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141
Writing/ner4all-case-study.bib
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@ -1,106 +1,45 @@
@misc{ollama_chroma_cookbook, @misc{hiltmann2025ner4allcontextneedusing,
title = {Ollama - Chroma Cookbook}, title={NER4all or Context is All You Need: Using LLMs for low-effort, high-performance NER on historical texts. A humanities informed approach},
url = {https://cookbook.chromadb.dev/integrations/ollama/embeddings/}, author={Torsten Hiltmann and Martin Dröge and Nicole Dresselhaus and Till Grallert and Melanie Althage and Paul Bayer and Sophie Eckenstaler and Koray Mendi and Jascha Marijn Schmitz and Philipp Schneider and Wiebke Sczeponik and Anica Skibba},
note = {Accessed: 2025-04-23}, year={2025},
year = {2024}, eprint={2502.04351},
month = apr archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.04351},
}
@misc{farcas2024run,
author = {Mihai Farcas},
title = {Run LLMs locally: 5 best methods (+ self-hosted AI starter kit)},
year = {2024},
month = {August},
day = {26},
url = {https://blog.n8n.io/local-llm/},
note = {Accessed: 2025-05-09}
}
@misc{haber2025n8nagent,
author = {Haber, Aleksandar},
title = {Tutorial on How to Integrate DeepSeek-R1 and the n8n Agent Development Framework},
year = {2025},
month = {feb},
day = {23},
url = {https://aleksandarhaber.com/tutorial-on-how-to-develop-private-and-secure-local-ai-agents-using-deepseek-r1-and-the-n8n-agent-development-framework/},
note = {Accessed: 2025-05-09}
} }
@misc{smart_connections_plugin, @misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title = {Just wanted to mention that the smart connections plugin is incredible. : r/ObsidianMD}, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
url = {https://www.reddit.com/r/ObsidianMD/comments/1fzmkdk/just_wanted_to_mention_that_the_smart_connections/}, author={DeepSeek-AI},
note = {Accessed: 2025-04-23}, year={2025},
year = {2024}, eprint={2501.12948},
month = oct archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.12948},
} }
@misc{khoj_plugin, @misc{deepcogito2025cogito14b,
title = {Khoj: An AI powered Search Assistant for your Second Brain - Share & showcase - Obsidian Forum}, author = {Deep Cogito},
url = {https://forum.obsidian.md/t/khoj-an-ai-powered-search-assistant-for-you-second-brain/53756}, title = {Cogito v1 Preview - Introducing IDA as a path to general superintelligence},
note = {Accessed: 2025-04-23}, year = {2025},
year = {2023}, url = {https://www.deepcogito.com/research/cogito-v1-preview},
month = jul urldate = {2025-05-09},
} }
@misc{supercharging_obsidian_search,
title = {Supercharging Obsidian Search with AI and Ollama},
author = {@airabbitX},
url = {https://medium.com/@airabbitX/supercharging-obsidian-search-with-local-llms-a-personal-journey-1e008eb649a6},
note = {Accessed: 2025-04-23},
year = {2024},
month = nov
}
@misc{export_to_common_graph_formats,
title = {Export to common graph formats - Plugins ideas - Obsidian Forum},
url = {https://forum.obsidian.md/t/export-to-common-graph-formats/4138},
note = {Accessed: 2025-04-23},
year = {2020},
month = feb
}
@misc{personal_knowledge_graphs_in_obsidian,
title = {Personal Knowledge Graphs in Obsidian},
author = {Volodymyr Pavlyshyn},
url = {https://volodymyrpavlyshyn.medium.com/personal-knowledge-graphs-in-obsidian-528a0f4584b9},
note = {Accessed: 2025-04-23},
year = {2024},
month = mar
}
