Tool calling
https://python.langchain.com/docs/how_to/tool_calling/
第6章 在GraphRAG中应用国产大模型
1、LangChain集成的Chat模型列表
https://python.langchain.com/docs/integrations/chat/
2、DeepSeek R1/V3
https://github.com/deepseek-ai
3、LangChain集成的Embedding模型列表
https://python.langchain.com/docs/integrations/text_embedding/
4、LangChain文心一言Chat模型文档
https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint/
5、LangChain讯飞星火Chat模型文档
https://python.langchain.com/docs/integrations/chat/sparkllm/
6、LangChain通义千问Chat模型文档
https://python.langchain.com/docs/integrations/chat/tongyi/
7、LangChain腾讯混元Chat模型文档
https://python.langchain.com/docs/integrations/chat/tencent_hunyuan/
8、LangChain文心一言Embedding模型文档
https://python.langchain.com/docs/integrations/text_embedding/baidu_qianfan_endpoint/
9、LangChain讯飞星火Embedding模型文档
https://python.langchain.com/docs/integrations/text_embedding/sparkllm/
10、LangChain通义千问Embedding模型文档
https://python.langchain.com/docs/integrations/text_embedding/dashscope/
11、LangChain Huggingface Embedding模型文档
https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub/
12、Refactor ChatHunyuan and support Hunyuan Embedding
https://github.com/langchain-ai/langchain/pull/23160/commits/4d7cc88c2368c77593fbb38d71127d574f623ff2
13、Add function call support in Sparkllm chat model
https://github.com/langchain-ai/langchain/pull/20607/files/454d92a2bd95be7bcd45564b89dbb74c5ce77243
14、Function calling
https://github.com/deepseek-ai/DeepSeek-R1/issues/9
15、《DeepSeek R1测试之八 各大云平台调用》
https://zhuanlan.zhihu.com/p/22540281019/
第7章 本地部署LLM
1、qwen2.5
https://ollama.com/library/qwen2.5
2、bge-m3
https://ollama.com/library/bge-m3
3、openbmb/MiniCPM3-4B
https://huggingface.co/openbmb/MiniCPM3-4B
4、Run custom GGUF model on Ollama
https://zohaib.me/run-custom-gguf-model-on-ollama/
5、MiniCPM3的函数调用例子文档
https://github.com/OpenBMB/MiniCPM/tree/main/demo/minicpm3/function_call
6、vLLM
https://github.com/vllm-project/vllm
7、vLLM支持的LLM列表
https://docs.vllm.ai/en/v0.6.2/models/supported_models.html
8、Running vLLM on Pascal
https://github.com/jasonacox/TinyLLM/tree/main/vllm#running-vllm-on-pascal
9、LLM的文档《Installation: Full Build》
https://docs.vllm.ai/en/latest/getting_started/installation.html#full-build-with-compilation
10、Support for compute capability <7.0
https://github.com/vllm-project/vllm/issues/963
11、Enable support for Pascal GPUs
https://github.com/vllm-project/vllm/pull/4290
12、OpenAI Compatible Server: How to write a tool parser plugin
https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#how-to-write-a-tool-parser-plugin
13、LangChain vLLM Chat
https://python.langchain.com/docs/integrations/chat/vllm/
14、LangChain ChatHuggingFace
https://python.langchain.com/docs/integrations/chat/huggingface/
15、HuggingFaceEndpoint
https://python.langchain.com/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html
16、HuggingFacePipeline
https://python.langchain.com/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_pipeline.HuggingFacePipeline.html
17、ChatHuggingFace源码
https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html#langchain_huggingface.chat_models.huggingface.ChatHuggingFace.bind_tools
18、issue#22379《Tools do not work with HuggingFace》
https://github.com/langchain-ai/langchain/issues/22379
19、Llama.cpp
https://github.com/abetlen/llama-cpp-python
20、LangChain Chat Llama.cpp
https://python.langchain.com/docs/integrations/chat/llamacpp/
21、Issue#957《Generic Function Calling》
https://github.com/abetlen/llama-cpp-python/pull/957
22、Issue#1351《Improve function calling (auto selection, parallel functions)》
https://github.com/abetlen/llama-cpp-python/pull/1351
23、ChatLlamaCpp API
https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html#langchain_community.chat_models.llamacpp.ChatLlamaCpp.bind_tools
第8章 开发GraphRAG APP
1、Python FastAPI
https://github.com/fastapi/fastapi
2、Python Uvicorn WEB服务器
https://www.uvicorn.org/
3、starlette项目
https://github.com/encode/starlette
4、How to delete messages
https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages/
5、Postman
https://www.postman.com/
6、Streamlit
https://streamlit.io/
7、Flask
https://github.com/pallets/flask
8、《精通Shiny》
http://www.oreilly.com.cn/index.php?func=book&isbn=978-7-5766-0656-0
9、Shiny for Python
https://shiny.posit.co/py/
10、Rstudio Server在线文档
https://posit.co/download/rstudio-server/
11、《RStudio IDE User Guide》
https://docs.posit.co/ide/user/
12、Shiny Server在线文档
https://posit.co/download/shiny-server/
13、配置Shiny Server为用户发布模式
https://docs.posit.co/shiny-server/#host-per-user-application-directories
14、Shiny Server Administrator's Guide
https://docs.posit.co/shiny-server/
15、《Shiny for Python APP开发》
https://zhuanlan.zhihu.com/p/658670798
16、《墨尔本房价回归模型Shiny for Python APP》
https://zhuanlan.zhihu.com/p/658996965
17、Shiny for Python在线文档
https://shiny.posit.co/py/docs/overview.html
18、Mastering Shiny Examples and Solutions for Python
https://youngroklee-ml.github.io/mastering-shiny-for-python/
19、Mastering Shiny Examples and Solutions for Python
https://github.com/youngroklee-ml/mastering-shiny-for-python
第9章 GraphRAG 应用评估
1、Ragas项目主页
https://github.com/explodinggradients/ragas/
2、RAGAS: Automated Evaluation of Retrieval Augmented Generation
https://arxiv.org/abs/2309.15217
3、Integrations
https://docs.ragas.io/en/stable/howtos/integrations/
4、Neo4j KGBuilder
https://github.com/neo4j-labs/llm-graph-builder/
5、ragas_eval.py
https://github.com/neo4j-labs/llm-graph-builder/blob/main/backend/src/ragas_eval.py
6、Evaluate a simple LLM application
https://docs.ragas.io/en/stable/getstarted/evals/
7、References: evaluate()
https://docs.ragas.io/en/stable/references/evaluate/
8、List of available metrics
https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/
9、How to estimate Cost and Usage of evaluations and testset generation
https://docs.ragas.io/en/stable/howtos/applications/_cost/
10、《使用 Ragas 评估 RAG（含代码）》
https://www.zhihu.com/tardis/bd/art/689730410?source_id=1001
11、英文原文
https://towardsdatascience.com/rag-evaluation-using-ragas-4645a4c6c477
12、RAGAS评估及指标解析
https://blog.csdn.net/daada123321/article/details/139601950
13、LLMs
https://docs.ragas.io/en/stable/references/llms/?h=llm_factory
14、Adapting metrics to target language
https://docs.ragas.io/en/stable/howtos/customizations/metrics/_metrics_language_adaptation/