Using LLMs with Ebooks and texts, developing Prompts, Agents, and Code with LangChain, using a Vector DB and Embeddings

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Marius Ciepluch d16d897502 added FAISS MMR query and Ollama Mistral LLM prompt and reply, with in-mem cache by LangChain		2024-04-05 12:01:32 +00:00
.github/workflows	Create code_quality.yml.yml	2024-03-08 09:54:23 +01:00
data	moved from sqlite to faiss for concurrency reasons	2024-04-04 10:38:12 +00:00
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Ask_Phrack.ipynb	publication of evening project	2024-03-03 17:31:37 +00:00
EverNote_Documents_To_FAISS_Colab_GPU.ipynb	added FAISS MMR query and Ollama Mistral LLM prompt and reply, with in-mem cache by LangChain	2024-04-05 12:01:32 +00:00
EverNote_To_OpenAI.ipynb	moved from sqlite to faiss for concurrency reasons	2024-04-04 10:38:12 +00:00
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Local_CPU_LLM_Bling_Non_Interactive.ipynb	Example Notebook with LLMware CPU LLM and LangChain	2024-03-17 11:30:55 +00:00
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requirements.gpu.txt	updated requirements txt to use faiss cpu	2024-04-04 11:22:38 +00:00
requirements.txt	updated requirements txt to use faiss cpu	2024-04-04 11:22:38 +00:00
sandbox.py	sandbox	2024-03-08 09:04:10 +00:00
vss.db	publication of evening project	2024-03-03 17:30:28 +00:00

Don't read this

Follow the sub-pages for more.

Local install w conda

conda create --name lang_chain python=3.11 
source ~/miniconda3/bin/activate 
conda activate lang_chain
conda install --file requirements.txt

pip install --upgrade --quiet  sqlite-vss

for Ubuntu Server 22.04 LTS minimal) specifically here:

sudo apt-get install libatlas-base-dev

pip install sentence-transformers