@misc{export_obsidian_to_rdf,
title = {How to export your Obsidian Vault to RDF},
author = {Volodymyr Pavlyshyn},
url = {https://volodymyrpavlyshyn.medium.com/how-to-export-your-obsidian-vault-to-rdf-00fb2539ed18},
note = {Accessed: 2025-04-23},
year = {2024},
month = mar
}
@misc{ai_empowered_zettelkasten_with_ner_and_graph_llm,
title = {AI empowered Zettelkasten with NER and Graph LLM - Knowledge management - Obsidian Forum},
url = {https://forum.obsidian.md/t/ai-empowered-zettelkasten-with-ner-and-graph-llm/79112},
note = {Accessed: 2025-04-23},
year = {2024},
month = mar
}
@misc{build_your_second_brain_with_khoj_ai,
title = {Build your second brain with Khoj AI},
url = {https://dswharshit.medium.com/build-your-second-brain-with-khoj-ai-high-signal-ai-2-87492730d7ce},
note = {Accessed: 2025-04-23},
year = {2024},
month = jun
}
@misc{second_brain_assistant_with_obsidian,
title = {Second Brain Assistant with Obsidian},
url = {https://www.ssp.sh/brain/second-brain-assistant-with-obsidian-notegpt/},
note = {Accessed: 2025-04-23},
year = {2025},
month = mar
}
@misc{basic_memory_ai_conversations_that_build_knowledge,
title = {Basic Memory | AI Conversations That Build Knowledge},
url = {https://basicmachines.co/},
note = {Accessed: 2025-04-23}
}
@misc{local_free_rag_with_question_generation,
title = {Local (Free) RAG with Question Generation using LM Studio, Nomic embeddings, ChromaDB and Llama 3.2 on a Mac mini M1},
author = {Oscar Galvis},
url = {https://lomaky.medium.com/local-free-rag-with-question-generation-using-lm-studio-nomic-embeddings-chromadb-and-llama-3-2-9758877e93b4},
note = {Accessed: 2025-04-23},
year = {2024},
month = oct
}
@misc{private_gpt_llama_cpp_based_scripts,
title = {privateGPT / llama.cpp based scripts},
url = {https://www.ssp.sh/brain/second-brain-assistant-with-obsidian-notegpt/},
note = {Accessed: 2025-04-23},
year = {2025},
month = mar
}

12
Writing/ner4all-case-study.md
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@ -595,11 +595,13 @@ further analysis. With the rapid advancements in local AI models, we anticipate
that such pipelines will become even more accurate and faster over time, that such pipelines will become even more accurate and faster over time,
continually democratizing access to advanced NLP for all domains. continually democratizing access to advanced NLP for all domains.
**Sources:** This implementation draws on insights from Ahmed et al. (2025) for **Sources:** This implementation draws on insights from
the prompt-based NER method, and uses tools like n8n and Ollama as documented in [@hiltmann2025ner4allcontextneedusing] for the prompt-based NER method, and uses
their official guides. The chosen models (DeepSeek-R1 and Cogito) are described tools like n8n and Ollama as documented in their official guides. The chosen
in their respective releases. All software and models are utilized in accordance models (DeepSeek-R1[@deepseekai2025deepseekr1incentivizingreasoningcapability]
with their licenses for a fully local deployment. and Cogito[@deepcogito2025cogito14b]) are described in their respective
releases. All software and models are utilized in accordance with their licenses
for a fully local deployment.
## About LLMs as 'authors' {.appendix} ## About LLMs as 'authors' {.appendix}

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@ -167,19 +167,11 @@ maintaining data privacy and reproducibility.
