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Using LLMs with Ebooks and texts, developing Prompts, Agents, and Code with LangChain, using a Vector DB and Embeddings
chunkingembeddingsgpt-4langchainllm-agentsllmsopenai-apiprompt-engineeringpythonsqlitevector-database
.github/workflows | ||
data | ||
scripts | ||
3.txt | ||
Ask_Phrack.ipynb | ||
EverNote_Documents_To_FAISS_Colab_GPU.ipynb | ||
EverNote_To_OpenAI.ipynb | ||
LICENSE | ||
Local_CPU_LLM_Bling_Non_Interactive.ipynb | ||
Local_CPU_LLM_Ollama_Mistral.ipynb | ||
Readme.md | ||
requirements.gpu.txt | ||
requirements.txt | ||
sandbox.py | ||
vss.db |
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
For the local SQlite VSS
pip install --upgrade --quiet sqlite-vss
for Ubuntu Server 22.04 LTS minimal) specifically here:
sudo apt-get install libatlas-base-dev
Probably too much, but it works
pip install sentence-transformers