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GPT4All KNIME - Creating Vector Store

<p>Create a Vector Store with GPT4All from a PDF and query it with local GPT4All instance</p><ul><li><p>you need GPT4All installed and ready to run on your machine</p></li><li><p>tested it with Llama 3.2</p></li><li><p>Embeddings done with GPT4All</p><p></p></li></ul>

URL: Medium: How to leverage open source LLMs locally via Ollama https://medium.com/low-code-for-advanced-data-science/how-to-leverage-open-source-llms-locally-via-ollama-2d6cf8c378b4
URL: use DeepSeek models with KNIME and GPT$All and Ollama https://forum.knime.com/t/deepseek-models-supported/86009/3?u=mlauber71
URL: Medium: Multimodal Prompting with local LLMs using KNIME and Ollama https://medium.com/low-code-for-advanced-data-science/multimodal-prompting-with-local-llms-using-knime-and-ollama-74928cf5d09f

Create a Vector Store with GPT4All from a PDF and query it with local GPT4All instance
  • you need Ollama installed and ready to run on your machine, also you should have GPt4All installed

  • tested it with Llama 3.2

  • Embeddings done with GPT4All

MEDIUM Blog: Creating a Local LLM Vector Store from PDFs with KNIME and GPT4All

https://medium.com/p/311bf61dd20e

MEDIUM Blog: KNIME, AI and local Large Language Models (LLM)

https://medium.com/p/cef650fc142b

joinSep("\n\n",

"You are an AI expert to give helpful answers.",

"Use the following pieces of context to answer the question at the end.",

"If you don't know the answer, just say that you don't know, don't try to make up an answer.",

join("{",joinSep("\n\n",$Retrieved documents$),"}"),

join("Question: {",$Question$,"}"),

"Helpful Answer:")

"engineered_prompt"
LLM Prompter
Chroma Vector Store Creator
adapt the number ofDocuments providedfor each question
Vector Store Retriever
prompt engineering"engineered_prompt"
String Manipulation
knowledge basevector_store_<..>.zip
Model Reader
"engineered_prompt"
LLM Prompter
vector_store_chroma.zip
Model Writer
remove sentences with less than 5 terms
Row Filter
FAISS Vector Store Creator
default: automaticallydownload an embeddingor use local embedding model like"all-minilm-l6-v2_f16.gguf"
GPT4All Embedding Model Selector
Question(s)
Table Creator
<...model name...>.ggufload the .gguf modelneeds KNIME 5.2+
Local GPT4All LLM Selector
current time
Date&Time Range Creator
extract sentencesin different rows=> you might put more effort into the pre-processing
Sentence Extractor
prompt engineering"engineered_prompt"
String Manipulation
knowledge basevector_store_<..>.zip
Model Reader
vector_store_faiss.zip
Model Writer
adapt the number ofDocuments providedfor each question
Vector Store Retriever
../gpt4all_models/place *.gguf models in this pathconfigure base path with F6
prepare GPT4all model path
<...model name...>.ggufload the .gguf modelneeds KNIME 5.2+you can change settings like choosing GPU
Local GPT4All LLM Selector
Date&Time Part Extractor
../gpt4all_models/place *.gguf models in this pathchosse embedding model like"all-minilm-l6-v2_f16.gguf"configure base path with F6
prepare GPT4all model path
knime://knime.workflow/data/Strip text from PDFs
PDF Parser

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