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</head> </head>
<body class="nav-sidebar docked nav-fixed quarto-light"><script id="quarto-html-before-body" type="application/javascript"> <body class="nav-sidebar docked nav-fixed quarto-light"><script id="quarto-html-before-body" type="application/javascript">
@ -897,7 +889,7 @@ council.</code></pre>
<p>By following this guide, we implemented the <strong>NER4All</strong> paper’s methodology with a local, reproducible setup. We used n8n to handle automation and prompt assembly, and a local LLM (via Ollama) to perform the heavy-duty language understanding. The result is a flexible NER pipeline that requires <strong>no training data or API access</strong> – just a well-crafted prompt and a powerful pretrained model. We demonstrated how a user can specify custom entity types and get their text annotated in one click or API call. The approach leverages the strengths of LLMs (vast knowledge and language proficiency) to adapt to historical or niche texts, aligning with the paper’s finding that a bit of context and expert prompt design can unlock high NER performance.</p> <p>By following this guide, we implemented the <strong>NER4All</strong> paper’s methodology with a local, reproducible setup. We used n8n to handle automation and prompt assembly, and a local LLM (via Ollama) to perform the heavy-duty language understanding. The result is a flexible NER pipeline that requires <strong>no training data or API access</strong> – just a well-crafted prompt and a powerful pretrained model. We demonstrated how a user can specify custom entity types and get their text annotated in one click or API call. The approach leverages the strengths of LLMs (vast knowledge and language proficiency) to adapt to historical or niche texts, aligning with the paper’s finding that a bit of context and expert prompt design can unlock high NER performance.</p>
<p>Importantly, this setup is <strong>easy to reproduce</strong>: all components are either open-source or freely available (n8n, Ollama, and the models). A research engineer or historian can run it on a single machine with sufficient resources, and it can be shared as a workflow file for others to import. By removing the need for extensive data preparation or model training, this lowers the barrier to extracting structured information from large text archives.</p> <p>Importantly, this setup is <strong>easy to reproduce</strong>: all components are either open-source or freely available (n8n, Ollama, and the models). A research engineer or historian can run it on a single machine with sufficient resources, and it can be shared as a workflow file for others to import. By removing the need for extensive data preparation or model training, this lowers the barrier to extracting structured information from large text archives.</p>
<p>Moving forward, users can extend this case study in various ways: adding more entity types (just update the definitions input), switching to other LLMs as they become available (perhaps a future 20B model with even better understanding), or integrating the output with databases or search indexes for further analysis. With the rapid advancements in local AI models, we anticipate that such pipelines will become even more accurate and faster over time, continually democratizing access to advanced NLP for all domains.</p> <p>Moving forward, users can extend this case study in various ways: adding more entity types (just update the definitions input), switching to other LLMs as they become available (perhaps a future 20B model with even better understanding), or integrating the output with databases or search indexes for further analysis. With the rapid advancements in local AI models, we anticipate that such pipelines will become even more accurate and faster over time, continually democratizing access to advanced NLP for all domains.</p>
<p><strong>Sources:</strong> This implementation draws on insights from Ahmed et al.&nbsp;(2025) for the prompt-based NER method, and uses tools like n8n and Ollama as documented in their official guides. The chosen models (DeepSeek-R1 and Cogito) are described in their respective releases. All software and models are utilized in accordance with their licenses for a fully local deployment.</p> <p><strong>Sources:</strong> This implementation draws on insights from <span class="citation" data-cites="hiltmann2025ner4allcontextneedusing">[<a href="#ref-hiltmann2025ner4allcontextneedusing" role="doc-biblioref">1</a>]</span> for the prompt-based NER method, and uses tools like n8n and Ollama as documented in their official guides. The chosen models (DeepSeek-R1<span class="citation" data-cites="deepseekai2025deepseekr1incentivizingreasoningcapability">[<a href="#ref-deepseekai2025deepseekr1incentivizingreasoningcapability" role="doc-biblioref">2</a>]</span> and Cogito<span class="citation" data-cites="deepcogito2025cogito14b">[<a href="#ref-deepcogito2025cogito14b" role="doc-biblioref">3</a>]</span>) are described in their respective releases. All software and models are utilized in accordance with their licenses for a fully local deployment.</p>
