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