Ollama - Chat with your PDF or Log Files - create and use a local vector store
To keep up with the fast pace of local LLMs I try to use more generic nodes and Python code to access Ollama and Llama3 - this workflow will run with KNIME 4.7
The chroma vector store will be persisted in a local SQLite3 database.
To get this to work you will have to install Ollama and a Python environment with the necessary packages (py3_knime_llama), downlaod the Llama3 model and an embedding model (https://ollama.com/blog/embedding-models)
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Medium: Llama3 and KNIME - Build your local Vector Store from PDFs and other Documents
https://medium.com/p/237eda761c1c
Medium - Chat with local Llama3 Model via Ollama in KNIME Analytics Platform - Also extract Logs into structured JSON Files
https://medium.com/p/aca61e4a690a
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You can get more example of how to work with your documents by checking these Python Codes that you could then adapt
https://github.com/ml-score/
P.S.: yes I am aware of the large empty white space but I have no idea how to remove it in KNIME 4 and have already contacted KNIME support
To keep up with the fast pace of local LLMs I try to use more generic nodes and Python code to access Ollama and Llama3 - this workflow will run with KNIME 4.7
The chroma vector store will be persisted in a local SQLite3 database.
To get this to work you will have to install Ollama and a Python environment with the necessary packages (py3_knime_llama), downlaod the Llama3 model and an embedding model (https://ollama.com/blog/embedding-models)
---
Medium: Llama3 and KNIME - Build your local Vector Store from PDFs and other Documents
https://medium.com/p/237eda761c1c
Medium - Chat with local Llama3 Model via Ollama in KNIME Analytics Platform - Also extract Logs into structured JSON Files
https://medium.com/p/aca61e4a690a
---
You can get more example of how to work with your documents by checking these Python Codes that you could then adapt
https://github.com/ml-score/
P.S.: yes I am aware of the large empty white space but I have no idea how to remove it in KNIME 4 and have already contacted KNIME support