</section> </section>
@ -908,44 +900,20 @@ council.</code></pre>
</div></section><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body" data-entry-spacing="0" role="list"> </div></section><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body" data-entry-spacing="0" role="list">
<div id="ref-ollama_chroma_cookbook" class="csl-entry" role="listitem"> <div id="ref-hiltmann2025ner4allcontextneedusing" class="csl-entry" role="listitem">
<div class="csl-left-margin">1. </div><div class="csl-right-inline"><a href="https://cookbook.chromadb.dev/integrations/ollama/embeddings/">Ollama - chroma cookbook</a>. 2024.</div> <div class="csl-left-margin">1. </div><div class="csl-right-inline">Hiltmann, Torsten, Martin Dröge, Nicole Dresselhaus, Till Grallert, Melanie Althage, Paul Bayer, Sophie Eckenstaler, et al. 2025. <a href="https://arxiv.org/abs/2502.04351">NER4all or context is all you need: Using LLMs for low-effort, high-performance NER on historical texts. A humanities informed approach</a>.</div>
</div> </div>
<div id="ref-smart_connections_plugin" class="csl-entry" role="listitem"> <div id="ref-deepseekai2025deepseekr1incentivizingreasoningcapability" class="csl-entry" role="listitem">
<div class="csl-left-margin">2. </div><div class="csl-right-inline"><a href="https://www.reddit.com/r/ObsidianMD/comments/1fzmkdk/just_wanted_to_mention_that_the_smart_connections/">Just wanted to mention that the smart connections plugin is incredible. : R/ObsidianMD</a>. 2024.</div> <div class="csl-left-margin">2. </div><div class="csl-right-inline">DeepSeek-AI. 2025. <a href="https://arxiv.org/abs/2501.12948">DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning</a>.</div>
</div> </div>
<div id="ref-khoj_plugin" class="csl-entry" role="listitem"> <div id="ref-deepcogito2025cogito14b" class="csl-entry" role="listitem">
<div class="csl-left-margin">3. </div><div class="csl-right-inline"><a href="https://forum.obsidian.md/t/khoj-an-ai-powered-search-assistant-for-you-second-brain/53756">Khoj: An AI powered search assistant for your second brain - share &amp; showcase - obsidian forum</a>. 2023.</div> <div class="csl-left-margin">3. </div><div class="csl-right-inline">Cogito, Deep. 2025. <a href="https://www.deepcogito.com/research/cogito-v1-preview">Cogito v1 preview - introducing IDA as a path to general superintelligence</a>.</div>
</div> </div>
<div id="ref-supercharging_obsidian_search" class="csl-entry" role="listitem"> <div id="ref-farcas2024run" class="csl-entry" role="listitem">
<div class="csl-left-margin">4. </div><div class="csl-right-inline">@airabbitX. 2024. <a href="https://medium.com/@airabbitX/supercharging-obsidian-search-with-local-llms-a-personal-journey-1e008eb649a6">Supercharging obsidian search with AI and ollama</a>.</div> <div class="csl-left-margin">4. </div><div class="csl-right-inline">Farcas, Mihai. 2024. <a href="https://blog.n8n.io/local-llm/">Run LLMs locally: 5 best methods (+ self-hosted AI starter kit)</a>.</div>
</div> </div>
<div id="ref-export_to_common_graph_formats" class="csl-entry" role="listitem"> <div id="ref-haber2025n8nagent" class="csl-entry" role="listitem">
<div class="csl-left-margin">5. </div><div class="csl-right-inline"><a href="https://forum.obsidian.md/t/export-to-common-graph-formats/4138">Export to common graph formats - plugins ideas - obsidian forum</a>. 2020.</div> <div class="csl-left-margin">5. </div><div class="csl-right-inline">Haber, Aleksandar. 2025. <a href="https://aleksandarhaber.com/tutorial-on-how-to-develop-private-and-secure-local-ai-agents-using-deepseek-r1-and-the-n8n-agent-development-framework/">Tutorial on how to integrate DeepSeek-R1 and the n8n agent development framework</a>.</div>
</div>
<div id="ref-personal_knowledge_graphs_in_obsidian" class="csl-entry" role="listitem">
<div class="csl-left-margin">6. </div><div class="csl-right-inline">Pavlyshyn, Volodymyr. 2024. <a href="https://volodymyrpavlyshyn.medium.com/personal-knowledge-graphs-in-obsidian-528a0f4584b9">Personal knowledge graphs in obsidian</a>.</div>
