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{
"cells": [
{
"cell_type": "code",
"metadata": {
"jukit_cell_id": "f0GEGDeLRe"
},
"source": [],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "bNvqfQkX7p"
},
"source": [
"# Utils\n",
"\n",
"Connect LLMs to tools and other sources of knowledge\n",
"\n",
"- Text Splitter\n",
"- Embeddigs:\n",
" - Return embeddigns (list of floats)\n",
"- Vectorstores\n",
" - Datastores to store embeddings of documents in vector form.\n",
" - Expose methods for passing in a string and retrieve similar document\n",
"- Python Repl\n",
"- Bash\n",
"- Requests Wrapper (requests lib)\n",
"- Search\n",
"\n",
"## Generic Utilities\n",
"\n",
"### Bash"
]
},
{
"cell_type": "code",
"metadata": {
"jukit_cell_id": "ph5kvpzBNj"
},
"source": [
"from langchain.utilities import BashProcess\n",
"\n",
"bash = BashProcess()\n",
"print(bash.run(\"ls\"))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "document-loaders.ipynb\ndocument-loaders.py\ndocuments\nllm.json\nllms.ipynb\nllms.py\npdf-loader.ipynb\npdf-loader.py\nquickstart.ipynb\nquickstart.py\ntest.ipynb\ntest.py\nutils.py\n\n"
}
],
"execution_count": 1
},
{
"cell_type": "code",
"metadata": {
"jukit_cell_id": "4qBrDqHb3v"
},
"source": [],
"outputs": [],
"execution_count": null
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "python",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}