</div>
<div id="ref-export_obsidian_to_rdf" class="csl-entry" role="listitem">
<div class="csl-left-margin">7. </div><div class="csl-right-inline">Pavlyshyn, Volodymyr. 2024. <a href="https://volodymyrpavlyshyn.medium.com/how-to-export-your-obsidian-vault-to-rdf-00fb2539ed18">How to export your obsidian vault to RDF</a>.</div>
</div>
<div id="ref-ai_empowered_zettelkasten_with_ner_and_graph_llm" class="csl-entry" role="listitem">
<div class="csl-left-margin">8. </div><div class="csl-right-inline"><a href="https://forum.obsidian.md/t/ai-empowered-zettelkasten-with-ner-and-graph-llm/79112">AI empowered zettelkasten with NER and graph LLM - knowledge management - obsidian forum</a>. 2024.</div>
</div>
<div id="ref-build_your_second_brain_with_khoj_ai" class="csl-entry" role="listitem">
<div class="csl-left-margin">9. </div><div class="csl-right-inline"><a href="https://dswharshit.medium.com/build-your-second-brain-with-khoj-ai-high-signal-ai-2-87492730d7ce">Build your second brain with khoj AI</a>. 2024.</div>
</div>
<div id="ref-second_brain_assistant_with_obsidian" class="csl-entry" role="listitem">
<div class="csl-left-margin">10. </div><div class="csl-right-inline"><a href="https://www.ssp.sh/brain/second-brain-assistant-with-obsidian-notegpt/">Second brain assistant with obsidian</a>. 2025.</div>
</div>
<div id="ref-basic_memory_ai_conversations_that_build_knowledge" class="csl-entry" role="listitem">
<div class="csl-left-margin">11. </div><div class="csl-right-inline"><a href="https://basicmachines.co/">Basic memory | AI conversations that build knowledge</a>.</div>
</div>
<div id="ref-local_free_rag_with_question_generation" class="csl-entry" role="listitem">
<div class="csl-left-margin">12. </div><div class="csl-right-inline">Galvis, Oscar. 2024. <a href="https://lomaky.medium.com/local-free-rag-with-question-generation-using-lm-studio-nomic-embeddings-chromadb-and-llama-3-2-9758877e93b4">Local (free) RAG with question generation using LM studio, nomic embeddings, ChromaDB and llama 3.2 on a mac mini M1</a>.</div>
</div>
<div id="ref-private_gpt_llama_cpp_based_scripts" class="csl-entry" role="listitem">
<div class="csl-left-margin">13. </div><div class="csl-right-inline"><a href="https://www.ssp.sh/brain/second-brain-assistant-with-obsidian-notegpt/">privateGPT / llama.cpp based scripts</a>. 2025.</div>
</div> </div>
</div></section><section class="quarto-appendix-contents" id="quarto-citation"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{2025, </div></section><section class="quarto-appendix-contents" id="quarto-citation"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{2025,
author = {, GPT-4.5 and Dresselhaus, Nicole}, author = {, GPT-4.5 and Dresselhaus, Nicole},

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<p>By following this guide, we implemented the <strong>NER4All</strong> paper’s methodology with a local, reproducible setup. We used n8n to handle automation and prompt assembly, and a local LLM (via Ollama) to perform the heavy-duty language understanding. The result is a flexible NER pipeline that requires <strong>no training data or API access</strong> – just a well-crafted prompt and a powerful pretrained model. We demonstrated how a user can specify custom entity types and get their text annotated in one click or API call. The approach leverages the strengths of LLMs (vast knowledge and language proficiency) to adapt to historical or niche texts, aligning with the paper’s finding that a bit of context and expert prompt design can unlock high NER performance.</p> <p>By following this guide, we implemented the <strong>NER4All</strong> paper’s methodology with a local, reproducible setup. We used n8n to handle automation and prompt assembly, and a local LLM (via Ollama) to perform the heavy-duty language understanding. The result is a flexible NER pipeline that requires <strong>no training data or API access</strong> – just a well-crafted prompt and a powerful pretrained model. We demonstrated how a user can specify custom entity types and get their text annotated in one click or API call. The approach leverages the strengths of LLMs (vast knowledge and language proficiency) to adapt to historical or niche texts, aligning with the paper’s finding that a bit of context and expert prompt design can unlock high NER performance.</p>
<p>Importantly, this setup is <strong>easy to reproduce</strong>: all components are either open-source or freely available (n8n, Ollama, and the models). A research engineer or historian can run it on a single machine with sufficient resources, and it can be shared as a workflow file for others to import. By removing the need for extensive data preparation or model training, this lowers the barrier to extracting structured information from large text archives.</p> <p>Importantly, this setup is <strong>easy to reproduce</strong>: all components are either open-source or freely available (n8n, Ollama, and the models). A research engineer or historian can run it on a single machine with sufficient resources, and it can be shared as a workflow file for others to import. By removing the need for extensive data preparation or model training, this lowers the barrier to extracting structured information from large text archives.</p>
<p>Moving forward, users can extend this case study in various ways: adding more entity types (just update the definitions input), switching to other LLMs as they become available (perhaps a future 20B model with even better understanding), or integrating the output with databases or search indexes for further analysis. With the rapid advancements in local AI models, we anticipate that such pipelines will become even more accurate and faster over time, continually democratizing access to advanced NLP for all domains.</p> <p>Moving forward, users can extend this case study in various ways: adding more entity types (just update the definitions input), switching to other LLMs as they become available (perhaps a future 20B model with even better understanding), or integrating the output with databases or search indexes for further analysis. With the rapid advancements in local AI models, we anticipate that such pipelines will become even more accurate and faster over time, continually democratizing access to advanced NLP for all domains.</p>
<p><strong>Sources:</strong> This implementation draws on insights from Ahmed et al.&nbsp;(2025) for the prompt-based NER method, and uses tools like n8n and Ollama as documented in their official guides. The chosen models (DeepSeek-R1 and Cogito) are described in their respective releases. All software and models are utilized in accordance with their licenses for a fully local deployment.</p> <p><strong>Sources:</strong> This implementation draws on insights from <span class="citation" data-cites="hiltmann2025ner4allcontextneedusing">[1]</span> for the prompt-based NER method, and uses tools like n8n and Ollama as documented in their official guides. The chosen models (DeepSeek-R1<span class="citation" data-cites="deepseekai2025deepseekr1incentivizingreasoningcapability">[2]</span> and Cogito<span class="citation" data-cites="deepcogito2025cogito14b">[3]</span>) are described in their respective releases. All software and models are utilized in accordance with their licenses for a fully local deployment.</p>
</section> </section>
@ -579,44 +579,20 @@ font-style: inherit;">N8N_DEFAULT_TIMEOUT</span></code> or in the workflow setti
</div></section><section class="quarto-appendix-contents" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body" data-entry-spacing="0"> </div></section><section class="quarto-appendix-contents" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body" data-entry-spacing="0">
<div id="ref-ollama_chroma_cookbook" class="csl-entry"> <div id="ref-hiltmann2025ner4allcontextneedusing" class="csl-entry">
<div class="csl-left-margin">1. </div><div class="csl-right-inline"><a href="https://cookbook.chromadb.dev/integrations/ollama/embeddings/">Ollama - chroma cookbook</a>. 2024.</div> <div class="csl-left-margin">1. </div><div class="csl-right-inline">Hiltmann, Torsten, Martin Dröge, Nicole Dresselhaus, Till Grallert, Melanie Althage, Paul Bayer, Sophie Eckenstaler, et al. 2025. <a href="https://arxiv.org/abs/2502.04351">NER4all or context is all you need: Using LLMs for low-effort, high-performance NER on historical texts. A humanities informed approach</a>.</div>
</div> </div>
<div id="ref-smart_connections_plugin" class="csl-entry"> <div id="ref-deepseekai2025deepseekr1incentivizingreasoningcapability" class="csl-entry">
<div class="csl-left-margin">2. </div><div class="csl-right-inline"><a href="https://www.reddit.com/r/ObsidianMD/comments/1fzmkdk/just_wanted_to_mention_that_the_smart_connections/">Just wanted to mention that the smart connections plugin is incredible. : R/ObsidianMD</a>. 2024.</div> <div class="csl-left-margin">2. </div><div class="csl-right-inline">DeepSeek-AI. 2025. <a href="https://arxiv.org/abs/2501.12948">DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning</a>.</div>
</div> </div>
<div id="ref-khoj_plugin" class="csl-entry"> <div id="ref-deepcogito2025cogito14b" class="csl-entry">
<div class="csl-left-margin">3. </div><div class="csl-right-inline"><a href="https://forum.obsidian.md/t/khoj-an-ai-powered-search-assistant-for-you-second-brain/53756">Khoj: An AI powered search assistant for your second brain - share &amp; showcase - obsidian forum</a>. 2023.</div> <div class="csl-left-margin">3. </div><div class="csl-right-inline">Cogito, Deep. 2025. <a href="https://www.deepcogito.com/research/cogito-v1-preview">Cogito v1 preview - introducing IDA as a path to general superintelligence</a>.</div>
</div> </div>
<div id="ref-supercharging_obsidian_search" class="csl-entry"> <div id="ref-farcas2024run" class="csl-entry">
<div class="csl-left-margin">4. </div><div class="csl-right-inline">@airabbitX. 2024. <a href="https://medium.com/@airabbitX/supercharging-obsidian-search-with-local-llms-a-personal-journey-1e008eb649a6">Supercharging obsidian search with AI and ollama</a>.</div> <div class="csl-left-margin">4. </div><div class="csl-right-inline">Farcas, Mihai. 2024. <a href="https://blog.n8n.io/local-llm/">Run LLMs locally: 5 best methods (+ self-hosted AI starter kit)</a>.</div>
</div> </div>
<div id="ref-export_to_common_graph_formats" class="csl-entry"> <div id="ref-haber2025n8nagent" class="csl-entry">
<div class="csl-left-margin">5. </div><div class="csl-right-inline"><a href="https://forum.obsidian.md/t/export-to-common-graph-formats/4138">Export to common graph formats - plugins ideas - obsidian forum</a>. 2020.</div> <div class="csl-left-margin">5. </div><div class="csl-right-inline">Haber, Aleksandar. 2025. <a href="https://aleksandarhaber.com/tutorial-on-how-to-develop-private-and-secure-local-ai-agents-using-deepseek-r1-and-the-n8n-agent-development-framework/">Tutorial on how to integrate DeepSeek-R1 and the n8n agent development framework</a>.</div>
</div>
<div id="ref-personal_knowledge_graphs_in_obsidian" class="csl-entry">
<div class="csl-left-margin">6. </div><div class="csl-right-inline">Pavlyshyn, Volodymyr. 2024. <a href="https://volodymyrpavlyshyn.medium.com/personal-knowledge-graphs-in-obsidian-528a0f4584b9">Personal knowledge graphs in obsidian</a>.</div>
</div>
<div id="ref-export_obsidian_to_rdf" class="csl-entry">
<div class="csl-left-margin">7. </div><div class="csl-right-inline">Pavlyshyn, Volodymyr. 2024. <a href="https://volodymyrpavlyshyn.medium.com/how-to-export-your-obsidian-vault-to-rdf-00fb2539ed18">How to export your obsidian vault to RDF</a>.</div>
</div>
<div id="ref-ai_empowered_zettelkasten_with_ner_and_graph_llm" class="csl-entry">
<div class="csl-left-margin">8. </div><div class="csl-right-inline"><a href="https://forum.obsidian.md/t/ai-empowered-zettelkasten-with-ner-and-graph-llm/79112">AI empowered zettelkasten with NER and graph LLM - knowledge management - obsidian forum</a>. 2024.</div>
</div>
<div id="ref-build_your_second_brain_with_khoj_ai" class="csl-entry">
<div class="csl-left-margin">9. </div><div class="csl-right-inline"><a href="https://dswharshit.medium.com/build-your-second-brain-with-khoj-ai-high-signal-ai-2-87492730d7ce">Build your second brain with khoj AI</a>. 2024.</div>
</div>
<div id="ref-second_brain_assistant_with_obsidian" class="csl-entry">
<div class="csl-left-margin">10. </div><div class="csl-right-inline"><a href="https://www.ssp.sh/brain/second-brain-assistant-with-obsidian-notegpt/">Second brain assistant with obsidian</a>. 2025.</div>
</div>
<div id="ref-basic_memory_ai_conversations_that_build_knowledge" class="csl-entry">
<div class="csl-left-margin">11. </div><div class="csl-right-inline"><a href="https://basicmachines.co/">Basic memory | AI conversations that build knowledge</a>.</div>
</div>
<div id="ref-local_free_rag_with_question_generation" class="csl-entry">
<div class="csl-left-margin">12. </div><div class="csl-right-inline">Galvis, Oscar. 2024. <a href="https://lomaky.medium.com/local-free-rag-with-question-generation-using-lm-studio-nomic-embeddings-chromadb-and-llama-3-2-9758877e93b4">Local (free) RAG with question generation using LM studio, nomic embeddings, ChromaDB and llama 3.2 on a mac mini M1</a>.</div>
</div>
<div id="ref-private_gpt_llama_cpp_based_scripts" class="csl-entry">
<div class="csl-left-margin">13. </div><div class="csl-right-inline"><a href="https://www.ssp.sh/brain/second-brain-assistant-with-obsidian-notegpt/">privateGPT / llama.cpp based scripts</a>. 2025.</div>
</div> </div>
</div></section><section class="quarto-appendix-contents" id="quarto-citation"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{2025, </div></section><section class="quarto-appendix-contents" id="quarto-citation"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{2025,
author = {, GPT-4.5 and Dresselhaus, Nicole}, author = {, GPT-4.5 and Dresselhaus, Nicole},

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"text": "Conclusion\nBy following this guide, we implemented the NER4All paper’s methodology with a local, reproducible setup. We used n8n to handle automation and prompt assembly, and a local LLM (via Ollama) to perform the heavy-duty language understanding. The result is a flexible NER pipeline that requires no training data or API access – just a well-crafted prompt and a powerful pretrained model. We demonstrated how a user can specify custom entity types and get their text annotated in one click or API call. The approach leverages the strengths of LLMs (vast knowledge and language proficiency) to adapt to historical or niche texts, aligning with the paper’s finding that a bit of context and expert prompt design can unlock high NER performance.\nImportantly, this setup is easy to reproduce: all components are either open-source or freely available (n8n, Ollama, and the models). A research engineer or historian can run it on a single machine with sufficient resources, and it can be shared as a workflow file for others to import. By removing the need for extensive data preparation or model training, this lowers the barrier to extracting structured information from large text archives.\nMoving forward, users can extend this case study in various ways: adding more entity types (just update the definitions input), switching to other LLMs as they become available (perhaps a future 20B model with even better understanding), or integrating the output with databases or search indexes for further analysis. With the rapid advancements in local AI models, we anticipate that such pipelines will become even more accurate and faster over time, continually democratizing access to advanced NLP for all domains.\nSources: This implementation draws on insights from Ahmed et al. (2025) for the prompt-based NER method, and uses tools like n8n and Ollama as documented in their official guides. The chosen models (DeepSeek-R1 and Cogito) are described in their respective releases. All software and models are utilized in accordance with their licenses for a fully local deployment.", "text": "Conclusion\nBy following this guide, we implemented the NER4All paper’s methodology with a local, reproducible setup. We used n8n to handle automation and prompt assembly, and a local LLM (via Ollama) to perform the heavy-duty language understanding. The result is a flexible NER pipeline that requires no training data or API access – just a well-crafted prompt and a powerful pretrained model. We demonstrated how a user can specify custom entity types and get their text annotated in one click or API call. The approach leverages the strengths of LLMs (vast knowledge and language proficiency) to adapt to historical or niche texts, aligning with the paper’s finding that a bit of context and expert prompt design can unlock high NER performance.\nImportantly, this setup is easy to reproduce: all components are either open-source or freely available (n8n, Ollama, and the models). A research engineer or historian can run it on a single machine with sufficient resources, and it can be shared as a workflow file for others to import. By removing the need for extensive data preparation or model training, this lowers the barrier to extracting structured information from large text archives.\nMoving forward, users can extend this case study in various ways: adding more entity types (just update the definitions input), switching to other LLMs as they become available (perhaps a future 20B model with even better understanding), or integrating the output with databases or search indexes for further analysis. With the rapid advancements in local AI models, we anticipate that such pipelines will become even more accurate and faster over time, continually democratizing access to advanced NLP for all domains.\nSources: This implementation draws on insights from [1] for the prompt-based NER method, and uses tools like n8n and Ollama as documented in their official guides. The chosen models (DeepSeek-R1[2] and Cogito[3]) are described in their respective releases. All software and models are utilized in accordance with their licenses for a fully local deployment.",
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